X Development LLC

3m Innovative Properties Company

Wing Aviation LLC

Other

Google Inc.

Philip Edwin Watson

Sarah Ann Laszlo

Gabriella Levine

Martin F Schubert

Keith Michael Kotchick

Michael Jason Grundmann

Andrew John Ouderkirk

Richard John Pokorny

Adam Eales Norton

Marc Dudley Radcliffe

Mark B O'Neill

Andrei Stefan Kazmierski

David J Lamb

Ikuko Ebihara

Christopher Chadwick Bowley

Yarn Chee Poon

Kenneth A Epstein

Richard C Allen

Leland Ray Whitney

Stephen J Willett

Georgios Evangelopoulos

Christian Ervin

Brian John Adolf

Mary Lou Jepsen

Adam D Haag

Matthew B Johnson

Ying-Yuh Lu

Behnam Bastani

Sergey Lamansky

Clifford L Biffle

Mark E Gardiner

Robert S Moshrefzadeh

Matthew Dixon Eisaman

Jie Yang

Robert M Emmons

Prabhakara Satyauolu Rao

Yaoqi Joe Liu

Kim Leong Tan

Larry A Meixner

Carlin Vieri

Joseph Hollis Sargent

Gregory Edward Gilligan

Steven J Rhyner

Xiaohui Cheng

Nicholas Charles Loomis

Pey Lung Fu

Hung-Yu Chen

John Allen Wheatley

Michael Alan Meis

Raj B Apte

Gary Timothy Boyd

Catherine A Leatherdale

Rolf W Biernath

Sanford Cobb, Jr

Craig R Schardt

Serge J Bierhuizen

John C Schultz

Kiran S Kanukurthy

Charles L Bruzzone

Tao Liu

Tommie W Kelley

Serge Joel Armand Bierhuizen

Johan Ulrich Lewin Jessen

Scott E Brigham

Philip J Ralli

Dale R Lutz

Michael Patrick Bauerly

John J Cael

Serge J A Bierhuizen

Belle Fu

Patrick H Marushin

Kathleen Watson

Patricia Prewitt

Nicole J Wagner

Kristina Liv Larsen

Rowan M Ogden

Robert Bennet

Julia Watson

Yachi Lee

Ya-Chi Lee

Eddy Giing-Li Chen

Joseph R Owens

Shenyi Tai

James E Anderson

Carl Ferman McCleary Smith

Aysja Johnson

Martin Friedrich Schubert

Kim L Tan

Gregory E Gilligan

A technique for dynamic generation of a therapeutic derivative story includes obtaining attribute data that describes characteristics of a content consumer along with situational details describing an emotional situation involving the content consumer. A relatability score for the therapeutic derivative story is determined. A content data structure (CDS) is selected. The CDS specifies story elements of a preexisting story. The story elements are associated with metadata constraints that constrain modification or use of the story elements. The metadata constraints indicate whether associated ones of the story elements are mutable story elements. One or more of the mutable story elements are adapted based on the attribute data or the situational details as constrained by the metadata constraints and to an extent determined at least in part by the relatability score to generate the therapeutic derivative story.

Derivative content creation using neural networks for therapeutic use

A sensor device includes a sensor housing defining a channel extending along a channel axis through the housing from a first side of the sensor housing to a second side of the sensor housing opposite the first side, at least one contact electrode extending from the first side of the housing, an electrically-conducting lead attached to the housing in electrical communication with the at least one contact electrode, and a locking mechanism located in the channel permitting one-way axial motion of a thread threaded through the channel from the first side to the second side.

Hair ratcheting electroencephalogram sensors

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate a student neural network output comprising a respective score for each of a plurality of classes; processing the training input using a brain emulation neural network to generate a brain emulation neural network output comprising a respective score for each of the plurality of classes; and adjusting current values of the student neural network parameters using gradients of an objective function that characterizes a similarity between: (i) the student neural network output for the training input, and (ii) the brain emulation neural network output for the training input.

Training artificial neural networks based on synaptic connectivity graphs

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate an output for the training input; processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output, wherein the discriminative score characterizes a prediction for whether the network input was generated using: (i) the student neural network, or (ii) a brain emulation neural network; and adjusting current values of the student neural network parameters using gradients of an objective function that depends on the discriminative score for the student neural network output.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a neural network architecture for performing a machine learning task. In one aspect, a method comprises: obtaining data defining a synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism; generating data defining a plurality of candidate graphs based on the synaptic connectivity graph; determining, for each candidate graph, a performance measure on a machine learning task of a neural network having a neural network architecture that is specified by the candidate graph; and selecting a final neural network architecture for performing the machine learning task based on the performance measures.

