Patent Application
20220086008
© Alitheon, Inc. 2020. A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, if and when they are made public, but otherwise reserves all copyright rights whatsoever. 37 CFR § 1.71(d).
This application pertains to methods, systems and software for secure registration, induction and authentication of physical objects through the use of digital fingerprints for manufacturing and supply chain environments.
There are many reasons why a manufacturer or a distributer would want to carefully monitor an item within their manufacturing process or across a supply chain. Two primary ones are to track the item and to ensure that the item that arrives at the next step or at the end user is not counterfeit or otherwise illegitimate. Historically, these two essentially independent needs have been treated as though they were a single requirement, with tracking taking precedence over preventing counterfeits from entering the supply chain. An example of such confusion was when a manufacturer or distributer believed that the presence of a legitimate serial number, in the form of a barcode for example, equated to the presence of a legitimate item. Serial numbers, particularly if they are applied to the object rather than being a part of the object, can be counterfeited themselves, often far more easily than the item itself. Tracking, when there is no danger of counterfeiting or of accidentally installing the wrong part during manufacturing, does not require identification provided a proxy such as a serial number can be attached.
If, however, there is a risk of counterfeiting or a risk that a person on an assembly line, say, will use the wrong part, then being able to determine the identity of the part becomes essential. The need remains for improvements to mitigate these two separate problems—tracking an object that may or may not have an attached serial number and ensuring that the item received at a manufacturing site or at a station within a facility is the correct item.
The following is a summary of the present disclosure to provide a basic understanding of some features and context. This summary is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Its sole purpose is to present some concepts of the present disclosure in simplified form as a prelude to a more detailed description that is presented later.
The technology disclosed herein addresses the two separate problems identified above—tracking an object that may or may not have an attached serial number and ensuring that the item received at a manufacturing site or at a station within a facility, is the correct item. It does so with a single technology based on digital fingerprints of the items in the manufacturing or supply chain.
In one embodiment, a system enables item registration and authentication of physical objects in varied environments and the storage of and access to the digital fingerprints occurring in the cloud. Its applicability to a particular environment is primarily achieved by changing the business rules, reference sets, and authentication parameters of the disclosed system. A “reference set” refers to a set of trusted digital fingerprints. For example, they may be digital fingerprints acquired (or extracted from image data acquired) under trusted circumstances for later reference and comparison.
The disclosed system is designed to do several things as the central part of an integrated system. First, to authenticate items in a supply or distribution chain as well as internally in a production facility. Second, it is designed to use digital fingerprinting in a native way as part of the core process, taking full advantage of the security provided by a digital fingerprinting process. Third, the registration/authentication systems are table-driven making it easy to change applications while using the same disclosed system.
In an embodiment, an example system may comprise a central digital computing server coupled to a machine-readable non-volatile memory. Preferably, the server may be provisioned in an on-demand cloud computing platform. The server is arranged to implement the following components, although the names given to the components are not critical:
a storage layer to store digital fingerprint records and process event records, each event record linked to the stored digital fingerprints used in the corresponding event;
an authentication layer including working memory for temporary data storage;
the memory storing digital fingerprint records, each digital fingerprint record associated to a physical object registered in the system;
a users layer to maintain user accounts, where each user and third-party system granted access to the system has a corresponding user account in the users layer;
a services registrar component to manage working memory allocations, including allocating a portion of the working memory as an authentication instance for one client for a selected product class; wherein the services registrar loads digital fingerprint records of the selected product class from the storage layer into the authentication instance for use in matching a digital fingerprint; and
a security layer to enforce rules governing network level access to the system, where the access rules are stored in the users layer, on a per user basis, and or stored in the services registrar component.
To enable the reader to realize one or more of the above-recited and other advantages and features of the present disclosure, a more particular description follows by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the disclosure and are not therefore to be considered limiting of its scope, the present disclosure will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:
The technology disclosed herein addresses the two separate problems—tracking an object that may or may not have an attached serial number and ensuring that an item received at a manufacturing site or at a station within a facility, is the correct item. It does so with a single technology based on digital fingerprints of the items in the manufacturing or supply chain. The following paragraphs describe the two functionalities in more detail.
