Patent Publication Number: US-11030882-B2

Title: Automated security subsystem activation

Description:
FIELD 
     The subject matter disclosed herein relates to security subsystem activation and more particularly relates to automated security subsystem activation. 
     BACKGROUND 
     A user may forget to activate a security subsystem. 
     BRIEF SUMMARY 
     An apparatus for automated security subsystem activation is disclosed. The apparatus includes a security subsystem, a processor, and a memory. The memory stores code executable by the processor. The processor monitors occupant activity from a plurality of electronic devices. The processor further determines the occupant activity satisfies a quiescence model. The processor activates the security subsystem in response to satisfying the quiescence model. A method and program product also perform the functions of the apparatus. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more particular description of the embodiments briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only some embodiments and are not therefore to be considered to be limiting of scope, the embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which: 
         FIG. 1A  is a schematic block diagram illustrating one embodiment of a activation system; 
         FIG. 1B  is a schematic block diagram illustrating one alternate embodiment of a activation system; 
         FIG. 1C  is a schematic block diagram illustrating one alternate embodiment of a activation system; 
         FIG. 2  is drawings illustrating embodiments of electronic devices; 
         FIG. 3  is a schematic block diagram illustrating one embodiment of security data; 
         FIG. 4A  is a schematic block diagram illustrating one embodiment of a computer; 
         FIG. 4B  is a schematic diagram illustrating one embodiment of a neural network; 
         FIG. 5A  is a schematic flow chart diagram illustrating one embodiment of a security subsystem activation method; 
         FIG. 5B  is a schematic flowchart diagram illustrating one embodiment of a model training method; 
         FIG. 5C  is a schematic flow chart diagram illustrating one embodiment of a security subsystem deactivation method; and 
         FIG. 5D  is a schematic flow chart diagram illustrating one embodiment of a location-based security subsystem activation method. 
     
    
    
     DETAILED DESCRIPTION 
     As will be appreciated by one skilled in the art, aspects of the embodiments may be embodied as a system, method or program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, embodiments may take the form of a program product embodied in one or more computer readable storage devices storing machine readable code, computer readable code, and/or program code, referred hereafter as code. The storage devices may be tangible, non-transitory, and/or non-transmission. The storage devices may not embody signals. In a certain embodiment, the storage devices only employ signals for accessing code. 
     Many of the functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like. 
     Modules may also be implemented in code and/or software for execution by various types of processors. An identified module of code may, for instance, comprise one or more physical or logical blocks of executable code which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module. 
     Indeed, a module of code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different computer readable storage devices. Where a module or portions of a module are implemented in software, the software portions are stored on one or more computer readable storage devices. 
     Any combination of one or more computer readable medium may be utilized. The computer readable medium may be a computer readable storage medium. The computer readable storage medium may be a storage device storing the code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. 
     More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Code for carrying out operations for embodiments may be written in any combination of one or more programming languages including an object oriented programming language such as Python, Ruby, Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the “C” programming language, or the like, and/or machine languages such as assembly languages. The code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to,” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise. 
     Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. 
     Aspects of the embodiments are described below with reference to schematic flowchart diagrams and/or schematic block diagrams of methods, apparatuses, systems, and program products according to embodiments. It will be understood that each block of the schematic flowchart diagrams and/or schematic block diagrams, and combinations of blocks in the schematic flowchart diagrams and/or schematic block diagrams, can be implemented by code. This code may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks. 
     The code may also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the storage device produce an article of manufacture including instructions which implement the function/act specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks. 
     The code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The schematic flowchart diagrams and/or schematic block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods and program products according to various embodiments. In this regard, each block in the schematic flowchart diagrams and/or schematic block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions of the code for implementing the specified logical function(s). 
     It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more blocks, or portions thereof, of the illustrated Figures. 
     Although various arrow types and line types may be employed in the flowchart and/or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and/or flowchart diagrams, and combinations of blocks in the block diagrams and/or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and code. 
     The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements. 
       FIG. 1A  is a schematic block diagram illustrating one embodiment of a activation system  100 . The activation system  100  may automatically activate and deactivate a security subsystem  120 . The security subsystem  120  may protect a space such as a dwelling, office, business, and the like from intrusion. The security subsystem  120  may include motion detectors, opening detectors, and the like for detecting the unwanted intrusion. 
