Patent Publication Number: US-2016232644-A1

Title: Difference image compression

Description:
TECHNICAL FIELD 
     The present application relates generally to systems and methods for editing image files and more specifically to systems and methods for generating information describing results of user edits to a source image file. 
     BACKGROUND 
     The rise of the computer age has resulted in a wide variety of electronic devices. One common type of electronic device is a personal electronic device that is intended to be carried around by a user, such as a smart phone, a tablet computer, or a smart watch. Each personal device has a large number of capabilities that increase its usefulness. 
     One common capability provided by personal devices is image editing functionality. Cameras that are built into a personal electronic device allow the personal electronic device to capture visual data for picture and videos. That data can be saved on the personal electronic device and displayed on the display associated with the device (e.g., a touch screen for a tablet computer). In some cases the personal electronic devices can modify the visual data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings. 
         FIG. 1  is a network diagram illustrating a network environment suitable for image processing, according to some example embodiments. 
         FIG. 2  is a block diagram illustrating components of a computing device suitable for a Difference File Generator, according to some example embodiments. 
         FIG. 3  is a block diagram illustrating the creation and caching of a first pixel difference file, according to some example embodiments. 
         FIG. 4  is a block diagram illustrating the creation and caching of a second pixel difference file, according to some example embodiments. 
         FIG. 5  is a block diagram illustrating the creation of an edited image by applying a pixel difference file to a source image, according to some example embodiments. 
         FIG. 6  is a flowchart illustrating a method for cached image processing, according to some example embodiments. 
         FIG. 7  is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium and perform any one or more of the methodologies discussed herein. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods and systems are directed to a Difference File Generator. Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details. 
     In various embodiments, the Difference File Generator receives at least one edit to at least one pixel of a source image file. The Difference File Generator generates a first edited version of the source image file having at least one edited pixel. Each edited pixel comprises a pixel resulting from the at least one edit to a respective pixel of the source image file. The Difference File Generator generates a pixel difference file comprising pixel difference data. The pixel difference data indicates a difference between each respective pixel of the source image file and a corresponding edited pixel in the first edited version of the source image file. 
     In one or more embodiments, the Difference File Generator receives one or more edits to a source image file. The one or more edits can be a user-selection of any type of image editing tool that allows for image filtering and/or image modification. For example, a filter can change a hue of one or more colors present in the source image file. Based on the received edits, the Difference File Generator creates an edited version of the source image file. The Difference File Generator compares the source image file and the edited version of the source image file. Based on the comparison, the Difference File Generator creates a pixel difference file. 
     The pixel difference file includes pixel difference data that correspond to one or more pixels in the source image file and the edited version of the source image file. The pixel difference file indicates a difference between pixels in the source image file and the edited version of the source image file. For example, for a first pixel from the source image file, a user-selected edit may modify the first pixel such that it has a modified color in the edited version of the source image file. The Difference File Generator generates first pixel difference data that numerically represents a difference between the first pixel&#39;s original color and the first pixel&#39;s modified color in the edited version of the source image file. It is understood that the embodiments described herein are not limited pixel difference data that describes a change in pixel color. The pixel difference data can describe a change in pixel location and/or pixel size. 
     In addition, a user-selected edit may modify a second pixel in the source image file. In various embodiments, the edit made to the first pixel may be different than an the edit made to the second pixel. The Difference File Generator generates second pixel difference data for the pixel difference file that numerically represents a difference between the second pixel in the source image file a modified pixel that results from applying the user-selected edit to the second pixel. 
     The Difference File Generator stores the pixel difference file in cache memory. By storing the pixel difference file in cache memory, the Difference File Generator allows for cache-based image processing. 
       FIG. 1  is a network diagram illustrating a network environment  100  suitable for image processing, according to some example embodiments. The network environment  100  includes a server machine  110 , a database  115 , and devices  130  and  150 , all communicatively coupled to each other via a network  190 . The server machine  110  may form all or part of a network-based system  105  (e.g., a cloud-based server system configured to provide one or more services to the devices  130  and  150 ). The server machine  110  and the devices  130  and  150  may each be implemented in a computer system, in whole or in part, as described below with respect to  FIG. 11 . 
     Also shown in  FIG. 1  are users  132  and  152 . One or both of the users  132  and  152  may be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the device  130 ), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The user  132  is not part of the network environment  100 , but is associated with the device  130  and may be a user of the device  130 . For example, the device  130  may be a desktop computer, a vehicle computer, a tablet computer, a navigational device, a portable media device, a smartphone, or a wearable device (e.g., a smart watch or smart glasses) belonging to the user  132 . Likewise, the user  152  is not part of the network environment  100 , but is associated with the device  150 . As an example, the device  150  may be a desktop computer, a vehicle computer, a tablet computer, a navigational device, a portable media device, a smartphone, or a wearable device (e.g., a smart watch or smart glasses) belonging to the user  152 . 