Neural architecture search based on synaptic connectivity graphs

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an artificial neural network architecture based on a synaptic connectivity graph. According to one aspect, there is provided a method comprising: obtaining a synaptic resolution image of at least a portion of a brain of a biological organism; processing the image to identify: (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain; generating data defining a graph representing synaptic connectivity between the neurons in the brain; determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain; and processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

Artificial neural network architectures based on synaptic connectivity graphs

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

Reservoir computing neural networks based on synaptic connectivity graphs

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an artificial neural network architecture corresponding to a sub-graph of a synaptic connectivity graph. In one aspect, there is provided a method comprising: obtaining data defining a graph representing synaptic connectivity between neurons in a brain of a biological organism; determining, for each node in the graph, a respective set of one or more node features characterizing a structure of the graph relative to the node; identifying a sub-graph of the graph, comprising selecting a proper subset of the nodes in the graph for inclusion in the sub-graph based on the node features of the nodes in the graph; and determining an artificial neural network architecture corresponding to the sub-graph of the graph.

Predicting neuron types based on synaptic connectivity graphs

A technique for dynamic generation of a derivative story includes obtaining content preferences from a content consumer. The content preferences indicate preferences for characteristics of the derivative story. A content data structure is identified based at least in part on the content preferences. The content data structure specifies story elements of a preexisting story. The story elements are defined at one or more different levels of story abstraction and associated with metadata constraints that constrain modification or use of the story elements within the derivative story. At least some of the metadata constraints indicate whether associated ones of the story elements are mutable story elements. One or more of the mutable story elements are adapted to the content preferences of the content consumer as constrained by the metadata constraints to generate the derivative story. The derivative story is then rendered via a user interface.

Personalized content creation using neural networks

The subject matter of this specification generally relates to modular vehicles including separable pod and base units. In some implementations, a computing system installed in a vehicle base identifies a vehicle pod that is detachably connected to a chassis on the vehicle base. In response to identifying that the vehicle pod is detachably connected to the chassis on the vehicle base, a communications link can be established between the computing system installed in the vehicle base and a computing system installed in the vehicle pod. Based on information obtained through the communications link, the computing system installed in the vehicle base can determine a particular configuration of the vehicle pod that is detachably connected to the chassis. The computing system can then verify that the vehicle base can safely transport the vehicle pod while the vehicle pod is detachably connected.

Modular vehicles with detachable pods

An example method may include receiving, from a client computing device, an indication of a target drop-off spot for an object within a first virtual model of a first region of a delivery destination. A second virtual model of a second region of the delivery destination may be determined based on sensor data received from one or more sensors on a delivery vehicle. A mapping may be determined between physical features represented in the first virtual model and physical features represented in the second virtual model to determine an overlapping region between the first and second virtual models. A position of the target drop-off spot within the second virtual model may be determined based on the overlapping region. Based on the position of the target drop-off spot within the second virtual model, the delivery vehicle may be navigated to the target drop-off spot to drop off the object.

Drop-off location planning for delivery vehicle

Examples implementations relate to determining path confidence for a vehicle. An example method includes receiving a request for a vehicle to navigate a target location. The method further includes determining a navigation path for the vehicle to traverse a first segment of the target location based on a plurality of prior navigation paths previously determined for traversal of segments similar to the first segment of the target location. The method also includes determining a confidence level associated with the navigation path. Based on the determined confidence level, the method additionally includes selecting a navigation mode for the vehicle from a plurality of navigation modes corresponding to a plurality of levels of remote assistance. The method further includes causing the vehicle to traverse the first segment of the target location using a level of remote assistance corresponding to the selected navigation mode for the vehicle.

Systems and methods for determining path confidence for unmanned vehicles

A wallpaper-like audio/visual system includes a display array of display pixels to emit an image, an array of speakers to emit audio, and driver circuitry coupled to the display array and the array of speakers to drive the display pixels and the speakers with the first and second signals, respectively, in response to receiving audio and visual input signals. The speakers are interspersed amongst the display pixels.

Display array with distributed audio

A bioamplifier for analyzing electroencephalogram (EEG) signals is disclosed. The bioamplifier includes an input terminal for receiving an EEG signal from a plurality of sensors coupled to a user. The bioamplifier also includes an analogue-to-digital converter arranged to receive the EEG signal from the input terminal and convert the EEG signal to a digital EEG signal. A data processing apparatus within the bioamplifier is arranged to receive the digital EEG signal from the analogue-to-digital converter and programmed to process, in real time the digital EEG signal using a first machine learning model to generate a cleaned EEG signal having a higher signal-to-noise ratio than the digital EEG signal. The bioamplifier further includes a power source to provide electrical power to the analogue-to-digital converter and the data processing apparatus. The bioamplifier includes a housing that contains the analogue-to-digital converter, the data processing apparatus, the power source, and the sensor input.

Electroencephalogram bioamplifier

A method for analyzing electroencephalogram (EEG) signals is disclosed. EEG signals are received from a sensor coupled to a user. Contextual information from one or both of the user and the user's environment is also received. The EEG signals are processed in real time using a machine learning model to predict an action of the user, which is associated with the contextual information. Output associated with the predicted action is then generated.

Fused electroencephalogram and machine learning for precognitive brain-computer interface for computer control

Growing community of inventors

Santa Cruz, CA, United States of America

Philip Edwin Watson