Google Inc.

Benjamin Irvine

Harry Tannenbaum

Shayan Sayadi

James Vanhook Singer

Jeffrey Adgate Dean

Kai Chen

Quoc V Le

Yi Zhang

Kun Zhang

James Wilson

Gregory Sean Corrado

Yingwei Cui

Andrew M Dai

Nissan Hajaj

Alvin Rishi Rajkomar

Peter Junteng Liu

Eyal Oren

James Wexler

Patrik Sundberg

Hector Yee

Michaela Hardt

Xiaomi Sun

Xiaobing Liu

Jacob Marcus

Alexander Mossin

Gerardo Flores

Kurt Litsch

Gavin Duggan

James Van Hook Singer

A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

System and method for predicting and summarizing medical events from electronic health records

The various implementations disclosed herein pertain to methods and systems for detecting a state of monitored objects in a premises. In one aspect, a method is performed at a computer system that is communicably coupled to sensor devices. The computer system receives and processes information of acoustic signals against a database of stored acoustic signatures characterizing predefined acoustic signals generated by electrically non-powered passive tags in response to physical motion of respective monitored objects associated with the passive tags. It receives a first acoustic signal characterized by a first acoustic signature and generated by a first passive tag. In response to the receiving, based on the first acoustic signature and information in the database, it identifies the first monitored object associated with the first acoustic signature, determines a first state of the first monitored object, and provides a notification of the first state to an occupant of the premises.

Systems and methods for monitoring objects and their states by using acoustic signals

A computer system is communicably coupled to one or more sensor devices. The computer system obtains a database of stored acoustic signatures characterizing predefined acoustic signals generated by passive tags in response to physical motion of the passive tags. The passive tags are associated with non-provisioned devices, and the acoustic signatures are associated with sets of executable instructions for provisioning the non-provisioned devices. A first acoustic signal characterized by a respective acoustic signature and generated by a first passive tag is detected. In response, and based on the respective acoustic signature and information in the database, a first non-provisioned device associated with the respective acoustic signature is identified, and a first set of executable instructions for provisioning the first non-provisioned device is identified. After, the computer system causes execution of the first set of executable instructions, thereby causing to commence a software process for provisioning the first non-provisioned device.

Systems and methods for provisioning devices using acoustic signals

A computer system is communicably coupled to one or more sensor devices. The computer system obtains a database of stored acoustic signatures characterizing predefined acoustic signals generated by passive tags in response to physical motion of respective monitored objects associated with the passive tags. A first acoustic signal characterized by a respective acoustic signature and generated by a first passive tag is detected. In response, and based on the respective acoustic signature and information in the database, a first monitored object associated with the respective acoustic signature is identified, and a first state of the first monitored object is determined. The determined first state is stored in the database, and an indication of the first state is provided to an associated monitoring service.

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San Francisco, CA, United States of America

Benjamin Irvine