Patent Publication Number: US-2013246109-A1

Title: System, article, and method for annotating resource variation

Description:
CROSS-REFERENCE TO RELATED OR CO-PENDING APPLICATIONS 
     This application relates to co-pending U.S. patent application Ser. No. 12/860,401, entitled “Tracking Major Appliance Efficiency,” (PDNo. 201001392-RI: 82264499) filed on Aug. 20, 2010, by Marwah et al., and U.S. patent application Ser. No. 12/859,931, entitled “Disaggregating Power Consumption,” (PDNo. 201001393-RI: 82264502) filed on Aug. 20, 2010, by Marwah et al. These related applications are commonly assigned to Hewlett-Packard Development Co. of Houston, Tex. 
    
    
     BACKGROUND OF THE INVENTION 
     Brief Background Introduction 
     The present invention relates generally to systems and methods for managing resources. As competition grows throughout the world for various resources, systems and services which help set prices and ensure delivery of such resources in a predictable, reliable way are ever more necessary. Current systems and services for managing resources often contain information bottlenecks which introduce inefficiencies that unnecessarily either increase price and/or limit their availability. Further improvements in resource management are desired. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some examples of the invention are described in the following figures: 
         FIG. 1  is one example of a system for annotating resource variation; 
         FIG. 2  is one example of a data structure for implementing the system; 
         FIG. 3  is one example of a first user interface showing data collected by the system; 
         FIG. 4  is one example of a second user interface showing resource usage identified by the system; 
         FIG. 5  is a flowchart of one example of a method for annotating resource variation; and 
         FIG. 6  is another example of the system for annotating resource variation. 
     
    
    
     DETAILED DESCRIPTION 
     Scarcity of resources and their price are closely linked in our modern worldwide economy. Managing such resources effectively and in harmony between resource producers and resource consumers can yield significant production and consumption efficiencies that benefit both. Effective and efficient resource management, perhaps effected through a resource ecosystem or a cloud service, can help promote conservation practices and environmental sustainability. Resources in this context include not only a home or business&#39; electric, water, and gas production, consumption, and recycling, but also can include any other resource, including: network bandwidth; use of network bandwidth; computation and storage resources available via cloud services; and so on. These resources can either be from sustainable, renewable, or non-renewable sources. 
     In one example embodiment of a world-wide deployment of the present invention, a cloud resource management service could monitor and maintain the resources of millions households and businesses, and millions of computing and access devices, encompassing an installed base of billions of energy consuming devices and appliances throughout the world. 
     some embodiments, demand response and consumer energy efficiency would drive the creation of products with appropriate hooks to enable user participation as peers within a negotiated energy-balanced ecosystem. Such embodiments have the potential to generate vast quantities of mineable data which, if managed and analyzed appropriately, would be of great benefit to consumers, device manufacturers, utility providers, and the public sector; contributing to a reduction in any nation&#39;s carbon footprint. 
     Additionally, a resource management service&#39;s collection of energy and resource information from billions of households would likely create an opportunity for “Resource Intelligence” brokers to provide 3rd parties (e.g. device manufacturers, utilities, and governments) a platform to analyze and deliver targeted products and services based on resource or energy-oriented analytics, performed against a massive energy intelligence repository that is collected. 
     To help achieve such current and future resource management goals, the present invention includes a cloud services based network resource manager and/or a local installation based resource manager, which together or individually provide mechanisms for users to detect, annotate, and understand their resource (e.g. energy) usage and/or generation in response to data obtained from a suite of installation based sensing systems. Such “installations” can be defined as an individual home, business, a political region (e.g. a city), a utility, a production line, a smart-grid, a region, a transmission line, a recycling facility, and so on, or any combination thereof. The present invention thus permits a user to associate specific user or local installation based behaviors, actions, or activities with detected resource events, anomalies, or other detected observations. 
