Apple Inc.

William Matthew Vieta

Wayne Carl Westerman

Xiaoyuan Tu

Christopher D Moore

Duncan Robert Kerr

Steven Porter Hotelling

Jeffrey Traer Bernstein

Julian K Missig

Avi Elias Cieplinski

Michael Boshra

Pavel Mrazek

Giovanni Gozzini

Patrick Steven Piemonte

Brian Michael King

Rudolph Van Der Merwe

Craig Alan Marciniak

Myra Mary Haggerty

Ari Y Benbasat

Alex Tremain Nelson

Edward Allen Valko

Ronald Keryuan Huang

Gregory B Novick

Benjamin B Lyon

Scott Thomas Smith

Christopher Tenzin Mullens

Jan Erik Solem

Matthew C Waldon

Ondrej Konopka

Karen Louise Jenkins

Matt Waldon

One embodiment may take the form of a method of operating a computing device to provide presence based functionality. The method may include operating the computing device in a reduced power state and collecting a first set of data from a first sensor. Based on the first set of data, the computing device determines if an object is within a threshold distance of the computing device and, if the object is within the threshold distance, the device activates a secondary sensor to collect a second set of data. Based on the second set of data, the device determines if the object is a person. If the object is a person, the device determines a position of the person relative to the computing device and executes a change of state in the computing device based on the position of the person relative to the computing device. If the object is not a person, the computing device remains in a reduced power state.

Presence sensing

One embodiment may take the form of a method of operating a computing device in a reduced power state and collecting a first set of data from at least one sensor. Based on the first set of data, the computing device determines a probability that an object is within a threshold distance of the computing device and, if so, the device activates at least one secondary sensor to collect a second set of data. Based on the second set of data, the device determines if the object is a person. If it is a person, a position of the person relative to the computing device is determined and the computing device changes its state based on the position of the person. If the object is not a person, the computing device remains in a reduced power state.

Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.

Motion pattern classification and gesture recognition

The present application is related to a computer for providing output to a user. The computer includes a processor and an input device in communication with the processor. The input device includes a feedback surface and at least one sensor in communication with the feedback surface, the at least one sensor configured to detect a user input to the feedback surface. The processor varies a down-stroke threshold based on a first factor and varies an up-stroke threshold based on a second factor. The down-stroke threshold determines a first output of the computing device, the up-stroke threshold determines a second output of the computing device, and at least one of the first factor or the second factor are determined based on the user input.

Thresholds for determining feedback in computing devices

The present application is related to a computer for providing output to a user. The computer includes a processor and an input device in communication with the processor. The input device includes a feedback surface and at least one sensor in communication with the feedback surface, the at least one sensor configured to detect a user input to the feedback surface. The processor varies a down-stroke threshold based on a first factor and varies an up-stroke threshold based on a second factor. The down-stroke threshold determines a first output of the computing device, the up-stroke threshold determines a second output of the computing device, and at least of the first factor or the second factor are determined based on the user input.

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.

Reconstruction of original touch image from differential touch image

A processing channel is operatively connected to a biometric device. The processing channel includes an analog-to-digital converter (ADC) connected to at least one of an amplifier and an offset circuit. Biometric signals representing captured biometric data, such as a fingerprint image, are controllably fitted into an input range of the ADC. The ADC input range can include one or more removed ranges and one or more adjustable allowed input ranges. One or more channel parameters are adjusted until a desired number of biometric signals are included in the one or more removed ranges.

Controllable signal processing in a biometric device

An electronic device includes a biometric sensing device connected to a processing channel that includes at least one amplifier having a gain. One or more processing devices is operatively connected to the biometric sensing device and adapted to compensate for signal fixed pattern noise in signals received from the processing channel. The signal fixed pattern noise can include signal measurement variation noise and gain variation noise. The biometric sensing device captures a new image or data, and at least one processing device compensates for the signal fixed pattern noise in the newly captured image or data.

Noise compensation in a biometric sensing device

A template of a biometric attribute for use with a biometric recognition device includes a long term component and a short term component. The long term component can include a plurality of nodes that each represents at least a portion of the biometric attribute. The short term component may include one or more newly captured nodes that each represents at least a portion of the biometric attribute.

Updating a template for a biometric recognition device

An electronic device may be provided with electronic components such as a touch screen display. The touch screen display may be controlled based on information from a proximity sensor. The proximity sensor may have a light source that emits infrared light and a light detector that detects reflected infrared light. When the electronic device is in the vicinity of a user's head, the proximity sensor may produce data indicative of the presence of the user's head. Variations in proximity sensor output due to user hair color and smudges on the proximity sensor can be accommodated by using a dynamically adjustable proximity sensor data threshold in processing proximity sensor data. Proximity sensor data may be analyzed in real time to detect signal strength fluctuations that are indicative of the presence of low-reflectively dark hair on the user's head. Threshold adjustments may be based on the presence of dark hair.

Electronic device with adaptive proximity sensor threshold

An error correction method and a mobile communication device incorporating the error correction method. The error correction method begins with receiving fingerprint image information that includes a set of swatches. A first one of the received swatches is matched to a second one of the swatches at an edge. A measure of fingerprint image manipulation is determined responsive to an amount of processing to perform and an amount of fingerprint image error. Then the fingerprint image information is manipulated in response to an optimum value of the measure.

Image manipulation utilizing edge detection and stitching for fingerprint recognition

An electronic device may include a finger biometric sensor, a memory, and a processor capable of generating a respective finger biometric template node for each finger positioning adjacent the finger biometric sensor. The processor may also be capable of allocating a set space of the memory to store a total number of finger biometric template nodes based upon a first number of fingers and with each finger having a first number of finger biometric template nodes associated therewith. In addition, the processor may reallocate the set space of the memory to store the total number of finger biometric template nodes but with a second number of finger biometric template nodes associated with at least one finger, with the second number of finger biometric template nodes being different than the first number of finger biometric template nodes.

Electronic device for reallocating finger biometric template nodes in a set memory space and related methods

An electronic device can include at least one fingerprint image sensor that obtains fingerprint image information, where the fingerprint information can include at least a first partial fingerprint image and a second partial fingerprint image. At least one fingerprint navigation sensor can be disposed to receive navigation information responsive to at least one of movement or orientation of a user's finger with respect to the at least one fingerprint image sensor. At least one processing unit can combine the first partial fingerprint image and the second partial fingerprint image into at least one combined fingerprint image utilizing the navigation information.

Growing community of inventors

Santa Clara, CA, United States of America

William Matthew Vieta