PATENT DOCUMENT

Publication Number: US-11422689-B1
Application Number: US-201816100077-A
Country: US
Kind Code: B1

Title: Detecting touch user interface on a display under partial wet conditions

Abstract:
An electronic device disclosed herein is adapted to determine a location of touch input when liquid is present. The electronic device may include a force detection assembly having force detection units. When a touch input is applied to the display layer, the force detection units generates a differential capacitance that is used to determine a location of the touch input. The electronic device may further include a confidence interval algorithm that uses the location and builds a confidence interval around the location. The electronic device may include a touch input components that can determine whether the liquid is present. The confidence interval algorithm determines whether the liquid is present within the confidence interval and provides an updated location of the touch input. The electronic device further includes a machine learning algorithm designed predict a software application the user intends to select using the touch input.

Claims:
What is claimed is: 
     
       1. A wearable electronic device, comprising:
 a transparent layer; 
 a display assembly covered by the transparent layer, the display assembly comprising a touch input component to determine whether a liquid is in contact with the transparent layer; 
 a force detection assembly to determine a first location of a touch input having an input location on the transparent layer and an amount of force applied to the transparent layer from the touch input; 
 a memory circuit; 
 a confidence interval algorithm stored on the memory circuit that, when accessed by a processor, generates a confidence interval centered around the first location and determines a second location of the touch input based on the first location when the liquid is in contact with the transparent layer within the confidence interval, the confidence interval including a circular area of the transparent layer, the area having a radius, wherein the size of the radius is at least partially based on the amount of force applied to the transparent layer; and 
 a machine learning algorithm stored on the memory circuit that, when accessed by the processor, generates a list of predicted user requests, determines whether a user request corresponding to the second location is on the list, and instructs the processor to execute the user request in response to determining the user request is on the list. 
 
     
     
       2. The wearable electronic device of  claim 1 , wherein the second location defines a user input to the display assembly that selects an icon presented on the display assembly. 
     
     
       3. The wearable electronic device of  claim 1 , wherein the force detection assembly comprises:
 a first force detection unit that measures a first capacitance based upon the amount of applied force; and 
 a second force detection unit that measures a second capacitance based upon the amount of applied force, wherein the first location is determined by a differential capacitance between the first capacitance and the second capacitance. 
 
     
     
       4. The wearable electronic device of  claim 1 , further comprising:
 an enclosure that is coupled with the transparent layer; and 
 a first band and a second band, the first band and the second band connected to the enclosure and configured to secure the enclosure to a user. 
 
     
     
       5. The wearable electronic device of  claim 1 , wherein the liquid comprises multiple liquid droplets, and wherein the confidence interval algorithm determines the second location when at least one liquid droplet is within the confidence interval. 
     
     
       6. The wearable electronic device of  claim 5 , wherein the confidence interval algorithm determines a location of a liquid droplet that is closest to the first location, and determines the second location as the location of the liquid droplet. 
     
     
       7. The wearable electronic device of  claim 1 , wherein the force detection assembly is adhesively coupled to the transparent layer. 
     
     
       8. The wearable electronic device of  claim 1 , wherein the machine learning algorithm generates the list of predicted user requests based on at least one of a location of the wearable electronic device, a state of acceleration of the wearable electronic device, or weather-related information. 
     
     
       9. The wearable electronic device of  claim 1 , wherein the radius of the confidence interval is inversely proportional to the amount of force applied to the transparent layer. 
     
     
       10. A wearable electronic device, comprising:
 an enclosure; 
 a display assembly comprising a display layer to display icons that represent software applications and a touch input component that detects an input location of a user touch input; 
 a confidence interval algorithm to generate a confidence interval including an area of the display assembly centered around the input location and having a radius, and determine a second location based on a location of a liquid that at least partially covers the display assembly as detected by the touch input component when the location of the liquid is at least partially within the confidence interval; and 
 a machine learning algorithm to generate a list of predicted software applications, the machine learning algorithm confirming that an icon corresponding to the second location is associated with a software application on the list of predicted software applications. 
 
     
     
       11. The wearable electronic device of  claim 10 , further comprising:
 a transparent layer coupled to the enclosure and covering the display assembly; and 
 a force detection assembly to determine an amount of applied force from the user touch input. 
 
     
     
       12. The wearable electronic device of  claim 11 , wherein the force detection assembly comprises:
 a first force detection unit that measures a first capacitance based upon an amount of applied force by the user touch input, and 
 a second force detection unit that measures a second capacitance based upon the amount of applied force, wherein the input location is determined by a differential capacitance between the first capacitance and the second capacitance. 
 
     
     
       13. The wearable electronic device of  claim 12 , wherein the display layer is positioned between the first force detection unit and the second force detection unit. 
     
     
       14. The wearable electronic device of  claim 10 , wherein the enclosure defines an internal volume encompassing:
 a processor circuit that executes instructions of the confidence interval algorithm and the machine learning algorithm; and 
 a memory circuit that stores the instructions of the confidence interval algorithm and the machine learning algorithm. 
 
     
     
       15. A method for selecting an icon on a display assembly of a wearable electronic device when a liquid is present on the display assembly, the method comprising:
 determining, by a force detection assembly, a first location of a touch input on the display assembly and an amount of force applied to the display assembly by the touch input; 
 determining, by a touch input component of the display assembly, the presence of the liquid on a transparent layer that covers the display assembly; 
 determining, by a confidence interval algorithm, a second location of the touch input based on confidence interval surrounding the first location, the confidence interval including a circular area centered around the first location and having a radius, and the determination of the presence the liquid; and 
 confirming, by a machine learning algorithm, that the second location is closer to an icon selection associated with an input location than the first location based on whether a software application associated with the icon selection is on a list of predicted software applications generated by the machine learning algorithm. 
 
     
     
       16. The method of  claim 15 , wherein determining, by the confidence interval algorithm, the second location of the touch input comprises:
 generating a user input location based on the first location; 
 generating a confidence interval around the user input location; and 
 determining whether the liquid is present at least partially within the confidence interval. 
 
     
     