     Any of the machines, databases, or devices shown in  FIG. 1  may be implemented in a general-purpose computer modified (e.g., configured or programmed) by software (e.g., one or more software modules) to be a special-purpose computer to perform one or more of the functions described herein for that machine, database, or device. For example, a computer system able to implement any one or more of the methodologies described herein is discussed below with respect to  FIG. 11 . As used herein, a “database” is a data storage resource and may store data structured as a text file, a table, a spreadsheet, a relational database (e.g., an object-relational database), a triple store, a hierarchical data store, or any suitable combination thereof. Moreover, any two or more of the machines, databases, or devices illustrated in  FIG. 1  may be combined into a single machine, and the functions described herein for any single machine, database, or device may be subdivided among multiple machines, databases, or devices. 
     The network  190  may be any network that enables communication between or among machines, databases, and devices (e.g., the server machine  110  and the device  130 ). Accordingly, the network  190  may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network  190  may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof. Accordingly, the network  190  may include one or more portions that incorporate a local area network (LAN), a wide area network (WAN), the Internet, a mobile telephone network (e.g., a cellular network), a wired telephone network (e.g., a plain old telephone system (POTS) network), a wireless data network (e.g., WiFi network or WiMax network), or any suitable combination thereof. Any one or more portions of the network  190  may communicate information via a transmission medium. As used herein, “transmission medium” refers to any intangible (e.g., transitory) medium that is capable of communicating (e.g., transmitting) instructions for execution by a machine (e.g., by one or more processors of such a machine), and includes digital or analog communication signals or other intangible media to facilitate communication of such software. 
       FIG. 2  is a block diagram illustrating components of the computing device  150 , according to some example embodiments. The computing device  150  is shown as including a Difference File Generator  155 . The Difference File Generator includes various modules, such as an edit receipt module  210 , an edited image generation module  220 , a pixel difference module  230 , a cache module  240 , a pixel difference retrieval module  250  and an edited image recreation module  260 , all configured to communicate with each other (e.g., via a bus, shared memory, or a switch). 
     Any one or more of the modules described herein may be implemented using hardware (e.g., one or more processors of a machine) or a combination of hardware and software. For example, any module described herein may configure a processor (e.g., among one or more processors of a machine) to perform the operations described herein for that module. Moreover, any two or more of these modules may be combined into a single module, and the functions described herein for a single module may be subdivided among multiple modules. Furthermore, according to various example embodiments, modules described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices. 
     In various example embodiments, the edit receipt module  210  is a hardware-implemented module that controls, manages and stores information related to receiving one or more user-selected edits to a source image file. 
     In various example embodiments, the edited image generation module  220  is a hardware-implemented module that controls, manages and stores information related to generating an edited version of the source image file according to the received user-selected edits. 
     In various example embodiments, the pixel difference module  230  is a hardware-implemented module that controls, manages and stores information related to generating a pixel difference file that include pixel difference data. The pixel difference data numerically represents differences between respective pixels in the source image file and correspond edited pixels in the edited version of the source image file. 
     In various example embodiments, the cache module  240  is a hardware-implemented module that controls, manages and stores one or more pixel difference files. 
     In various example embodiments, the pixel difference retrieval module  250  is a hardware-implemented module that controls, manages and stores information related to retrieving a pixel difference file from the cache module  204 . 
     In various example embodiments, the edited image recreation module  260  is a hardware-implemented module that controls, manages and stores information applying a retrieved pixel difference file to a source image file in order to recreate the edited version of the source image file without having to execute the user-selected edits. 
       FIG. 3  is a block diagram illustrating the creation and caching of a first pixel difference file, according to some example embodiments. A source image  302  is made up of a plurality of pixels. The plurality of pixels includes at least a first pixel  302 - 1 , a second pixel  302 - 2  and a third pixel  302 - 3 . 
     The Difference File Generator (“DFG”)  155  receives one or more user-selected applied to the source image  302 . For example, a first user-selected edit modifies the first pixel  302 - 1  and a second user-selected edit modifies the second pixel  302 - 2 . Based on the received edits, the DFG  155  generates a first edited version of the source image  304 . The first edited version of the source image  304  includes an edited version of the first pixel  302 - 1 - 1 , an edited version of the second pixel  302 - 2 - 1  and the third pixel  302 - 3 . 
     The DFG  155  compares the edited version of the first pixel  302 - 1 - 1  to the first pixel  302 - 1  and generates first pixel difference data  306 - 1 . The first pixel difference data  306 - 1  numerically represents a difference between the edited version of the first pixel  302 - 1 - 1  and the first pixel  302 - 1 . The DFG  155  also compares the edited version of the second pixel  302 - 2 - 1  to the second pixel  302 - 2  and generates second pixel difference data  306 - 2 . The second pixel difference data  306 - 2  numerically represents a difference between the edited version of the second pixel  302 - 2 - 1  and the second pixel  302 - 2 . 