Barcodes and other proxies provide little security against counterfeiting but are useful for tracking items in environments where counterfeiting is not a concern. There are, however, many places where a barcode or other proxy is impossible or impractical. First, of course, if they are affixed to the item, they can detach, leaving the item unidentifiable. Second, if the part continually undergoes change in a manufacturing process, such proxies can be obscured or destroyed entirely. Third, many items such as bolts and small gears are too small for serial number or QR codes. And finally, some objects are meant to be attractive and most proxies are not items of beauty. In all of these situations, the current disclosure teaches a system that uses native features of the item to identify it as it is being tracked. Other patents of ours such as U.S. Pat. No. 10,346,852 “Preserving Authentication under Item Change” are useful in tracking objects that change during manufacturing or in the supply chain but are only peripheral to what is described here where the native features are used as part of a tracking system.
When ID proxies are impractical or get obscured during manufacturing, tracking becomes particularly difficult. At manufacturing sites that produce many different components this leads to similar but incorrect components being added to items, often with serious consequences. By using native features for identity determination or confirmation, installing similar but incorrect parts in a component or manufactured unit becomes much less likely.
A further consideration is worth stating: a barcode or other attached proxy is useful for tracking an object only if it is attached (uniquely) to one specific object. If the object or the barcode or both are counterfeit, whatever it is the system acts like it is tracking, it isn't tracking the correct object itself. This interplay between tracking and counterfeit prevention is discussed more in the following section.
While barcodes and QR codes are useful for tracking items, their use in preventing counterfeiting is misapplied and of little utility. If an item can be counterfeited, any identification proxy can be counterfeited as well, often with considerably greater ease than the item itself. To discourage counterfeiters, complex features are often added to the item, to the ID proxy, or to both based on the idea that complex features are difficult for the counterfeiter to duplicate. In addition to making the items less attractive, this has led to an arms race between counterfeiters and manufacturers, an arms race that the counterfeiters are winning. What one person can make intentionally another can copy intentionally.
Because anticounterfeiting efforts have concentrated on the complexity problem, and because tracking is still necessary, manufacturers have had to do both—add needless complexity to their items (to prevent counterfeiting) and add ID proxies (for tracking). The result is ever-increasing expenses with little evidence they are effective at reducing counterfeiting. The technology described herein uses the natural complexity of the physical object—the result of characteristics of the materials, accidental variations due to manufacturing, or other causes—to identify the item at each location in a tracking process, whether within a manufacturing facility or within a broader supply chain. These natural or native features of the physical object are expressed in a digital fingerprint. We use terms like “natural” or “native” to reference features of the object itself as distinguished from artificial or added labels, tags, bar codes or other proxies.
There are many different ways that the disclosed system can be implemented. What is discussed here is one possible embodiment of the taught system. This discussion is general, describing the various components of the system and how they work together to produce the unique benefits of the disclosed system, but it is presented solely to convey information on the taught system and not to limit it.
Referring now to
For inbound communication, this affects who and what systems may communicate with the system 100. The access rules may be further customized per customer for specific business needs. A “customer” refers to an entity that is a user of the system 100, for example, a manufacturer. For example, with one customer, enhanced rules in the Application-Specific Interface (API) 106 may enforce the business requirement that particular accounts may perform authentications only for certain product classes. “Accounts” here refers to individual user accounts maintained in the Users layer 124. The users and third-party systems granted access to the System 100 are registered in the Users layer 124. Each user or third-party system has one or more roles so that the Security layer 104 may authorize each request separately.
For outbound communication, the Security layer 104 provides application layer security over and above security configured at the network and operations level. For example, the Security layer 104 may limit the flow of business events to a specific set of product classes when these events must be filtered for security or compliance purposes. Business events may be, for example, in the outbound case, who gets the report on what object. There is, for example, no need for the assembly line making transmissions of type “a” to get reports on components meant for type “b.” Further, if the system is used in a multi-manufacturer supply chain, it is likely the entity controlling the chain would not want information related to components made by one manufacturer to be able to get information about other manufacturers. Further, if there are directives (as there often are with classified components, for example, that reports be issued in a particular format to a particular group of people and no others), then it is a matter of both security (because the parts are classified) and compliance (since there are those directives). These are merely examples of business events than can be controlled by the security layer.
The System 100 has an ever-growing set of digital fingerprints of registered objects. While all digital fingerprints are accessible in the Storage layer 130, a subset of the digital fingerprints are loaded into working memory (not shown) for faster authentication (see Authentication). The Services Registrar component 120 manages the working memory allocations. This component may be used to:
In a presently preferred embodiment, for one customer, each Authentication instance serves as a distinct working memory for a product class. Since all product classes are not required to be in working memory at all times, the Service Registrar component 120 rotates product classes into Authentication instances as needed. This reduces the number of Authentication instances required, which in turn saves on operational costs.
