US20150370272A1

US20150370272A1 - Intelligent configuration of a smart environment based on arrival time

Info

Publication number: US20150370272A1
Application number: US14/531,805
Authority: US
Inventors: Prashant Papiahgari Reddy; Mark Malhotra; Evan Jarman Fisher; Todd Hester; Mark Andrew McBride; Yoky Matsuoka
Original assignee: Google LLC
Current assignee: Google LLC
Priority date: 2014-06-23
Filing date: 2014-11-03
Publication date: 2015-12-24
Also published as: US20160261425A1; US20190058985A1; US20200045522A1; US20150370621A1; US9491571B2; US10075828B2; US10638292B2; US20150372832A1; US9456297B2; US20150372833A1; US10440545B2; US9838830B2; US20150372834A1; US20170192402A1; US20150373149A1; US9668085B2; US20150372999A1; US10764735B2; US9854386B2; US20180376313A1

Abstract

A method includes receiving an estimated time of arrival (ETA) relating to an arrival to an environment, an arrival of an event, arrival of an activity, or a combination thereof; and controlling, configuring, or controlling and configuring a smart device based upon the ETA.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No. 62/016,052, filed Jun. 23, 2014, the disclosure of which is hereby incorporated in its entirety for all purposes.

BACKGROUND

The present disclosure relates generally to smart devices. More specifically, the present disclosure relates to a service for controlling smart devices based upon an estimated time of arrival (ETA).
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Various types of controllable smart electronic devices capable of controlling building environments, such as controlling temperatures and the like are now disposed throughout many buildings including homes and offices. Traditionally, control of these smart electronic devices has been triggered primarily when a user is within physical proximity of the devices. However, as the sophistication of smart devices increase, many new applications for these devices may be desired. As a result, the ability to control these devices in new ways may be highly desirable.

SUMMARY

A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below.
Embodiments of the present disclosure relate to an electronic device, such as a computer, thermostat or a hazard detector (e.g., smoke detector), that may be controlled based upon an estimated time of arrival (ETA) to a destination or other activity and/or event. As a result, the electronic device may pre-condition to a desired state prior to the arrival to the destination and/or the arrival of the activity and/or event, resulting in a more desirable user experience.
Various refinements of the features noted above may exist in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:

FIG. 1 is a block diagram of a smart home device, in accordance with an embodiment;

FIG. 2 is a block diagram of a connected smart home environment that includes a number of smart home devices, in accordance with an embodiment;

FIG. 3 is a block diagram illustrating a manner of controlling and/or accessing the smart home environment using services over the internet, in accordance with an embodiment;

FIG. 4 is a block diagram of processing paradigms that may be used to control devices of the smart home environment, in accordance with an embodiment;

FIG. 5 is a schematic drawing illustrating a system for providing control of the smart electronic device of FIG. 1 using an ETA, in accordance with an embodiment;

FIG. 6 is a flowchart of a method for providing control of the smart electronic device of FIG. 1 using an ETA, in accordance with an embodiment;

FIG. 7 is a flowchart of a method for providing control of the smart electronic device of FIG. 1 using an ETA, in accordance with an embodiment;

FIG. 8 is a temperature profile of a thermostat using the ETA control system, wherein preconditioning is active on the thermostat, in accordance with an embodiment;

FIG. 9 is a state diagram of a thermostat using the ETA control system, wherein preconditioning is active on the thermostat, in accordance with an embodiment;

FIG. 10 is a temperature profile of a thermostat using the ETA control system, wherein preconditioning is disabled on the thermostat, in accordance with an embodiment;

FIG. 11 is a state diagram of a thermostat using the ETA control system, wherein preconditioning is disabled on the thermostat, in accordance with an embodiment;

FIG. 12 is a temperature profile of a thermostat using the ETA control system, wherein preconditioning is enabled during an away mode, in accordance with an embodiment;

FIG. 13 is a flowchart illustrating a method for validating an ETA, in accordance with an embodiment;

FIG. 14 is a flowchart illustrating a method for defining a pre-conditioning window statically, in accordance with an embodiment;

FIG. 15 is a schematic drawing of a system using a static pre-conditioning window, in accordance with an embodiment;

FIG. 16 is a flowchart illustrating a method for defining a pre-conditioning window dynamically, in accordance with an embodiment;

FIG. 17 is a schematic drawing of a system using a dynamic pre-conditioning window, in accordance with an embodiment; and

FIG. 18 is a schematic drawing of a system for providing control of the smart electronic device of FIG. 1 using ETA conflict logic, in accordance with an embodiment.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
A number of smart home devices may serve the inhabitants of a home. A smart thermostat, such as the Nest® Learning Thermostat by Nest Labs, Inc. (a company of Google, Inc.), may learn the inhabitants' behavior and adjust the temperature to suit their preferences. A smart hazard detector, such as the Nest® Protect by Nest Labs, may communicate with other smart home devices while performing hazard detection functions to keep the inhabitants safe. A person may interact with the smart home devices using a first-party application program running on a personal device. Even so, it may be undesirable in some situations to use a third-party application to interact with the devices.
Sometimes device performance is not instantaneous, requiring pre-conditioning time to elevate to a desired end state. For example, HVAC systems may take a certain pre-conditioning time to cool and/or heat to a desired comfort level.
According to embodiments of this disclosure, an estimated time of arrival (ETA) of arriving at a conditioned environment, of arriving at an activity or event, etc. may be used to control and/or modify configurations of these devices. Accordingly, pre-conditioning may occur prior to the ETA.