     In addition, the present invention permits users to predict the resource effects of their future actions, which the resource managers then monitor and quantify actual resource effects achieved, thereby in effect collecting empirical data on many millions of local “tests” which when aggregated and analyzed can help ground-truth the plans of other resource users or product and service providers. 
     The present invention&#39;s use of annotation enables more resource usage and production patterns to be detected, especially given the complexity of individual home and business environments, and the ever expanding product base for consumer home entertainment and appliances, and business equipment and machinery. 
     Details of the present invention are now discussed. 
       FIG. 1  is one example of a system  100  for annotating resource variations.  FIG. 2  is one example of a data structure  200  for implementing the system  100 .  FIG. 3  is one example of a first user interface  300  showing data collected by the system  100 .  FIG. 4  is one example of a second user interface  400  showing resource usage identified by the system  100 . Due to the integrated operation of the system  100  with the data structure  200 , and the illustrative benefits of reviewing the data collected and resource usage identified by the system  100 ,  FIGS. 1 through 4  are discussed together, when necessary, to facilitate understanding of the present invention. 
     The basic architecture of the system  100  includes one or more installations  102  and  103  connected to a set of resources  104  and a network resource manager  107  through a set of resource gateways  106 . In this example embodiment, the resources include: electric  108 , water  110 , gas  112 , telecommunication (Telecom)  114 , and/or any other resource  116  provided to or received from one or more of the installations  102  and  103 . The resource gateways  106  can include a main power cable, a water pipe, a gas pipe, a land phone-line, a wireless link, and supporting network connections for exchanging resources  104  and associated information. The network resource manager  107 , in one example, is effected by a cloud-service. 
     The example installation  102  includes a user  118 , a local resource manager  120 , and a first installation zone  122  through an N-th installation zone  124 . The first installation zone  122  includes a set of sensors  126 ,  132 , and  136 , connected to monitor and/or control those resources  104  used or generated by a set of devices  128 ,  130 ,  134 , and  138 . Note that device # 2   130  (e.g. a refrigerator) is monitored both by sensor # 1   126  (e.g. perhaps monitoring, the refrigerator&#39;s compressor and tight bulbs) and by sensor # 2   132  (e.g. perhaps monitoring the refrigerator&#39;s water and ice dispensers). The N-th installation zone  124  includes sensors and devices as well, but which may be connected in its own unique way. 
     The sensors can be of any type, including: an electric sensor, a water sensor, a gas sensor, a data sensor, a network sensor, a volume sensor, a weight sensor, a temperature sensor, a chemical sensor, a biological sensor, a light sensor, and a motion sensor. Also note, that in certain embodiments, there already exists a sensor (e.g. electric meter, water meter, gas meter) connected to one or more of the resource gateways  106 . Such a sensor can function as a overall sensor for the entire installation (e.g. an electric meter can also function as a “whole house” electric Current(A) sensor). 
     The network resource manager  107  and local resource manager  120  (a.k.a. Energy Intelligence Managers (EIM)) respectively perform remote and local monitoring and control of resources  104  consumed or generated by the devices  128 ,  130 ,  134 , and  138  within the installation  102 . This monitoring can logically be thought of as a resource sensing layer. This resource sensing layer can be built using a Zigbee wireless network of energy sensing nodes (e.g. sensors  126 ,  132 , and  136 ) which collect information streams from a suite of Smart-Grid enabled devices. The wireless network relays the resource usage or generation data streams to the network managers  107  and  120  along with an operational consumption profile. The network managers  107  and  120  could also control device attributes or install self-implementing policies at each device. 
     For the network resource manager  107  and/or local resource manager  120  to know the zones, sensors, devices, and resources to be consumed or generated at each installation, an installation profile needs to be completed for the installation  102 . The user  118  can complete this profile, or analytics within the resource managers  107  and  120  can automatically profile the installation  102 . 