Traceability is key to object identification and authentication. The Storage layer 130 ensures that all digital fingerprints that have been entered into the System 100 are retained. They may be made inactive if they are not needed at a particular time, but they can be restored later. This is handled through an archival and versioning process.
In more detail, all processing events preferably are retained for auditing purposes. These events are linked to the digital fingerprints used for that event. The events may include:
The Authentication layer 140 serves as the working memory of digital fingerprints. This current set of digital fingerprints represents the set of real-world objects from which an incoming request seeks a match. In a preferred embodiment, an authentication instance may be implemented as a combination of both software and hardware. Its input is generally an image (from the item being authenticated) and operational instructions (what kind of part is this supposed to be, whose part is it, etc.). The hardware isn't strictly a part of the system but is used by the authentication instance. It may comprise the computer processor, working memory, the specific reference memory (on a disc but swapped into working memory when required), and related systems. The software converts the image to a digital fingerprint, fetches reference digital fingerprints, makes the comparisons, and generates and exports reports on the results of the comparisons. The authentication instance has allocated memory and processor time for its use. It has access to data but strictly speaking the authentication instance is the program that does the authentication. There may be many of them running concurrently (for example, as multiple threads on a processor).
Cameras take pictures of real-world objects and the System 100 processes them as image or image data. These images may be converted into digital fingerprints onsite (where the images are captured) or in the System. For the latter case, this conversion takes place in the fingerprint extraction layer 150. The conversion of images to digital fingerprints makes use of a number of extraction parameters. These parameters vary between product classes. Each conversion, therefore, requires a set of extraction parameters. These may be tracked in the Services Registrar layer 120.
“Fingerprint Catalog” refers to a package of instructions to the system on how to carry out a particular set of digital fingerprint matches. (The moniker is not important.) Put another way, the Fingerprint Catalog specifies necessary information for a given class of objects. In general, the catalog includes information necessary to register and or authenticate the objects of the corresponding class. In a presently preferred embodiment, a Fingerprint Catalog may be stored in the Services Registrar layer 120 and it may contain some or all of the following parameters and settings:
Extraction parameters. Extraction parameters guide how a digital fingerprint is created. They include parameters informing image processing steps as well parameters informing feature extraction algorithms. Examples may include: imageScalingFactor, extractionMode.
Authentication parameters. Authentication parameters guide how a query digital fingerprint is matched to a reference set digital fingerprint. These parameters inform applicable or available digital fingerprint matching algorithms. Examples may include: scaleMin, scaleMax, allowedAngleIntervals
User settings. These are of course dependent on the particular user. They include such information as what data from the user (meta data, list of references, etc.) should be included in the records for the object, the format(s) to be used in reporting back to them, etc.
Registration station settings. In some embodiments, registration station settings may include API URL, image cropping settings, computer vision-based triggering settings.
Fixed authentication settings. These are fixed in the sense that they do not change from instance to instance or with changes to the category of objects. These settings may include API URL, image cropping settings, and computer vision-based triggering settings.
Mobile device settings. In some embodiments, these settings may include digital fingerprint catalog filter, and application preferences.
Icons, logos, images, colors for UI. Customers generally provide these for use in the mobile application. Some examples include app icon, landing page company logo, images for each product class, and skin colors for the entire app.
The Storage 130, Digital Fingerprint Extraction 150, and Authentication 140 layers require substantial coordination. That may be provided by the Services Aggregation Service 160. Some examples may include:
Any customer-specific logic preferably exists in the Application-Specific Interface 106. Furthermore, customer-specific storage requirements are also defined in this layer. For example, for one customer, real-world serial numbers must be associated to all digital fingerprints and events. For another customer, there is no serial number; only a match to a product class is required.
Transaction requirements in the System also vary between customers. As a result, these may be defined, for example, as rules in the Application-Specific Interface 106. Some examples of transaction rules may include:
WebUI 122 represents a Customer-specific web portal for select people to look at the history and state of the Cloud Service. It also allows appropriate Users the ability to generate reports and manage reference sets (see
This interface represents interaction with a Customer's software system via their exposed front-end. This allows for real-time propagation of Event data from the described cloud service to the Customer's software system for immediate action based on their own business logic.