The Smart Home Environment

By way of introduction, FIG. 1 is a block diagram of one example of a smart home device 10. In one embodiment, the smart home device 10 may include one or more sensors 12, a user-interface component 14, a power supply 16 (e.g., including a power connection and/or battery), a network interface 18, memory 20, and one or more processors 22. These components are intended to be representative and are not intended to be exhaustive. By way of example, the smart home device 10 may be a Nest® Learning Thermostat—1st Generation T100577, a Nest® Learning Thermostat—2nd Generation T200577, or a Nest® Protect, each of which is made by Nest Labs, Inc., a company of Google, Inc.
The sensors 12 may detect various properties of the environment of the smart home device 10. These may include acceleration, temperature, humidity, water, supplied power, proximity, external motion, device motion, sound signals, ultrasound signals, light signals, fire, smoke, carbon monoxide, global-positioning-satellite (GPS) signals, radio-frequency (RF), other electromagnetic signals or fields, or the like. As such, the sensors 12 may include temperature sensor(s), humidity sensor(s), hazard-related sensor(s) or other environmental sensor(s), accelerometer(s), microphone(s), optical sensors up to and including camera(s) (e.g., charged coupled-device or video cameras), active or passive radiation sensors, GPS receiver(s) or radiofrequency identification detector(s). While FIG. 1 illustrates an embodiment with a single sensor 12, many embodiments may include multiple sensors 12. In some instances, the smart home device 10 may include one or more primary sensors and one or more secondary sensors. For example, the primary sensor(s) may sense data central to the core operation of the device (e.g., sensing a temperature in a thermostat or sensing smoke in a smoke detector), while the secondary sensor(s) may sense other types of data (e.g., motion, light or sound), which can be used for energy-efficiency objectives or smart-operation objectives.
One or more user-interface components 14 in the smart home device 10 may receive input from the user and/or present information to the user when the user interacts in person with the smart home device 10. For example, the user may mechanically move a sliding component (e.g., along a vertical or horizontal track) or rotate a rotatable ring (e.g., along a circular track) to adjust a temperature setting. The power-supply component 16 may include a power connection and/or a local battery. For example, the power connection may connect the smart home device 10 to a power source such as a line voltage source. In some instances, an AC power source can be used to repeatedly charge a (e.g., rechargeable) local battery, such that the battery may be used later to supply power to the smart home device 10 when the AC power source is not available. In cases where the smart home device 10 will not have access to an external supply of power, the power-supply component 16 may be a non-rechargeable battery that is sized appropriately to last at least as long as a planned lifespan of the smart home device under normal operating conditions. The network interface 18 may include a component that enables the smart home device 10 to communicate between devices. As such, the network interface 18 may enable the smart home device 10 to communicate with other devices 10 via a wired or wireless network. The network interface 18 may include a wireless card or some other transceiver connection to facilitate this communication.
The memory device 20 may store instructions to execute on the processor 22. In one example, the memory device 20 may include an article of manufacture such as flash memory, a hard drive, random access memory, or the like. The processor 22 may include a general-purpose processor that carries out computer code stored in the memory device 20, a special-purpose processor or application-specific integrated circuit, or some combination of these. The processor 22 may also represent any other suitable type of hardware/firmware/software processing platforms. In certain embodiments, the processor 22 includes a high-power processor that may execute computationally intensive operations, such as operating the user-interface component 14 and the like, and a low-power processor that may manage less complex processes such as detecting a hazard or temperature from the sensor 12. In one embodiment, the low-power processor may wake or initialize the high-power processor for computationally intensive processes.
By way of example, when the processor 22 includes both a high-power processor and a low-power processor, the low-power processor may detect when a location (e.g., a house or room) is occupied (i.e., includes a presence of a human) and/or whether it is occupied by a specific person or is occupied by a specific number of people (e.g., relative to one or more thresholds). In one embodiment, this detection can occur by analyzing microphone signals, detecting user movements (e.g., in front of a device), detecting openings and closings of doors or garage doors, detecting wireless signals, detecting an internet protocol (IP) address of a received signal, detecting operation of one or more devices within a time window, or any other suitable techniques. The high-power processor and the low-power processor may include image recognition technology to identify particular occupants or objects. In certain embodiments, the high-power processor and the low-power processor may detect the presence of a human using a passive infrared (PIR) sensor 24.
In some instances, the high-power processor of the processor 22 may predict desirable settings and/or implement those settings. For example, based on the presence detection, the high-power processor may adjust device settings to, e.g., conserve power when nobody is home or in a particular room or to accord with user preferences (e.g., general at-home preferences or user-specific preferences). As another example, based on the detection of a particular person, animal or object (e.g., a child, pet or lost object), the high-power processor may initiate an audio or visual indicator of where the person, animal or object is or may initiate an alarm or security feature if an unrecognized person is detected under certain conditions (e.g., at night or when lights are off).
In some instances, devices may interact with each other such that events detected by a first device influences actions of a second device. For example, a first device can detect that a user has entered into a garage (e.g., by detecting motion in the garage, detecting a change in light in the garage or detecting opening of the garage door). The first device can transmit this information to a second device via the network interface 18, such that the second device can, e.g., adjust a home temperature setting, a light setting, a music setting, and/or a security-alarm setting. As another example, a first device can detect a user approaching a front door (e.g., by detecting motion or sudden light pattern changes). The first device may, e.g., cause a general audio or visual signal to be presented (e.g., such as sounding of a doorbell) or cause a location-specific audio or visual signal to be presented (e.g., to announce the visitor's presence within a room that a user is occupying).
Keeping the foregoing in mind, FIG. 2 illustrates an example of a smart-home environment 30 within which one or more of the smart home devices 10 of FIG. 1, methods, systems, services, and/or computer program products described further herein can be applicable. The depicted smart-home environment 30 includes a structure 32, which can include, e.g., a house, office building, garage, or mobile home. It will be appreciated that devices can also be integrated into a smart-home environment 30 that does not include an entire structure 32, such as an apartment, condominium, or office space. Further, the smart home environment can control and/or be coupled to devices outside of the actual structure 32. Indeed, several devices in the smart home environment need not physically be within the structure 32 at all. For example, a device controlling a pool heater or irrigation system can be located outside of the structure 32.
The depicted structure 32 includes a number of rooms 38, separated at least partly from each other via walls 40. The walls 40 can include interior walls or exterior walls. Each room can further include a floor 42 and a ceiling 44. Devices can be mounted on, integrated with and/or supported by a wall 40, floor 42 or ceiling 44.
In some embodiments, the smart-home environment 30 of FIG. 2 includes a number of smart home devices 10, including intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and/or with a central server or a cloud-computing system to provide any of a variety of useful smart-home objectives. The smart-home environment 30 may include one or more intelligent, multi-sensing, network-connected thermostats 46 (hereinafter referred to as “smart thermostats 46”), one or more intelligent, network-connected, multi-sensing hazard detection units 50 (hereinafter referred to as “smart hazard detectors 50”), and one or more intelligent, multi-sensing, network-connected entryway interface devices 52 (hereinafter referred to as “smart doorbells 52”). According to embodiments, the smart thermostat 46 may include a Nest® Learning Thermostat—1st Generation T100577 or Nest® Learning Thermostat—2nd Generation T200577 by Nest Labs, Inc., among others. The smart thermostat 46 detects ambient climate characteristics (e.g., temperature and/or humidity) and controls a HVAC system 48 accordingly.
The smart hazard detector 50 may detect the presence of a hazardous substance or a substance indicative of a hazardous substance (e.g., smoke, fire, or carbon monoxide). The smart hazard detector 50 may include a Nest® Protect that may include sensors 12 such as smoke sensors, carbon monoxide sensors, and the like. As such, the hazard detector 50 may determine when smoke, fire, or carbon monoxide may be present within the building.
The smart doorbell 52 may detect a person's approach to or departure from a location (e.g., an outer door), control doorbell functionality, announce a person's approach or departure via audio or visual means, or control settings on a security system (e.g., to activate or deactivate the security system when occupants go and come). The smart doorbell 52 may interact with other devices 10 based on whether someone has approached or entered the smart-home environment 30.
In some embodiments, the smart-home environment 30 further includes one or more intelligent, multi-sensing, network-connected wall switches 54 (hereinafter referred to as “smart wall switches 54”), along with one or more intelligent, multi-sensing, network-connected wall plug interfaces 56 (hereinafter referred to as “smart wall plugs 56”). The smart wall switches 54 may detect ambient lighting conditions, detect room-occupancy states, and control a power and/or dim state of one or more lights. In some instances, smart wall switches 54 may also control a power state or speed of a fan, such as a ceiling fan. The smart wall plugs 56 may detect occupancy of a room or enclosure and control supply of power to one or more wall plugs (e.g., such that power is not supplied to the plug if nobody is at home).
Still further, in some embodiments, the smart home device 10 within the smart-home environment 30 may further includes a number of intelligent, multi-sensing, network-connected appliances 58 (hereinafter referred to as “smart appliances 58”), such as refrigerators, stoves and/or ovens, televisions, washers, dryers, lights, stereos, intercom systems, garage-door openers, floor fans, ceiling fans, wall air conditioners, pool heaters, irrigation systems, security systems, and so forth. According to embodiments, the network-connected appliances 58 are made compatible with the smart-home environment by cooperating with the respective manufacturers of the appliances. For example, the appliances can be space heaters, window AC units, motorized duct vents, etc. When plugged in, an appliance can announce itself to the smart-home network, such as by indicating what type of appliance it is, and it can automatically integrate with the controls of the smart-home. Such communication by the appliance to the smart home can be facilitated by any wired or wireless communication protocols known by those having ordinary skill in the art. The smart home also can include a variety of non-communicating legacy appliances 68, such as old conventional washer/dryers, refrigerators, and the like which can be controlled, albeit coarsely (ON/OFF), by virtue of the smart wall plugs 56. The smart-home environment 30 can further include a variety of partially communicating legacy appliances 70, such as infrared (“IR”) controlled wall air conditioners or other IR-controlled devices, which can be controlled by IR signals provided by the smart hazard detectors 50 or the smart wall switches 54.
According to embodiments, the smart thermostats 46, the smart hazard detectors 50, the smart doorbells 52, the smart wall switches 54, the smart wall plugs 56, and other devices of the smart-home environment 30 are modular and can be incorporated into older and new houses. For example, the smart home devices 10 are designed around a modular platform consisting of two basic components: a head unit and a back plate, which is also referred to as a docking station. Multiple configurations of the docking station are provided so as to be compatible with any home, such as older and newer homes. However, all of the docking stations include a standard head-connection arrangement, such that any head unit can be removably attached to any docking station. Thus, in some embodiments, the docking stations are interfaces that serve as physical connections to the structure and the voltage wiring of the homes, and the interchangeable head units contain all of the sensors 12, processors 28, user interfaces 14, the power supply 16, the network interface 18, and other functional components of the devices described above.
Many different commercial and functional possibilities for provisioning, maintenance, and upgrade are possible. For example, after years of using any particular head unit, a user will be able to buy a new version of the head unit and simply plug it into the old docking station. There are also many different versions for the head units, such as low-cost versions with few features, and then a progression of increasingly-capable versions, up to and including extremely fancy head units with a large number of features. Thus, it should be appreciated that the various versions of the head units can all be interchangeable, with any of them working when placed into any docking station. This can advantageously encourage sharing and re-deployment of old head units—for example, when an important high-capability head unit, such as a hazard detector, is replaced by a new version of the head unit, then the old head unit can be re-deployed to a back room or basement, etc. According to embodiments, when first plugged into a docking station, the head unit can ask the user (by 2D LCD display, 2D/3D holographic projection, voice interaction, etc.) a few simple questions such as, “Where am I” and the user can indicate “living room”, “kitchen” and so forth.
