Processing events generated by internet of things (IoT)

• US 10,324,773 B2
• Filed: 04/01/2016
• Issued: 06/18/2019
• Est. Priority Date: 09/17/2015
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Wing, Aubrey, Mamut, Tatyana, Che, Arthur Ping-Tak, Chin, Tiffany Joy, Poon, Amy Kar Sin, Layman, Andrew
• Primary Examiner(s): Phantana-angkool, David
• Application Number:
  US15/089,097
• Publication Number:
  US 20170083386A1
• Time in Patent Office: 1,173 Days
• Field of Search: 715810
• CPC Class Codes: G06F 1/30 Means for acting in the event of power-supply failure or interruption, e.g. power-supply fluctuations for resetting only G06F1/24
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

The technology disclosed relates to processing events generated by Internet of Things (IoT) devices. In particular, it relates to storing a machine-readable declarative specification of stateful event processing of an automated multi-step progression of monitoring of Internet of Things (IoT) devices that generate events. It includes compiling into tangible memory, in response to the declarative specification, a state processing network that implements a multi-step progression of monitoring events generated by the IoT devices. The state processing network implements both the time based transition triggers and the event based transition triggers after being initiated by the IoT devices. Further, data structures, which record monitoring status of particular IoT devices, are compiled. Finally, the state processing network further selects an alternative action to trigger, and triggers the selected action using at least some data from a particular event being processed.

Claims

1. A method of processing events generated by Internet of Things (IoT) devices, the method including:

storing a machine-readable declarative specification of stateful event processing of an automated multi-step progression of monitoring of IoT devices that generate events, wherein the declarative specification includes;

states in the multi-step progression;

time based transition triggers;

event based transition triggers;

definitions of conditions applicable to the event based triggers; and

alternative actions responsive to state transitions caused by the time based transition triggers and the event based transition triggers;

compiling into tangible memory, in response to the declarative specification;

a state processing network that implements the automated multi-step progression of monitoring the events generated by the IoT devices, the time based transition triggers and the event based transition triggers after being initiated by the IoT devices, and a first plurality of data structures that record monitoring status of the IoT devices, wherein the state processing network further selects an alternative action to trigger, and triggers the alternative action using at least some data from a particular event being processed;

compiling a second plurality of data structures corresponding to a first event that indicates a first anomalous performance of a particular IoT device installed with a plurality of other IoT devices under a device deployment scheme;

compiling a third plurality of data structures corresponding to event history indicating various anomalous performances of the plurality of other IoT devices at different times;

using the state processing network to determine historical frequency of the first anomalous performance among the plurality of other IoT devices by applying a definition of a threshold condition to the first event, the event history, and variables derived from the first event and the event history; and

using the state processing network to select and trigger a potential mismatch action when the historical frequency meets the threshold condition, wherein the potential mismatch action represents a potential mismatch between the particular IoT device and the plurality of other IoT devices and the device deployment scheme.

2. The method of claim 1, wherein the state processing network further selects and triggers a global action using the at least some data from the particular event being processed.

3. The method of claim 1, wherein the particular IoT device is a smart uninterruptible power supply (UPS) device and the first anomalous performance is spike in power voltage.

4. The method of claim 1, wherein the event history of the other IoT devices includes a surge in power load, a swing in power voltage, circuit breaks, a spike in power voltage, low battery voltage, or battery failure.

5. The method of claim 1, wherein the historical frequency is a first historical frequency, the definition is a first definition, the event history is first event history, the threshold condition is a first threshold condition, the particular IoT device is a first IoT device, and further including:

compiling a fourth plurality of data structures corresponding to a second event that indicates a second anomalous performance of a second IoT device;

compiling a fifth plurality of data structures corresponding to second event history indicating a plurality of anomalous performances of the second IoT device;

using the state processing network to determine second historical frequency of the second anomalous performance within a predetermined time window by applying a second definition of a second threshold condition and a time period condition to the second event and the second event history and to variables derived from the second event and the second event history; and

using the state processing network to select and trigger a false positive action when the second historical frequency fails to meet the second threshold condition and the time period condition.

6. The method of claim 5, wherein the second IoT device is a smart thermostat and the second anomalous performance is low battery voltage.

7. The method of claim 5, wherein the second event history of the second plurality of IoT devices includes low connectivity signal, lengthy constant readings, low battery voltage, or battery failure.