     The DFG  155  generates a first pixel difference file  306 , which includes the first and second pixel difference data  306 - 1 ,  306 - 2 . The DFG  155  stores the first pixel difference file  306  in cache memory  310 . 
       FIG. 4  is a block diagram illustrating the creation and caching of a second pixel difference file, according to some example embodiments. 
     The Difference File Generator (“DFG”)  155  receives one or more user-selected applied to the same source image  302 . For example, a third user-selected edit modifies the third pixel  302 - 3 , but leaves the first and second pixels  302 - 1 ,  302 - 2  unmodified. Based on the received edits, the DFG  155  generates a second edited version of the source image  404 . The second edited version of the source image  404  includes an edited version of the third pixel  302 - 3 - 1 , as well as the first and second pixels  302 - 1 ,  302 - 2   
     The DFG  155  compares the edited version of the third pixel  302 - 3 - 1  to the third pixel  302 - 3  and generates third pixel difference data  406 - 1 . The third pixel difference data  406 - 1  numerically represents a difference between the edited version of the third pixel  302 - 3 - 1  and the third pixel  302 - 3 . The DFG  155  generates a second pixel difference file  406 , which includes the third pixel difference data  406 - 1 . The DFG  155  stores the second pixel difference file  406  in in cache memory  310  along with the previously-generated first pixel difference file  306 . 
       FIG. 5  is a block diagram illustrating the creation of an edited image by applying a pixel difference file to a source image, according to some example embodiments. 
     The DFG  155  includes a pixel difference file processor  505  to apply the first pixel difference file  306  to the source image  302 . For example, in one embodiment, the first pixel  302 - 1  has a pixel color value of “249” and the edited version of the first pixel  302 - 1 - 1  has a pixel color value of “246. Therefore, the first pixel difference data  306 - 1  in the first pixel difference file  306  has a value of “−3”. In addition, the second pixel  302 - 2  has a pixel color value of “237” and the edited version of the second pixel  302 - 2 - 1  has a pixel color value of “236”. Therefore, the second pixel difference data  306 - 2  in the first pixel difference file  306  has a value of 
     The pixel difference file processor  505  applies the first and second pixel difference data  306 - 1 ,  306 - 2  from the first pixel difference file  306  to the source image  302 . The color value of the first pixel  302 - 1  is adjusted from “249” to “246” and the color value of the second pixel  302 - 2  is adjusted from “237” to “236”. Based on the adjustments made to the color values of the first and second pixels  302 - 1 ,  302 - 2 , the pixel difference file processor  505  generates a recreation of the first edited image  504  without having to execute the user-selected edits that created the first edited image  304 . 
       FIG. 6  is a flowchart illustrating a method  600  for cached image processing, according to some example embodiments. Operations in the method  600  may be performed by the device  150 , using modules described above with respect to  FIG. 2 . As shown in  FIG. 6 , the method  600  includes operations  610 ,  620  and  630 . 
     At operation  610 , the DFG  155  receives at least one edit to at least one pixel in a source image file. In various embodiments, a first edit applied to a first pixel of the source image file can be difference than a second edit applied to a second pixel of the source image file. 
     At operation  620 , the DFG  155  generates a first edited version of the source image file. Each edited pixel in the first edited version of the source image file comprises a pixel resulting from an edit(s) to a respective pixel of the source image file. 
     At operation  630 , the DFG  155  generates a pixel difference file. The pixel difference file comprises pixel difference data that indicates a difference between each respective pixel of the source image file and a corresponding edited pixel in the first edited version of the source image file. 
     In some embodiments, to generate the pixel difference file, the DFG  155  compares a first pixel of the source image file and a first edited pixel. The first edited pixel resulting from an edit(s) applied to the first pixel. The DFG  155  generate first pixel difference data that numerically represents a difference between the first pixel and the first edited pixel. The DFG  155  incorporates the first pixel difference data in the pixel difference file. 
     According to various example embodiments, one or more of the methodologies described herein may facilitate a Difference File Generator. 
     When these effects are considered in aggregate, one or more of the methodologies described herein may obviate a need for certain efforts or resources that otherwise would be involved in one or more embodiments of the Difference File Generator. Efforts expended by a user in generating a pixel difference file may be reduced by one or more of the methodologies described herein. Computing resources used by one or more machines, databases, or devices (e.g., within the network environment  100 ) may similarly be reduced. Examples of such computing resources include processor cycles, network traffic, memory usage, data storage capacity, power consumption, and cooling capacity. 