The smart-home environment 30 may also include communication with devices outside of the physical home but within a proximate geographical range of the home. For example, the smart-home environment 30 may include a pool heater monitor 34 that communicates a current pool temperature to other devices within the smart-home environment 30 or receives commands for controlling the pool temperature. Similarly, the smart-home environment 30 may include an irrigation monitor 36 that communicates information regarding irrigation systems within the smart-home environment 30 and/or receives control information for controlling such irrigation systems. According to embodiments, an algorithm is provided for considering the geographic location of the smart-home environment 30, such as based on the zip code or geographic coordinates of the home. The geographic information is then used to obtain data helpful for determining optimal times for watering, such data may include sun location information, temperature, dewpoint, soil type of the land on which the home is located, etc.
By virtue of network connectivity, one or more of the smart-home devices of FIG. 2 can further allow a user to interact with the device even if the user is not proximate to the device. For example, a user can communicate with a device using a computer (e.g., a desktop computer, laptop computer, or tablet) or other portable electronic device (e.g., a smartphone) 66. A web page or app can be configured to receive communications from the user and control the device based on the communications and/or to present information about the device's operation to the user. For example, the user can view a current set point temperature for a device and adjust it using a computer. The user can be in the structure during this remote communication or outside the structure.
As discussed, users can control the smart thermostat and other smart devices in the smart-home environment 30 using a network-connected computer or portable electronic device 66. In some examples, some or all of the occupants (e.g., individuals who live in the home) can register their device 66 with the smart-home environment 30. Such registration can be made at a central server to authenticate the occupant and/or the device as being associated with the home and to give permission to the occupant to use the device to control the smart devices in the home. An occupant can use their registered device 66 to remotely control the smart devices of the home, such as when the occupant is at work or on vacation. The occupant may also use their registered device to control the smart devices when the occupant is actually located inside the home, such as when the occupant is sitting on a couch inside the home. It should be appreciated that instead of or in addition to registering devices 66, the smart-home environment 30 makes inferences about which individuals live in the home and are therefore occupants and which devices 66 are associated with those individuals. As such, the smart-home environment “learns” who is an occupant and permits the devices 66 associated with those individuals to control the smart devices of the home.
In some instances, guests desire to control the smart devices. For example, the smart-home environment may receive communication from an unregistered mobile device of an individual inside of the home, where said individual is not recognized as an occupant of the home. Further, for example, a smart-home environment may receive communication from a mobile device of an individual who is known to be or who is registered as a guest.
According to embodiments, a guest-layer of controls can be provided to guests of the smart-home environment 30. The guest-layer of controls gives guests access to basic controls (e.g., a judicially selected subset of features of the smart devices), such as temperature adjustments, but it locks out other functionalities. The guest layer of controls can be thought of as a “safe sandbox” in which guests have limited controls, but they do not have access to more advanced controls that could fundamentally alter, undermine, damage, or otherwise impair the occupant-desired operation of the smart devices. For example, the guest layer of controls will not permit the guest to adjust the heat-pump lockout temperature.
A use case example of this is when a guest is in a smart home, the guest could walk up to the thermostat and turn the dial manually, but the guest may not want to walk around the house “hunting” the thermostat, especially at night while the home is dark and others are sleeping. Further, the guest may not want to go through the hassle of downloading the necessary application to their device for remotely controlling the thermostat. In fact, the guest may not have the home owner's login credentials, etc., and therefore cannot remotely control the thermostat via such an application. Accordingly, according to embodiments of the invention, the guest can open a mobile browser on their mobile device, type a keyword, such as “NEST” into the URL field and tap “Go” or “Search”, etc. In response, the device presents the guest with a user interface which allows the guest to move the target temperature between a limited range, such as 65 and 80 degrees Fahrenheit. As discussed, the user interface provides a guest layer of controls that are limited to basic functions. The guest cannot change the target humidity, modes, or view energy history.
According to embodiments, to enable guests to access the user interface that provides the guest layer of controls, a local webserver is provided that is accessible in the local area network (LAN). It does not require a password, because physical presence inside the home is established reliably enough by the guest's presence on the LAN. In some embodiments, during installation of the smart device, such as the smart thermostat, the home owner is asked if they want to enable a Local Web App (LWA) on the smart device. Business owners will likely say no; home owners will likely say yes. When the LWA option is selected, the smart device broadcasts to the LAN that the above referenced keyword, such as “NEST”, is now a host alias for its local web server. Thus, no matter whose home a guest goes to, that same keyword (e.g., “NEST”) is always the URL you use to access the LWA, provided the smart device is purchased from the same manufacturer. Further, according to embodiments, if there is more than one smart device on the LAN, the second and subsequent smart devices do not offer to set up another LWA. Instead, they register themselves as target candidates with the master LWA. And in this case the LWA user would be asked which smart device they want to change the temperature on before getting the simplified user interface for the particular smart device they choose.
According to embodiments, a guest layer of controls may also be provided to users by means other than a device 66. For example, the smart device, such as the smart thermostat, may be equipped with walkup-identification technology (e.g., face recognition, RFID, ultrasonic sensors) that “fingerprints” or creates a “signature” for the occupants of the home. The walkup-identification technology can be the same as or similar to the fingerprinting and signature creating techniques described in other sections of this application. In operation, when a person who does not live in the home or is otherwise not registered with the smart home or whose fingerprint or signature is not recognized by the smart home “walks up” to a smart device, the smart device provides the guest with the guest layer of controls, rather than full controls.
As described below, the smart thermostat 46 and other smart devices “learn” by observing occupant behavior. For example, the smart thermostat learns occupants' preferred temperature set-points for mornings and evenings, and it learns when the occupants are asleep or awake, as well as when the occupants are typically away or at home, for example. According to embodiments, when a guest controls the smart devices, such as the smart thermostat, the smart devices do not “learn” from the guest. This prevents the guest's adjustments and controls from affecting the learned preferences of the occupants.
According to some embodiments, a smart television remote control is provided. The smart remote control recognizes occupants by thumbprint, visual identification, RFID, etc., and it recognizes a user as a guest or as someone belonging to a particular class having limited control and access (e.g., child). Upon recognizing the user as a guest or someone belonging to a limited class, the smart remote control only permits that user to view a subset of channels and to make limited adjustments to the settings of the television and other devices. For example, a guest cannot adjust the digital video recorder (DVR) settings, and a child is limited to viewing child-appropriate programming.
According to some embodiments, similar controls are provided for other instruments, utilities, and devices in the house. For example, sinks, bathtubs, and showers can be controlled by smart spigots that recognize users as guests or as children and therefore prevent water from exceeding a designated temperature that is considered safe.
In some embodiments, in addition to containing processing and sensing capabilities, each of the devices 34, 36, 46, 50, 52, 54, 56, and 58 (collectively referred to as “the smart devices”) is capable of data communications and information sharing with any other of the smart devices, as well as to any central server or cloud-computing system or any other device that is network-connected anywhere in the world. The required data communications can be carried out using any of a variety of custom or standard wireless protocols (Wi-Fi, ZigBee, 6LoWPAN, etc.) and/or any of a variety of custom or standard wired protocols (CAT6 Ethernet, HomePlug, etc.).
According to embodiments, all or some of the smart devices can serve as wireless or wired repeaters. For example, a first one of the smart devices can communicate with a second one of the smart device via a wireless router 60. The smart devices can further communicate with each other via a connection to a network, such as the Internet 62. Through the Internet 62, the smart devices can communicate with a central server or a cloud-computing system (device service) 64. The central server or cloud-computing system (device service) 64 can be associated with a manufacturer, support entity, or service provider associated with the device. For one embodiment, a user may be able to contact customer support using a device itself rather than needing to use other communication means such as a telephone or Internet-connected computer. Further, software updates can be automatically sent from the central server or cloud-computing system (device service) 64 to devices (e.g., when available, when purchased, or at routine intervals).
According to embodiments, the smart devices combine to create a mesh network of spokesman and low-power nodes in the smart-home environment 30, where some of the smart devices are “spokesman” nodes and others are “low-powered” nodes. Some of the smart devices in the smart-home environment 30 are battery powered, while others have a regular and reliable power source, such as by connecting to wiring (e.g., to 120V line voltage wires) behind the walls 40 of the smart-home environment. The smart devices that have a regular and reliable power source are referred to as “spokesman” nodes. These nodes are equipped with the capability of using any wireless protocol or manner to facilitate bidirectional communication with any of a variety of other devices in the smart-home environment 30 as well as with the central server or cloud-computing system (device service) 64. On the other hand, the devices that are battery powered are referred to as “low-power” nodes. These nodes tend to be smaller than spokesman nodes and can only communicate using wireless protocols that require very little power, such as Zigbee, 6LoWPAN, etc. Further, some, but not all, low-power nodes are incapable of bidirectional communication. These low-power nodes send messages, but they are unable to “listen”. Thus, other devices in the smart-home environment 30, such as the spokesman nodes, cannot send information to these low-power nodes.
As described, the smart devices serve as low-power and spokesman nodes to create a mesh network in the smart-home environment 30. Individual low-power nodes in the smart-home environment regularly send out messages regarding what they are sensing, and the other low-powered nodes in the smart-home environment—in addition to sending out their own messages—repeat the messages, thereby causing the messages to travel from node to node (i.e., device to device) throughout the smart-home environment 30. The spokesman nodes in the smart-home environment 30 are able to “drop down” to low-powered communication protocols to receive these messages, translate the messages to other communication protocols, and send the translated messages to other spokesman nodes and/or the central server or cloud-computing system (device service) 64. Thus, the low-powered nodes using low-power communication protocols are able send messages across the entire smart-home environment 30 as well as over the Internet 62 to the central server or cloud-computing system (device service) 64. According to embodiments, the mesh network enables the central server or cloud-computing system (device service) 64 to regularly receive data from all of the smart devices in the home, make inferences based on the data, and send commands back to one of the smart devices to accomplish some of the smart-home objectives described herein.
As described, the spokesman nodes and some of the low-powered nodes are capable of “listening”. Accordingly, users, other devices, and the central server or cloud-computing system (device service) 64 can communicate controls to the low-powered nodes. For example, a user can use the portable electronic device (e.g., a smartphone) 66 to send commands over the Internet 62 to the central server or cloud-computing system (device service) 64, which then relays the commands to the spokesman nodes in the smart-home environment 30. The spokesman nodes drop down to a low-power protocol to communicate the commands to the low-power nodes throughout the smart-home environment, as well as to other spokesman nodes that did not receive the commands directly from the central server or cloud-computing system (device service) 64.
An example of a low-power node is a smart night light 65. In addition to housing a light source, the smart night light 65 houses an occupancy sensor, such as an ultrasonic or passive IR sensor, and an ambient light sensor, such as a photoresistor or a single-pixel sensor that measures light in the room. In some embodiments, the smart night light 65 is configured to activate the light source when its ambient light sensor detects that the room is dark and when its occupancy sensor detects that someone is in the room. In other embodiments, the smart night light 65 is simply configured to activate the light source when its ambient light sensor detects that the room is dark. Further, according to embodiments, the smart night light 65 includes a low-power wireless communication chip (e.g., ZigBee chip) that regularly sends out messages regarding the occupancy of the room and the amount of light in the room, including instantaneous messages coincident with the occupancy sensor detecting the presence of a person in the room. As mentioned above, these messages may be sent wirelessly, using the mesh network, from node to node (i.e., smart device to smart device) within the smart-home environment 30 as well as over the Internet 62 to the central server or cloud-computing system (device service) 64.
