Automation of a programmable device

• US 9,618,911 B2
• Filed: 09/30/2014
• Issued: 04/11/2017
• Est. Priority Date: 12/02/2009
• Status: Active Grant

• Current Assignee: Velvetwire LLC
• Original Assignee: Velvetwire LLC
• Inventors: Bodnar, Eric O.
• Primary Examiner(s): JARRETT, RYAN A
• Application Number:
  US14/503,288
• Publication Number:
  US 20150018982A1
• Time in Patent Office: 924 Days
• Field of Search: None
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/25377 New sequence as function of deviation from predicted result, state
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05D 23/19 characterised by the use of electric means G05D23/1393 takes precedence
• CPC Class Codes: G05D 23/1904 variable in time
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A method and apparatus for a computer-implemented adaptive automation module comprising an event recorder to store one or more events for a predetermined period, and a timeline pattern generator logic to create a timeline for the predetermined period. The module further comprising marker creator logic to generate a marker to abstract the timeline data from the event data for controlling a device.

Claims

1. A method for automation of an environmental controller comprising:

receiving an event from a sensor, the event indicating a presence or absence of a person;

recording a timeline pattern based on a plurality of the events received from the sensor, the timeline pattern reflecting times of day associated with the plurality of events;

establishing a marker on the timeline pattern based on the record of events, the marker delineating a transition within the timeline pattern;

adjusting the position of the marker to reflect variation in the timeline pattern to approximate location consistency of the events in the timeline;

computing a weight for the marker that reflects time consistency of the events, the marker being more heavily weighted for consistently timed transitions, the computing comprising assigning the weight to the marker based on a proximity of one or more events occurring within a time range of the marker; and

subsequently triggering output based on the marker to adjust settings of the environmental controller based on the expected transition indicated by the marker.

2. The method of claim 1, wherein the recording of the timeline pattern further comprises:

archiving the plurality of events from the sensors into an event histogram; and

collapsing the event histogram to create the timeline pattern.

3. The method of claim 1, wherein the sensor indicates presence of a device associated with the person.

4. The method of claim 3, wherein the sensor provides data to the controller using a network.

5. The method of claim 1, further comprising:

associating the events with external data comprising at least one of;

time of day information, ambient light information, weather information, pricing information, and demand response data.

6. The method of claim 1, wherein the weight for the marker is computed based on a time range of the marker and a number of events within the time range of the marker.

7. The method of claim 1, wherein establishing a new marker comprises, for each slot in the timeline pattern comprising a plurality of slots:

determining whether an activity value for a current slot in the timeline pattern matches an activity value for a subsequent slot in the timeline pattern; and

adding the new marker to the timeline when the activity value for the current slot and the activity value for the subsequent slot do not match.

8. The method of claim 1, wherein adjusting the marker further comprises:

scanning the record of events for at least one event occurring after the marker and prior to a next marker;

recording a time of the at least one event;

computing an average time for the marker based on the time of the at least one event; and

repositioning the marker based on the average time.

9. A computer-implemented adaptive automation module comprising:

memory including an event recorder to store one or more events for a predetermined period, each event based on data from a sensor indicating a presence or absence of a person;

microprocessor including a timeline pattern generator logic to create a timeline for the predetermined period;

the microprocessor including a marker creator logic to generate a marker to create an intermediate data set from an event data represented by the timeline data, the marker representing transitions, wherein the microprocessor uses the marker for controlling a device, the device used to adapt an environment for the person, the marker creator logic further comprising a marker adjuster to adjust a location of the marker on the timeline based on the one or more events, wherein the marker adjustor scans the one or more events for an outlier event occurring near the marker, and computes an adjusted time for the marker based on a time of the outlier event.

10. The module of claim 9, wherein the marker creator logic comprises:

a marker generator to generate the marker based on the transitions as indicated by the one or more events on the timeline;

a marker weight assignor to assign a weight to the marker, based on a time range of the marker and a number of events within the time range of the marker.

11. The module of claim 10, wherein the timeline comprises a plurality of slots, and the module further comprising:

the marker generator evaluating each of the plurality of slots in the timeline, determine whether an activity value matches the activity value for a subsequent slot in the timeline pattern, and add a marker to a list of markers if the activity value for the current slot and the activity value for the next slot do not match.

12. The module of claim 10, further comprising:

the marker weight assignor is further to identify in-range events occurring within a time range of a marker, and assigning the weight to the marker based on a proximity of the in-range events to the marker.

13. The module of claim 9, wherein the data from the sensor is received via a network.

14. The module of claim 9, further comprising:

the module to broadcast automation decisions made based on the intermediate data set.

15. The module of claim 9, further comprising the timeline pattern generator logic generating an event histogram based on the one or more events, and collapsing the event histogram to create the timeline.

16. The module of claim 9, further comprising a low pass filter to remove short duration events from the timeline.

17. The module of claim 9, further comprising:

the marker creator to associate the marker with external data comprising at least one of;

time of day information, ambient light information, weather information, pricing information, and demand response data.

18. A adaptive automation module comprising:

a receiver to receive data from one or more sensors, the data indicating a presence or an absence of a person within a space;

a storage medium to store the data;

a processor associated with the storage medium and configured to;

create a timeline pattern based on a record of events, the record of events derived from the data;

generate an intermediate data set of markers from the timeline pattern, the markers adjusted to indication locations on the timeline pattern with consistent transitions, based on the record of events;

assign a weight to the marker based on a time range of the marker, a number of events within the time range of the marker, and a proximity of the in-range events to the marker; and

use the markers to control the adaptive automation module.