Intel Corporation

Falkonry Inc.

Beverly Klemme

Rajashree Raji Baskaran

Sergio E Sian

Nikunj R Mehta

Daniel Kearns

Peter Nicholas Pritchard

Deeksha Karanjgaokar

A computer-implemented method of predicting an event horizon is disclosed. The method comprises maintaining condition data indicating a plurality of conditions occurring on one or more physical systems at a plurality of points in time. The method further comprises receiving an input feature vector representing a given condition occurring at a specific time during a specific period of time. The method also comprises generating, using a particular trained machine learning model of a plurality of trained machine learning models, a forecast value that indicates an amount of time from the specific time to an occurrence of a particular target condition on a particular physical system, the particular target condition being different from the given condition, each trained machine learning model corresponding to a distinct target condition. In addition, the method comprises causing, based on the forecast value, an action to be executed on the particular physical system.

Computing an explainable event horizon estimate

Apparatus and methods for bone conduction detection are disclosed herein. An example apparatus includes memory; machine-readable instructions; and processor circuitry to execute the machine-readable instructions to associate a vibration signal with a voice or with motion, the vibration signal transmitted via a bone structure of a user; permit access to a user application based on the association of the vibration signal with the voice; and deny access to the user application based on the association of the vibration signal with the motion.

Apparatus and methods for bone conduction context detection

A method for generating forecast predictions that indicate an event horizon of an entity or remaining useful life of a consumable using machine learning techniques is provided. Using a server computer system, feature data comprising features vectors that represent a set of signal data over a range of time is stored. Condition data comprising conditions occurring on the entity at particular moments in time is stored. Label data that comprises a plurality of time values that each indicate a difference in time between one condition and another condition is stored. A training dataset is created by combining the feature data, the condition data, and the label data into a single dataset. The training dataset is partitioned by condition. A machine learning model is trained on each target condition training dataset. The trained machine learning models are used to generate forecast values that each indicate an amount of time to an occurrence of a target condition associated with an entity.

Apparatus and methods for bone conduction detection are disclosed herein. An example apparatus includes a first sensor to output a first vibration signal associated with a first bone structure of a user, a second sensor to output a second vibration signal associated with a second bone structure of the user, memory, and at least one processor to execute instructions to determine a sound associated with at least one of the first vibration signal or the second vibration signal originated external to the user; determine a direction from which the sound originated relative to the user; and generate a signal to cause an output device to present an alert to the user, the alert indicative of the direction of the sound.

Apparatus and methods for bone conduction detection are disclosed herein. An example wearable device includes a first sensor positioned to generate first vibration information from a bone structure of a user and a second sensor positioned to generate second vibration information from the bone structure of the user. The first vibration information and the second vibration information include sound data and motion data. The motion data is indicative of a motion by the user. The example wearable device includes a signal modifier to generate a modified signal including the sound data based on the first vibration information and the second vibration information. The example wearable device includes a communicator to transmit the modified signal for output via a speaker.

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San Jose, CA, United States of America

Beverly Klemme