Other examples of low-powered nodes include battery-operated versions of the smart hazard detectors 50. These smart hazard detectors 50 are often located in an area without access to constant and reliable power and, as discussed in detail below, may include any number and type of sensors, such as smoke/fire/heat sensors, carbon monoxide/dioxide sensors, occupancy/motion sensors, ambient light sensors, temperature sensors, humidity sensors, and the like. Furthermore, smart hazard detectors 50 can send messages that correspond to each of the respective sensors to the other devices and the central server or cloud-computing system (device service) 64, such as by using the mesh network as described above.
Examples of spokesman nodes include smart thermostats 46, smart doorbells 52, smart wall switches 54, and smart wall plugs 56. These devices 46, 52, 54, and 56 are often located near and connected to a reliable power source, and therefore can include more power-consuming components, such as one or more communication chips capable of bidirectional communication in any variety of protocols.
In some embodiments, these low-powered and spokesman nodes (e.g., devices 46, 50, 52, 54, 56, 58, and 65) can function as “tripwires” for an alarm system in the smart-home environment. For example, in the event a perpetrator circumvents detection by alarm sensors located at windows, doors, and other entry points of the smart-home environment 30, the alarm could be triggered upon receiving an occupancy, motion, heat, sound, etc. message from one or more of the low-powered and spokesman nodes in the mesh network. For example, upon receiving a message from a smart night light 65 indicating the presence of a person, the central server or cloud-computing system (device service) 64 or some other device could trigger an alarm, provided the alarm is armed at the time of detection. Thus, the alarm system could be enhanced by various low-powered and spokesman nodes located throughout the smart-home environment 30. In this example, a user could enhance the security of the smart-home environment 30 by buying and installing extra smart nightlights 65. However, in a scenario where the perpetrator uses a radio transceiver to jam the wireless network, the smart home devices 10 may be incapable of communicating with each other. Therefore, as discussed in detail below, the present techniques provide network communication jamming attack detection and notification solutions to such a problem.
In some embodiments, the mesh network can be used to automatically turn on and off lights as a person transitions from room to room. For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network. Using the messages that indicate which rooms are occupied, the central server or cloud-computing system (device service) 64 or some other device activates and deactivates the smart wall switches 54 to automatically provide light as the person moves from room to room in the smart-home environment 30. Further, users may provide pre-configuration information that indicates which smart wall plugs 56 provide power to lamps and other light sources, such as the smart night light 65. Alternatively, this mapping of light sources to wall plugs 56 can be done automatically (e.g., the smart wall plugs 56 detect when a light source is plugged into it, and it sends a corresponding message to the central server or cloud-computing system (device service) 64). Using this mapping information in combination with messages that indicate which rooms are occupied, the central server or cloud-computing system (device service) 64 or some other device activates and deactivates the smart wall plugs 56 that provide power to lamps and other light sources so as to track the person's movement and provide light as the person moves from room to room.
In some embodiments, the mesh network of low-powered and spokesman nodes can be used to provide exit lighting in the event of an emergency. In some instances, to facilitate this, users provide pre-configuration information that indicates exit routes in the smart-home environment 30. For example, for each room in the house, the user provides a map of the best exit route. It should be appreciated that instead of a user providing this information, the central server or cloud-computing system (device service) 64 or some other device could automatically determine the routes using uploaded maps, diagrams, architectural drawings of the smart-home house, as well as using a map generated based on positional information obtained from the nodes of the mesh network (e.g., positional information from the devices is used to construct a map of the house). In operation, when an alarm is activated (e.g., when one or more of the smart hazard detector 50 detects smoke and activates an alarm), the central server or cloud-computing system (device service) 64 or some other device uses occupancy information obtained from the low-powered and spokesman nodes to determine which rooms are occupied and then turns on lights (e.g., nightlights 65, wall switches 54, wall plugs 56 that power lamps, etc.) along the exit routes from the occupied rooms so as to provide emergency exit lighting.
Further included and illustrated in the smart-home environment 30 of FIG. 2 are service robots 69 each configured to carry out, in an autonomous manner, any of a variety of household tasks. For some embodiments, the service robots 69 can be respectively configured to perform floor sweeping, floor washing, etc. in a manner similar to that of known commercially available devices such as the ROOMBA™ and SCOOBA™ products sold by iRobot, Inc. of Bedford, Mass. Tasks such as floor sweeping and floor washing can be considered as “away” or “while-away” tasks for purposes of the instant description, as it is generally more desirable for these tasks to be performed when the occupants are not present. For other embodiments, one or more of the service robots 69 are configured to perform tasks such as playing music for an occupant, serving as a localized thermostat for an occupant, serving as a localized air monitor/purifier for an occupant, serving as a localized baby monitor, serving as a localized hazard detector for an occupant, and so forth, it being generally more desirable for such tasks to be carried out in the immediate presence of the human occupant. For purposes of the instant description, such tasks can be considered as “human-facing” or “human-centric” tasks.
When serving as a localized thermostat for an occupant, a particular one of the service robots 69 can be considered to be facilitating what can be called a “personal comfort-area network” for the occupant, with the objective being to keep the occupant's immediate space at a comfortable temperature wherever that occupant may be located in the home. This can be contrasted with conventional wall-mounted room thermostats, which have the more attenuated objective of keeping a statically-defined structural space at a comfortable temperature. According to one embodiment, the localized-thermostat service robot 69 is configured to move itself into the immediate presence (e.g., within five feet) of a particular occupant who has settled into a particular location in the home (e.g. in the dining room to eat their breakfast and read the news). The localized-thermostat service robot 69 includes a temperature sensor, a processor, and wireless communication components configured such that control communications with the HVAC system, either directly or through a wall-mounted wirelessly communicating thermostat coupled to the HVAC system, are maintained and such that the temperature in the immediate vicinity of the occupant is maintained at their desired level. If the occupant then moves and settles into another location (e.g. to the living room couch to watch television), the localized-thermostat service robot 69 proceeds to move and park itself next to the couch and keep that particular immediate space at a comfortable temperature.
Technologies by which the localized-thermostat service robot 69 (and/or the larger smart-home system of FIG. 2) can identify and locate the occupant whose personal-area space is to be kept at a comfortable temperature can include, but are not limited to, RFID sensing (e.g., person having an RFID bracelet, RFID necklace, or RFID key fob), synthetic vision techniques (e.g., video cameras and face recognition processors), audio techniques (e.g., voice, sound pattern, vibration pattern recognition), ultrasound sensing/imaging techniques, and infrared or near-field communication (NFC) techniques (e.g., person wearing an infrared or NFC-capable smartphone), along with rules-based inference engines or artificial intelligence techniques that draw useful conclusions from the sensed information (e.g., if there is only a single occupant present in the home, then that is the person whose immediate space should be kept at a comfortable temperature, and the selection of the desired comfortable temperature should correspond to that occupant's particular stored profile).
When serving as a localized air monitor/purifier for an occupant, a particular service robot 69 can be considered to be facilitating what can be called a “personal health-area network” for the occupant, with the objective being to keep the air quality in the occupant's immediate space at healthy levels. Alternatively or in conjunction therewith, other health-related functions can be provided, such as monitoring the temperature or heart rate of the occupant (e.g., using finely remote sensors, near-field communication with on-person monitors, etc.). When serving as a localized hazard detector for an occupant, a particular service robot 69 can be considered to be facilitating what can be called a “personal safety-area network” for the occupant, with the objective being to ensure there is no excessive carbon monoxide, smoke, fire, etc., in the immediate space of the occupant. Methods analogous to those described above for personal comfort-area networks in terms of occupant identifying and tracking are likewise applicable for personal health-area network and personal safety-area network embodiments.
According to some embodiments, the above-referenced facilitation of personal comfort-area networks, personal health-area networks, personal safety-area networks, and/or other such human-facing functionalities of the service robots 69, are further enhanced by logical integration with other smart sensors in the home according to rules-based inferencing techniques or artificial intelligence techniques for achieving better performance of those human-facing functionalities and/or for achieving those goals in energy-conserving or other resource-conserving ways. Thus, for one embodiment relating to personal health-area networks, the air monitor/purifier service robot 69 can be configured to detect whether a household pet is moving toward the currently settled location of the occupant (e.g., using on-board sensors and/or by data communications with other smart-home sensors along with rules-based inferencing/artificial intelligence techniques), and if so, the air purifying rate is immediately increased in preparation for the arrival of more airborne pet dander. For another embodiment relating to personal safety-area networks, the hazard detector service robot 69 can be advised by other smart-home sensors that the temperature and humidity levels are rising in the kitchen, which is nearby to the occupant's current dining room location, and responsive to this advisory the hazard detector service robot 69 will temporarily raise a hazard detection threshold, such as a smoke detection threshold, under an inference that any small increases in ambient smoke levels will most likely be due to cooking activity and not due to a genuinely hazardous condition.
The above-described “human-facing” and “away” functionalities can be provided, without limitation, by multiple distinct service robots 69 having respective dedicated ones of such functionalities, by a single service robot 69 having an integration of two or more different ones of such functionalities, and/or any combinations thereof (including the ability for a single service robot 69 to have both “away” and “human facing” functionalities) without departing from the scope of the present teachings. Electrical power can be provided by virtue of rechargeable batteries or other rechargeable methods, such as an out-of-the-way docking station to which the service robots 69 will automatically dock and recharge its batteries (if needed) during periods of inactivity. Preferably, each service robot 69 includes wireless communication components that facilitate data communications with one or more of the other wirelessly communicating smart-home sensors of FIG. 2 and/or with one or more other service robots 69 (e.g., using Wi-Fi, Zigbee, Z-Wave, 6LoWPAN, etc.), and one or more of the smart-home devices 10 can be in communication with a remote server over the Internet. Alternatively or in conjunction therewith, each service robot 69 can be configured to communicate directly with a remote server by virtue of cellular telephone communications, satellite communications, 3G/4G network data communications, or other direct communication method.
Provided according to some embodiments are systems and methods relating to the integration of the service robot(s) 69 with home security sensors and related functionalities of the smart home system. The embodiments are particularly applicable and advantageous when applied for those service robots 69 that perform “away” functionalities or that otherwise are desirable to be active when the home is unoccupied (hereinafter “away-service robots”). Included in the embodiments are methods and systems for ensuring that home security systems, intrusion detection systems, and/or occupancy-sensitive environmental control systems (for example, occupancy-sensitive automated setback thermostats that enter into a lower-energy-using condition when the home is unoccupied) are not erroneously triggered by the away-service robots.
Provided according to one embodiment is a home automation and security system (e.g., as shown in FIG. 2) that is remotely monitored by a monitoring service by virtue of automated systems (e.g., cloud-based servers or other central servers, hereinafter “central server”) that are in data communications with one or more network-connected elements of the home automation and security system. The away-service robots are configured to be in operative data communication with the central server, and are configured such that they remain in a non-away-service state (e.g., a dormant state at their docking station) unless permission is granted from the central server (e.g., by virtue of an “away-service-OK” message from the central server) to commence their away-service activities. An away-state determination made by the system, which can be arrived at (i) exclusively by local on-premises smart device(s) based on occupancy sensor data, (ii) exclusively by the central server based on received occupancy sensor data and/or based on received proximity-related information such as GPS coordinates from user smartphones or automobiles, or (iii) any combination of (i) and (ii) can then trigger the granting of away-service permission to the away-service robots by the central server. During the course of the away-service robot activity, during which the away-service robots may continuously detect and send their in-home location coordinates to the central server, the central server can readily filter signals from the occupancy sensing devices to distinguish between the away-service robot activity versus any unexpected intrusion activity, thereby avoiding a false intrusion alarm condition while also ensuring that the home is secure. Alternatively or in conjunction therewith, the central server may provide filtering data (such as an expected occupancy-sensing profile triggered by the away-service robots) to the occupancy sensing nodes or associated processing nodes of the smart home, such that the filtering is performed at the local level. Although somewhat less secure, it would also be within the scope of the present teachings for the central server to temporarily disable the occupancy sensing equipment for the duration of the away-service robot activity.
