Abstract: The dynamic adjusting of the conditions for identifying inputs as touching a touch-sensitive device is discloses. In some examples, in addition to using a signal density make threshold to identify an input patch as touching the surface, a signal density stability threshold can be used to identify the input patch as touching the surface. In some examples, a weighted average of peak signal density contributions from recent identified touches can be computed to dynamically adjust the make threshold for new input patches. In other examples, a new input patch identified as associated with the same path as an earlier touch can have its “make” threshold dynamically adjusted based on the earlier touch without computing a weighted average.

Abstract: Embodiments are related to user input devices that accept complex user input including a combination of touch and push (or pick) input. Embodiments of the invention provide for selective ignoring or rejection of input received from such devices in order to avoid interpreting unintentional user actions as commands. Furthermore, some input signals can be modified. The selective rejection or modification can be performed by the user interface device itself or by a computing device that includes or is attached to the user interface device. The selective rejection or modification may be performed by a module that processes input signals, performs the necessary rejections and modifications and sends revised input signals to higher level modules.

Abstract: Pre-processing can be applied to raw signal measurements resulting from stimulation from an input device, such as an active stylus, having a non-linear signal profile. The pre-processing can include a non-linear transformation, which can linearize the signal profile and thereby reduce wobble resulting from location detection algorithms. The transformation can be selected based on the signal profile for the stylus and the ideal profile for the location detection algorithms. In some examples, the transformation can be applied to linearize the entire signal profile, but in other examples, the non-linear transformation can be applied only to specific regions of the signal profile. The pre-processing can also discard raw signal measurements that are at least a threshold distance from the peak signal measurement or raw signal measurements below a threshold signal level.

Abstract: The detection of an orientation of a stylus relative to a touch sensitive surface is disclosed. In one example, a touch image of the stylus tip and the hand used to grasp the stylus can be captured by the touch sensor panel and analyzed to determine the stylus' orientation relative to the surface of the touch sensor panel. The analysis can include estimating the size of the user's hand, determining the distance away from the user's hand at which the stylus tip makes contact with the touch sensor panel, and determining an angle of tilt based on the estimated size of the user's hand and the distance between the tip and the user's hand.

Abstract: This relates to a method of extrapolating proximity information to generate a border column or row of touch nodes (also known as touch pixels) and then fitting an ellipse to the contact patch including the extrapolated border touch nodes. Additionally, a contact can be identified as a thumb based on both its major axis and its distance to an edge of the touch sensing surface.

Abstract: Apparatus and methods are disclosed for simultaneously tracking multiple finger and palm contacts as hands approach, touch, and slide across a proximity-sensing, multi-touch surface. Identification and classification of intuitive hand configurations and motions enables unprecedented integration of typing, resting, pointing, scrolling, 3D manipulation, and handwriting into a versatile, ergonomic computer input device.

Abstract: The use of multiple stimulation signals having one or more frequencies and one or more phases to generate an image of touch on a touch sensor panel is disclosed. Each of a plurality of sense channels can be coupled to a column in a touch sensor panel and can have one or more mixers. Each mixer in the sense channel can utilize a circuit capable generating a demodulation frequency of a particular frequency. At each of multiple steps, various phases of one or more selected frequencies can be used to simultaneously stimulate the rows of the touch sensor panel, and the one or more mixers in each sense channel can be configured to demodulate the signal received from the column connected to each sense channel using the one or more selected frequencies. After all steps have been completed, the demodulated signals from the one or more mixers can be used in calculations to determine an image of touch for the touch sensor panel at each of the one or more frequencies.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: Techniques for identifying irregular objects in contact with, or in close proximity to, a touch-surface are described. An irregularity measure is determined based on the regions intrinsic characteristics (e.g., energy content) rather than on the shape (or pattern) of the pixels within the region.

Abstract: This relates to a method of extrapolating proximity information to generate a border column or row of touch nodes (also known as touch pixels) and then fitting an ellipse to the contact patch including the extrapolated border touch nodes. Additionally, a contact can be identified as a thumb based on both its major axis and its distance to an edge of the touch sensing surface.

Abstract: The use of multiple stimulation signals having one or more frequencies and one or more phases to generate an image of touch on a touch sensor panel is disclosed. Each of a plurality of sense channels can be coupled to a column in a touch sensor panel and can have one or more mixers. Each mixer in the sense channel can utilize a circuit capable generating a demodulation frequency of a particular frequency. At each of multiple steps, various phases of one or more selected frequencies can be used to simultaneously stimulate the rows of the touch sensor panel, and the one or more mixers in each sense channel can be configured to demodulate the signal received from the column connected to each sense channel using the one or more selected frequencies. After all steps have been completed, the demodulated signals from the one or more mixers can be used in calculations to determine an image of touch for the touch sensor panel at each of the one or more frequencies.