       17. The method of  claim 16 , wherein the second location corresponds to a location of the liquid.

Description:
FIELD 
     The following description relates to electronic devices with touch input displays. In particular, the following description relates to portable electronic devices and wearable electronic devices with enhanced accuracy of touch input detection when the touch input display (or a transparent layer covering the touch input display) is wet. 
     BACKGROUND 
     Current electronic devices are known to include displays with touch input capability. Touch input technology may include multiple electrodes that form several capacitors that define an electrostatic field. When a user touches a region of the touch input display, the electrostatic field is altered, as determined by a change in capacitance, in that region. The electronic device can determine the location of the user&#39;s touch input based on the capacitance change. 
     The touch input display provides a dynamic user input that can alter the display. However, the performance of the display decreases when water is present. For instance, water can alter the electrostatic field by providing an electrical ground to the display. Some electronic devices deactivate the touch input component under these circumstances. Other electronic devices generate a false positive by inaccurately determining a touch input. Alternatively, ungrounded water on the display may cause the touch input display to be non-responsive to a touch input from a user. In either event, the touch input capability of the electronic device is limited to other input mechanisms, such as buttons. 
     SUMMARY 
     In one aspect, a wearable electronic device is described. The wearable electronic device may include a transparent layer. The wearable electronic device may further include a display assembly covered by the transparent layer. The display assembly may include a touch input component configured to receive a touch input at the transparent layer. The touch input component is capable of determining whether a liquid is in contact with the transparent layer. The wearable electronic device may further include a force detection assembly configured to determine an amount of force applied to the transparent layer from the touch input. The force detection assembly is capable of determining a first location of the touch input. The wearable electronic device may further include a memory circuit. The wearable electronic device may further include a confidence interval algorithm stored on the memory circuit and configured to generate a confidence interval around the first location. In some instances, when the liquid is at least partially within the confidence interval, a second location is determined based upon the first location, the second location being closer an actual location of the touch input than the first location. 
     In another aspect, a wearable electronic device is described. The wearable electronic device may include an enclosure. The wearable electronic device may further include a band coupled to the enclosure, the band configured to secure the enclosure with a user. The wearable electronic device may further include a display assembly i) a display layer configured to present icons that represent software applications, and ii) a touch input component that detects a user input to select an icon from the icons. The wearable electronic device may further include a confidence interval algorithm configured to build a confidence interval around a first location, determined by the touch input component, and evaluate a location of a liquid that covers the display assembly, the location of the liquid determined by the touch input component. In some instances, when the liquid is at least partially within the confidence interval algorithm, the location of the liquid is used as a second location of the user input. The wearable electronic device may further include a machine learning algorithm stored on a memory circuit carried by the enclosure. The machine learning algorithm is configured to predict a software application selected by a user from the software applications. Also, the machine learning algorithm capable of confirming that the second location provides an input to select the icon from the user input. 
     In another aspect, a method for selecting an icon on a display assembly of a wearable electronic device when a liquid is present on the display assembly is described. The method may include determining, by a force detection assembly, a first location of a touch input to the icon, the force detection assembly configured to detect an amount of applied force by the touch input. The method may further include determining, by a touch input component of the display assembly, whether the liquid is present on a transparent layer that covers the display assembly. The method may further include determining, by a confidence interval algorithm, a second location of the touch input, wherein the confidence interval algorithm uses the first location and a determination of the presence the liquid to determine the second location. 
     Other systems, methods, features and advantages of the embodiments will be, or will become, apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description and this summary, be within the scope of the embodiments, and be protected by the following claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosure will be readily understood by the following detailed description in conjunction with the accompanying drawings, wherein like reference numerals designate like structural elements, and in which: 
         FIG. 1  illustrates an isometric view of an embodiment of an electronic device, in accordance with some described embodiments. 
         FIG. 2  illustrates an isometric view of an alternative embodiment of an electronic device, in accordance with some described embodiments. 
         FIG. 3  illustrates a plan view that partially shows the electronic device in  FIG. 1 , further showing a force detection assembly. 
         FIG. 4  illustrates a cross sectional view of the electronic device shown in  FIG. 3  taken along line A-A, showing additional features of the force detection assembly. 
         FIG. 5  illustrates a cross sectional view of the electronic device shown in  FIG. 3  taken along line B-B, showing additional features of the electronic device. 
         FIG. 6  illustrates a plan view that partially shows the electronic device, showing liquid droplets partially covering the transparent layer. 
         FIG. 7  illustrates an isometric view of the transparent layer and the force detection assembly, showing the force detection assembly responding to a touch input to the transparent layer. 
         FIG. 8  illustrates a cross sectional view of the transparent layer and the force detection assembly shown in  FIG. 7 , showing a change in separation between the electrode layers of the force detection unit. 
         FIG. 9  illustrates a cross sectional view of the transparent layer and the force detection assembly shown in  FIG. 7 , showing a change in separation between the electrode layers of two separate force detection units. 
         FIG. 10  illustrates a plan view that partially shows the electronic device, showing a user interacting with the electronic device, in accordance with some described embodiments. 
         FIG. 11  illustrates a plan view that partially shows the electronic device, showing a confidence interval generated by an algorithm, in accordance with some described embodiments. 
         FIG. 12  illustrates a plan view that partially shows the electronic device, showing the user providing an increased applied force during interaction with the electronic device. 
         FIG. 13  illustrates a plan view that partially shows the electronic device, showing an updated confidence interval generated by the algorithm, in accordance with some described embodiments. 
         FIG. 14  illustrates a plan view partially showing the electronic device, showing the electronic device presenting several software applications on the display assembly, in accordance with some described embodiments. 
         FIG. 15  illustrates a plan view of the electronic device, showing the display assembly  108  presenting a selected software application. 
         FIG. 16  illustrates a plan view partially showing the electronic device, showing the electronic device presenting several software applications on the display assembly, in accordance with some described embodiments. 
         FIG. 17  illustrates a plan view of the electronic device, showing the display opening a selected software application subsequent to a touch input. 
         FIG. 18  illustrates a plan view that partially shows an alternate embodiment of an electronic device, showing an alternate embodiment of a force detection assembly. 
         FIG. 19  illustrates a plan view that partially shows an alternate embodiment of an electronic device, showing an alternate embodiment of a force detection assembly. 
         FIG. 20  illustrates a block diagram of a portable electronic device, in accordance with some described embodiments. 
         FIG. 21  illustrates a block diagram of an electronic device, showing several inputs and associated outputs generated based on the inputs used to locate a touch input to the electronic device, in accordance with some described embodiments. 
         FIG. 22  illustrates a schematic diagram of a machine learning algorithm, in accordance with some described embodiments. 
         FIG. 23  illustrates a flowchart showing a method for selecting an icon on a display layer of a wearable electronic device when a liquid is present, in accordance with some described embodiments. 
     
    
    
     Those skilled in the art will appreciate and understand that, according to common practice, various features of the drawings discussed below are not necessarily drawn to scale, and that dimensions of various features and elements of the drawings may be expanded or reduced to more clearly illustrate the embodiments of the present invention described herein. 
     DETAILED DESCRIPTION 
     Reference will now be made in detail to representative embodiments illustrated in the accompanying drawings. It should be understood that the following descriptions are not intended to limit the embodiments to one preferred embodiment. To the contrary, it is intended to cover alternatives, modifications, and equivalents as can be included within the spirit and scope of the described embodiments as defined by the appended claims. 
     In the following detailed description, references are made to the accompanying drawings, which form a part of the description and in which are shown, by way of illustration, specific embodiments in accordance with the described embodiments. Although these embodiments are described in sufficient detail to enable one skilled in the art to practice the described embodiments, it is understood that these examples are not limiting such that other embodiments may be used, and changes may be made without departing from the spirit and scope of the described embodiments. 
     The following disclosure relates to reliable user interface with a display of an electronic device when liquid is present on the display. Touchscreens, or displays with a touch input component, often use capacitive technology to detect the location of a touch input, or user input, to the touchscreen by measuring a change in electrostatic field caused by the touch input. In some cases, the liquid causes a change in the electrostatic field, regardless of whether the touch input is detected. As a result, the liquid reduces the ability of the touch input component to accurately detect the location of the touch input, and in some cases, the touch input component cannot detect the touch input at all. However, electronic devices described herein may include several enhancements to overcome these issues. 
     For example, electronic devices described herein may include a force detection assembly designed to detect an amount of force applied to the display by a touch input. The force detection assembly relies on a change in capacitance due to the change in distance between electrodes of the capacitors. The capacitance C can be determined by 
             C   =         ɛ   0     ⁢     ɛ   r     ⁢   A     d           
where ε 0  is the permittivity of air, ε r  is the permittivity of the dielectric material between the electrodes, A is the area of the electrode, and d is the distance between the electrodes. It can readily be determined by one of ordinary skill in the art that capacitance C is inversely proportional to the distance d.
 