According to another embodiment, functionality similar to that of the central server in the above example can be performed by an on-site computing device such as a dedicated server computer, a “master” home automation console or panel, or as an adjunct function of one or more of the smart-home devices of FIG. 2. In such an embodiment, there would be no dependency on a remote service provider to provide the “away-service-OK” permission to the away-service robots and the false-alarm-avoidance filtering service or filter information for the sensed intrusion detection signals.
According to other embodiments, there are provided methods and systems for implementing away-service robot functionality while avoiding false home security alarms and false occupancy-sensitive environmental controls without the requirement of a single overall event orchestrator. For purposes of the simplicity in the present disclosure, the home security systems and/or occupancy-sensitive environmental controls that would be triggered by the motion, noise, vibrations, or other disturbances of the away-service robot activity are referenced simply as “activity sensing systems,” and when so triggered will yield a “disturbance-detected” outcome representative of the false trigger (for example, an alarm message to a security service, or an “arrival” determination for an automated setback thermostat that causes the home to be heated or cooled to a more comfortable “occupied” setpoint temperature). According to one embodiment, the away-service robots are configured to emit a standard ultrasonic sound throughout the course of their away-service activity, the activity sensing systems are configured to detect that standard ultrasonic sound, and the activity sensing systems are further configured such that no disturbance-detected outcome will occur for as long as that standard ultrasonic sound is detected. For other embodiments, the away-service robots are configured to emit a standard notification signal throughout the course of their away-service activity, the activity sensing systems are configured to detect that standard notification signal, and the activity sensing systems are further configured such that no disturbance-detected outcome will occur for as long as that standard notification signal is detected, wherein the standard notification signal comprises one or more of: an optical notifying signal; an audible notifying signal; an infrared notifying signal; an infrasonic notifying signal; a wirelessly transmitted data notification signal (e.g., an IP broadcast, multicast, or unicast notification signal, or a notification message sent in an TCP/IP two-way communication session).
According to some embodiments, the notification signals sent by the away-service robots to the activity sensing systems are authenticated and encrypted such that the notifications cannot be learned and replicated by a potential burglar. Any of a variety of known encryption/authentication schemes can be used to ensure such data security including, but not limited to, methods involving third party data security services or certificate authorities. For some embodiments, a permission request-response model can be used, wherein any particular away-service robot requests permission from each activity sensing system in the home when it is ready to perform its away-service tasks, and does not initiate such activity until receiving a “yes” or “permission granted” message from each activity sensing system (or from a single activity sensing system serving as a “spokesman” for all of the activity sensing systems). One advantage of the described embodiments that do not require a central event orchestrator is that there can (optionally) be more of an arms-length relationship between the supplier(s) of the home security/environmental control equipment, on the one hand, and the supplier(s) of the away-service robot(s), on the other hand, as it is only required that there is the described standard one-way notification protocol or the described standard two-way request/permission protocol to be agreed upon by the respective suppliers.
According to still other embodiments, the activity sensing systems are configured to detect sounds, vibrations, RF emissions, or other detectable environmental signals or “signatures” that are intrinsically associated with the away-service activity of each away-service robot, and are further configured such that no disturbance-detected outcome will occur for as long as that particular detectable signal or environmental “signature” is detected. By way of example, a particular kind of vacuum-cleaning away-service robot may emit a specific sound or RF signature. For one embodiment, the away-service environmental signatures for each of a number of known away-service robots are stored in the memory of the activity sensing systems based on empirically collected data, the environmental signatures being supplied with the activity sensing systems and periodically updated by a remote update server. For another embodiment, the activity sensing systems can be placed into a “training mode” for the particular home in which they are installed, wherein they “listen” and “learn” the particular environmental signatures of the away-service robots for that home during that training session, and thereafter will suppress disturbance-detected outcomes for intervals in which those environmental signatures are heard.
For still another embodiment, which is particularly useful when the activity sensing system is associated with occupancy-sensitive environmental control equipment rather than a home security system, the activity sensing system is configured to automatically learn the environmental signatures for the away-service robots by virtue of automatically performing correlations over time between detected environmental signatures and detected occupancy activity. By way of example, for one embodiment an intelligent automated nonoccupancy-triggered setback thermostat such as the Nest Learning Thermostat can be configured to constantly monitor for audible and RF activity as well as to perform infrared-based occupancy detection. In particular view of the fact that the environmental signature of the away-service robot will remain relatively constant from event to event, and in view of the fact that the away-service events will likely either (a) themselves be triggered by some sort of nonoccupancy condition as measured by the away-service robots themselves, or (b) occur at regular times of day, there will be patterns in the collected data by which the events themselves will become apparent and for which the environmental signatures can be readily learned. Generally speaking, for this automatic-learning embodiment in which the environmental signatures of the away-service robots are automatically learned without requiring user interaction, it is more preferable that a certain number of false triggers be tolerable over the course of the learning process. Accordingly, this automatic-learning embodiment is more preferable for application in occupancy-sensitive environmental control equipment (such as an automated setback thermostat) rather than home security systems for the reason that a few false occupancy determinations may cause a few instances of unnecessary heating or cooling, but will not otherwise have any serious consequences, whereas false home security alarms may have more serious consequences.
According to embodiments, technologies including the sensors of the smart devices located in the mesh network of the smart-home environment in combination with rules-based inference engines or artificial intelligence provided at the central server or cloud-computing system (device service) 64 are used to provide a personal “smart alarm clock” for individual occupants of the home. For example, user-occupants can communicate with the central server or cloud-computing system (device service) 64 via their mobile devices 66 to access an interface for the smart alarm clock. There, occupants can turn on their “smart alarm clock” and input a wake time for the next day and/or for additional days. In some embodiments, the occupant may have the option of setting a specific wake time for each day of the week, as well as the option of setting some or all of the inputted wake times to “repeat”. Artificial intelligence will be used to consider the occupant's response to these alarms when they go off and make inferences about the user's preferred sleep patterns over time.
According to embodiments, the smart device in the smart-home environment 30 that happens to be closest to the occupant when the occupant falls asleep will be the device that transmits messages regarding when the occupant stopped moving, from which the central server or cloud-computing system (device service) 64 will make inferences about where and when the occupant prefers to sleep. This closest smart device will as be the device that sounds the alarm to wake the occupant. In this manner, the “smart alarm clock” will follow the occupant throughout the house, by tracking the individual occupants based on their “unique signature”, which is determined based on data obtained from sensors located in the smart devices. For example, the sensors include ultrasonic sensors, passive IR sensors, and the like. The unique signature is based on a combination of walking gate, patterns of movement, voice, height, size, etc. It should be appreciated that facial recognition may also be used.
According to an embodiment, the wake times associated with the “smart alarm clock” are used by the smart thermostat 46 to control the HVAC in an efficient manner so as to pre-heat or cool the house to the occupant's desired “sleeping” and “awake” temperature settings. The preferred settings can be learned over time, such as by observing which temperature the occupant sets the thermostat to before going to sleep and which temperature the occupant sets the thermostat to upon waking up.
According to an embodiment, a device is positioned proximate to the occupant's bed, such as on an adjacent nightstand, and collects data as the occupant sleeps using noise sensors, motion sensors (e.g., ultrasonic, IR, and optical), etc. Data may be obtained by the other smart devices in the room as well. Such data may include the occupant's breathing patterns, heart rate, movement, etc. Inferences are made based on this data in combination with data that indicates when the occupant actually wakes up. For example, if—on a regular basis—the occupant's heart rate, breathing, and moving all increase by 5% to 10%, twenty to thirty minutes before the occupant wakes up each morning, then predictions can be made regarding when the occupant is going to wake. Other devices in the home can use these predictions to provide other smart-home objectives, such as adjusting the smart thermostat 46 so as to pre-heat or cool the home to the occupant's desired setting before the occupant wakes up. Further, these predictions can be used to set the “smart alarm clock” for the occupant, to turn on lights, etc.
According to embodiments, technologies including the sensors of the smart devices located throughout the smart-home environment in combination with rules-based inference engines or artificial intelligence provided at the central server or cloud-computing system (device service) 64 are used to detect or monitor the progress of Alzheimer's Disease. For example, the unique signatures of the occupants are used to track the individual occupants' movement throughout the smart-home environment 30. This data can be aggregated and analyzed to identify patterns indicative of Alzheimer's. Oftentimes, individuals with Alzheimer's have distinctive patterns of migration in their homes. For example, a person will walk to the kitchen and stand there for a while, then to the living room and stand there for a while, and then back to the kitchen. This pattern will take about thirty minutes, and then the person will repeat the pattern. According to embodiments, the remote servers or cloud computing architectures 64 analyze the person's migration data collected by the mesh network of the smart-home environment to identify such patterns.
In addition, FIG. 3 illustrates an embodiment of an extensible devices and services platform 80 that can be concentrated at a single server or distributed among several different computing entities without limitation with respect to the smart-home environment 30. The extensible devices and services platform 80 may include a processing engine 86, which may include engines that receive data from devices of smart-home environments (e.g., via the Internet or a hubbed network), to index the data, to analyze the data and/or to generate statistics based on the analysis or as part of the analysis. The analyzed data can be stored as derived home data 88.
Results of the analysis or statistics can thereafter be transmitted back to the device that provided home data used to derive the results, to other devices, to a server providing a web page to a user of the device, or to other non-device entities. For example, use statistics, use statistics relative to use of other devices, use patterns, and/or statistics summarizing sensor readings can be generated by the processing engine 86 and transmitted. The results or statistics can be provided via the Internet 62. In this manner, the processing engine 86 can be configured and programmed to derive a variety of useful information from the home data 82. A single server can include one or more engines.
The derived data can be highly beneficial at a variety of different granularities for a variety of useful purposes, ranging from explicit programmed control of the devices on a per-home, per-neighborhood, or per-region basis (for example, demand-response programs for electrical utilities), to the generation of inferential abstractions that can assist on a per-home basis (for example, an inference can be drawn that the homeowner has left for vacation and so security detection equipment can be put on heightened sensitivity), to the generation of statistics and associated inferential abstractions that can be used for government or charitable purposes. For example, processing engine 86 can generate statistics about device usage across a population of devices and send the statistics to device users, service providers or other entities (e.g., that have requested or may have provided monetary compensation for the statistics).