     Unfortunately, the security subsystem  120  can only protect the space if the security subsystem  120  is activated. In addition, if the security subsystem  120  is activated when occupants of the space wish to exit, the security subsystem  120  may generate a false alarm, inconveniencing the occupants. The embodiments automatically activate the security subsystem  120 . 
     In the depicted embodiment, the activation system  100  includes a server  105 , a network  115 , the security subsystem  120 , and one or more electronic devices  110 . The network  115  may be the Internet, a local area network, a wide-area network, a Wi-Fi network, a mobile telephone network, or combinations thereof. The server  105 , electronic device  110 , and security subsystem  120 , may communicate through the network  115 . The activation system  100  may monitor occupant activity from a plurality electronic devices  110 . The activation system  100  may further determine if the occupant activity satisfies a quiescence model. In addition, the activation system may activate the security subsystem  120  in response to satisfying a quiescence model as will be described hereafter. 
     In the depicted embodiment, the server  105  may monitor the occupant activity from the plurality of electronic devices  110 , determine the occupant activity satisfies the quiescence model, and activate the security subsystem  120 . 
       FIG. 1B  is a schematic block diagram illustrating one embodiment of a activation system  100 . In the depicted embodiment, the activation system  100  includes the network  115 , the plurality of electronic devices  110 , and the security subsystem  120 . In one embodiment, the security subsystem  120  monitors the occupant activity from the plurality of electronic devices  110 . In addition, the security subsystem  120  may determine that the occupant activity satisfies the quiescence model and activate itself. 
     In an alternative embodiment, one or more electronic devices  110  may monitor the occupant activity from the plurality of electronic devices  110 . The one or more electronic devices  110  may determine that the occupant activity satisfies the quiescence model and activate the security subsystem  120 . 
       FIG. 1C  is a schematic block diagram illustrating one embodiment of a activation system  100 . In the depicted embodiment, one or more electronic devices  110  communicate directly with the security subsystem  120  through a connection such as an infrared connection and/or a Bluetooth connection. The security subsystem  120  may monitor the occupant activity from the plurality of electronic devices  110 , determine that the occupant activity satisfies the quiescence model, and activate itself. Alternatively, the one or more electronic devices  110  may determine that the occupant activity satisfies the quiescence model and activate the security subsystem  120 . 
       FIG. 2  is drawings illustrating embodiments of electronic devices  100 . In the depicted embodiment, the electronic devices  110  include a controller  110   a , a mobile telephone  110   b , a wearable electronic device  110   c , and a camera  110   d . Other electronic devices  110  may also be employed. 
       FIG. 3  is a schematic block diagram illustrating one embodiment of security data  200 . The security data  200  maybe organized as a data structure in a memory. In the depicted embodiment, the security data  200  includes occupant activity data  201 , occupant identity data  203 , exit permissions  205 , exit data  207 , threshold data  209 , location data  211 , dress data  213 , and lighting data  215 . In addition, the security data  200  may include the quiescence model  221 , an exit model  223 , and an away time model  225 . 
     The occupant activity data  201  may record occupant activity from the electronic devices  110 . The occupant activity may include but is not limited to direct occupant interaction with the electronic device  110 , motions of the electronic device  110 , images of the occupant captured by the electronic device  110 , sounds of the occupant captured by the electronic device  110 , and/or a charging status of the electronic device  110 . 
     The occupant identity data  203  may record biometric data for identifying one or more occupants. The biometric data may include images of the occupants, voice prints of the occupants, fingerprints of the occupants, calendars of the occupants, historical schedules of the occupants, and the like. 
     The exit permissions  205  may record which occupants are allowed to exit the space during which times. In addition, the exit permissions  205  may specify conditions for exiting the space. For example, an adult occupant may have exit permission at any time, while a teenage occupant may have exit permission from 6:00 to 22:00 and a child occupant may only have exit permission when accompanied by an adult. 
     The exit data  207  may record historical exits of occupants from the space. The threshold data  209  may record one or more thresholds for determining when the quiescence model  221 , the exit model  223 , and/or the away time model  225  are satisfied. 