Connected Devices: Several devices may connect to this system, the most important being for registering objects and later authenticating them. All connections go through the security layer 104. To illustrate, an external registration station 162, component, or device may utilize a registration API to register a physical object into the system 100. See description below of
In one implementation, a registration station 162 may comprise a fixed rig including lighting and camera(s). Each Registration station 162 has a unique identifier and accesses the cloud system via a customer or use case specific URL. The Registration station 162 may receive processing parameters from the cloud system via the same API. The Registration station 162 captures images of physical objects. It may have its own digital fingerprint extraction service or rely on the one in the cloud system (e.g., the fingerprint extraction layer 150). Images and (if created locally) digital fingerprints for authentication are uploaded to the cloud system via the API.
In some embodiments, a fixed rig includes lighting and camera(s). Each authentication station 168 preferably has a unique identifier and accesses the present system 100 via a customer or use case specific URL. The Authentication station 168 receives processing parameters from the Alitheon cloud via the same API. The Authentication station 168 captures images of objects. It may have its own digital fingerprint extraction service or rely on the one in the cloud (e.g., the fingerprint extraction layer 150 shown in
Mobile units 172 may be used for registration and or for authentication in connection with a system of the type described herein. The system may be used for any of the combinations of mobile/fixed registration and mobile/fixed authentication. In one example, the mobile unit 172 may comprise a smart phone with a camera, for example, an Apple iPhone®. The mobile unit 172 may include external lighting and/or lenses. In some embodiments, the mobile unit 172 may not include creation of digital fingerprints, which may instead be created by the fingerprint extraction service 150 using inputs from a fixed registration rig described below. In general, the mobile unit 172 may create the digital fingerprints either at registration or at authentication. The mobile unit 172, in one embodiment, may utilize a REST API. REST is an acronym for REpresentational State Transfer—a software architectural style that defines a set of constraints to be used for creating Web services. See https://en.wikipedia.org/wiki/Representational_state transfer for more detail.
An authorized User of a mobile app must login for the app to function. Based on the User, the mobile app is provided a customer or use case specific URL via a cloud API. In a preferred embodiment, the mobile app receives digital fingerprint catalog data via the specific URL based on User. Images captured by the app preferably are sent to the cloud system using the same URL.
Example Optics for fixed Registration and Authentication Stations:
In
“Digital fingerprinting” refers to the creation and use of digital records (digital fingerprints) derived from properties of a physical object, which digital records are typically stored in a database. Digital fingerprints maybe used to reliably and unambiguously identify or authenticate corresponding physical objects, track them through supply chains, record their provenance and changes over time, and for many other uses and applications.
Digital fingerprints store information, preferably in the form of numbers or “feature vectors,” that describes features that appear at particular locations, called points of interest, of a two-dimensional (2-D) or three-dimensional (3-D) object. In the case of a 2-D object, the points of interest are preferably on a surface of the corresponding object; in the 3-D case, the points of interest may be on the surface or in the interior of the object. In some applications, an object “feature template” may be used to define locations or regions of interest for a class of objects. The digital fingerprints may be derived or generated from digital data of the object which may be, for example, image data.
While the data from which digital fingerprints are derived is often images, a digital fingerprint may contain digital representations of any data derived from or associated with the object. For example, digital fingerprint data may be derived from an audio file. That audio file in turn may be associated or linked in a database to an object. Thus, in general, a digital fingerprint may be derived from a first object directly, or it may be derived from a different object (or file) linked to the first object, or a combination of the two (or more) sources. In the audio example, the audio file may be a recording of a person speaking a particular phrase. The digital fingerprint of the audio recording may be stored as part of a digital fingerprint of the person speaking. The digital fingerprint (of the person) may be used as part of a system and method to later identify or authenticate that person, based on their speaking the same phrase, in combination with other sources.
Returning to the 2-D and 3-D object examples mentioned above, feature extraction or feature detection may be used to characterize points of interest. In an embodiment, this may be done in various ways. Two examples include Scale-Invariant Feature Transform (or SIFT) and Speeded Up Robust features (or SURF). Both are described in the literature. For example: “Feature detection and matching are used in image registration, object tracking, object retrieval etc. There are number of approaches used to detect and matching of features as SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Feature), FAST, ORB etc. SIFT and SURF are most useful approaches to detect and matching of features because of it is invariant to scale, rotate, translation, illumination, and blur.” MISTRY, Darshana et al., Comparison of Feature Detection and Matching Approaches: SIFT and SURF, GRD Journals—Global Research and Development Journal for Engineering|Volume 2|Issue 4|March 2017.
