Continuous sensor monitoring for identification of predictive anomalies
OwnerRamin Firoozyeinventor
Life of this patent
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Abstract
Systems and methods for automatically and continuously monitoring user data collected through different devices and sensors and analyzing the user data for identification of any predictive anomalies are provided. A user monitoring gateway implemented in a user environment continuously receives ambient data associated with a user from different devices and sensors in the user environment. The ambient data is processed to detect any predictive anomalies associated with the user. The predictive anomalies are associated with different categories and actions are performed to address the predictive anomalies.
Claims21
A computer-implemented method, comprising:
- continuously receiving ambient data associated with a user, wherein the ambient data is continuously received through a set of communications channels associated with different sensors and a gateway device, and wherein the ambient data is continuously received to dynamically detect any predictive anomalies associated with the user;
- processing the ambient data through a machine learning algorithm to detect one or more predictive anomalies associated with the user, wherein the machine learning algorithm is trained using a dataset of sample ambient data and corresponding sample predictive anomalies associated with the sample ambient data;
- associating the one or more predictive anomalies with a category according to a set of characteristics associated with the one or more predictive anomalies and the user;
- identifying one or more actions performable to address the one or more predictive anomalies, wherein the one or more actions are identified according to the category and a set of preferences associated with the user;
- and
- executing the one or more actions through the gateway device to address the one or more predictive anomalies.
A system, comprising:
- one or more processors;
- and
- memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
- continuously receive ambient data associated with a user, wherein the ambient data is continuously received through a set of communications channels associated with different sensors and a gateway device, and wherein the ambient data is continuously received to dynamically detect any predictive anomalies associated with the user;
- process the ambient data through a machine learning algorithm to detect one or more predictive anomalies associated with the user, wherein the machine learning algorithm is trained using a dataset of sample ambient data and corresponding sample predictive anomalies associated with the sample ambient data;
- associate the one or more predictive anomalies with a category according to a set of characteristics associated with the one or more predictive anomalies and the user;
- identify one or more actions performable to address the one or more predictive anomalies, wherein the one or more actions are identified according to the category and a set of preferences associated with the user;
- and
- execute the one or more actions through the gateway device to address the one or more predictive anomalies.
A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
- continuously receive ambient data associated with a user, wherein the ambient data is continuously received through a set of communications channels associated with different sensors and a gateway device, and wherein the ambient data is continuously received to dynamically detect any predictive anomalies associated with the user;
- process the ambient data through a machine learning algorithm to detect one or more predictive anomalies associated with the user, wherein the machine learning algorithm is trained using a dataset of sample ambient data and corresponding sample predictive anomalies associated with the sample ambient data;
- associate the one or more predictive anomalies with a category according to a set of characteristics associated with the one or more predictive anomalies and the user;
- identify one or more actions performable to address the one or more predictive anomalies, wherein the one or more actions are identified according to the category and a set of preferences associated with the user;
- and
- execute the one or more actions through the gateway device to address the one or more predictive anomalies.
Dependencies are inferred from the claim text.