According to some embodiments, the home data 82, the derived home data 88, and/or another data can be used to create “automated neighborhood safety networks.” For example, in the event the central server or cloud-computing architecture 64 receives data indicating that a particular home has been broken into, is experiencing a fire, or some other type of emergency event, an alarm is sent to other smart homes in the “neighborhood.” In some instances, the central server or cloud-computing architecture 64 automatically identifies smart homes within a radius of the home experiencing the emergency and sends an alarm to the identified homes. In such instances, the other homes in the “neighborhood” do not have to sign up for or register to be a part of a safety network, but instead are notified of an emergency based on their proximity to the location of the emergency. This creates robust and evolving neighborhood security watch networks, such that if one person's home is getting broken into, an alarm can be sent to nearby homes, such as by audio announcements via the smart devices located in those homes. It should be appreciated that this can be an opt-in service and that, in addition to or instead of the central server or cloud-computing architecture 64 selecting which homes to send alerts to, individuals can subscribe to participate in such networks and individuals can specify which homes they want to receive alerts from. This can include, for example, the homes of family members who live in different cities, such that individuals can receive alerts when their loved ones in other locations are experiencing an emergency.
According to some embodiments, sound, vibration, and/or motion sensing components of the smart devices are used to detect sound, vibration, and/or motion created by running water. Based on the detected sound, vibration, and/or motion, the central server or cloud-computing architecture 64 makes inferences about water usage in the home and provides related services. For example, the central server or cloud-computing architecture 64 can run programs/algorithms that recognize what water sounds like and when it is running in the home. According to one embodiment, to map the various water sources of the home, upon detecting running water, the central server or cloud-computing architecture 64 sends a message an occupant's mobile device asking if water is currently running or if water has been recently run in the home and, if so, which room and which water-consumption appliance (e.g., sink, shower, toilet, etc.) was the source of the water. This enables the central server or cloud-computing architecture 64 to determine the “signature” or “fingerprint” of each water source in the home. This is sometimes referred to herein as “audio fingerprinting water usage.”
In one illustrative example, the central server or cloud-computing architecture 64 creates a signature for the toilet in the master bathroom, and whenever that toilet is flushed, the central server or cloud-computing architecture 64 will know that the water usage at that time is associated with that toilet. Thus, the central server or cloud-computing architecture 64 can track the water usage of that toilet as well as each water-consumption application in the home. This information can be correlated to water bills or smart water meters so as to provide users with a breakdown of their water usage.
According to some embodiments, sound, vibration, and/or motion sensing components of the smart devices are used to detect sound, vibration, and/or motion created by mice and other rodents as well as by termites, cockroaches, and other insects (collectively referred to as “pests”). Based on the detected sound, vibration, and/or motion, the central server or cloud-computing architecture 64 makes inferences about pest-detection in the home and provides related services. For example, the central server or cloud-computing architecture 64 can run programs/algorithms that recognize what certain pests sound like, how they move, and/or the vibration they create, individually and/or collectively. According to one embodiment, the central server or cloud-computing architecture 64 can determine the “signatures” of particular types of pests.
For example, in the event the central server or cloud-computing architecture 64 detects sounds that may be associated with pests, it notifies the occupants of such sounds and suggests hiring a pest control company. If it is confirmed that pests are indeed present, the occupants input to the central server or cloud-computing architecture 64 confirms that its detection was correct, along with details regarding the identified pests, such as name, type, description, location, quantity, etc. This enables the central server or cloud-computing architecture 64 to “tune” itself for better detection and create “signatures” or “fingerprints” for specific types of pests. For example, the central server or cloud-computing architecture 64 can use the tuning as well as the signatures and fingerprints to detect pests in other homes, such as nearby homes that may be experiencing problems with the same pests. Further, for example, in the event that two or more homes in a “neighborhood” are experiencing problems with the same or similar types of pests, the central server or cloud-computing architecture 64 can make inferences that nearby homes may also have such problems or may be susceptible to having such problems, and it can send warning messages to those homes to help facilitate early detection and prevention.
In some embodiments, to encourage innovation and research and to increase products and services available to users, the devices and services platform 80 expose a range of application programming interfaces (APIs) 90 to third parties, such as charities 94, governmental entities 96 (e.g., the Food and Drug Administration or the Environmental Protection Agency), academic institutions 98 (e.g., university researchers), businesses 100 (e.g., providing device warranties or service to related equipment, targeting advertisements based on home data), utility companies 102, and other third parties. The APIs 90 are coupled to and permit third-party systems to communicate with the central server or the cloud-computing system (device service) 64, including the services 84, the processing engine 86, the home data 82, and the derived home data 88. For example, the APIs 90 allow applications executed by the third parties to initiate specific data processing tasks that are executed by the central server or the cloud-computing system (device service) 64, as well as to receive dynamic updates to the home data 82 and the derived home data 88.
For example, third parties can develop programs and/or applications, such as web or mobile apps, that integrate with the central server or the cloud-computing system (device service) 64 to provide services and information to users. Such programs and application may be, for example, designed to help users reduce energy consumption, to preemptively service faulty equipment, to prepare for high service demands, to track past service performance, etc., or to perform any of a variety of beneficial functions or tasks now known or hereinafter developed. To ensure the efficient functioning of the smart home devices 10 that these programs and/or applications interact with, these programs and/or applications may provide ETAs which may influence control and/or configuration settings of the smart home devices 10.
According to some embodiments, third-party applications make inferences from the home data 82 and the derived home data 88, such inferences may include when are occupants home, when are they sleeping, when are they cooking, when are they in the den watching television, and when they are showering. The answers to these questions may help third-parties benefit consumers by providing them with interesting information, products and services as well as with providing them with targeted advertisements.
In one example, a shipping company creates an application that makes inferences regarding when people are at home. The application uses the inferences to schedule deliveries for times when people will most likely be at home. The application can also build delivery routes around these scheduled times. This reduces the number of instances where the shipping company has to make multiple attempts to deliver packages, and it reduces the number of times consumers have to pick up their packages from the shipping company.
In another example, a car company may develop a car navigation application that can determine an estimated time of arrival (ETA) to a home. The application may continually update a device service through the APIs associated with the
To further illustrate, FIG. 4 describes an abstracted functional view 110 of the extensible devices and services platform 80 of FIG. 3, with particular reference to the processing engine 86 as well as devices, such as those of the smart-home environment 30 of FIG. 2. Even though devices situated in smart-home environments will have an endless variety of different individual capabilities and limitations, they can all be thought of as sharing common characteristics in that each of them is a data consumer 112 (DC), a data source 114 (DS), a services consumer 116 (SC), and a services source 118 (SS). Advantageously, in addition to providing the essential control information needed for the devices to achieve their local and immediate objectives, the extensible devices and services platform 80 can also be configured to harness the large amount of data that is flowing out of these devices. In addition to enhancing or optimizing the actual operation of the devices themselves with respect to their immediate functions, the extensible devices and services platform 80 can be directed to “repurposing” that data in a variety of automated, extensible, flexible, and/or scalable ways to achieve a variety of useful objectives. These objectives may be predefined or adaptively identified based on, e.g., usage patterns, device efficiency, and/or user input (e.g., requesting specific functionality).
For example, FIG. 4 shows processing engine 86 as including a number of paradigms 120. Processing engine 86 can include a managed services paradigm 120 a that monitors and manages primary or secondary device functions. The device functions can include ensuring proper operation of a device given user inputs, estimating that (e.g., and responding to an instance in which) an intruder is or is attempting to be in a dwelling, detecting a failure of equipment coupled to the device (e.g., a light bulb having burned out), implementing or otherwise responding to energy demand response events, or alerting a user of a current or predicted future event or characteristic. Processing engine 86 can further include an advertising/communication paradigm 120 b that estimates characteristics (e.g., demographic information), desires and/or products of interest of a user based on device usage. Services, promotions, products or upgrades can then be offered or automatically provided to the user. Processing engine 86 can further include a social paradigm 120 c that uses information from a social network, provides information to a social network (for example, based on device usage), and/or processes data associated with user and/or device interactions with the social network platform. For example, a user's status as reported to their trusted contacts on the social network could be updated to indicate when they are home based on light detection, security system inactivation or device usage detectors. As another example, a user may be able to share device-usage statistics with other users. In yet another example, a user may share HVAC settings that result in low power bills and other users may download the HVAC settings to their smart thermostat 46 to reduce their power bills.
The processing engine 86 can include a challenges/rules/compliance/rewards paradigm 120 d that informs a user of challenges, competitions, rules, compliance regulations and/or rewards and/or that uses operation data to determine whether a challenge has been met, a rule or regulation has been complied with and/or a reward has been earned. The challenges, rules or regulations can relate to efforts to conserve energy, to live safely (e.g., reducing exposure to toxins or carcinogens), to conserve money and/or equipment life, to improve health, etc. For example, one challenge may involve participants turning down their thermostat by one degree for one week. Those that successfully complete the challenge are rewarded, such as by coupons, virtual currency, status, etc. Regarding compliance, an example involves a rental-property owner making a rule that no renters are permitted to access certain owner's rooms. The devices in the room having occupancy sensors could send updates to the owner when the room is accessed.
The processing engine 86 can integrate or otherwise utilize extrinsic information 122 from extrinsic sources to improve the functioning of one or more processing paradigms. Extrinsic information 122 can be used to interpret data received from a device, to determine a characteristic of the environment near the device (e.g., outside a structure that the device is enclosed in), to determine services or products available to the user, to identify a social network or social-network information, to determine contact information of entities (e.g., public-service entities such as an emergency-response team, the police or a hospital) near the device, etc., to identify statistical or environmental conditions, trends or other information associated with a home or neighborhood, and so forth.
An extraordinary range and variety of benefits can be brought about by, and fit within the scope of, the described extensible devices and services platform 80, ranging from the ordinary to the profound. Thus, in one “ordinary” example, each bedroom of the smart-home environment 30 can be provided with a smart wall switch 54, a smart wall plug 56, and/or smart hazard detectors 50, all or some of which include an occupancy sensor, wherein the occupancy sensor is also capable of inferring (e.g., by virtue of motion detection, facial recognition, audible sound patterns, etc.) whether the occupant is asleep or awake. If a serious fire event is sensed, the remote security/monitoring service or fire department is advised of how many occupants there are in each bedroom, and whether those occupants are still asleep (or immobile) or whether they have properly evacuated the bedroom. While this is, of course, a very advantageous capability accommodated by the described extensible devices and services platform 80, there can be substantially more “profound” examples that can truly illustrate the potential of a larger “intelligence” that can be made available. By way of perhaps a more “profound” example, the same bedroom occupancy data that is being used for fire safety can also be “repurposed” by the processing engine 86 in the context of a social paradigm of neighborhood child development and education. Thus, for example, the same bedroom occupancy and motion data discussed in the “ordinary” example can be collected and made available (properly anonymized) for processing in which the sleep patterns of schoolchildren in a particular ZIP code can be identified and tracked. Localized variations in the sleeping patterns of the schoolchildren may be identified and correlated, for example, to different nutrition programs in local schools.