In an embodiment, features may be used to represent information derived from a digital image in a machine-readable and useful way. Features may be point, line, edges, and blob of an image etc. There are areas such as image registration, object tracking, and object retrieval etc. that require a system or processor to detect and match correct features. Therefore, it may be desirable to find features in ways that are invariant to rotation, scale, translation, illumination, noisy and blur images. The search of points of interest from one object image to corresponding images can be very challenging work. The search may preferably be done such that same physical interest points can be found in different views. Once located, points of interest and their respective characteristics may be aggregated to form the digital fingerprint (generally including 2-D or 3-D location parameters).
In an embodiment, features may be matched, for example, based on finding a minimum threshold distance. Distances can be found using Euclidean distance, Manhattan distance etc. If distances of two points are less than a prescribed minimum threshold distance, those key points may be known as matching pairs. Matching a digital fingerprint may comprise assessing a number of matching pairs, their locations or distance and other characteristics. Many points may be assessed to calculate a likelihood of a match, since, generally, a perfect match will not be found. In some applications a “feature template” may be used to define locations or regions of interest for a class of objects.
In this application, the term “scan” is used in the broadest sense, referring to any and all means for capturing an image or set of images, which may be in digital form or transformed into digital form. Images may, for example, be two dimensional, three dimensional, or in the form of a video. Thus a “scan” may refer to an image (or digital data that defines an image) captured by a scanner, a camera, a specially adapted sensor or sensor array (such as a CCD array), a microscope, a smartphone camera, a video camera, an x-ray machine, a sonar, an ultrasound machine, a microphone (or other instruments for converting sound waves into electrical energy variations), etc. Broadly, any device that can sense and capture either electromagnetic radiation or mechanical wave that has traveled through an object or reflected off an object or any other means to capture surface or internal structure of an object is a candidate to create a “scan” of an object. Various means to extract “fingerprints” or features from an object may be used; for example, through sound, physical structure, chemical composition, or many others. The remainder of this application will use terms like “image” but when doing so, the broader uses of this technology should be implied. In other words, alternative means to extract “fingerprints” or features from an object should be considered equivalents within the scope of this disclosure. Similarly, terms such as “scanner” and “scanning equipment” herein may be used in a broad sense to refer to any equipment capable of carrying out “scans” as defined above, or to equipment that carries out “scans” as defined above as part of their function.
More information about digital fingerprinting can be found in various patents and publications assigned to Alitheon, Inc. including, for example, the following: DIGITAL FINGERPRINTING, U.S. Pat. No. 8,6109,762; OBJECT IDENTIFICATION AND INVENTORY MANAGEMENT, U.S. Pat. No. 9,152,862; DIGITAL FINGERPRINTING OBJECT AUTHENTICATION AND ANTI-COUNTERFEITING SYSTEM, U.S. Pat. No. 9,443,298; PERSONAL HISTORY IN TRACK AND TRACE SYSTEM, U.S. Pat. No. 10,037,537; PRESERVING AUTHENTICATION UNDER ITEM CHANGE, U.S. Pat. App. Pub. No. 2017-0243230 A1. These references are incorporated herein by this reference.
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated.
The system and method disclosed herein may be implemented via one or more components, systems, servers, appliances, other subcomponents, or distributed between such elements. When implemented as a system, such systems may include an/or involve, inter alia, components such as software modules, general-purpose CPU, RAM, etc. found in general-purpose computers. In implementations where the innovations reside on a server, such a server may include or involve components such as CPU, RAM, etc., such as those found in general-purpose computers.
Additionally, the system and method herein may be achieved via implementations with disparate or entirely different software, hardware and/or firmware components, beyond that set forth above. With regard to such other components (e.g., software, processing components, etc.) and/or computer-readable media associated with or embodying the present inventions, for example, aspects of the innovations herein may be implemented consistent with numerous general purpose or special purpose computing systems or configurations. Various exemplary computing systems, environments, and/or configurations that may be suitable for use with the innovations herein may include, but are not limited to: software or other components within or embodied on personal computers, servers or server computing devices such as routing/connectivity components, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, consumer electronic devices, network PCs, other existing computer platforms, distributed computing environments that include one or more of the above systems or devices, etc.
In some instances, aspects of the system and method may be achieved via or performed by logic and/or logic instructions including program modules, executed in association with such components or circuitry, for example. In general, program modules may include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular instructions herein. The inventions may also be practiced in the context of distributed software, computer, or circuit settings where circuitry is connected via communication buses, circuitry or links. In distributed settings, control/instructions may occur from both local and remote computer storage media including memory storage devices.