       FIG. 2  shows an example installation profile  202  data structure  200 . In this profile  202 , the installation  102  is defined as a “Home &amp; Address”. The resources  104  monitored at this installation  102  are: electric, water, and network bandwidth. The zones  204  for monitoring electricity are: the kitchen, family room, roof, and garage. The zones  204  for monitoring water are: the kitchen, and yard. The zones  204  for monitoring network bandwidth are: the family room, and office. The sensors  206  are defined as collecting sensor data  208  for various devices  210 , as shown. For example, in the family room zone  204 , the lights sensor  206  is collecting Current (A) sensor data  208  from the following group of devices  210 : a filament light bulb; an LED array; a CFI (compact fluorescent light; and one or more halogen lights. Other example sets of sensor data  208  and associated devices  210  are also shown in  FIG. 2 . The example installation profile  202  may also include an operational profile (not shown) for each device  210 . The operational profile can specify whether a device is: “always on”; periodically on; occasionally on; or follows a pre-programmed daily, weekly, etc. schedule (e.g. such as possible with HVAC thermostat controllers). 
     The installation profile can also include other installation  102  attributes and metadata, some of which may be obtainable through public records, and social networking sites. These other attributes can include: geographic location, square footage, number of individuals therein, year built, permits for renovations, and so on. 
     Over time as the system  100  operates and collects data, not only from the installation  102  but also from other installations  103 , the resource managers  107  and  120  often will be able to automatically identify devices at either the installation, zone, or sensor level as the system&#39;s  100  analytics learn the energy usage patterns of specific devices commonly used. Depending upon the robustness of these analytics, as few as just one sensor  206  may be used (e.g. collecting sensor data  208  from just one standard home electric-meter sensor, instead of a larger number of Smart-plug sensors located throughout the home) to identify and track multiple devices in the installation  102 . The resource managers  107  and  120  may also flag possible discrepancies in the installation profile, for explanation by the user  118 . 
     Once the installation  102  and installation profile  202  have been defined and sensor data has been collected, then analysis, annotation, and mediation of resource  104  usage and/or generation by the installation  102  can begin. The annotation and mediation functions are now discussed. The user interfaces in  FIG. 3  and  FIG. 4  are illustrative of the present invention&#39;s functionality, and are used to facilitate this discussion. Even though the invention will now be further discussed in the context of electrical power consumption within a home, this teaching can apply to other example embodiments involving any other installation  102  or resource  104 . 
     The user interface  300  in  FIG. 3  shows an example of an electrical resource variation over time. The user interface  300  includes a resource selection  302  pull-down menu for selecting which of the resources  104  to monitor (e.g. an “electric” resource). Another pull-down menu for zone selection  304  (e.g. “Whole Home”) corresponds to the zones  122  and  124  in  FIG. 1 , and zone  204  in  FIG. 2 . The device selection  306  initially corresponds to sensor  126 ,  132 ,  136 ,  206  selection, since little if any sensor data has yet been collected. However, over time as the system  100  collects more and more sensor data and annotations, the device selection  306  can correspond to specific devices  128 ,  130 ,  134 ,  138  or  210 , since the resource managers  107  and  120  contain analytics which will separate out device resource variations within sensor resource variations in situations where one sensor is connected to multiple devices over time (see  FIG. 4  for an example). Note that while  FIG. 3  does not show an “installation selection”, the network resource manager  107  would be able to make such a selection. Similarly, the example in  FIG. 3  does not include an “optional” device  306  selection, and instead just specifies the “Whole Home” zone. 
     In response to the user&#39;s  118  selections, the user interface  300  displays the selected resource data  308  on a graph having a resource usage axis  310  and a time axis  312 . As can be seen from this graph, the resource data  308  varies over time. Note that while  FIG. 3  shows resource data  308  indicating that electricity is being “consumed”, in alternate installations  102  the presence of a solar cell device (not shown) would “generate” electricity and shift the resource data  308  plot down toward (if not below) the time axis  312 . 