     The location data  211  may record a location of each occupant. The locations may be determined from the occupant activity data  201 . For example, Global Positioning System (GPS) coordinates from the mobile telephone electronic device  110   b  of an occupant may be used to determine the occupants location. In addition, an image from the camera electronic device  110   d  may be matched to the occupant identity data  203  to determine a location of the occupant. 
     The dress data  213  may record the clothing worn by an occupant when the occupant exits the space. The dress data  213  may be used to determine whether the occupant is dressed to exit the space. The lighting data  215  may record the lighting of the space at a specified time each day such as 3:00 AM. The lighting data  215  may be used to determine if the lighting of the space satisfies a quiescence level. 
     The quiescence model  221 , exit model  223 , and/or away time model  225  maybe organized as neural networks as will be described hereafter. In addition, the quiescence model  221 , exit model  223 , and away time model  225  may comprise one or more algorithms. 
       FIG. 4A  is a schematic block diagram illustrating one embodiment of a computer  400 . The computer  400  may be embodied in one or more of the server  105 , an electronic device  110 , and/or the security subsystem  120 . In the depicted embodiment, the computer  400  includes a processor  405 , a memory  410 , and communication hardware  415 . The memory  410  may include a semiconductor storage device, hard disk drive, an optical storage device, a micromechanical storage device, or combinations thereof. The memory  410  may store code. The processor  405  may execute the code. The communication hardware  415  may communicate with other devices such as the network  115 . 
       FIG. 4B  is a schematic block diagram illustrating one embodiment of a neural network  475 . In the depicted embodiment, the neural network  475  includes input neurons  450 , hidden neurons  455 , and output neurons  460 . The neural network  475  may be organized as a convolutional neural network, a recurrent neural network, and the like. 
     The neural network  475  may be trained with training data. The training data may include occupant activity data  201 , exit data  207 , and/or location data  211 . The neural network  475  may be trained using one or more learning functions while applying the training data to the input neurons  450  and known result values for the output neurons  460 . Subsequently, the neural network  465  may receive actual data at the input neurons  450  and make predictions at the output neurons  460  based on the actual data. The actual data may include data from the occupant activity data  201 , exit data  207 , and/or location data  211 . 
     In one embodiment, the quiescence model  221  is the neural network  475  recursively trained from a plurality of occupant activity data  201  and the exit data  207 , location data  211 , dress data  213 , and/or lighting data  215 . In a certain embodiment, the quiescence model  221  is the neural network  475  recursively trained from the plurality of occupant activity data  201  and a last exit of the exit data  207 . The last exit may be the last instance of an occupant exiting the space before a specified time such as 5:00 AM. For example, the quiescence model  221  may be satisfied when historical conditions of the exit data  207 , the location data  211 , the dress data  213 , and the lighting data  215  are satisfied after a last exit of the day. 
     In one embodiment, the exit model  223  is the neural network  475  recursively trained from the plurality of occupant activity data  201  and the exit data  207 , the location data  211 , the dress data  213 , and/or the lighting data  215 . For example, the exit model  223  may be satisfied when historical conditions of the exit data  207  are replicated. In addition, the exit model  223  may be satisfied when historical conditions of the exit data  207 , location data  211 , dress data  213 , and lighting data  215  are satisfied. 
     In one embodiment, the away time model  225  is the neural network  475  recursively trained from the plurality of occupant activity data  201  and the exit data  207 , the location data  211 , and the dress data  213 . The away time model  215  may forecast an away time. The away time may forecast a time interval before any occupant returns to the space. 
       FIG. 5A  is a schematic flow chart diagram illustrating one embodiment of a security subsystem activation method  500 . The method  500  may automatically activate the security subsystem  120 . The method  500  may be performed by the processor  405  of the computer  400  and/or the neural network  475 . 
     The method  500  starts, and in one embodiment, the processor  405  monitors  501  the occupant activity from the plurality of electronic devices  110 . The occupant activity may be stored as occupant activity data  201 . In one embodiment, the processor  405  queries an electronic device  110  for the occupant activity data  201 . In addition, an electronic device  110  may report the occupant activity data  201  to the processor  405 . 
     The processor  405  may identify  503  the occupant of the occupant activity. In one embodiment, the occupant is identified  503  based on the electronic device  110 . For example, if the occupant activity data  201  is from an electronic device  110  of a first occupant, the occupant activity data  201  may be associated with the first occupant. 