     The force detection assembly may include one or more (modular) force detection units. When multiple force detection units are used, a difference in detected capacitance among the force detection units may result from a touch input, and the difference is used to determine the location of the touch input. The electronic device can use the location information from the force detection assembly to determine the location of the touch input, even when the electronic device deactivates the touch input component due to the detected presence of liquid. 
     Alternatively, the touch input component can remain active under the presence of liquid. For example, in combination with the force detection assembly, electronic devices described herein may integrate the touch input component and use a confidence internal algorithm to more accurately identify a location of the touch input, thereby providing the electronic device with a greater confidence level that the determined touch input corresponds to the actual touch input. In an example scenario, when one or more liquid droplets are located on the display, the touch input component may register a potential touch input at each location of the liquid droplets. Using the force detection assembly, the confidence interval algorithm can generate a centroid, or center of mass location, that defines a center point of the touch input. This may be referred to as a touch input centroid or a user input centroid. The confidence internal algorithm can build a confidence interval around the touch input centroid, corresponding to an estimated region on the display in which the touch input by the user may have occurred. The confidence internal algorithm can determine which liquid droplet(s) are within (or at least partially within) the confidence interval and which liquid droplet(s) are not within the confidence interval. Using location information from the touch input component, the confidence interval algorithm can further create centroids for each liquid droplet that is determined to be at least partially within the confidence interval. This may be referred to as a liquid droplet centroid (or centroids). The confidence internal algorithm can then determine the distance (in a two-dimensional plane) from each liquid droplet centroid to the touch input centroid, and subsequently determine the location of the liquid droplet centroid that is closest to the touch input centroid. The electronic device can use the location information of the selected liquid droplet centroid to determine the location of the touch input, or at least an approximated location. 
     It should be noted that electronic devices described herein can distinguish the touch input centroid (determined by the force detection assembly) from the liquid droplet centroids (determined by the touch input component) not only by using the different sensing components, but also by the difference in applied force. Regarding the latter, an applied force associated with the user&#39;s touch input is substantially greater as compared to an applied force associated with liquid droplets. Moreover, the force detection assembly may be configured to require a threshold amount of applied force that is greater than an applied force commonly known for a liquid droplet (or droplets) such that the force detection assembly does not detect the liquid droplet(s). 
     In combination with the force detection assembly, the touch input component, and the confidence internal algorithm, electronic devices described herein may integrate a machine learning algorithm. The machine learning algorithm can access historical data of user preferences stored on the electronic device and predict a desired user request (to the electronic device) based on the historical data. As an example, when the user is selecting an icon (representing a software application) on the display, the machine learning algorithm can use location-based data to predict the software application intended for selection by the user based a current location of the user (more specifically, the current location of the electronic device). The predicted software application may include a software application most commonly used by the user at the current location, as determined by a ranking of software applications used at the current location. In another example, when the user is selecting an icon on the display, the machine learning algorithm can use time-based data to predict the software application intended for selection by the user based on a current time. The predicted software application may include a software application most commonly used at the current time (or an interval around the current time), as determined by a ranking of software applications used at the current time. 
     The machine learning algorithm may provide a confirmation that the location of the touch input, as determined by the force detection assembly or the confidence interval algorithm, is accurate. For example, when the machine learning algorithm predicts the software application corresponding to the icon that is selected by the touch input, the location of which is determined by the confidence interval algorithm, the electronic device can then determine, using the machine learning algorithm, that the intended software application to be opened is correct. Accordingly, the machine learning algorithm can provide the electronic device with a greater confidence level that the determined touch input corresponds to the user-intended selection of the software application. 
     These and other embodiments are discussed below with reference to  FIGS. 1-23 . However, those skilled in the art will readily appreciate that the detailed description given herein with respect to these Figures is for explanatory purposes only and should not be construed as limiting. 
       FIG. 1  illustrates an isometric view of an embodiment of an electronic device  100 , in accordance with some described embodiments. As shown in  FIG. 1 , the electronic device  100  includes a wearable electronic device. For example, the electronic device  100  includes an enclosure  102 , or housing, as well as a band  104   a  and a band  104   b  coupled to the enclosure  102 . The band  104   a  may couple together with the band  104   b  to form a loop that secures around a user&#39;s appendage (such as a wrist) in order to secure the enclosure  102  (and more generally, the electronic device  100 ) to the user&#39;s appendage. The enclosure  102  may be formed from materials, such as steel (including stainless steel), ceramic, plastic, aluminum, or some combination thereof, as non-limiting examples. The enclosure  102  defines an internal volume designed to carry several components, such as a processor circuit, a memory circuit, a flexible circuit, a battery, a speaker module, and a microphone, as non-limiting examples. 
     The electronic device  100  may further include a transparent layer  106  coupled to the enclosure  102 . The transparent layer  106  can provide a transparent protective cover for a display assembly  108  (shown as a dotted line). The display assembly  108  may include a display layer (not shown in  FIG. 1 ) designed to present visual information in the form of motion images, still images, and/or textual information. The display layer may further present icons representing software application that can be executed and presented on the display layer. In order to interact with the display layer to change the visual information, the display assembly  108  may further include a touch input component (not shown in  FIG. 1 ). This will be further shown below. Furthermore, the electronic device  100  may include an input mechanism  112  that represents a mechanical feature used to alter the visual information presented by the display layer of the display assembly  108 . As shown in  FIG. 1 , the input mechanism  112  is a dial designed to rotate about an axis of rotation to alter the visual information, and can also be depressed and actuated toward the enclosure  102  to alter the visual information. Although not shown, the electronic device  100  may further include additional input mechanisms in the form of button and switches, both of which are mechanical features with which a user can interact. 
       FIG. 2  illustrates an isometric view of an alternative embodiment of an electronic device  200 , in accordance with some described embodiments. As shown in  FIG. 2 , the electronic device  200  includes a mobile wireless communication device, such as a smartphone or a tablet computer. The electronic device  200  includes an enclosure  202 , or housing. The enclosure  202  defines an internal volume designed to carry several components, such as a processor circuit, a memory circuit, a flexible circuit, a battery, a speaker module, and a microphone, as non-limiting examples. The electronic device  200  may further include a transparent layer  206  designed to provide a transparent protective cover. The electronic device  200  may further include a display assembly  208  (shown as a dotted line). The display assembly  208  may include display layer (not shown in  FIG. 2 ) that is designed to present visual information in the form of motion images, still images, and/or textual information. The display layer of the display assembly  208  may further present icons representing software application that can be executed and presented on the display assembly  208 . In order to interact with the display assembly  208  to change the visual information, the display assembly  208  may further include a touch input component (not shown in  FIG. 2 ) and a force detection assembly (not shown in  FIG. 2 ). These types of features for electronic devices will be further shown below. Furthermore, the electronic device  200  may include an input mechanism  212  that represents a button that can be depressed to alter the visual information presented by the display assembly  208 . Although not shown, the electronic device  200  may further include additional input mechanisms in the form of button and switches, both of which are mechanical features with which a user can interact. 
       FIG. 3  illustrates a plan view that partially shows the electronic device  100  in  FIG. 1 , further showing a force detection assembly  114 . As shown, the transparent layer  106  covers the force detection assembly  114 . In this regard, the force detection assembly  114  is designed to determine an amount of applied force to the transparent layer  106 . In particular, the force detection assembly  114  is designed to determine an amount of applied force from a user while interacting with the display assembly  108  by, for example, depressing the transparent layer  106 . While the a touch input component of the display assembly  108  and the force detection assembly  114  rely upon the user interacting with the display assembly  108 , the touch input component of the display assembly  108  responds on a touch input by the presence of a user&#39;s finger (with minimal force required), while the force detection assembly  114  requires a threshold amount of applied force. In this regard, the electronic device  100  may include two distinguishable touch input events. The force detection assembly  114  may include multiple force detection units, such as a force detection unit  118   a , a force detection unit  118   b , a force detection unit  118   c , and a force detection unit  118   d . Each force detection unit may include a pair of electrode layers used to form several capacitors. When a user depresses, or touches, the transparent layer  106 , the transparent layer  106  can move, thereby causing the distance between some electrode layers to change. The change in the distance between the electrode layers may cause a change in capacitance of the capacitors. 
       FIG. 4  illustrates a cross sectional view of the electronic device  100  shown in  FIG. 3  taken along line A-A, showing additional features of the force detection assembly  114 . As shown, the force detection assembly  114  is secured with the transparent layer  106  and the enclosure  102  by an adhesive layer  116   a  and an adhesive layer  116   b , respectively. A force detection unit  118   a , representative of other force detection units, is shown. The force detection unit  118   a  may include an electrode layer  122   a  and an electrode layer  122   b , with a dielectric material  124  separating the electrode layer  122   a  from the electrode layer  122   b . The dielectric material  124  may include a compliant material, such as silicone, designed to compress in response to at least some forces applied to the transparent layer  106 . 
