Microsoft Technology Licensing, LLC

David Alan Stevens

Aleksandar Uzelac

Weidong Zhao

Takahiro Shigemitsu

John Graham Pierce

Briggs A Willoughby

Craig Steven Ranta

Pravin Kumar Santiago

Timothy Allen Wright

Jeffrey C Maier

Robert T Perry

Stanimir Naskov Kirilov

Andrey Borissov Batchvarov

Andrew David Wilson

Hrvoje Benko

John Carlton Platt

Nigel Stuart Keam

Bryan Douglas Scott

Jack Wilson Stokes, Iii

David Earl Washington

John Lawrence Miller

Changsin Lee

Frederic Morin

Daniel Ethan Keller

Iii Jack W Stokes

Touchscreen testing techniques are described. In one or more implementations, a piece of conductor (e.g., metal) is positioned as proximal to a touchscreen device and the touchscreen device is tested by simulating a touch of a user. This technique may be utilized to perform a variety of different testing of a touchscreen device, such as to test latency and probabilistic latency. Additional techniques are also described including contact geometry testing techniques.

Contact geometry tests

Touchscreen testing techniques are described. In one or more implementations, a conductor is placed proximal to a touchscreen device and the touchscreen device is tested by simulating a touch of a user by placing the conductor in a grounded state and lack of a touch by the user by placing the conductor in an ungrounded state.

Touchscreen testing

Aspects of the subject matter described herein relate to paragraph snapping. In aspects, trending data is collected and prepared for sending to one or more target machines. Upon receiving the trending data, a target machines installs the trending data locally and deletes previously installed trending data. After installation, the trending data may be used to suggest text in response to input from a user. If a user selects suggested text, the text may be added to a local dictionary of the target machine.

Trending suggestions

In embodiments of prediction-based touch contact tracking, touch input sensor data is recognized as a series of components of a contact on a touch-screen display. A first component of the contact can be identified, and a second component can be determined to correlate to the contact. The first component and the second component can then be associated to represent a tracking of the contact. Subsequent components of the contact can be determined and associated with the previous components of the contact to further represent the tracking of the contact.

Prediction-based touch contact tracking

Probabilistic latency modeling

In embodiments of angular contact geometry, touch input sensor data is recognized as a touch input on a touch-screen display, such as a touch-screen display integrated in a mobile phone or portable computing device. A sensor map is generated from the touch input sensor data, and the sensor map represents the touch input. The sensor map can be generated as a two-dimensional array of elements that correlate to sensed contact from a touch input. An ellipse can then be determined that approximately encompasses elements of the sensor map, and the ellipse represents a contact shape of the touch input.

Angular contact geometry

Panning latency measurement techniques are described. In one or more implementations, a test apparatus includes one or more motors configured to move one or more contacts at least proximal to one or more sensors of a device to be detectable as a movement. The test apparatus also includes one or more modules implemented at least partially in hardware to measure latency of the device to recognize the movement of the one or more contacts.

Latency measurement

In embodiments of multi-finger detection and component resolution, touch input sensor data is recognized as a component of a multi-finger gesture on a touch-screen display. An ellipse is determined that approximately encompasses the component, and the ellipse has a primary axis and a secondary axis that are orthogonal. A distribution is then generated that projects sensor data elements from the primary axis based on detected intensity of the touch input sensor data. A histogram function can then be generated based on the distribution, where the histogram function indicates individual contacts of the component and separation of the individual contacts.

Multi-finger detection and component resolution

One or more techniques and/or systems are provided for suggesting a word and/or phrase to a user based at least upon a prefix of one or more characters that the user has inputted. Words in a database are respectively assigned a unique identifier. Generally, the unique identifiers are assigned sequentially and contiguously, beginning with a first word alphabetically and ending with a last word alphabetically. When a user inputted prefix is received, a range of unique identifiers corresponding to words respectively having a prefix that matches the user inputted prefix are identified. Typically, the range of unique identifiers corresponds to substantially all of the words that begin with the given prefix and does not correspond to words that do not begin with the given prefix. The unique identifiers may then be compared to a probability database to identify which words have a higher probability of being selected by the user.

Text prediction

In embodiments of multi-pass touch contact tracking, touch input sensor data is recognized as a series of components of a contact on a touch-screen display. The components can be determined to correlate to the contact based on multi-pass nearest-neighbor contact mapping that includes forward nearest-neighbor contact mapping of the components and reverse nearest-neighbor contact mapping of the components. The components can then be associated to represent a tracking of the contact. Subsequent components of the contact can also be determined and associated with the previous components of the contact to further represent the tracking of the contact.

Multi-pass touch contact tracking

A system level automatic gain control ('System AGC') automatically initializes and controls analog microphone gain in an environment where multiple independent applications simultaneously receive an input from a single analog microphone or microphone array. In one embodiment, the System AGC also prevents those applications from acting to separately control the gain by intercepting external gain control commands and responding to the corresponding application with a corresponding digital gain applied to the input signal from the microphone. Consequently, the System AGC avoids problems relating to oscillations and instability in the microphone gain resulting from multiple applications trying to simultaneously control the gain while preventing each application from adversely affecting the quality of another application's audio capture signal. Further, in one embodiment, the System AGC also acts to maximize the signal to noise (SNR) ratio of the microphone without introducing clipping as a function of a sampled background environment.

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Sammamish, WA, United States of America

David Alan Stevens