     The resource manager  107 ,  120  contain a set of data analytics which automatically analyze the resource data  308 . These analytics mine the resource data  308  for patterns of energy use, both at the individual (i.e. one installation) and aggregate (i.e. many installations) level. The resource manager  107 ,  120  also creates representational models of the resource data  308  on an hourly, daily, weekly, and yearly bases. 
     Alternately, the user  118  may view the resource data  308  in the user interface  300  and reach various conclusions regarding variations in the resource data  308 . 
     Depending upon the analytics applied by the resource manager  107 ,  120  and the user  118 , explanations for certain change-points, trends, missing data, patterns, anomalies, or other variations in the resource data  308  may be useful for better managing the resources  104 . Such variations can be caused: when the user  118  replaces a refrigerator with a more energy efficient model resulting in a drop in electric power consumption; or when the user  118  installs a backyard lighting system, resulting in an increased power consumption (but only at night); or when the user  118  goes on a vacation for a week resulting in an electric power reduction. Other variations in the resource data  308  may be caused: when a TV is turned on; when in-laws visit; by a kitchen cooking fest, or when new energy efficient lighting is installed. 
     In order to capture the event or user  118  behavior which caused the variations in the electric resource data  308 , and to capture any other interesting and/or ambiguous resource data  308  variations, the resource manager  107 ,  120  can automatically generate and display annotation requests  314 , on the user interface  300 , proximate to a region of the resource data  308  curve which displays the interesting and/or ambiguous electrical resource data variation. The resource manager  107 ,  120  can also generate and display its own resource manager annotations  315  on the resource data  308  curve. 
     The resource manager  107 ,  120  may add these annotation requests  314  and resource manager annotations  315  on the basis of the resource manager&#39;s  107 ,  120  own built-in analytics, or based on specific or aggregated feedback and tips (Note: a “tip” is a type of annotation) from users  118  at the other installations  103 . In fact, community feedback and tips from other installations  103  may be a significant source of resource manager annotations  315  if there is a significant similarity between the resources and devices at both the installation  102  and the other installations  101 . Often the collective wisdom of a community of resource managing installations can be as, if not more, insightful than many programmed analytical resource management tools. 
     The user  118  at the installation  102  can also flag regions of the resource data  308  curve and add their own user annotation  316  to explain, or hypothesize an explanation, why a variation in the resource data  308  occurred. User  118  generated annotations can also become “tips” which are transmitted to the network resource manager  107 , and thereby broadcast to and used by the other installations  103  for their own resource usage and/or generation annotations. 
     The user  118  can also contradict or disagree with the resource manager annotation  315  added by the resource manager&#39;s  107 ,  120  own built-in analytics, or based on specific or aggregated feedback and tips from users  118  at the other installations  103 . Sometimes the analytical tools and community tips are wrong. 
     The user  118 , at the installation or one of the other installations  103 , responds to the annotation request  314  or adds their own user annotation  316  using an annotation dialog window  318 . Information can be entered into the dialog window  318  as either “structured information” (e.g. a pull down menu with pre-populated selections) or as “unstructured information” (e.g. free form comments and remarks). In  FIG. 3 , an example set of “structure information” selections includes: “Turning an existing device on or off”, “A one-time event”, “Ongoing change in use”, “Adding/removing/upgrading a device”, and “Other/I&#39;m not sure”. However, the annotations can take many different forms as well, including: occupancy based annotations (e.g. “one-time events” such as a specific vacation, business trip, or visit); behavior-change based annotations (e.g. took shorter showers, turned computer off at night, lowered the thermostat, turned TV off when not watching, changed to energy efficient lighting changed to energy efficient appliances, do laundry at night); and devices based annotations (e.g. device on; device off; adding device; remove device; upgrade device; holiday lights). 
     The user  118  and network manager  107 ,  120  can generate anticipated and aspirational annotations as well at future dates (i.e. before actual resource data  308  has even been collected). For example, the user  118  may specify a future action, to be taken, which the user  118  thinks may result in future energy savings or better energy generation. 