     The aforementioned electrode layers of the force detection unit  118   a  may act as several capacitors. For example, as shown in the enlarged view, the electrode layer  122   a  and the electrode layer  122   b  may form a capacitor  126   a , a capacitor  126   b , a capacitor  126   c , and a capacitor  126   d . These capacitors may represent several additional capacitors of the force detection unit  118   a . Also, each capacitor includes two electrodes plates. For example, the capacitor  126   a  includes an electrode plate  128   a  and an electrode plate  128   b . A voltage differential may be applied between the electrode plate  128   a  and the electrode plate  128   b , causing an electrostatic field between the electrode plate  128   a  and the electrode plate  128   b . The remaining capacitors of the force detection unit  118   a  may include similar features. 
       FIG. 5  illustrates a cross sectional view of the electronic device  100  shown in  FIG. 3  taken along line B-B, showing additional features of the electronic device  100 . As shown, the display assembly  108  includes a touch input component  132  and a display layer  134 . The touch input component  132  and the display layer  134  may include a rectangular shape similar to that of the transparent layer  106 , and may cover a substantial portion of the backside of the transparent layer  106 . Also, the touch input component  132  may cover the display layer  134 . The touch input component  132  may include a touch sensitive layer. For example, the touch input component  132  may include an indium tin oxide (“ITO”) applied to a back surface of the transparent layer  106 . The touch input component  132  may use capacitive sensing technology that provides an electrostatic field. In this regard, when a user provides a touch input to the electronic device  100  to alter visual information on the display layer  134 , the touch input component  132  responds by a change in the electrostatic field at a location corresponding to the touch input. The electronic device  100  may use the location of the changed electrostatic field to determine the location of the touch input. 
     The display assembly  108  is surrounded by a force detection unit  118   b  and a force detection unit  118   c . As shown, the force detection unit  118   b  and the force detection unit  118   c  may include electrode layers (not labeled) designed to form several capacitors, in a manner similar to what is shown and described for the force detection unit  118   a  (shown in  FIG. 4 ). Any additional force detection units may include similar properties and features. The touch input component  132  and the display layer  134  may electrically couple to a circuit board  136  by flexible circuits  138 . During operation of the touch input component  132  and/or the display layer  134 , the flexible circuits  138  may generate capacitance. However, the force detection unit  118   b  and the force detection unit  118   c  (as well as remaining force detection units) may undergo a calibration operation to adjust for the capacitance generated from the flexible circuits  138 . 
     In  FIGS. 4 and 5 , the transparent layer  106  is horizontal, or at least generally parallel with respect to a horizontal plane  140  (shown in  FIG. 5 ). As a result, the separation between the electrode layers (such as the electrode layer  122   a  and the electrode layer  122   b ) of the force detection units is the same, or least generally the same. However, when an applied force causes the transparent layer  106  to move such that the transparent layer  106  is no longer parallel with respect to the horizontal plane  140 , the separation between some electrode layers can change. Moreover, the distance between the electrode layers at one location of a force detection unit may vary with respect to the distant between electrode layers at another location, causing a capacitance change that can be used by the electronic device  100  to determine a location of a touch input. This will be further shown and discussed below. 
       FIG. 6  illustrates a plan view that partially shows the electronic device  100 , showing liquid droplets partially covering the transparent layer  106 . As shown, a liquid droplet  142   a , a liquid droplet  142   b , a liquid droplet  142   c , and a liquid droplet  142   d  are on the transparent layer  106 . The liquid droplets may include an water-based liquid. Due to the presences of the liquid droplets on the transparent layer  106 , the touch input component  132  (shown in  FIG. 5 ) of the display assembly  108  may detect at least some of the liquid droplets and register the detected liquid droplet as touch inputs by a user. In this regard, the liquid droplets can result in false positives, and may obscure or limit the ability of the electronic device  100  to detect a location of the user input. However, in some instances, when the touch input component  132  (shown in  FIG. 5 ) of the display assembly  108  detects a predetermined change in the electrostatic field associated with the presence of a liquid droplet (or droplets) on the transparent layer  106 , the electronic device  100  may deactivate the touch input component  132  and use the force detection assembly  114  to determine a user input location. For example, a user input location  144  (denoted as an “x”) represents a location of a user interaction, or touch input, with the display assembly  108 , as determined by the force detection assembly  114 . The process by which the force detection assembly  114  determines the user input location  144  will be described below. 
       FIG. 7  illustrates an isometric view of the transparent layer  106  and the force detection assembly  114 , showing the force detection assembly  114  responding to a touch input to the transparent layer. When the applied force from the user input location  144  causes movement of the transparent layer  106 , at least some of the applied force is transmitted to the force detection assembly  114 . This is due in part to the transparent layer  106  being formed from a relatively rigid layer whereas the dielectric material of the force detection assembly  114  is formed from a compliant material. As a result, a corresponding movement of the force detection assembly  114  may occur. For example, the force detection assembly  114  (in particular, the dielectric material) may initially include a dimension  146  (or first height). Due to the applied force from the user input location  144 , the force detection assembly  114  may also include a dimension  148  (or second height) that is less than the dimension  146 . This is due to the user input location  144  being substantially closer to a corner of the force detection assembly  114  associated with the dimension  148 . 
     As a result, the electrode layers of the force detection units may undergo relative movement. For example,  FIG. 7  shows the electrode layer  122   a  and the electrode layer  122   b  of the force detection unit  118   a . The electrode layer  122   a  is separated from the electrode layer  122   b  by a dimension, or gap, at one end (closer to the dimension  146 ) that is greater than the dimension between the electrode layer  122   a  and the electrode layer  122   b  at an opposing end (closer to the dimension  148 ). This may result in a difference in capacitance, or capacitance differential, across the electrode layers. Furthermore, as a result of the user input location  144 , the dimension between electrode layers of different force detection units may differ. For example, the dimension between the electrode layer  122   a  and the electrode layer  122   b  is less than the dimension between an electrode layer  122   c  and an electrode layer  122   d , with the electrode layer  122   c  and the electrode layer  122   d  being part of the force detection unit  118   c.    
       FIG. 8  illustrates a cross sectional view of the transparent layer  106  and the force detection assembly  114  shown in  FIG. 7 , showing a change in separation between the electrode layers of the force detection unit  118   a . As shown, a distance  152  separates the electrode layer  122   a  from the electrode layer  122   b  at one end of the force detection unit  118   a , while a distance  154  separates the electrode layer  122   a  from the electrode layer  122   b  at the other end of the force detection unit  118   a . Due to the applied force from the user input location  144  being biased at one end of the transparent layer  106 , the distance  152  is less than the distance  154 . As a result, the distance between the electrode plates of the capacitors (formed by the electrode layer  122   a  and the electrode layer  122   b ) may be different in different locations of the force detection unit  118   a . For example, the electrode plates of the capacitor  126   a  are further apart than the electrode plates of the capacitor  126   d . Accordingly, for the same area of the electrode plates and the same applied charge to the capacitor  126   a  and the capacitor  126   d , the capacitance of the capacitor  126   d  is greater than that of the capacitor  126   a . Further, the capacitance between consecutive electrode plates gradually increases from the capacitor  126   a  to the capacitor  126   d , due to the gradual decrease in distance between the electrode plates (as shown in  FIG. 8 ). The electronic device  100  can use the capacitance information from the force detection unit  118   a  to determine a location, or at least an approximate location, of the user input location  144  along the Y-axis. 
       FIG. 9  illustrates a cross sectional view of the transparent layer  106  and the force detection assembly  114  shown in  FIG. 7 , showing a change in separation between the electrode layers of two separate force detection units. As shown, a distance  156  separates the electrode layer  122   a  from the electrode layer  122   b , while a distance  158  separates the electrode layer  122   c  from the electrode layer  122   d . Due to the applied force from the user input location  144  being biased at one end of the transparent layer  106 , the distance  156  is less than the distance  158 . As a result, the distance between the electrode plates of the capacitors of the force detection unit  118   a  (formed by the electrode layer  122   a  and the electrode layer  122   b ) may be different than the distance between the electrode plates of the capacitors of the force detection unit  118   c  (formed by the electrode layer  122   c  and the electrode layer  122   d ). For example, the electrode plates of the capacitor  126   e  are further apart than the electrode plates of the capacitor  126   d . Accordingly, for the same area of the electrode plates and the same applied charge to the capacitor  126   d  and the capacitor  126   e , the capacitance of the capacitor  126   d  is greater than that of the capacitor  126   e . Generally, given the location of the user input location  144 , the electrode plates of capacitors of the force detection unit  118   a  are closer together than the electrode plates of the force detection unit  118   c , and accordingly, the capacitance of a capacitor of the force detection unit  118   a  is greater than the capacitance of a capacitor of the force detection unit  118   c . The electronic device  100  can use the differential capacitance information, between the force detection unit  118   a  and the force detection unit  118   c , to determine a location, or at least an approximate location, of the user input location  144  along the X-axis. 
       FIGS. 8 and 9  show that capacitive information of a single force detection unit (such as the force detection unit  118   a ) can provide a location of the user input location  144  in one axis, while the differential capacitive information between two force detection units (such as the force detection unit  118   a  and the force detection unit  118   c ) can provide a location of the user input location  144  in another axis. Given the capacitive and differential capacitance information, the electronic device  100  force can determine a location of the user input location  144  along a two-dimensional (X-Y) plane using the force detection assembly  114 . Although not indicated in the described example, capacitive information and/or differential capacitance information from the force detection unit  118   b  and/or the force detection unit  118   d  can further be used to more accurately determine the location of the user input location  144 . 
     Referring to  FIG. 6 , the display assembly  108  can be divided into four quadrants, labeled as a quadrant  162   a , a quadrant  162   b , quadrant  162   c , and a quadrant  162   d . Based on the capacitance information obtained by the force detection unit  118   a  (shown in  FIGS. 7 and 8 ), the electronic device  100  can determine the user input location  144  more likely located in the quadrant  162   c , as compared to the quadrant  162   a  or the quadrant  162   b . Further, based on the differential capacitance information between the force detection unit  118   a  and the force detection unit  118   c  (shown in  FIG. 9 ), the electronic device  100  can determine the user input location  144  more likely located in the quadrant  162   c , as compared to the quadrant  162   a  or the quadrant  162   d . Taken together, the electronic device  100  can determine the user input location  144  is located in the quadrant  162   c . Accordingly, the electronic device  100  can determine at least an approximate location of the user input location  144  without using the touch input component  132  (shown in  FIG. 5 ) of the display assembly  108 . This may be useful when liquid droplets area present on the transparent layer  106  and capable of causing false positive user inputs. It should be noted that the force detection assembly  114  may respond to locate the user input location  144  when the user input location  144  is in any of the remaining quadrants. 