ETA-Based Control of Smart Device

FIG. 5 illustrates a system 400 for providing control of the smart electronic device of FIG. 1 using an ETA, in accordance with an embodiment. In the illustrated system 400, one or more smart devices 10 may be controlled by an ETA smart device controller 402. The controller 402 may be a processor-based system that receives an ETA 404 from an ETA source 406.
The ETA 404 may, in some embodiments, be an estimated time of arrival to the smart home environment. For example, an automobile may include a navigation system that acts as the ETA source 406 to provide a time-based ETA (e.g. 4:02 PM) to the controller 402. Other ETA sources 406 that may provide similar ETAs 402 may include a navigation app on a smart device, a portable global positioning system (GPS), etc.
Alternatively, in some embodiments, the ETA may be associated with estimated time of arrival of an event. For example, as will be discussed in more detail below, the ETA source 406 may include an alarm clock, activity monitor, etc. that is able to determine a household occupant's wake time. Accordingly, the ETA 404 may be an ETA for the user's wake time.
Indeed, the ETA 404 may represent any estimate of arrival time for any event. For example, the ETA 404 may represent an estimated sleep time, an estimated away time, etc. The ETAs 404 may be based upon data from one or more devices 10 that may be used to derive the ETA 404.
Based upon the received ETA 404, the controller 402 may provide ETA-based control of one or more smart devices 10. For example, as will be discussed in more detail below, a thermostat may be controlled to heat and/or cool an environment prior to an ETA of returning back to the environment. In some embodiments, the controller 402 may provide a control operation 408 to one or more of the devices 10. The control operation 408 may be a device-readable instruction that may be interpreted by the smart devices 10. Alternatively, the controller 402 may provide a configuration setting change 410 to the smart devices 10.
In some embodiments, the controller 402 may provide the control operation 408 and/or configuration setting 410 to one or more smart devices 10 to affect control of a secondary smart device 10′ and/or 10″. For example, the smart device 10 may forward the control operation 408 and/or configuration setting change 410 to device 10, which may forward the control operation 408 and/or change 410 to the devices 10′ and/or 10″ (e.g., via communication channel 412).
Turning now to a more detailed discussion of the ETA-based control, FIG. 6 illustrates a flowchart of a method 430 for providing control of the smart electronic device of FIG. 1 using an ETA, in accordance with an embodiment.
First, an ETA is received (block 432). The ETA may be a time (e.g., 4:32 PM and/or 16:32), a duration (e.g. 30 minutes from now), or other indicator.
The received indicator may be validated (block 434). For example, as will be discussed in more detail below, one or more criteria for acknowledging a received ETA may be created. The criteria may help ensure that purposeful and accurate ETA-based control occurs. For example, in one embodiment, the criteria may require multiple consistent ETAs to be received prior to using an ETA to control a device. If the criteria are not met (e.g., the ETA is not valid) the ETA is ignored and the process 430 begins again.
When the ETA is valid, a determination is made as to whether the current time matches the ETA (e.g., it is 4:32 PM and the ETA is 4:32 PM and/or the ETA is 0 minutes) (decision block 436). If the ETA is not met, the process 430 begins again. However, when the ETA matches the current time, control of the smart device is executed (block 438).
It may be beneficial to create a pre-conditioning window for triggering ETA-based control. FIG. 7 illustrates a flowchart of a method 450 for providing control of the smart electronic device of FIG. 1 using a pre-conditioning window, in accordance with an embodiment.
First, an ETA is received (block 452). The ETA may be a time (e.g., 4:32 PM and/or 16:32), a duration (e.g. 30 minutes from now), or other indicator.
The received indicator may be validated (block 454). For example, as will be discussed in more detail below, one or more criteria for acknowledging a received ETA may be created. The criteria may help ensure that purposeful and accurate ETA-based control occurs. For example, in one embodiment, the criteria may require multiple consistent ETAs to be received prior to using an ETA to control a device. If the criteria are not met (e.g., the ETA is not valid) the ETA is ignored and the process 430 begins again.
A pre-conditioning window may be obtained (block 456). For example, the pre-conditioning window may be obtained as a static instruction stored in a tangible, non-transitory, computer-readable medium. Alternatively, the pre-conditioning window may be derived based upon a desired pre-conditioning time for a device. For example, for an embodiment of controlling a thermostat, the pre-conditioning window may be based upon an estimated pre-conditioning time (e.g., a time to reach a programmed set point).
When the ETA is valid, a determination is made as to whether the ETA is in the pre-conditioning window (e.g., it is 4:32 PM and the ETA is 4:32 PM and/or the ETA is 0 minutes) (decision block 458). If the ETA is not met, the process 450 begins again. However, when the ETA is in the pre-conditioning window, pre-conditioning of the smart device is triggered (block 460).
FIG. 8 is a temperature profile 490 of a thermostat using the ETA control system, where preconditioning is active on the thermostat, in accordance with an embodiment. Line 492 represents a scheduled/programmed temperature for the thermostat (e.g., a programmed temperature when occupants are present). Line 494 represents the away temperature (e.g., the programmed temperature when occupants are not present).
As illustrated, when the thermostat is in away mode 500 (e.g., when occupants are not present within an environment), the thermostat's programmed temperature 498 is set to the away temperature 494. Accordingly, the ambient temperature 486 approaches the programmed away temperature 494.
When the ETA 502 is received (and is optionally validated), an ETA 502 set point is generated. A pre-conditioning window 502 may be created based upon the ETA 502. For example, to reach the programmed temperature 492, the HVAC may need to begin cooling and/or heating at the beginning of the pre-conditioning widow 502. Accordingly, when the pre-conditioning window 502 is reached, pre-conditioning begins (e.g., the thermostat begins climate control at the programmed temperature 492. Thus, the 486 temperature progresses towards the programmed temperature 492. If the pre-conditioning time is estimated properly, the ambient temperature 486 should reach or come relatively close to the programmed temperature 492 at the ETA 502, as illustrated by region 504. Accordingly, when an occupant arrives (e.g., at home and/or a particular event), set-points may be accurately met. Thus, an HVAC system may cool an environment to a programmed level by arrival home and/or when waking.
FIG. 9 is a state diagram 520 of a thermostat using the ETA control system, where pre-conditioning is active on the thermostat, in accordance with an embodiment. When the thermostat is in an away mode (block 522), the thermostat stays in away mode until an ETA is received. When an ETA is received, the thermostat waits for the pre-conditioning window (e.g., the time needed to condition the environment to an ETA set point). If the pre-conditioning window is not reached, the thermostat stays in away mode (block 522). However, when the pre-conditioning window is reached, preconditioning commences (block 526). Thus, when the ETA passes, the pre-conditioning finishes (block 528). If, at any point, the ETA is cancelled and/or deleted, the thermostat may return to away mode (block 522). Further, if, after the pre-conditioning is finished, occupancy is not detected or occupancy is not detected for a certain duration after pre-conditioning ends, the thermostat may return to away mode (block 522).
FIG. 10 is a temperature profile 550 of a thermostat using the ETA control system, where preconditioning is disabled on the thermostat, in accordance with an embodiment. Line 492 represents a scheduled/programmed temperature for the thermostat (e.g., a programmed temperature when occupants are present). Line 494 represents the away temperature (e.g., the programmed temperature when occupants are not present). Line 496 represents the ambient air temperature (e.g., the HVAC controlled air temperature).
As illustrated, when the thermostat is in away mode 500 (e.g., when occupants are not present within an environment), the thermostat's programmed temperature 498 is set to the away temperature 494. Accordingly, the ambient temperature 486 approaches the programmed away temperature 494.
When the ETA 502 is received (and is optionally validated), an ETA 502 set point is generated. In contrast to the embodiment of FIG. 8, which included a pre-conditioning window, the current embodiment does not pre-condition. Instead, upon reaching the ETA 502, the HVAC transitions to active conditioning. Thus, an HVAC system may cool an environment at or near an ETA (e.g., arrival home and/or when waking).
FIG. 11 is a state diagram 560 of a thermostat using the ETA control system, wherein preconditioning is disabled on the thermostat, in accordance with an embodiment. When the thermostat is in a home mode (e.g., when an occupant is at the conditioned environment) (block 562), the thermostat conditions at a scheduled temperature (e.g. line 492 of FIG. 10). When the thermostat transitions to away mode (block 564) (e.g., auto-away when occupants are no longer detected or manual away triggered by an operator of the HVAC system), the thermostat stays in away mode until an ETA is received. When an ETA is received, the ETA logic may be implemented. Because the current ETA scheme is related to enabling HVAC environment conditioning, the current ETA scheme may be implemented when the thermostat is in an away mode (or any other mode where thermostat environment conditioning is not active). In the current embodiment, when a thermostat is in an inactive conditioning state and an ETA passes, the thermostat transitions back to an active conditioning state (e.g., a home state (block 562)).
In some embodiments, it may be beneficial to retain an “away” or “home” state, while modifying activities typically associated with those states. For example, FIG. 12 is a temperature profile of a thermostat using the ETA control system, wherein preconditioning is enabled during an away mode, in accordance with an embodiment.
Line 492 represents a scheduled/programmed temperature for the thermostat (e.g., a programmed temperature when occupants are present). Line 494 represents the away temperature (e.g., the programmed temperature when occupants are not present).
When the ETA 502 is received (and is optionally validated), an ETA 502 set point is generated. A pre-conditioning window 502 may be created based upon the ETA 502. For example, to reach the programmed temperature 492, the HVAC may need to begin cooling and/or heating at the beginning of the pre-conditioning widow 502. Accordingly, when the pre-conditioning window 502 is reached, pre-conditioning begins (e.g., the thermostat begins climate control at the programmed temperature 492. Thus, the ambient temperature 496 progresses towards the programmed temperature 492. If the pre-conditioning time is estimated properly, the ambient temperature 486 should reach or come relatively close to the programmed temperature 492 at the ETA 502, as illustrated by region 504.
As illustrated, when the thermostat is in away mode 500 (e.g., when occupants are not present within an environment) and the pre-conditioning window 506 has not been reached, the thermostat's programmed temperature 498 is set to the away temperature 494. Accordingly, the ambient temperature 486 approaches the programmed away temperature 494.
When the pre-conditioning window 506 is reached, the programmed temperature 498 is set to a pre-conditioning temperature, thus causing the ambient temperature 496 to progress towards the scheduled temperature 492.
Accordingly, when an ETA 502 is reached (e.g., at home and/or a particular event), the scheduled temperature 492 set-points may be accurately met. Further, the “away” mode 500 may still accurately represent that the HVAC system is away (e.g., either auto-away or manual away), even when pre-conditioning is enabled. Thus, an HVAC system may condition an environment to a programmed level by arrival home and/or when waking.
Additionally, the ETA logic may be used to adjust set point times for particular events/activities associated with set points. For example, in the illustrated embodiment, a sleep set point 582 triggers a temperature adjustment of the scheduled temperature 492. The sleep set point 582 may be adjusted to provide a more comfortable and/or more energy efficient sleep environment. The sleep set point 582 time may be adjusted based on a number of attributes. For example, the sleep set point 582 time may be adjusted based upon an indication that sleep will occur at a particular time. In one embodiment, the sleep set point 582 time may be set based upon a period of time after bedroom lights are turned off after a particular threshold time period (e.g., sleep set point 582 time=Lights out time+15 minutes, when the lights out is at or after 10:00 PM).
Any number of set points and associated times may be added to the HVAC system. For example, ETA set points for multiple occupants may be set (allowing for adjusted temperatures for each occupant). Further, wake set points may be set. Wake set point times may be calculated based upon data received from an alarm clock, an alarm clock app, an activity monitor, etc.
In some embodiments, an “arriving” mode may implemented within the thermostat. The arriving mode may be a transitional mode between “home” and “away” that is implemented upon receiving an indication that someone will be arriving at the controlled environment. For example, when an ETA is provided to the thermostat, the thermostat may take this as an indication that a user is on their way to the environment, regardless of when that user will actually arrive at the environment.
The “arriving” mode may be useful in scenarios where it may be desirable for multiple thermostats to perform in the same manner, based upon an ETA. For example, in a two-story house, one thermostat may have a longer pre-conditioning period than another thermostat. Accordingly, using the pre-conditioning schemes discussed above, the thermostats may begin conditioning the environment at separate times. Alternatively, by placing the thermostats in “arriving” mode, the thermostats may both begin conditioning based upon receiving an indication that someone will arrive, instead of a pre-conditioning estimate for each individual thermostat. Accordingly, the thermostats may both use the same trigger point (e.g., 5 minutes after receiving an indication that someone will eventually arrive at the environment) to begin conditioning the environment. Thus, the thermostats will have common execution, at least with regards to execution tasks associated with the “arriving” mode.
FIG. 13 is a flowchart illustrating a process 600 for validating an ETA, in accordance with an embodiment. First, an ETA is received (block 602). The ETA may be associated with a trip or event indicator, which may be used to associate received ETAs with other received ETAs of the trip or event.
Next, a determination is made as to whether the ETA meets a basic validity check (decision block 604). For example, the basic validity check may include a determining whether the ETA is in future, whether the ETA meets formatting guidelines, whether the ETA is within a maximum ETA interval, etc.
If the ETA is valid according to the basic validation, a determination is made as to whether the ETA meets the minimum time between ETA samples (decision block 606). For example, it may be beneficial to only use ETAs that are received at least a certain time interval between one another. The minimum time between samples may be obtained (block 608) either from an external or internal data source.
The interval between the samples may be obtained by comparing other ETAs associated with the trip/event id and determining the difference in time between the ETAs (e.g., based upon stored timestamp data associated with the ETAs). If the received ETA fails the basic validation (decision block 604) and/or does not meet the minimum time between ETA samples (decision block 606), the ETA sample is disregarded (block 610) and reception of new ETAs (block 602) commences.
To limit unintended control, a minimum number of conforming ETA estimates may be mandated prior to enabling set point control. This allows control to occur after the system is more confident that the ETA should be used. As with obtaining the minimum time between samples, the minimum number of conforming samples may be obtained from either internal and/or external data sources.
If the ETA meets the time interval between the ETA samples (decision block 606), a sample count is incremented (block 612). If the incremented sample count is not greater than or equal to a minimum number of conforming samples (decision block 616), the ETA is noted, but is not used for control of the system. The system continues receiving ETAs (block 602) until the sample count is greater than or equal to the number of minimum conforming samples. Once the sample count is greater than or equal to the minimum number of conforming samples, the ETA is noted as valid and is used as a basis of control for the system (block 618).
FIG. 14 is a flowchart illustrating a process 640 for defining a pre-conditioning window statically, in accordance with an embodiment. The process 640 begins by obtaining a static early arrival window (block 642). In one embodiment, the state early arrival window may be stored as a machine-readable instruction in a tangible, non-transitory, machine-readable medium of the ETA service. Additionally and/or alternatively, the static early arrival window may be provided from an external data source.
The pre-conditioning window (or other control set point time) may then be defined based upon the static early arrival window (block 644). For example, when the static early arrival window is 20 minutes, the pre-conditioning window is set to 20 minutes prior to the ETA.
FIG. 15 is a schematic drawing of a system 650 using a static pre-conditioning window, in accordance with an embodiment. The illustration of FIG. 15 provides two houses, a small house 652 and a large house 654. Further, the small house 652 is illustrated with milder weather conditions 656 than the extreme weather conditions 658 of the large house 654. Both the small house 652 and the large house 654 use the static 15 minute early arrival window 660. Accordingly, despite the varied conditions of the small and large houses 652 and 654, the pre-conditioning initiation time for these homes will be the same. For example, if an application from a device (e.g., vehicle 664, activity monitor bracelet 66, and/or alarm clock 668) provides an ETA 662 of 5:00 to both houses 652 and 654, pre-conditioning will begin at 4:45 (5:00-15 minutes) at both houses 652 and 654.
FIG. 16 is a flowchart illustrating a process 680 for defining a pre-conditioning window dynamically, in accordance with an embodiment. The process 680 begins by obtaining a pre-conditioning estimate (block 682). As described in U.S. Pat. No. 8,606,374, filed Sep. 14, 2010, U.S. Pat. No. 8,452,457, filed Sep. 30, 2012, U.S. Pat. No. 8,630,742, filed Sep. 30, 2012, U.S. application Ser. No. 13/866,602, filed Apr. 19, 2013, and U.S. patent application Ser. No. 14/256,741, filed Apr. 18, 2014, all of which are incorporated herein by reference herein in their entirety for all purposes, a number of factors may be used to determine an environment's pre-conditioning estimate. For example, the build quality, size, ceiling height, location, etc. of a conditioned environment may be used to determine a pre-conditioning estimate. Further, ambient weather conditions, etc. may also be used.
Once the pre-conditioning estimate is determined, the pre-conditioning window is defined based on the pre-conditioning estimate (block 684). Accordingly, an environment's particular characteristics may be used to control pre-conditioning times (or pre-conditioning times for any other event and/or activity set points).
FIG. 17 is a schematic drawing of a system 700 using a dynamic pre-conditioning window, in accordance with an embodiment. Similar to FIG. 15, the illustration of FIG. 17 provides two houses, a small house 652 and a large house 654. Further, the small house 652 is illustrated with milder weather conditions 656 than the extreme weather conditions 658 of the large house 654.
In contrast to the illustration of FIG. 15, in FIG. 17, the small house 652 and the large house 654 use the personalized pre-conditioning estimates as a basis for determining each house's pre-conditioning window. As mentioned herein, the varied conditions of the small and large houses 652 and 654 may result in varied pre-conditioning times. For example, because the large house 654 has more space to condition, the pre-conditioning may take longer. Further, the large house has extreme weather conditions 658 in comparison to the mild conditions 656 of the small house 652. This may also add to the pre-conditioning time of the large house 654. Indeed, as illustrated, the pre-conditioning window 702 for the small house is 15 minutes, whereas the pre-conditioning window 704 for the large house 654 is 45 minutes. Accordingly, if an application from a device (e.g., vehicle 664, activity monitor bracelet 66, and/or alarm clock 668) provides an ETA 662 of 5:00 to both houses 652 and 654, pre-conditioning will begin at 4:45 (5:00-15 minutes) at the small house 652 and at 4:15 (5:00-45 minutes) for the large house 654.
In some instances, there may be multiple sources of ETAs 404. Accordingly, control and/or configuration of devices based upon ETAs may use conflict resolution logic to determine which of a plurality of ETAs 404 should trigger control and/or configuration changes. FIG. 18 is a schematic drawing of a system 730 for providing control of the smart electronic devices 10 of FIG. 1 using ETA conflict logic 732, in accordance with an embodiment. In the illustrated system 7300, one or more smart devices 10 may be controlled by an ETA smart device controller 402. The controller 402 may be a processor-based system that receives ETAs 404 and 404′ from a plurality of ETA sources 406 (e.g., in- vehicle applications 664 and 664′).
Based upon the received ETAs 404 and 404′, the controller 402, when used in conjunction with conflict logic 732, may provide appropriate multi-ETA-based control and/or configuration changes to one or more smart devices 10.
The conflict logic 732 may provide control and/or configuration changes based upon both ETAs 404 and 404′ when control and/or configuration changes do not conflict based upon multiple ETAs. For example, in a system where control of a smart television is to be turned on at the arrival of the in-vehicle application 664 and where control of an HVAC is to be pre-conditioned based upon the arrival of the in-vehicle application 664′, both ETA-based controls may be implemented, because they are not mutually-exclusive control events.
However, when mutually-exclusive events occur, the conflict resolution logic 732 may prioritize a particular ETA 404 or 404′ to trigger control. For example, in a system where pre-conditioning is triggered by either ETA 404 or 404′, the conflict resolution logic 732 may enable pre-conditioning to occur by prioritizing one ETA over the other. The logic 732 may prioritize a particular ETA based on any number of factors. For example, an operator of the system 730 may provide an indication to prioritize one ETA over another. Additionally or alternatively, the logic 732 may prioritize an ETA that is closer in time than the other ETAs. Thus, in the pre-conditioning example, pre-conditioning will begin based upon the earliest arrival time, providing a pre-conditioned environment for all occupants arriving at the environment.
While prioritization of a particular ETA may occur for mutually-exclusive control and/or configuration change events, the alternative ETAs may continue to be tracked. By tracking the alternative ETAs, the conflict logic 732 may adjust prioritization of the ETAs when changes occur. For example, when the conflict logic 732 prioritizes ETAs based upon the closest ETA in time, ETA 404 may obtain priority. However, if the ETA 404 changes, making it further in time than ETA 404′, the logic 732 may dynamically change priority to ETA 404′.
In some embodiments, it may be beneficial to obtain a confidence level prior to adjusting ETA prioritization. Accordingly, the logic 732, in some embodiments, may retain a current priority until the prioritization change would result in a particular time interval difference for a control and/or configuration change. For example, prioritization of ETA 404′ over ETA 404 may be implemented when a control or configuration change would be impacted by a certain time interval (e.g., 10 minutes or more).
Additionally or alternatively, the prioritization may be changed after an ETA changed is confirmed by a particular number of sent ETAs. For example, the prioritization may remain fixed until ETA 404 is sent a certain number of times (e.g., 3 times) and each of the sent ETAs indicates that the ETA has indeed changed.
Further, the controller 402 may predict deviations from a particular provided ETA 404 or 404′ which may be used in the control and/or configuration changes as well as the prioritization by the logic 732. For example, in some embodiments, the controller 402 may notice that ETA 404′ is consistently later than an actual arrival time. This can be discerned by comparing the actual arrival time (e.g., as determined based upon occupancy sensors at the environment) with the ETA. When the controller 402 notices a pattern of deviation from an actual arrival time, the controller 402 may derive a modified ETA based upon the observed deviation. Accordingly, in an example where ETA 404 is based upon an assumption of traveling at a posted speed limit, a modified ETA may be derived using particular observations of actual arrival time, which may impacted by any number of factors (e.g., whether the driver is a slow driver, a fast driver, etc.).
As may be appreciated, ETA-based control and/or configuration of smart devices allows pre-conditioning of an environment for arrival at the environment and/or arrival of an event. Accordingly, added comfort and/or efficiency may be obtained using ETA-based control and/or configuration.