The software, circuitry and components herein may also include and/or utilize one or more type of computer readable media. Computer readable media can be any available media that is resident on, associable with, or can be accessed by such circuits and/or computing components. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and can accessed by computing component. Communication media may comprise computer readable instructions, data structures, program modules and/or other components. Further, communication media may include wired media such as a wired network or direct-wired connection, however no media of any such type herein includes transitory media. Combinations of the any of the above are also included within the scope of computer readable media.
In the present description, the terms component, module, device, etc. may refer to any type of logical or functional software elements, circuits, blocks and/or processes that may be implemented in a variety of ways. For example, the functions of various circuits and/or blocks can be combined with one another into any other number of modules. Each module may even be implemented as a software program stored on a tangible memory (e.g., random access memory, read only memory, CD-ROM memory, hard disk drive, etc.) to be read by a central processing unit to implement the functions of the innovations herein. Or, the modules can comprise programming instructions transmitted to a general-purpose computer or to processing/graphics hardware via a transmission carrier wave. Also, the modules can be implemented as hardware logic circuitry implementing the functions encompassed by the innovations herein. Finally, the modules can be implemented using special purpose instructions (SIMD instructions), field programmable logic arrays or any mix thereof which provides the desired level performance and cost.
As disclosed herein, features consistent with the disclosure may be implemented via computer-hardware, software and/or firmware. For example, the systems and methods disclosed herein may be embodied in various forms including, for example, a data processor, such as a computer that also includes a database, digital electronic circuitry, firmware, software, or in combinations of them. Further, while some of the disclosed implementations describe specific hardware components, systems and methods consistent with the innovations herein may be implemented with any combination of hardware, software and/or firmware. Moreover, the above-noted features and other aspects and principles of the innovations herein may be implemented in various environments. Such environments and related applications may be specially constructed for performing the various routines, processes and/or operations according to the invention or they may include a general-purpose computer or computing platform selectively activated or reconfigured by code to provide the necessary functionality. The processes disclosed herein are not inherently related to any particular computer, network, architecture, environment, or other apparatus, and may be implemented by a suitable combination of hardware, software, and/or firmware. For example, various general-purpose machines may be used with programs written in accordance with teachings of the invention, or it may be more convenient to construct a specialized apparatus or system to perform the required methods and techniques.
Aspects of the method and system described herein, such as the logic, may also be implemented as functionality programmed into any of a variety of circuitry, including programmable logic devices (“PLDs”), such as field programmable gate arrays (“FPGAs”), programmable array logic (“PAL”) devices, electrically programmable logic and memory devices and standard cell-based devices, as well as application specific integrated circuits. Some other possibilities for implementing aspects include memory devices, microcontrollers with memory (such as EEPROM), embedded microprocessors, firmware, software, etc. Furthermore, aspects may be embodied in microprocessors having software-based circuit emulation, discrete logic (sequential and combinatorial), custom devices, fuzzy (neural) logic, quantum devices, and hybrids of any of the above device types. The underlying device technologies may be provided in a variety of component types, e.g., metal-oxide semiconductor field-effect transistor (“MOSFET”) technologies like complementary metal-oxide semiconductor (“CMOS”), bipolar technologies like emitter-coupled logic (“ECL”), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, and so on.
It should also be noted that the various logic and/or functions disclosed herein may be enabled using any number of combinations of hardware, firmware, and/or as data and/or instructions embodied in various machine-readable or computer-readable media, in terms of their behavioral, register transfer, logic component, and/or other characteristics. Computer-readable media in which such formatted data and/or instructions may be embodied include, but are not limited to, non-volatile storage media in various forms (e.g., optical, magnetic or semiconductor storage media) though again does not include transitory media. Unless the context clearly requires otherwise, throughout the description, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in a sense of “including, but not limited to.” Words using the singular or plural number also include the plural or singular number respectively. Additionally, the words “herein,” “hereunder,” “above,” “below,” and words of similar import refer to this application as a whole and not to any particular portions of this application. When the word “or” is used in reference to a list of two or more items, that word covers all of the following interpretations of the word: any of the items in the list, all of the items in the list and any combination of the items in the list.
Although certain presently preferred implementations of the invention have been specifically described herein, it will be apparent to those skilled in the art to which the invention pertains that variations and modifications of the various implementations shown and described herein may be made without departing from the spirit and scope of the invention. Accordingly, it is intended that the invention be limited only to the extent required by the applicable rules of law.
While the foregoing has been with reference to a particular embodiment of the disclosure, it will be appreciated by those skilled in the art that changes in this embodiment may be made without departing from the principles and spirit of the disclosure, the scope of which is defined by the appended claims.