       FIG. 10  illustrates a plan view that partially shows the electronic device  100 , showing a user  164  interacting with the electronic device  100 , in accordance with some described embodiments. As shown, the liquid droplet  142   a , the liquid droplet  142   b , the liquid droplet  142   c , and the liquid droplet  142   d  are present on the transparent layer  106 . The liquid droplet  142   a , the liquid droplet  142   c , and the liquid droplet  142   d  may be detected by the touch input component (not shown in  FIG. 10 ) of the display assembly  108 , which can trigger a false touch input (or inputs). The liquid droplet  142   b  is in contact with the enclosure  102 , and may provide an electrical ground (when the enclosure  102  includes an electrically conductive material), which can cause the display assembly  108  to incorrectly determine a touch input by the user  164 . 
     To determine an initial location of the user input location  144 , the electronic device  100  can use information from the force detection assembly  114 , such as the capacitive and the differential capacitive information, in a manner shown and described above. In addition to the location information determined using the force detection assembly  114 , the electronic device  100  may also use additional resources, such as the touch input component  132  (shown in  FIG. 5 ) to determine the location of the user input location  144 . In this regard, in some instances, the electronic device  100  may not deactivate the touch input component  132 . 
       FIG. 11  illustrates a plan view that partially shows the electronic device  100 , showing a confidence interval  170  generated by a confidence interval algorithm, in accordance with some described embodiments. The confidence interval algorithm is designed to use the location information of a user input, as determined by the force detection assembly  114 , and provide an additional step to more accurately determine an actual location of the user input, or at least provide the electronic device  100  with greater confidence that the determined touch input corresponds to (or is aligned with) the actual location of the touch input. The confidence interval algorithm may include a set of instructions stored on memory circuit that is subsequently run by a processor circuit. These features will be discussed in a schematic diagram below. The confidence interval algorithm (and/or another program of the electronic device  100 ) can use the location of the user input location  144  (shown in  FIG. 10 ), as determined by the force detection assembly  114 , and create a user input centroid  174 . In some instances, the force detection assembly  114  is used to create the user input centroid  174 , or a combination of the force detection assembly  114  and the confidence interval algorithm is used. The user input centroid  174  may refer to a center of mass location of the user input location  144 . Accordingly, the user input centroid  174  may generally define a central point of the user input location  144 . The user input centroid  174  may define a centroid location for a touch input by a user. 
     The confidence interval algorithm may build a confidence interval  170  (circle shown as a dotted line) around the user input centroid  174  As shown, the confidence interval  170  includes a radius r 1  that extends from the user input centroid  174 . The radius r 1  of the confidence interval  170  is based in part on the amount of applied force (as determined by the force detection assembly  114 ). For example, when the force detection assembly  114  determines a relatively high applied force by the user, the confidence interval  170  may include a smaller radius, as compared to the radius r 1 . The relatively high force provides provide the electronic device  100  can create additional certainty that the user input location  144  (shown in  FIG. 10 ) is correctly and accurately determined. This will be further shown below. 
     The electronic device  100  may use information from the touch input component (shown in  FIG. 3 ) of the display assembly  108  to determine the location of the liquid droplets, and the confidence interval algorithm may determine whether the liquid droplets fall within the confidence interval  170 . As shown, the liquid droplet  142   c  and the liquid droplet  142   d  at least partially fall within the confidence interval  170 , while an outer perimeter of the liquid droplet  142   a  and the liquid droplet  142   b  do not fall within the confidence interval  170 . As a result, the confidence interval algorithm can then determine that the liquid droplet  142   c  and the liquid droplet  142   d  are closer in proximity to the user input centroid  174 , as compared to the liquid droplet  142   a  and the liquid droplet  142   b . In this regard, the liquid droplet  142   c  and the liquid droplet  142   d  may define a subset of liquid droplets used by the confidence algorithm for further evaluation of the location of the touch input. 
     The confidence interval algorithm (and/or another program) can use location as well as the diameter of the liquid droplets, as determined by the display assembly  108 , and create liquid droplet centroids for the liquid droplets that fall at least partially within the confidence interval  170 . In some instances, the display assembly  108  is used to create the user input centroid  174 , or a combination of the display assembly  108  and the confidence interval algorithm is used. The liquid droplet centroids may generally define a central point for each droplet. The liquid droplet centroids may define a centroid location for each liquid droplet. For example, the confidence interval algorithm may create a liquid droplet centroid  178   c  and a liquid droplet centroid  178   d  based on the location and diameter of the liquid droplet  142   c  and the liquid droplet  142   d , respectively. Subsequently, the confidence interval algorithm can evaluate the distance from the user input centroid  174  to the liquid droplet centroids, and determine which liquid droplet centroid is closer to the user input centroid  174 . As shown in  FIG. 11 , the liquid droplet centroid  178   c  is closer to the user input centroid  174 , as compared to the liquid droplet centroid  178   d . As a result, the confidence interval algorithm can provide location information of the liquid droplet centroid  178   c  to the electronic device  100 . The electronic device  100  may use the location information of the liquid droplet centroid  178   c  as the location of the user input. In this regard, the liquid droplet centroid  178   c  may represent an updated user input, as the liquid droplet centroid  178   c  may be a revised user input as compared to that of the user input location  144  (shown in  FIG. 10 ), with the location of the revised user input more accurately representing the location of the actual touch input by the user. Accordingly, the electronic device  100  may use both the display assembly  108  and the force detection assembly  114  to determine a location of the user input when liquid is present on the transparent layer  106  and detected by the display assembly  108 . While  FIGS. 10 and 11  show and describe the touch input component of the display assembly  108  determining a location of the liquid droplets, other implementations are possible. For example, the touch input component of the display assembly  108  can be configured to determine whether a liquid droplet(s) is/are present, as opposed to determining both the presence and location of liquid droplets. In other words, in some implementations, the touch input component of the display assembly  108  only determines whether or not liquid is present on the transparent layer  106 . In this implementation, the force detection assembly  114  can determine the location of the user input location  144 . 
       FIGS. 12 and 13  illustrate a scenario in which a user  164  provides a user input with a greater amount of applied force, as compared to scenario showed in  FIGS. 10 and 11 .  FIG. 12  illustrates a plan view that partially shows the electronic device  100 , showing the user  164  providing an increased applied force (denoted by several lines near the user  164 ) during interaction with the electronic device  100 . As shown, the force detection assembly  114  determines the user input location  144  of the user  164 . 
       FIG. 13  illustrates a plan view that partially shows the electronic device  100 , showing an updated confidence interval generated by the confidence interval algorithm, in accordance with some described embodiments. As shown, the confidence interval algorithm (and/or another program) creates the user input centroid  174  and builds a confidence interval  270  around the user input centroid  174 . Due to the increase applied force by the user  164  (as shown in  FIG. 12 ), the electronic device  100  can determine, with greater accuracy, the location of the touch input. As a result, the confidence interval  270  includes a radius r 2 , which is shorter than the radius r 1  (shown in  FIG. 11 ), and the confidence interval  270  includes an area that is smaller than that of the confidence interval  170  (shown in  FIG. 11 ). As shown in  FIG. 13 , an outer perimeter of the liquid droplet  142   d  is not within the confidence interval  270 , and the liquid droplet  142   c  represents the only liquid droplet at least partially within the confidence interval  270 . The confidence interval algorithm may generate the liquid droplet centroid  178   c  and the electronic device  100  may use the location information of the liquid droplet centroid  178   c  as the location of the user input, similar to a manner previously described. When fewer liquid droplets fall within a confidence interval  270  that is relatively smaller in area, the electronic device  100  may determine the location the touch input with relatively higher confidence. 
       FIGS. 12 and 13  illustrate a step for enhanced accuracy for locating the touch input. In this manner, a manufacturer of the electronic device  100  may notify users the providing a relatively higher applied force to the transparent layer  106  (without causing damage) can provide better accuracy for determining the location of the touch input. 
     In combination with using both a force detection assembly and a touch input component of a display assembly, the electronic device  100  can integrate additional features. For example, the electronic device  100  may incorporate a machine learning algorithm (or algorithms). The machine learning algorithm can be designed to receive information and data from other software applications and features from user interaction with the electronic device  100 . The information and data may include frequency of use of software applications, location information (such as where the user used the software applications), and time/day information (such as when the user used the software applications), as non-limiting examples. Based on the received information and data, the machine learning algorithm can “learn” the user&#39;s behavior and predict an output from the behavior. The predicted output may include the software application the user intends to select/open when using the electronic device  100 . 
       FIGS. 14-17  show examples of incorporating a machine learning algorithm with a touch input event to the display assembly  108  of the electronic device  100 . When a user provides a touch input to the display assembly  108 , the user intends some desired outcome. The desired outcome may include opening or closing a software application, as non-limiting examples. In this regard, when the force detection assembly  114  and the display assembly  108  (including a touch input component) determine the location of the touch input by a user to the display assembly  108 , the machine learning algorithm may predict the software application the user intended to open or close. Accordingly, when the electronic device  100  determines the user&#39;s touch input corresponds to, for example, opening the software application and the machine learning algorithm predicts the same software application, the machine learning algorithm provides a confirmation to the electronic device  100  of the user&#39;s intent to open the software application. The machine learning algorithm may be beneficial during situations in which liquid droplets are on the transparent layer  106  and detected by the display assembly  108 . 