     These aspirational annotations can also providing an opportunity for the network manager  107 ,  120  to measure, validate, and quantify actual energy savings achieved, and verify if user&#39;s  118  resource prediction was correct. 
     Thus the present invention&#39;s annotation functionality helps both the user  118  and the network manager  107 ,  120  associate and link detected resource variations with events and actions within the installation  102 . 
     The network manager  107 ,  120  can also use its internal analytics in conjunction with the collective “annotations” received from both the user  118  and the other installations  103  to disaggregate the resource usage of multiple devices which are being monitored by a common sensor. 
     Some examples of such aggregately monitored devices include: devices  128  and  130  commonly monitored by sensor  126 ; devices  130  and  134  commonly monitored by sensor  132 ; the  FIG. 2  power strip collecting a common set of Current (A) sensor data  208  from: a lamp and a fan; and the  FIG. 2  lights sensor  206  collecting a common set of Current (A) sensor data  208  from: a filament light bulb; an LED array; a CFL (compact fluorescent light); and one or more halogen lights. 
     The user interface  400  of  FIG. 4 , presents an example graph of composite resource usage  402  verses time  404 , commonly monitored by sensor. The resource usage  402  in this example is collected from a single Current(Amps) sensor monitoring a power strip (not shown). Initially the single Current sensor only sees the “outline” of the Current(A) consumption (i.e. resource usage  402 ) which has been idealized to a series of step regions for illustrative purposes. 
     Based just on an “outline” of the Current(A) consumption alone, the resource manager  107 ,  120  might not be able to disambiguate the fan device  406 , the lamp device  408 , the TV device  410 , and the TV device baseline usage  412 , each drawing Current(A) from the power strip. However, the resource manager  107 ,  120  can generate “annotation requests” at each of the times  414  though  440 . If the user  118  responds to these “annotation requests” (e.g.  314 ), the resource manager  107 ,  120  will collect and analyze the user&#39;s  118  annotation responses, entered through their corresponding “annotation dialog window” (e.g.  318 ). Some example user annotations are as follows: 
     Time  414 —Fan ON; 
     Time  416 —Lamp ON; 
     Time  418 —Lamp OFF; 
     Time  420 —Lamp &amp; TV ON; 
     Time  422 —TV OFF; 
     Time  424 —Lamp &amp; Fan OFF; 
     Time  426 —TV ON; 
     Time  428 —TV OFF; 
     Time  430 —Fan, Lamp, &amp; TV ON; 
     Time  432 —Lamp &amp; TV OFF; 
     Time  434 —TV ON; 
     Time  436 —TV OFF; 
     Time  438 —Fan OFF; 
     Time  440 —UnPlug TV; 
     Given these example user “ON-OFF” annotations and the Current(A) consumption data from the power strip, the resource manager  107 ,  120  would be able to disambiguate the fan device  406 , the lamp device  408 , the TV device  410 , and even the TV device baseline usage  412  (due to the “Time  440 —UnPlug TV” annotation). Note, these annotations could also have included device MODE changes, such as STANDBY, LOW POWER, HIGH POWER, and various other power states juxtaposed between ON and OFF. 
     In other example embodiments, the resource manager  107 ,  120  can include predefined “disambiguation models” for various devices, which could analyze the installation&#39;s  102  resource usage and generation “patterns” from individual sensors to automatically disaggregate a set of devices connected to that sensor. Thus, using user, community, and model annotations, the system  100  can detect “device specific” usage patterns and associate resource usage “per device”, as opposed to “per zone”, even if only one sensor (e.g. Smartplug) collects data for all the devices. 
