Biointellisense, Inc.

Striiv, Inc.

Advanced Micro Devices Corporation

David Jonq Wang

Mark A Ross

James R Mault

Henry Leung

Thomas S Wong

Aloysius T Tam

Eduardo Chan

David F Naren

Sam Jones

Eleanor Nina Drake

Barry Alan Hoberman

Stuart T Auvinen

Patrick Wang

Zongde Qiu

Jim L Michelsen

Ronald Jen-Chuan Chwang

Tracy G Cheng

Arthur Joseph Swenson

Conlan Liang Ma

Brian Chris Ro

Wayne Eugene Greenwood

Anh T Nguyen

Conway Thomas Chen

A method to measure sound-producing behaviors of a subject with a power- and bandwidth-limited electronic device that includes a processor includes measuring, by a microphone communicatively coupled to the processor, sound in a vicinity of the subject to generate an audio data signal that represents the sound. The method also includes measuring, by at least one second sensor communicatively coupled to the processor, at least one parameter other than sound to generate at least a second data signal that represents the at least one parameter other than sound. The method also includes detecting one or more sound-producing behaviors of the subject based on: both the audio data signal and the second data signal; or information derived from both the audio data signal and the second data signal.

Sensor fusion to validate sound-producing behaviors

A method to determine medication adherence may include detecting whether a wearable electronic device is in use. The method may include detecting one or more markers in sweat vapor of the user using a chemical marker sensor of the wearable electronic device. The method may include, in response to detecting that the system is in use, determining whether the user has taken a therapeutic agent that includes the one or more markers based on detecting the one or more markers in the sweat vapor.

Medication adherence and/or counterfeit detection wearable electronic device

An adhesive device may include a first adhesive surface configured to be adhered to skin of a user, and a second adhesive surface opposite the first adhesive surface and configured to be adhered to a medical device. The adhesive device may also include an intermediate region between the first adhesive surface and the second adhesive surface, where the intermediate region includes a detector compound embedded in the intermediate region configured to change based on interaction of the detector compound with a target molecule.

Adhesive device

Multi-lobe sensor

According to an aspect of an embodiment, a method may include obtaining, at a mobile biometric-data hub, a first biometric reading of a user. The method may also include, based on the first biometric reading, associating the mobile biometric-data hub with the user. The method may also include wirelessly receiving, at the mobile biometric-data hub, a second biometric reading from a biometric sensor distinct from the mobile biometric-data hub. The method may also include determining that the second biometric reading is of the user. The method may also include, based on the second biometric reading being of the user and based on the association between the mobile biometric-data hub and the user, tagging the second biometric reading with an identifier of the user. The method may also include communicating the tagged second biometric reading from the mobile biometric-data hub to a network-attached device.

Mobile biometric-data hub

A system to determine medication adherence may include a chemical marker sensor, a verification sensor, and a processor. The chemical marker sensor may be configured to detect one or more markers in sweat vapor of a user. The verification sensor may be configured to detect whether the system is in use. The processor may be communicatively coupled to the chemical marker sensor and the verification sensor and may be configured to determine whether the user has taken a therapeutic agent that includes the one or more markers based on signals generated by the chemical marker sensor and the verification sensor.

A system to measure sweat vapor may include a skin contact sensor, a sweat vapor sensor, and a processor. The skin contact sensor may be configured to measure one or more aspects indicative of whether the measuring device is in contact with skin of a user of the measuring device. The sweat vapor sensor may be configured to measure one or more properties of sweat vapor of the user; and the processor may be communicatively coupled to the skin contact sensor and the sweat vapor sensor. The processor may be configured to confirm operation of the measuring device based on the one or more aspects measured by the skin contact sensor, and the sweat vapor sensor receiving the sweat vapor.

Operation-verifying wearable vapor sensor

A method to create autonomous decision logic (ADL) may include receiving a request to create the ADL. The request includes a selection of an input and a selection of an output. When executed by a client device, the ADL is configured to provide the output based at least partially on an identification of the input. The method further includes electronically packaging the input and the output to generate the ADL. The method includes sending the ADL via a transport layer to the client device for execution of the ADL on the client device. The client device is configured to generate encrypted data pertaining to the execution of the ADL. The method includes receiving, from the client device, the encrypted data pertaining to the execution of the ADL. The method further includes decrypting the encrypted data. The method includes presenting at least some of the decrypted data on a display.

Methods and systems for journaling and coaching using a wearable device

Fitness tracker wrist band

A method to implement multifactor authentication of a user may include performing biometric authentication of a person that bears the wearable electronic device and at least one of: performing knowledge-based authentication of the person or presenting an access control token of the wearable electronic device to an access reader that performs token-based authentication of the person. Performing biometric authentication may include receiving a first biometric signal generated by a wearable electronic device and determining a person-specific biometric characteristic of the person therefrom; comparing the person-specific biometric characteristic to a user-specific biometric characteristic of the user determined from a second biometric signal generated when the wearable electronic device was born by the user; and based on the comparing, determining a confidence level that the person is the user to determine a positive or negative authentication of the person as the user.

Multifactor authentication through wearable electronic device

Fitness tracker

An example embodiment includes a life pattern detection system. The life pattern detection system includes a sensor, a data acquisition device, and a first processor. The sensor is configured to monitor actions of a user. The data acquisition device is configured to harvest signals from the sensor and produce sensor data. The first processor is configured to receive the sensor data and background data, and to execute a life pattern application including a first series of analytical steps that determines a predictable set of actions from the sensor data and the background data.

Life pattern detection

Electronic device

A method for detecting a half-full condition of a first-in, first-out memory array. The method of the invention includes the steps of a) moving a write pointer through the array to write data to alternating rows of the memory array; b) moving a read pointer through the array to read data from the alternating rows of the memory array in first-in, first-out order; and c) providing a half-full indication when the read pointer and the write pointer point to adjacent rows in the memory array. This method eliminates the need to route lines across the array to detect a half-full condition, thereby reducing die and power requirements and offering an increase in speed.

Growing community of inventors

Palo Alto, CA, United States of America

David Jonq Wang