     In one embodiment, the occupant is identified  503  by comparing the occupant identity data  203  to the occupant activity data  201 . For example, the occupant may be identified  503  by matching an image of the occupant to a video stream from an electronic device  110 . 
     The processor  405  may determine  505  the occupant has exit permission. In one embodiment, the processor  405  compares the identity of the occupant to the stored exit permissions  205  to determine  505  whether the occupant has exit permission. 
     The processor  405  determines  507  the occupant activity satisfies the quiescence model  221 . In one embodiment, the occupant activity satisfies the quiescence model in response to one or more of a determination that the occupant is asleep, a determination that the occupant is not dressed to exit, a determination that a quiescence motion pattern is satisfied, and a determination that lighting satisfies a quiescence level. 
     The processor  405  may determine that the occupant is asleep based on one or more of an eye status of the occupant, a last motion of the occupant, a location of the occupant, and a breathing status of the occupant. In one embodiment, the processor  405  may calculate a sleep score SS using Equation 1, wherein ES is a length of time the eye status is closed, LM is elapsed time from the last motion, BD is true if the occupant is in bed, BS is true if the occupant&#39;s breathing status indicates sleep, and k1-4 are nonzero constants.
 
SS= k 1*ES+ k 2*LM+ k 3*BD+ k 4*BS  Equation 1
 
     In one embodiment, the processor  405  determines that the occupant is not dressed to exit if the occupants current attire does not match the dress data  213 . In addition, the processor  405  may determine that the quiescence motion pattern is satisfied if the elapsed time from the last motion of the occupant is less than a last motion threshold of the threshold data  209 . 
     In one embodiment, the processor  405  determines that the lighting of the space satisfies the quiescence level if the current lighting of the space is equivalent to the lighting data  215 . In addition, the lighting of the space may be equivalent to the lighting data  215  if the lighting of the space is within one standard deviation of the average lighting data  215 . 
     In one embodiment, the quiescence model  221  is the neural network  475 . The neural network  475  may receive the occupant activity data  201  and determine whether the occupant activity satisfies the quiescence model  221 . Because the neural network  475  is trained on the occupant activity data  201  combined with the exit data  207 , the neural network  475  and quiescence model  221  embodied therein makes judgments on whether the quiescence model  221  is satisfied that cannot be reproduced by human judgment. As a result, the neural network  475  improves the functioning of the computer  400  beyond the abilities of an algorithm that may be processed by a human. 
     If the quiescence model  221  is not satisfied, the processor  405  continues to monitor  501  the occupant activity. If the quiescence model  221  is satisfied, the processor  405  activates  509  the security subsystem  120  and the method  500  ends. The method  500  determines when subsequent exits of the space are unlikely and automatically activates  509  the security subsystem  120 . 
       FIG. 5B  is a schematic flowchart diagram illustrating one embodiment of a model training method  550 . The method  550  may train a model such as the quiescence model  221 , the exit model  223 , and/or the away tie model  225 . The method  550  may be performed by the processor  405  and/or neural network  475 . 
     The method  550  starts, and in one embodiment, the processor  405  presents  551  historical occupant activity data  201  from the plurality of electronic devices  110  to the neural network  475 . In addition, the processor  405  may present  551  a result associated with the historical occupant activity data  201  such as the exit data  207  and/or the location data  211 . 
     The processor  405  may further train  553  the model using the occupant activity data  201 . The training may be supervised by providing results associated with the occupant activity data  201 . Alternatively, the training may be unsupervised. The processor  405  may apply a learning method to the hidden neurons  455  of the neural network  475  to trained  553  the model. The learning method may set coefficients of the hidden neurons  455  to values that govern the function of the hidden neurons  455 . The setting of the coefficients may be beyond human computational abilities. In one embodiment, the model is retrained  553  using each day&#39;s occupant activity data  201  with more recent occupant activity data  201  weighted more heavily. 
     The processor  405  may determine  555  if an erroneous alarm is received from the security subsystem  120 . The erroneous alarm may be an alarm triggered by an occupant of the space. If no erroneous alarm occurs, the processor  405  may continue to determine  555  if erroneous alarm is received. If the erroneous alarm is determined  555 , the processor  405  may recursively train  557  the model with additional weighting on the occupant activity that caused the erroneous alarm. 