Abstract: Apparatus and methods are disclosed for simultaneously tracking multiple finger and palm contacts as hands approach, touch, and slide across a proximity-sensing, multi-touch surface. Identification and classification of intuitive hand configurations and motions enables unprecedented integration of typing, resting, pointing, scrolling, 3D manipulation, and handwriting into a versatile, ergonomic computer input device.

Abstract: Techniques for identifying irregular objects in contact with, or in close proximity to, a touch-surface are described. An irregularity measure is determined based on the regions intrinsic characteristics (e.g., energy content) rather than on the shape (or pattern) of the pixels within the region.

Abstract: Pre-stored no-touch or no-hover (no-event) sensor output values can initially be used when a sensor panel subsystem is first booted up to establish an initial baseline of sensor output values unaffected by fingers or other objects touching or hovering over the sensor panel during boot-up. This initial baseline can then be normalized so that each sensor generates the same output value for a given amount of touch or hover, providing a uniform response across the sensor panel and enabling subsequent touch or hover events to be more easily detected. After the initial normalization process is complete, the pre-stored baseline can be discarded in favor of a newly captured no-event baseline that may be more accurate than the pre-stored baseline due to temperature or other variations.

Abstract: An image jaggedness filter is disclosed that can be used to detect the presence of ungrounded objects such as water droplets or coins, and delay periodic baseline adjustments until these objects are no longer present. To do otherwise could produce inaccurate normalized baseline sensor output values. The application of a global baseline offset is also disclosed to quickly modify the sensor offset values to account for conditions such as rapid temperature changes. Background pixels not part of any touch regions can be used to detect changes to no-touch sensor output values and globally modify the sensor offset values accordingly. The use of motion dominance ratios and axis domination confidence values is also disclosed to improve the accuracy of locking onto dominant motion components as part of gesture recognition.

Abstract: “Real-world” gestures such as hand or finger movements/orientations that are generally recognized to mean certain things (e.g., an “OK” hand signal generally indicates an affirmative response) can be interpreted by a touch or hover sensitive device to more efficiently and accurately effect intended operations. These gestures can include, but are not limited to, “OK gestures,” “grasp everything gestures,” “stamp of approval gestures,” “circle select gestures,” “X to delete gestures,” “knock to inquire gestures,” “hitchhiker directional gestures,” and “shape gestures.” In addition, gestures can be used to provide identification and allow or deny access to applications, files, and the like.

Abstract: The application of a watershed algorithm to pixels and their touch values obtained from a scan of a touch sensor panel to determine patches corresponding to images of touch is disclosed. Prior to applying the watershed algorithm, background pixels having little or no touch values can be eliminated. A primary merge algorithm can then merge adjacent patches together when the saddle point between them is shallow as compared to the peak represented by the patches. However, if two candidate patches for merging have a total number of pixels below a certain threshold, these two patches may not be merged under the assumption that the patches might have been caused by different fingertips. Conversely, if two candidate patches for merging have a total number of pixels above a certain threshold, these two patches can be merged under the assumption that the patches were caused by a single thumb or palm.

Abstract: An image jaggedness filter is disclosed that can be used to detect the presence of ungrounded objects such as water droplets or coins, and delay periodic baseline adjustments until these objects are no longer present. To do otherwise could produce inaccurate normalized baseline sensor output values. The application of a global baseline offset is also disclosed to quickly modify the sensor offset values to account for conditions such as rapid temperature changes. Background pixels not part of any touch regions can be used to detect changes to no-touch sensor output values and globally modify the sensor offset values accordingly. The use of motion dominance ratios and axis domination confidence values is also disclosed to improve the accuracy of locking onto dominant motion components as part of gesture recognition.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: The suppression of errant motion regarding a mouse is disclosed. Mouse and touch information can be integrated to determine whether a gesture made on a surface of the mouse is errant, such as when a mouse is being moved and the fingers holding the mouse inadvertently move on the mouse surface. A gesture motion that is small relative to mouse motion can be considered errant and therefore at least partially suppressed, while a gesture motion that is large relative to mouse motion can be considered an intended gesture and therefore processed. Similarly, mouse and touch information can be integrated to determine whether a mouse motion is errant, such as when a robust gesture being made on the mouse surface inadvertently moves the mouse. A mouse motion that is small relative to gesture motion can be considered errant and therefore at least partially suppressed, while a mouse motion that is large relative to gesture motion can be considered an intended motion and therefore processed.