Google Inc.

Other

James Brooks Miller

Richard Carl Gossweiler, Iii

Michael Andrew Cleron

Debanjan Mukherjee

Tyler Reed Kugler

Kishore Sundara-Rajan

Philip Quinn

Alexey Polyudov

David Avraham Brown

Daniel Marc Gatan Shiplacoff

Jeffrey D Yaksick

Amar Gandhi

Shumin Zhai

Firas Sammoura

Seungyon Lee

Tal Dayan

Kelvin Kwong

Jay Pierre Civelli

Hugh Brendan McMahan

James Cooper

Zheng Sun

Maya Ben Ari

Jean-Michel Roland Trivi

Rich Gossweiler

Isaac William Reynolds

Christiaan Prins

Brandon Keely

Yun-Ling Lee

Ido Ofir

Karl Rosaen

Tanton Holt Gibbs

Ning Zheng

Rongjing Xiang

Michael Adam Cohen

Selim Flavio Cinek

Benjamin David Poiesz

Christoph Emanuel Studer

Andrew Abramson

Lee Brandon Keely

Neel Mamidi Rao

Jennifer Ellen Fernquist

Thomas Ramon Karlo

Andres Morales

Shawn Willden

Jorim Dorian Jaggi

Paul Daniel Lawrence

Adrian Roos

Ming Jia

Richard Carl Gossweiler Iii

Stuart Murray Scott

Jennifer Seth

Kevin Chyn

A method includes identifying, within an image that represents a fingerprint, a plurality of keypoints. The method also includes determining a plurality of pixel vectors by transforming, for each respective keypoint, a corresponding two-dimensional (2D) array of pixel values associated with the respective keypoint into a corresponding pixel vector, and obtaining a plurality of filter vectors that represent, for each respective filter of a plurality of filters, a transformation of a corresponding 2D array of filter values of the respective filter into a corresponding filter vector. The method additionally includes convolving the plurality of keypoints with the plurality of filters by multiplying, using a tensor processing hardware, a pixel matrix formed by the plurality of pixel vectors and a filter matrix formed by the plurality of filter vectors, and determining, based on the convolving, a representation of the fingerprint.

Hardware acceleration of fingerprint data processing

This document describes techniques and systems that enable grid-based enrollment for face authentication. The techniques and systems include overlaying a three-dimensional (3D) tracking window over a preview image of the user's face displayed via a display device. The 3D tracking window includes a plurality of segments, which persist to correspond to an approximate direction that the user's face is facing. Based on the tracking, segments are highlighted to indicate the approximate direction that the user's face is facing, a camera captures enrollment images of the user's face facing that direction, and embeddings are generated based on the enrollment images and stored in a fixed grid of pose cells corresponding to various facial poses for use in face authentication. Responsive to generation and storage of the embeddings, an indication that the one or more segments are completed is provided.

Grid-based enrollment for face authentication

This document describes methods and systems of adaptive fingerprint-enrollment to finger characteristics using local under-display fingerprint sensors, UDFPS, in an electronic device. The electronic device includes an adaptive-enrollment module that determines characteristics of a fingerprint based on information corresponding to a touch input detected by a touch-display device, including size and shape of an area of the touch input. Based on the fingerprint characteristics, a number and location of enrollment touches used for completing enrollment of the fingerprint are adjusted to minimize the number of enrollment touches required to complete the enrollment, minimize the amount of time needed to complete the enrollment, and maximize coverage of the fingerprint. The adaptive-enrollment module also provides visual guidance to guide the user to touch the adjusted locations of the enrollment touches and, if needed, feedback to instruct the user to adjust the location of their finger to align with the visual guidance.

Adaptive fingerprint-enrollment to finger characteristics using local under-display fingerprint sensor in an electronic device

Systems and techniques are provided for trust agents. Trust agents may be enabled. A state determination may be received from each of the enabled trust agents. The state determination may indicate either a trusted state or an untrusted state. The received state determinations may be combined to determine a security state. A security measure may be enabled or disabled based on the determined security state.

Trust agents

A computer-implemented method is described that includes providing a watch defining a body and a watch face on a surface of the body, the body having one or more sensors arranged to sense user inputs in an area adjacent to the body. The method further comprises sensing a motion of an object in the area adjacent to, but not touching, the body using the one or more sensors and changing a display of a pointing element on a graphical user interface on the watch in coordination with the sensed motion.

Gesture-based small device input

A method includes a processor of an electronic device receiving first input signals from a first sensor in response to user contact at a first edge of the device and second input signals from a second sensor in response to user contact at a second edge of the electronic device. The first and second sensors are covered by a housing of the device. The processor determines an external context of the device based on analysis of the first input signals and the second input signals. The determined external context indicates at least a position of the device relative to a user or an orientation of the device relative to a user. Responsive to determining the external context, the electronic device executes a particular user input action.

Electronic device response to force-sensitive interface

An apparatus utilizes multiple strain gauge ('SG') sensing units which are each disposed adjacent an inner surface of the device housing. Electrical voltage generated by the SGs is amplified by one or more amplifiers to maximize the resolution between a voltage output of an SG when in a non-pressed state and a voltage output of the SG when in a pressed state. Additionally, an electronic circuit is configured to identify a baseline voltage output for an SG over a period of time for comparing to a voltage output for the SG when the SG is in a pressed state such that the pressed state of the SG can be identified by the electronic circuit by comparing a current output voltage of the SG to the identified baseline voltage.

Apparatus for sensing user input

Techniques and apparatuses are described that provide an ultra-low power mode for a low-cost force-sensing device. These techniques extend battery life of the device by minimizing power consumption for potential wake-up events. To do this, a high-pass filter (e.g., differentiator) is used to evaluate sensor signals in a time domain to provide an estimate of a rate of change of the signal. When the rate of change of the signal deviates from a baseline value by a threshold amount, then a microcontroller is woken to evaluate a large number of historical samples, such as 200 or more milliseconds worth of historical data. If a human gesture is not recognized, then the microcontroller returns to an idle state, but if a human gesture is recognized, then a high-power application processor is woken to execute an application configured to perform an operation mapped to the human gesture.

Ultra-low power mode for a low-cost force-sensing device

A method includes one or more processors of an electronic device receiving signals from multiple sensors located along an edge of the device. The signals are received in response to external contact being provided to the edge of the device. At least one processor determines a distribution of forces applied to the sensors based on the input signals. Based on the determined distribution of forces, the processor determines: i) a location of the external contact that is offset from a location of each of the multiple sensors, and ii) a magnitude of the force of the external contact. The processor detects whether sensing criteria has been satisfied based on an analysis of: i) the location of the external contact and ii) the magnitude of the force of the external contact. Responsive to detecting that sensing criteria has been satisfied, the processor executes a user input action.

Force-sensitive user input interface for an electronic device

Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Aggregation of asynchronous trust outcomes in a mobile device

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Sunnyvale, CA, United States of America

James Brooks Miller