       FIG. 14  illustrates a plan view partially showing the electronic device  100 , showing the electronic device  100  presenting several software applications on the display assembly  108 , in accordance with some described embodiments. For purposes of simplicity, the force detection assembly  114  (shown in  FIG. 3 ) is not shown. The display assembly  108  can present several icons, with each icon representing a software application that can be executed and presented on the display assembly  108 . As shown, the display assembly  108  is presenting an icon  182   a , an icon  182   b , an icon  182   c , and an icon  182   d . Also, a liquid droplet  242   a  and a liquid droplet  242   b  are present on the transparent layer  106  and may be detected by the display assembly  108 . 
     In the example, a user intends to select/open the icon  182   d . As shown, the icon  182   d  represents a fitness/activity software application, which may include a calorie-burning software application or another fitness-related burning application. However, due in part to the liquid droplet  242   a  and the liquid droplet  242   b , the display assembly  108  may not accurately locate the user&#39;s intended touch input to the icon  182   d . In this regard, the force detection assembly  114  (not shown in  FIG. 14 ) can determine a location of a touch input. The display assembly  108  can determine the locations and size of the liquid droplet  242   a  and the liquid droplet  242   b . The confidence interval algorithm can use the location of the touch input to generate a user input centroid  374  and build a confidence interval  370  around the user input centroid  374 . The confidence interval algorithm (and/or another program of the electronic device  100 ) can then determine whether the liquid droplet  242   a  and/or the liquid droplet  242   b  at least partially fall within the confidence interval  370 . 
     As shown in  FIG. 14 , only the liquid droplet  242   b  falls at least partially within the confidence interval  370 . Accordingly, the confidence interval algorithm can determine that the liquid droplet  242   b  is closer to the user input centroid  374 , as compared to the liquid droplet  242   a . The confidence interval algorithm can create a liquid droplet centroid  278   b  based upon the liquid droplet  242   b  and the droplet location information provided by the display assembly  108 . As a result, the confidence interval algorithm may provide an input to the electronic device  100  that the location of the touch input corresponds to the location of the liquid droplet centroid  278   b.    
     The location of the touch input, as determined by the confidence interval algorithm, may indicate the user intends to select/open the icon  182   d . However, due in part to the liquid droplets, the determined location of the touch input may be accurate but with less confidence, or may not be accurate. In order to confirm the selection, the electronic device  100  may use the machine learning algorithm. For example, the machine learning algorithm can include a location-based machine learning algorithm that receives context-aware information, such as current location of the user&#39;s (or technically, current location of the electronic device  100 ). The machine learning algorithm can also receive historical information related to the software applications that the user uses at the current location (or within a radius around the current location), along with a ranking of the software applications. The ranking can include a list of the software application in descending order from most frequently used to least frequently used. The machine learning algorithm can also receive a list of software applications currently represented by an icon on the display assembly  108 . In some instances, the machine learning algorithm may cross-reference and subsequently remove software applications from the ranked list if they do not appear on the list of software applications currently represented by an icon(s) on the display assembly  108 , thereby reducing the number of possible software applications from which to select (and increasing the probability of correctly selecting the software application intended for selection by the user). Using this information, the machine learning algorithm can predict which software application the user intended to select, and accordingly, can predict which the icon to which the user intended to provide a user input. 
     In the example in  FIG. 14 , the current location  184  of the user is a fitness center, or gymnasium. The machine learning algorithm may receive the current location  184 , along with the list of software applications used by the user, in a ranked order, at the current location  184 . The machine learning algorithm can also receive a list of software applications, with each software application represented by one of the icon  182   a , the icon  182   b , the icon  182   c , and the icon  182   d . If, for example, the user uses a fitness-related software application, represented by the icon  182   d , at the current location  184  more frequently than other software applications (including those represented by the aforementioned icons), the machine learning algorithm may predict the user intended to select the icon  182   d . The electronic device  100  may receive the predicted information from the machine learning algorithm as a confirmation that the user intended to select the icon  182   d , as determined by the confidence interval algorithm. 
       FIG. 15  illustrates a plan view of the electronic device  100 , showing the display assembly  108  presenting a selected software application. The software application  192  is a fitness/activity software application represented by the icon  182   d  (shown in  FIG. 14 ). The software application  192  includes several options for the user to choose from. For example, the software application  192  includes a walking option  194   a  and a running option  194   b . Due to the presence of the liquid droplet  242   a  and the liquid droplet  242   b , the electronic device  100  may subsequently require at least some combination the force detection assembly  114 , the display assembly  108  (including the touch input component), the confidence interval algorithm, and the machine learning algorithm to predict which option the user selects from the software application  192 . The process for determining a location of the touch input to the display assembly  108  to determine whether the walking option  194   a  and the running option  194   b  is selected can be repeated using the processes described above. In order to further predict a subsequent user selection, the machine learning algorithm may receive additional information, such as a ranking (in terms of frequency) of the type of activity (walking option  194   a  or running option  194   b ) the user selects from the software application  192  while at the current location  184 , the type of activity most frequently used at the current time, and/or the type of activity most frequently used during the current day of the week. 
       FIG. 16  illustrates a plan view partially showing the electronic device  100 , showing the electronic device  100  presenting several software applications on the display assembly  108 , in accordance with some described embodiments. For purposes of simplicity, the force detection assembly  114  (shown in  FIG. 3 ) is not shown. As shown, the display assembly  108  is presenting an icon  282   a , an icon  282   b , an icon  282   c , and an icon  282   d . The liquid droplet  342   a  and the liquid droplet  342   b  are again present on the transparent layer  106  and may be detected by the display assembly  108 . 
     In the example, a user intends to select/open the icon  282   d . As shown, the icon  282   d  represents an email software application. However, due in part to the liquid droplet  342   a  and the liquid droplet  342   b , the display assembly  108  may not accurately the user&#39;s intended touch input to the icon  282   d . In this regard, the force detection assembly  114  (not shown in  FIG. 16 ) can determine a location of a touch input. The display assembly  108  can determine the locations and size of the liquid droplet  342   a  and the liquid droplet  342   b . The confidence interval algorithm can use the location of the touch input to generate a user input centroid  474  and build a confidence interval  470  around the user input centroid  474 . The confidence interval algorithm can then determine whether the liquid droplet  342   a  and/or the liquid droplet  342   b  at least partially fall within the confidence interval  370 . 
     As shown in  FIG. 16 , only the liquid droplet  342   b  falls at least partially within the confidence interval  370 . The confidence interval algorithm can determine that the liquid droplet  342   a  is closer to the user input centroid  474 , as compared to the liquid droplet  342   b . The confidence interval algorithm can create a liquid droplet centroid  378   a  based upon the liquid droplet  342   b  and the droplet location information provided by the display assembly  108 . As a result, the confidence interval algorithm may provide an input to the electronic device  100  that the location of the touch input corresponds to the location of the liquid droplet centroid  378   a.    
     The location of the touch input, as determined by the confidence interval algorithm, may indicate the user intends to select/open the icon  282   b . However, as shown in  FIG. 16 , the liquid droplet centroid  378   a  is not overlapping the icon  282   b . Accordingly, due in part to the liquid droplets, the determined location of the touch input may be accurate but with less confidence, or may not be accurate. In order to confirm the selection, the electronic device  100  may again use the machine learning algorithm. The machine learning algorithm can include a time-based machine learning algorithm that receives context-aware information, such as the current time. The machine learning algorithm can also receive historical information related to the software applications that the user uses at the current time (or within a time interval around the current time), along with a ranking of the software applications. The ranking can include a list of the software application in descending order from most frequently used to least frequently used. The machine learning algorithm can also receive a list of software applications currently represented by an icon on the display assembly  108 . In some instances, the machine learning algorithm may cross-reference and subsequently remove software applications from the ranked list if they do not appear on the list of software applications currently represented by an icon(s) on the display assembly  108 , thereby reducing the number of possible software applications from which to select (and increasing the probability of correctly selecting the software application intended for selection by the user). Using this information, the machine learning algorithm can predict which software application the user intended to select, and accordingly, can predict which the icon to which the user intended to provide a user input. 
     In the example in  FIG. 16 , the current time  196  is displayed on the display assembly  108 . The machine learning algorithm may receive the current time  196 , along with the list of software applications, in ranked order, used by the user at the current time  196 . The machine learning algorithm can also receive a list of software applications, with each software application represented by one of the icon  282   a , the icon  282   b , the icon  282   c , and the icon  282   d . If, for example, the user uses an email software application, represented by the icon  282   b , at the current time  196  more frequently than other software applications (including those represented by the aforementioned icons), the machine learning algorithm may predict the user intended to select the icon  282   b . The electronic device  100  may receive the predicted information from the machine learning algorithm as a confirmation that the user intended to select the icon  282   b , as determined by the confidence interval algorithm. 