     In one embodiment, this “one sensor” could be a single “electric power meter” for an entire home. Such disambiguation techniques could thus greatly reduce the price of monitoring a home&#39;s energy usage since there is no need to purchase, install, and maintain Smartplugs and Smartappliances which may be costly to deploy in bulk. This cost savings applies to the other sensors (e.g. water, gas, bandwidth, etc.) as well. 
     Once the resource manager  107 ,  120  has collected “annotations”, and performed any necessary sensor/device “disattibiguations”, the resource manager  107 ,  120  can generate one or more “notifications”, “Notifications” are herein defined broadly to include: community tips, action requests, task assignments, voluntary user actions, remediation requests, rewards, badges, certifications, warnings, penalties, device control signals, and so on. In one example, the resource manager  107 ,  120  could present users  118  with “actionable insights and options” to help the user  118  either reduce their resource consumption or enhance their resource production. For instance, by comparing a devices energy profile against current “Best-In-Class” device performance data, the resource manager  107 ,  120  can present a customized ROI (Return On Investment) plan of action to the user  118 , thereby encouraging replacement of an energy wasting device. 
     Notifications can also be thematically driven according to a given resource model. Some example “resource models” include: Minimizing Individual Home Energy Consumption; Maximizing Utility Energy Production; Minimizing Community Home Energy Consumption; Increasing Inter-Community Sharing of Energy Saving Tips; Identifying Activity Profiles for Selected Devices (e.g. a gaming system, to enable better parental monitoring); and so on. Each of these notifications can help engage the user  118 , driving resource awareness, and yielding better resource management practices, and all levels in the supply chain. 
     Once a set of notifications have been generated, the resource manager  107 ,  120  can later verify (i.e. validate) the resultant user&#39;s  118  device and/or behavioral changes (e.g. refrigerator energy usage decreased alter user replaced refrigeration with a “Best-In-Class” model, as recommended). 
     The resource manager  107 ,  120  can also use the user&#39;s  118  feedback after following the notification&#39;s instructions, to validate the accuracy and effectiveness of the notifications themselves. The installation  102  community can also vote on the usefulness and value of the notifications. Over time, any ineffective or off-point notifications will be rewritten, updated, or otherwise improved upon. 
     As an additional incentive for encouraging an engaged set of users  118  who participate in or generate a robust dialog, tips, and other notifications, the system  100  can include a rewards methodology, including points, badges, certifications, coupons, discounts, responsibilities, etc. These rewards can be granted at any point in the system&#39;s  100  construction or operation, including when users  118 : install the system  100  at their installation; build their installation profile (e.g. zones, sensors, and devices); annotate the resource data (e.g.  308 ) from their installation  102 , or other installations  103 ; post valuable energy saving tips; and/or test-out (i.e. validate) energy saving community suggestions. In one embodiment, the rewards can be incorporated into a “gaming environment”. 
       FIG. 5  is a flowchart of one example of a method  500  for annotating resource variations. The blocks comprising the flowchart can be effected in any order, unless a specific order is explicitly stated. Also, those skilled in the art will recognize that while one example of the present invention&#39;s method is now discussed, the material in this specification can be combined in a variety of ways to yield other examples as well. The method next discussed is to be understood within a context provided by this and other portions of this detailed description. 
     The method  500  begins in block  502 , where a resource  104  variation  308  associated with a set of devices  128  is detected. Next in block  504 , the user is presented with an annotation request  314 , in response to the detected resource variation. Then in block  506 , a set of user annotations  316  are received in response to the annotation request  314 . In block  508 , the resource variation is labeled with the user annotations  316 . Then in block  510 , the resource variation are divided into a set of resource variations corresponding to each of the devices, using the set of user annotations. Next in block  512 , the user is presented with a set of notifications, in response to the detected resource variation  308 . In block  514 , a user annotation  316  is received from the user, which includes a planned change to the installation  102  and which is anticipated to have a planned effect on the detected resource variation  308 . Then in block  516 , whether the planned effect occurred is verified. Next in block  518 , a tip  316  is received from a community user at another community installation  103 , in response to the annotation request  314 . Then in block  520 , the tip is incorporated into a separate resource manager annotation  315 , generated by the network resource manager  107  in response to the annotation request  314 . In block  522 , the tip is presented to the set of community installations. Then in block  524 , receiving feedback on a usefulness of the tip, from the community. 