       FIG. 7  is a block diagram illustrating components of a machine  700 , according to some example embodiments, able to read instructions  724  from a machine-readable medium  722  (e.g., a non-transitory machine-readable medium, a machine-readable storage medium, a computer-readable storage medium, or any suitable combination thereof) and perform any one or more of the methodologies discussed herein, in whole or in part. Specifically,  FIG. 7  shows the machine  700  in the example form of a computer system (e.g., a computer) within which the instructions  724  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  700  to perform any one or more of the methodologies discussed herein may be executed, in whole or in part. 
     In alternative embodiments, the machine  700  operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine  700  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a distributed (e.g., peer-to-peer) network environment. The machine  700  may be a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a cellular telephone, a smartphone, a set-top box (STB), a personal digital assistant (PDA), a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  724 , sequentially or otherwise, that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute the instructions  724  to perform all or part of any one or more of the methodologies discussed herein. 
     The machine  700  includes a processor  702  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), or any suitable combination thereof), a main memory  704 , and a static memory  706 , which are configured to communicate with each other via a bus  708 . The processor  702  may contain microcircuits that are configurable, temporarily or permanently, by some or all of the instructions  724  such that the processor  702  is configurable to perform any one or more of the methodologies described herein, in whole or in part. For example, a set of one or more microcircuits of the processor  702  may be configurable to execute one or more modules (e.g., software modules) described herein. 
     The machine  700  may further include a graphics display  710  (e.g., a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, a cathode ray tube (CRT), or any other display capable of displaying graphics or video). The machine  700  may also include an alphanumeric input device  712  (e.g., a keyboard or keypad), a cursor control device  714  (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, an eye tracking device, or other pointing instrument), a storage unit  716 , an audio generation device  718  (e.g., a sound card, an amplifier, a speaker, a headphone jack, or any suitable combination thereof), and a network interface device  720 . 
     The storage unit  716  includes the machine-readable medium  722  (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions  724  embodying any one or more of the methodologies or functions described herein. The instructions  724  may also reside, completely or at least partially, within the main memory  704 , within the processor  702  (e.g., within the processor&#39;s cache memory), or both, before or during execution thereof by the machine  700 . Accordingly, the main memory  704  and the processor  702  may be considered machine-readable media (e.g., tangible and non-transitory machine-readable media). The instructions  724  may be transmitted or received over the network  190  via the network interface device  720 . For example, the network interface device  720  may communicate the instructions  724  using any one or more transfer protocols (e.g., hypertext transfer protocol (HTTP)). 
     In some example embodiments, the machine  700  may be a portable computing device, such as a smart phone or tablet computer, and have one or more additional input components  730  (e.g., sensors or gauges). Examples of such input components  730  include an image input component (e.g., one or more cameras), an audio input component (e.g., a microphone), a direction input component (e.g., a compass), a location input component (e.g., a global positioning system (GPS) receiver), an orientation component (e.g., a gyroscope), a motion detection component (e.g., one or more accelerometers), an altitude detection component (e.g., an altimeter), and a gas detection component (e.g., a gas sensor). Inputs harvested by any one or more of these input components may be accessible and available for use by any of the modules described herein. 
     As used herein, the term “memory” refers to a machine-readable medium able to store data temporarily or permanently and may be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium  722  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing the instructions  724  for execution by the machine  700 , such that the instructions  724 , when executed by one or more processors of the machine  700  (e.g., processor  702 ), cause the machine  700  to perform any one or more of the methodologies described herein, in whole or in part. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as cloud-based storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, one or more tangible (e.g., non-transitory) data repositories in the form of a solid-state memory, an optical medium, a magnetic medium, or any suitable combination thereof. 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute software modules (e.g., code stored or otherwise embodied on a machine-readable medium or in a transmission medium), hardware modules, or any suitable combination thereof. A “hardware module” is a tangible (e.g., non-transitory) unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In some embodiments, a hardware module may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a field programmable gate array (FPGA) or an ASIC. A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software encompassed within a general-purpose processor or other programmable processor. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, and such a tangible entity may be physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software (e.g., a software module) may accordingly configure one or more processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors. 
     Similarly, the methods described herein may be at least partially processor-implemented, a processor being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. As used herein, “processor-implemented module” refers to a hardware module in which the hardware includes one or more processors. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an application program interface (API)). 
     The performance of certain operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations. 
     Some portions of the subject matter discussed herein may be presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a machine memory (e.g., a computer memory). Such algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an “algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as “data,” “content,” “bits,” “values,” “elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities. 
     Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or any suitable combination thereof), registers, or other machine components that receive, store, transmit, or display information. Furthermore, unless specifically stated otherwise, the terms “a” or “an” are herein used, as is common in patent documents, to include one or more than one instance. Finally, as used herein, the conjunction “or” refers to a non-exclusive “or,” unless specifically stated otherwise.