Claims

1. A method, comprising:

receiving, at an electronic device, an estimated time of arrival (ETA) relating to an arrival to an environment, an arrival of an event, arrival of an activity, or a combination thereof; and

controlling, configuring, or controlling and configuring, via the electronic device, a smart device based upon the ETA;

wherein the ETA is calculated by a third-party separate from the electronic device and the smart device.

2. The method of claim 1, wherein the smart device comprises a thermostat.

3. The method of claim 2, comprising:

validating the ETA based at least upon validation criteria of the electronic device, the smart device, or both; and

controlling, configuring, or controlling and configuring the smart device based upon the ETA only when the ETA is validated.

4. The method of claim 3, wherein validating the ETA comprises:

validating the ETA by associating the ETA with a particular trip, event, or activity; and

analyzing a set of ETAs associated with the particular trip, event, or activity, to determine if the validation criteria is met.

5. The method of claim 4, wherein validating the ETA comprises:

validating the ETA by determining if the set of ETAs including the ETA is greater than or equal to a minimum number of consistent ETA samples provided in the validation criteria.

6. The method of claim 3, wherein validating the ETA comprises:

validating the ETA by determining if the ETA was received at an interval of at least a minimum time interval between ETA samples from a previously-received ETA, as provided by the validation criteria.

7. The method of claim 1, comprising:

if the ETA is time-based: determining a pre-conditioning window extending in time from the ETA to a time prior to the ETA; or

if the ETA is duration-based: determining a pre-conditioning window that is a duration less than or equal to the ETA; and

controlling, configuring, or controlling and configuring the smart device during the pre-conditioning window.

8. The method of claim 7, wherein determining the pre-conditioning window comprises defining the time prior to the ETA by a static early arrival window, wherein the static early arrival window comprises a static amount of time to begin pre-conditioning prior to the ETA.

9. The method of claim 7, wherein determining the pre-conditioning window comprises defining the time prior to the ETA based upon a dynamic pre-conditioning estimate for the environment, wherein the dynamic pre-conditioning window comprises a dynamic amount of time to begin pre-conditioning prior to the ETA, based upon a pre-conditioning estimate specific to the environment.

10. An electronic device, comprising:

a processor configured to:

receive an estimated time of arrival (ETA) relating to an arrival to a conditioned environment, an arrival of an event, arrival of an activity, or a combination thereof; and

provide data to a smart device based upon the ETA, the data triggering control, configuration changes, or control and configuration changes of the smart device;

11. The electronic device of claim 10, wherein the electronic device and the smart device are the same device.

12. The electronic device of claim 10, wherein the smart device comprises a thermostat.

13. The electronic device of claim 12, wherein the data is configured to change, add, or change and add a set point of the thermostat.

14. The electronic device of claim 12, wherein the data is configured to change a mode of the thermostat from an away mode to a home mode.

15. The electronic device of claim 10, wherein the data is configured to trigger pre-conditioning of an environment, such that the environment is conditioned to a programmed temperature at or near the ETA.

16. The electronic device of claim 10, wherein the processor is configured to determine a pre-conditioning window based upon a static early arrival window, wherein the static early arrival window comprises a static amount of time to begin pre-conditioning prior to the ETA.

17. The electronic device of claim 10, wherein the processor is configured to determine a pre-conditioning window based upon a custom pre-conditioning estimate for the environment, wherein the dynamic pre-conditioning window comprises a dynamic amount of time to begin pre-conditioning prior to the ETA, based upon a pre-conditioning estimate specific to the environment.

18. A tangible, non-transitory, machine-readable medium, comprising machine-readable instructions to:

wherein the ETA is calculated by a third-party separate from the smart device and a machine implementing the machine-readable instructions.

19. The tangible, non-transitory, machine-readable medium of claim 18, comprising machine-readable instructions to:

receive a plurality of ETAs;

observe a pattern of inaccuracies between the provided plurality of ETAs and corresponding actual time of arrivals;

derive a modified ETA for a subsequently received ETA based upon the pattern of inaccuracies; and

trigger control, configuration changes, or control and configuration changes based upon the modified ETA for the subsequently received ETA.

20. The tangible, non-transitory, machine-readable medium of claim 18, comprising machine-readable instructions to:

receive a plurality of ETAs relating to different arrival to the condition environment, arrivals of an activity, or both;

determine whether the plurality of ETAs result in mutually exclusive ETA-based control events, configuration change events, or both;

prioritizing one of the plurality of ETAs when the plurality of ETAs result in mutually exclusive ETA-based control events, configuration change events, or both; and

optionally dynamically modify prioritization of the one of the plurality of ETAs when one or more of the plurality of ETAs changes.