       FIG. 17  illustrates a plan view of the electronic device  100 , showing the display assembly  108  presenting a selected software application. The software application  292  is an email software application represented by the icon  282   b  (shown in  FIG. 16 ). The software application  292  includes several options for the user to choose from. For example, the software application  292  includes an email icon  294   a  and an email icon  294   b , with each email icon represent an email that can be selected and read by the user. Due to the presence of the liquid droplet  342   a  and the liquid droplet  342   b , the electronic device  100  may subsequently require at least some combination of the force detection assembly  114 , the display assembly  108  (including the touch input component), the confidence interval algorithm, and the machine learning algorithm to predict which option the user selects from the software application  292 . The process for determining a location of the touch input to the display assembly  108  to determine whether the email icon  294   a  and the email icon  294   b  is selected can be repeated using the processes described above. 
     While  FIGS. 14-17  provide examples in which the electronic device  100  uses the machine learning algorithm in conjunction with both the force detection assembly  114  and the touch input component of the display assembly  108  to determine a location of a touch input, the electronic device  100  may rely upon fewer inputs. For example, in some implementations, the machine learning algorithm is used with the touch input component of the display assembly  108  to determine a location of the touch input, and any information provided by the force detection assembly  114  is not used to determine the location. For example, when the location is determined by the touch input component of the display assembly  108 , the machine learning algorithm can predict the software application and can act as a confirmation of the selected software application based on touch input information provided by the touch input component of the display assembly  108 . Also, the example scenarios provided in  FIGS. 14-17  are not intended to be limiting. The machine learning algorithm may be implemented to predict different user preferences as a confirmation to the location of the touch input. For example, when using a media player software application (represented by the icon  282   a  in  FIG. 16 ), the electronic device  100  can use the machine learning algorithm to predict a volume setting of a speaker module (not shown in  FIGS. 14-17 ) of the electronic device  100  based upon the current location of the user and/or the current time of day. When the electronic device  100  includes communication capability with other users, the electronic device  100  can use the machine learning algorithm to predict which of the other users that the user intends to contact based upon, for example, the current location of the user, the current day, and/or the current time of day. When the electronic device  100  includes an accelerometer, the accelerometer can determine whether the user of the electronic device  100  is accelerating or decelerating, the electronic device  100  can use the machine learning algorithm to predict a particular type of fitness-related activity, based on the acceleration or deceleration. When the electronic device  100  includes wireless capabilities, the electronic device  100  can receive weather-related information (such as rain, high temperatures, low temperatures, humidity, snow, high winds, as non-limiting examples). In this regard, the electronic device  100  can use the machine learning algorithm to predict a weather software application. In the foregoing examples, the machine learning algorithm can be used in conjunction with information provided by the force detection assembly and/or the touch input component of the display assembly. 
       FIGS. 18 and 19  show different configurations of a force detection assembly in an electronic device. It should be noted that the electronic devices shown in  FIGS. 18 and 19  may include any features described herein for an electronic device. 
       FIG. 18  illustrates a plan view that partially shows an alternate embodiment of an electronic device  500 , showing an alternate embodiment of a force detection assembly  514 . As shown, the electronic device  500  may include an enclosure  502  and a transparent layer  506  coupled to the enclosure  502 . The force detection assembly  514  may surround a display assembly  508 . The force detection assembly  514  may include a force detection unit  518   a  along a corner of the enclosure  502 , and a force detection unit  518   b  along a corner of the enclosure  502 . The corners along which the force detection units are located may include opposing corners. However, other configurations are possible. The force detection units may include multiple electrode layers that form several capacitors, similar to force detection units previously described. 
       FIG. 19  illustrates a plan view that partially shows an alternate embodiment of an electronic device  600 , showing an alternate embodiment of a force detection assembly  614 . As shown, the electronic device  600  may include an enclosure  602  and a transparent layer  606  coupled to the enclosure  602 . The force detection assembly  614  may surround a display assembly  608 . The force detection assembly  614  may include a force detection unit  618   a  along one half of the enclosure  602 , and a force detection unit  618   b  along the other half of the enclosure  602 . As shown, the halves along which the force detection units are located may include a “left-right” pair. However, other configurations are possible. The force detection units may include multiple electrode layers that form several capacitors, similar to force detection units previously described. 
     The force detection assemblies shown in  FIGS. 18 and 19  may be able to determine a location of a touch input to within one of four quadrants of the display assembly, despite a reduced number of force detection units as compared to a prior embodiment. 
       FIG. 20  illustrates a block diagram of a portable electronic device  700 , in accordance with some embodiments. The portable electronic device  700  is capable of implementing the various techniques described herein. The portable electronic device  700  may include any features described herein for an electronic device. Also, electronic devices described herein may include any feature or features described for the portable electronic device  700 . In some embodiments, the portable electronic device  700  takes the form of the electronic device  100  (shown in  FIG. 1 ). The portable electronic device  700  can include one or more processors  710  for executing functions of the portable electronic device  700 . The one or more processors  710  can refer to at least one of a central processing unit (CPU) and at least one microcontroller for performing dedicated functions. 
     According to some embodiments, the portable electronic device  700  can include a display unit  720 . The display unit  720  is capable of presenting a user interface that includes icons (representing software applications), textual images, and/or motion images. In some examples, each icon can be associated with a respective function (such as a software application) that can be executed by the one or more processors  710 . In some cases, the display unit  720  includes a display layer (not illustrated), which can include a liquid-crystal display (LCD), light-emitting diode display (LED), organic light-emitting diode display (OLED), or the like. According to some embodiments, the display unit  720  includes a touch input detection component and/or a force detection assembly that can be configured to detect changes in an electrical parameter (e.g., electrical capacitance value) when the user&#39;s appendage (acting as a capacitor) comes into proximity with the display unit  720  (or in contact with a transparent layer that covers the display unit  720 ). The display unit  720  is connected to the one or more processors  710  via one or more connection cables  722 . 
     According to some embodiments, the portable electronic device  700  can include one or more environmental sensors  730  capable of detecting environmental conditions that are present within, or general proximate to, the portable electronic device  700 . In some examples, the one or more environmental sensors  730  may include a humidity sensor, a temperature sensor, a liquid sensor, an ambient pressure sensor, underwater depth sensor, a magnetic field sensor, a strain gage, a capacitive sensor, a barometer, a microphone, and/or a thermometer. In some embodiments, the one or more environmental sensors  730  can determine whether the portable electronic device  700  is exposed to a specific environmental stimulus (e.g., moisture). In response, the one or more processors  710  can modify a notification that is presented by the display unit  720  that corresponds to the specific environmental stimulus. The one or more environmental sensors  730  is/are connected to the one or more processors  710  via one or more connection cables  732 . 
     According to some embodiments, the portable electronic device  700  can include one or more input/output components  740  (also referred to as “I/O components”) that enable communication between a user and the portable electronic device  700 . In some cases, the one or more input/output components  740  can refer to a button or a switch that is capable of actuation by the user. In some cases, the one or more input/output components  740  can refer to a soft key that is flexibly programmable to invoke any number of functions. In some examples, the one or more input/output components  740  can refer to a switch having a mechanical actuator (e.g., spring-based switch, slide-switch, rocker switch, rotating dial, etc.) or other moving parts that enable the switch to be actuated by the user. In some examples, the one or more input/output components  740  can include a capacitive switch that is integrated with the display unit  720 . Also, the one or more input/output components  740  can include a force detect assembly that includes several force detection units, each of which is designed to detection an amount of applied force (by, for example, a touch input) to the display unit  720 . The one or more input/output components  740  can include accelerometer that determine whether the portable electronic device  700 , and to what extent, is accelerating or decelerating. When the one or more input/output components  740  are used, the input/output components  740  can generate an electrical signal that is provided to the one or more processors  710  via one or more connection cables  742 . 
     According to some embodiments, the portable electronic device  700  can include a power supply  750  that is capable of providing energy to the operational components of the portable electronic device  700 . In some examples, the power supply  750  can refer to a rechargeable battery. The power supply  750  can be connected to the one or more processors  710  via one or more connection cables  752 . The power supply  750  can be directly connected to other devices of the portable electronic device  700 , such as the one or more input/output components  740 . In some examples, the portable electronic device  700  can receive power from another power sources (e.g., an external charging device) not shown in  FIG. 20 . 
     According to some embodiments, the portable electronic device  700  can include memory  760 , which can include a single disk or multiple disks (e.g., hard drives), and includes a storage management module that manages one or more partitions within the memory  760 . In some cases, the memory  760  can include flash memory, semiconductor (solid state) memory or the like. The memory  760  can also include a Random Access Memory (RAM) and a Read-Only Memory (ROM). The ROM can store programs, utilities or processes to be executed in a non-volatile manner. The RAM can provide volatile data storage, and stores instructions related to the operation of the portable electronic device  700 . In some embodiments, the memory  760  refers to a non-transitory computer readable medium, where an operating system (OS) is established at the memory  760  that can be configured to execute software applications, confidence interval algorithms, and/or machine learning algorithms that are stored at the memory  760 . The one or more processors  710  can also be used to execute software applications, confidence interval algorithms, and/or machine learning algorithms that are stored at the memory  760 . In some embodiments, a data bus  762  can facilitate data transfer between the memory  760  and the one or more processors  710 . 
     According to some embodiments, the portable electronic device  700  can include a wireless communications component  770 . A network/bus interface  772  can couple the wireless communications component  770  to the one or more processors  710 . The wireless communications component  770  can communicate with other electronic devices via any number of wireless communication protocols, including at least one of a global network (e.g., the Internet), a wide area network, a local area network, a wireless personal area network (WPAN), or the like. In some examples, the wireless communications component  770  can transmit data to the other electronic devices over IEEE 802.11 (e.g., a Wi-Fi® networking system), Bluetooth (IEEE 802.15.1), ZigBee, Wireless USB, Near-Field Communication (NFC), a cellular network system (e.g., a 3G/4G/5G network such as UMTS, LTE, etc.), or the like. 