8. A non-transitory computer readable storage medium impressed with computer program instructions to process events generated by Internet of Things (IoT) devices, the instructions, when executed on a processor, implement a method comprising:

storing a machine-readable declarative specification of stateful event processing of an automated multi-step progression of monitoring of IoT devices that generate events, wherein the declarative specification includes;

states in the multi-step progression;

time based transition triggers;

event based transition triggers;

definitions of conditions applicable to the event based triggers; and

alternative actions responsive to state transitions caused by the time based transition triggers and the event based transition triggers;

compiling into tangible memory, in response to the declarative specification;

a state processing network that implements a multi-step progression of monitoring events generated by the IoT devices, the time based transition triggers and the event based transition triggers after being initiated by the IoT devices, and a first plurality of data structures that record monitoring status of the IoT devices, wherein the state processing network further selects an alternative action to trigger, and triggers the alternative action using at least some data from a particular event being processed;

compiling a second plurality of data structures corresponding to a first event that indicates a first anomalous performance of a particular IoT device;

compiling a third plurality of data structures corresponding to event history indicating various anomalous performances of the particular IoT device at different times;

using the state processing network to determine historical frequency of the first anomalous performance within a predetermined time window by applying a definition of a threshold condition and a time period condition to the first event and the event history and to variables derived from the first event and the event history; and

using the state processing network to select and trigger a false positive action when the historical frequency fails to meet the threshold condition and the time period condition.

9. The non-transitory computer readable storage medium of claim 8, wherein the state processing network further selects and triggers a global action using the at least some data from the particular event being processed.

10. The non-transitory computer readable storage medium of claim 8, wherein the particular IoT device is a first IoT device, the historical frequency is a first historical frequency, the definition is a first definition, the threshold condition is a first threshold condition, the event history is first event history, implementing the method further comprising:

compiling a fourth plurality of data structures corresponding to a second event that indicates a second anomalous performance of a second IoT device installed with a second plurality of IoT devices under a second device deployment scheme;

compiling a fifth plurality of data structures corresponding to second event history indicating various anomalous performances of a second plurality of IoT devices;

using the state processing network to determine second historical frequency of the second anomalous performance among the second plurality of IoT devices by applying a second definition of a second threshold condition to the second event and the second event history and to variables derived from the second event and the second event history; and

using the state processing network to select and trigger a potential mismatch action when the second historical frequency meets the second threshold condition, wherein the potential mismatch action represents a potential mismatch between the second IoT device and the second plurality of IoT devices and the second device deployment scheme.

11. The non-transitory computer readable storage medium of claim 10, wherein the second IoT device is a smart uninterruptible power supply (UPS) device and the second anomalous performance is spike in power voltage.

12. The non-transitory computer readable storage medium of claim 10,

wherein the second event history of the plurality of other IoT devices includes a surge in power load, a swing in power voltage, circuit breaks, a spike in power voltage, low battery voltage, or battery failure.

13. The non-transitory computer readable storage medium of claim 8, wherein the particular IoT device is a smart thermostat and the first anomalous performance is low battery voltage.

14. The non-transitory computer readable storage medium of claim 8, wherein the event history of the plurality of other IoT devices includes low connectivity signal, lengthy constant readings, low battery voltage, or battery failure.

15. A system including one or more processors coupled to memory, the memory loaded with computer instructions to process events generated by Internet of Things (IoT) devices, the instructions, when executed on the processors, implement actions comprising:

storing a machine-readable declarative specification of stateful event processing of an automated multi-step progression of monitoring of Internet of Things (IoT) devices that generate events, wherein the declarative specification includes;

states in the multi-step progression;

time based transition triggers;

event based transition triggers;

definitions of conditions applicable to the event based triggers; and

alternative actions responsive to state transitions caused by the time based transition triggers and the event based transition triggers;

compiling into tangible memory, in response to the declarative specification;

a state processing network that implements a multi-step progression of monitoring events generated by the IoT devices, the time based transition triggers and the event based transition triggers after being initiated by the IoT devices, and a first plurality of data structures that record monitoring status of the IoT devices; and

wherein the state processing network further selects an alternative action to trigger, and triggers the alternative action using at least some data from a particular event being processed;

compiling a second plurality of data structures corresponding to a first event that indicates a first anomalous performance of the particular IoT device;

compiling a third plurality of data structures corresponding to event history indicating various anomalous performances of the particular IoT device at different times;

using the state processing network to determine historical frequency of the first anomalous performance within a predetermined time window by applying a definition of a threshold condition and a time period condition to the first event and the event history and to variables derived from the first event and the event history; and

using the state processing network to select and trigger a false positive action when the historical frequency fails to meet the threshold condition and the time period condition.

16. The system of claim 15, wherein the state processing network further selects and triggers a global action using the at least some data from the particular event being processed.

17. The system of claim 15, wherein the particular IoT device is a first IoT device, the historical frequency is a first historical frequency, the definition is a first definition, the threshold condition is a first threshold condition, the event history is first event history, further implementing actions comprising:

compiling a fourth plurality of data structures corresponding to a second event that indicates a second anomalous performance of a second IoT device installed with a plurality of IoT devices under a second device deployment scheme;

compiling a fifth plurality of data structures corresponding to second event history indicating various anomalous performances of the plurality of IoT devices;

using the state processing network to determine a second historical frequency of the second anomalous performance among the plurality of IoT devices by applying a second definition of a second threshold condition to the second event and the second event history and to variables derived from the second event and the second event history; and

using the state processing network to select and trigger a potential mismatch action when the second historical frequency meets the second threshold condition, wherein the potential mismatch action represents a potential mismatch between the second IoT device and the plurality of IoT devices and the second device deployment scheme.