       FIG. 6  is another example  600  of the system  100  for annotating resource variations. The diagram  600  shows input data  602  being received by a computing device  604 . The computing device  604  includes a processor  606 , a storage device  608 , and a machine-readable storage medium  610 . Instructions within the machine-readable storage medium  610  control how the processor  606  interprets and transforms the input data  602 , using data within the storage device  608 . 
     The instructions stored in the machine-readable storage medium  610  include: block  612 , detecting a resource  104  variation  308  associated with the device  128 ; and block  614 , labeling the resource variation with a user annotation  316  generated by the user  118 . 
     The processor (such as a central processing unit, CPU, microprocessor, application-specific integrated circuit (ASIC), etc.) controls the overall operation of the storage device (such as random access memory (RAM) for temporary data storage, read only memory (ROM) for permanent data storage, firmware, flash memory, external and internal hard-disk drives, and the like). The processor device communicates with the storage device and machine-readable storage medium using a bus and performs operations and tasks that implement one or more blocks stored in the machine-readable storage medium. 
     As Used Herein and in the Claims, these Words are Further Defined as Follows: 
     The term “cloud” is a computer network accessible over the internet and/or web that is dynamically scalable with virtualized resources, such as printing resources. Users are not required to have knowledge or expertise in the infrastructure of the cloud that relies on the internet to satisfy the computing or (printing needs of users. The cloud provides computer and/or printing device services with business applications that are accessible from a web browser while software and data are stored on servers in the cloud. For example, a printing cloud system supports infrastructure for printing device services, platform for the printing device services, and software for the printing device services. 
     The term “file” or “a set of files” refers to any collection of files, such as a directory of files. A “file” can refer to any data object (e.g., a document, a bitmap, an image, an audio clip, a video clip, software source code, software executable code, etc.). A “file” can also refer to a directory (a structure that contains other files). 
     Functional and software instructions described above are typically embodied as a set of executable instructions which are effected on a computer which is programmed with and controlled by said executable instructions. Such instructions are loaded for execution on a processor (such as one or more CPUs). The processor includes microprocessors, microcontrollers, processor modules or subsystems (including one or more microprocessors or microcontrollers), or other control or computing devices. A “processor” can refer to a single component or to plural components. 
     In one example, one or more blocks or steps discussed herein are automated. In other words, apparatus, systems, and methods occur automatically. The terms “automated” or “automatically” (and like variations thereof) mean controlled operation of an apparatus, system, and/or process using computers and/or mechanical/electrical devices without the necessity of human intervention, observation, effort and/or decision. 
     In some examples, the methods illustrated herein and data and instructions associated therewith are stored in respective storage devices, which are implemented as one or more computer-readable or computer-usable storage media or mediums. The storage media include different forms of memory including semiconductor memory devices such as DRAM, or SRAM, Erasable and Programmable Read-Only Memories (EPROMs), Electrically Erasable and Programmable Read-Only Memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; and optical media such as Compact Disks (CDs) or Digital Versatile Disks (DVDs). Note that the instructions of the software discussed above can be provided on one computer-readable or computer-usable storage medium, or alternatively, can be provided on multiple computer-readable or computer-usable storage media distributed in a large system having possibly plural nodes. Such computer-readable or computer-usable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture can refer to any manufactured single component or multiple components. 
     In the foregoing description, numerous details are set forth to provide an understanding of the present invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these details. While the invention has been disclosed with respect to a limited number of examples, those skilled in the art will appreciate numerous modifications and variations thereof. It is intended that the following claims cover such modifications and variations as fall within the true spirit and scope of the invention.