       FIG. 21  illustrates a block diagram of an electronic device  800 , showing several inputs and associated outputs generated based on the inputs used to locate a touch input to the electronic device  800 , in accordance with some described embodiments. The inputs and associated outputs may enable the electronic device  800  to locate a touch input  801  to a display assembly  808 , including instances in which a touch input component  832  of the display assembly  808  detects liquid  803  (which may include one or more liquid droplets). 
     When the display assembly  808  receives the touch input  801 , a force detection assembly  814  can determine an amount of applied force from the touch input  801 . The force detection assembly  814  may include one or more force detection units, as previously described. In addition to determining the amount of applied force, the force detection assembly  814  can also determine a location of the touch input  801 , through capacitance and/or differential capacitance information from the force detection units. In the regard, the force detection assembly  814  can provide touch input location information  805  to a processor  810 . The touch input location information  805  may be referred to as a first, or initial, location of the touch input  801 . 
     In some instances, the touch input component  832  detects the liquid  803  and subsequently deactivates, and the touch input location information  805  provided by the force detection assembly  814  is used to locate the touch input  801 . However, in other instances, the touch input component  832  remains active and determines the location of the liquid  803 . In this manner, the electronic device  800  may include a confidence interval module  807  designed to execute instructions of a confidence interval algorithm. The confidence interval module  807  can use the location of the touch input  801 , as determined by the touch input location information  805 , and create a user input centroid to represent the first location of the touch input  801 . The confidence interval module  807  can build a confidence interval  809  around the user input centroid, and determine (using the touch input component  832 ) whether any droplet(s) of the liquid  803  are within (or at least partially within) the confidence interval  809 . The confidence interval module  807  can build a liquid droplet centroid for each droplet of the liquid  803  determined to be within (or at least partially within) the confidence interval  809 , and evaluate which liquid droplet centroid is closest to the user input centroid. The confidence interval module  807  can use the location of the liquid droplet centroid that is closest to the user input centroid as a location of the touch input  801 , and can provide this information as touch input location information  811  to the processor  810 . The touch input location information  811  may be referred to as a second, updated, or revised, location of the touch input  801 . Further, the touch input location information  811  may provide a more accurate determination of the location of the actual touch input by the user, as compared to the location based on the touch input location information  805  from the force detection assembly  814 . 
     In some instances, the electronic device  800  includes a machine learning module  813  designed to execute instructions of a machine learning algorithm. The machine learning module  813  may access user information  815  to predict a software application (or an icon that represents the software application) the user intended to select/open by the touch input  801 . The user information  815  may include historical data related to use of software applications on the electronic device  800  by the user. The user information  815  may include which software application(s) are used at a current location of the electronic device  800  and/or at a current time and day, as non-limiting examples. When multiple software applications result, the multiple software applications can be ranked by frequency of use at the current location and/or at the current day and time. Given this information along with current location of the electronic device  800  and/or current time and day information, the machine learning module  813  can predict which software application the user intended to select by touch input  801 . Accordingly, the machine learning module  813  may provide a confirmation that the touch input  801  was correctly located (using the force detection assembly  814  or the confidence interval module  807 ) on the display assembly  808  to select the icon, and ultimately select the software application to user intended to use. 
     In some instances, the location of the touch input, as determined by the confidence interval algorithm, may provide a confirmation that the machine learning module  813  correctly predicts the software application selected by the user. For example, when the location of the touch input  801 , as determined by the confidence interval module  807 , causes a selection of an icon that opens the software application predicted by machine learning module  813 , the electronic device  800  can determine with relatively high confidence that the intended software application to be opened is correct. 
       FIG. 22  illustrates a schematic diagram of a machine learning system  901 , in accordance with some described embodiments. The machine learning system  901  can communicate with software application stored on an electronic device described herein. Further, the machine learning system  901  can receive location information, as well as day and time information. The machine learning system  901  may include a machine learning algorithm that is stored on the memory  760  (shown in  FIG. 20 ). The machine learning system  901  may access information from the machine learning system  901 . 
     The machine learning system  901  may include a machine learning and adaption application program interface (“API”)  903 . The machine learning and adaption API  903  may interface with machine learning and adaption techniques and exchanges data and information with the machine learning system  901 . 
     The machine learning system  901  may include a machine learning and adaption application engine  905  that learn from information received by the machine learning system  901 . The machine learning and adaption application engine  905  can classify the received data and information from software applications, classify and rank the data information by, for example, frequency of use of the software application, and predict a software application that the user intends to select. 
     The machine learning system  901  may include a data aggregation engine  907 . The data aggregation engine  907  can aggregate and/or combine data from applications and store the data and make the data accessible to the machine learning and adaption application engine  905 . 
       FIG. 23  illustrates a flowchart  1000  showing a method for selecting an icon on a display assembly of a wearable electronic device when a liquid is present, in accordance with some described embodiments. The liquid may be in the form of one or more liquid droplets located over the display assembly and positioned on a transparent layer that covers and protects the display assembly. 
     In step  1002 , a force detection assembly is used to determine a first location of a touch input to the icon. The force detection assembly is configured to detect an amount of force by the touch input. The force detection assembly may include force detection units that include electrode layers that form several capacitors. The gap, or distance, between electrode layers may change when the force is applied to the touch input, which may change the capacitance across each force detection unit. Also, different force detection units may form difference capacitance levels, and a differential capacitance between the different force detection units can be used to determine the first location of the touch input. 
     In step  1004 , a touch input component of the display assembly is used to determine whether the liquid is present. The may include a determination whether the liquid is present on a transparent layer that covers the display assembly. Moreover, the determination whether the liquid is present on the transparent layer may include a determination whether one or more liquid droplets are present on the transparent layer. The touch input component may include capacitance technology designed to generate an electrostatic field. The electrostatic field may be altered at a location corresponding to the touch input, as well as a location (or locations) corresponding to the liquid droplet(s). 
     In step  1006 , a confidence interval is used to determine a second location of the touch input. The confidence interval can use the first location and the determination of the presence of the liquid to determine the second location. The confidence interval can create a user input location based upon the first location. The confidence interval can build a confidence interval around the user input location. Further, when it is determined that multiple liquid droplets are present on the transparent layer, the confidence interval can determine whether a liquid droplet(s) falls within, or at least partially within, the confidence interval. The confidence interval can select, as a second location of the touch input, the liquid droplet that is closer (or closest) to the user input location. In some instances, the second location represents a more accurate representation of the touch input, as desired by the user. 
     The various aspects, embodiments, implementations or features of the described embodiments can be used separately or in any combination. Various aspects of the described embodiments can be implemented by software, hardware or a combination of hardware and software. The described embodiments can also be embodied as computer readable code on a computer readable medium for controlling manufacturing operations or as computer readable code on a computer readable medium for controlling a manufacturing line. The computer readable medium is any data storage device that can store data which can thereafter be read by a computer system. Examples of the computer readable medium include read-only memory, random-access memory, CD-ROMs, HDDs, DVDs, magnetic tape, and optical data storage devices. The computer readable medium can also be distributed over network-coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. 
     The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the described embodiments. However, it will be apparent to one skilled in the art that the specific details are not required in order to practice the described embodiments. Thus, the foregoing descriptions of the specific embodiments described herein are presented for purposes of illustration and description. They are not targeted to be exhaustive or to limit the embodiments to the precise forms disclosed. It will be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings.

Metadata:
Filing Date: 20180809
Publication Date: 20220823
Grant Date: 20220823
Priority Date: 20180809
Inventors: SPENCER, MAEGAN K.
BUSHNELL, TYLER S.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06N20/00", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/0447", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04182", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04142", "inventive": true, "first": false, "tree": "[]"}, {"code": "G04G21/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G04G17/045", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0445", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F2200/1634", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F1/1658", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F1/1643", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0488", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F1/163", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F1/1626", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/04842", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0482", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0414", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/044", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0488", "inventive": true, "first": true, "tree": "[]"}, {"code": "G04G21/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04817", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04817", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0414", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/044", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0488", "inventive": true, "first": true, "tree": "[]"}, {"code": "G04G21/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 82929999