       FIG. 5C  is a schematic flow chart diagram illustrating one embodiment of a security subsystem deactivation method  600 . The method  600  may automatically deactivate the security subsystem  120 . The method  600  may be performed by the processor  405  and/or the neural network  475 . 
     The method  600  starts, and in one embodiment, the processor  405  determines  601  the occupant activity satisfies the exit model  223 . In a certain embodiment, the exit model  223  is satisfied if the current dress of the occupant conforms to the dress data  213  and the location of the occupant is within a proximity threshold of the proximity data  209  of an exit. If the occupant activity does not satisfy the exit model  223 , the processor  405  continues to determine  601  whether the occupant activity satisfies the exit model  223 . 
     If the occupant activity satisfies the exit model  223  the processor  405  determines  603  if the occupant has exit permission. In one embodiment, the processor  405  identifies the occupant and retrieves the occupant&#39;s exit permissions  205  to determine if the occupant has exit permission. If the occupant does not have exit permission, the processor  405  continues to determine  601  whether the occupant activity satisfies the exit model  223 . 
     In response to the occupant activity satisfying the exit model  223  and the occupant having exit permission, the processor  405  may deactivate  605  the security subsystem  120  and the method  600  ends. The method  600  allows the activation system  100  to detect an occupant that is about to exit the space and deactivate  605  the security subsystem  120  if the occupant has exit permission. 
     In one embodiment, the exit model  223  is the neural network  475 . The neural network  475  may receive the occupant activity data  201  and determine whether the occupant activity satisfies the exit model  223 . Because the neural network  475  is trained on the occupant activity data  201  combined with the exit data  207 , the neural network  475  and exit model  223  embodied therein makes judgments on whether the exit model  223  is satisfied that cannot be reproduced by human judgment. As a result, the neural network  475  improves the functioning of the computer  400  beyond the abilities of an algorithm that may be processed by a human. 
       FIG. 5D  is a schematic flow chart diagram illustrating one embodiment of a location-based security subsystem activation method  650 . The method  650  may automatically activate the security subsystem  120  when the occupants of the space are likely to be away from the space for an extended period. The method  650  may be performed by the processor  405  and/or the neural network  475 . 
     The method  650  starts, and in one embodiment, the processor  405  determines  651  the location of each occupant of the space. The location of each occupant may be determined  651  from the location of an electronic device  110  associated with each occupant. Alternatively, the location of each occupant may be determined  651  from an image of each occupant captured by an electronic device  110 . In one embodiment, the location of an occupant is determined  651  to be outside a boundary of the space. 
     The processor  405  may forecast  653  the away time wherein all occupants of the space are outside the boundary of the space using the away time model  225 . The away time model  225  may be the neural network  475 . The away time model  225  may forecast the away time based on the occupant activity data  201  based on criteria that cannot be reproduced by human judgment. As a result, the neural network  475  improves the functioning of the computer  400  beyond the abilities of an algorithm that may be processed by a human. 
     The processor  405  may determine  655  if the away time exceeds an away time threshold of the threshold data  209 . If the away time does not exceed the away time threshold, the processor  405  continues to determine  651  the locations of the occupants. If the away time exceeds the away time threshold, the processor  405  may activate  657  the security subsystem  120 . As a result, if the occupants do not activate the security subsystem  120  when leaving for the day, the activation system  100  automatically activates  657  the security subsystem  120 . 
     The embodiments determine that the occupant activity satisfies the quiescence model  221  and automatically activate the security subsystem  120 . As a result, if the occupants retire for the evening without activating the security subsystem  120 , the embodiments automatically activate the security subsystem  120  to increase the protection for the space and its occupants. In addition, the embodiments may automatically deactivate the security subsystem  120  in response to the exit model  223  being satisfied for an occupant and the occupant having exit permission. As a result, an occupant that decides to exit the space will not inadvertently trigger the security subsystem  120 . 
     The embodiments may further determine that the occupants of the space are away for at least an away time threshold and automatically activate security subsystem  120 . As a result, even if the occupants forget to activate the security subsystem  120  when leaving for the day, the security subsystem  120  is automatically activated, protecting the space. Thus the embodiments improve the functioning of the security subsystem  120 . 
     Embodiments may be practiced in other specific forms. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.