Abstract:
A cognitive proximate recommendation method, system, and non-transitory computer readable medium, include identifying a requested item based on a user request, first extracting a requested feature and a requested value of the requested feature for the requested item, and returning a return item from a plurality of return items stored in the database by: second extracting a return feature corresponding to the requested feature for each of the plurality of return items, third extracting a return value of the return feature, and calculating a proximal distance between the return value for each of the plurality of return items and the requested value of the requested item.

Description:
BACKGROUND 
       [0001]    The present invention relates generally to a cognitive proximate recommendation method, and more particularly, but not by way of limitation, to a system, method, and recording medium for recommending an item in response to a user query having a lowest proximal distance between values of extracted features of the items and the requested item by the user. 
         [0002]    Industry is trending towards so called “cognitive models” enabled via “Big Data” platforms. Such cognitive models are aimed to remember prior interactions with users and continuously learn and refine the responses for future interactions. For example, cognitive agents are being used for welcoming customers at business door steps and are expected to evolve intelligent with generations. Such agents could be enriched for better customer handling by building the intelligence of the agents. 
         [0003]    Conventional cognitive models for searching and returning answers have proposed searching for information within social networks. The conventional search assist techniques receive a query, such as a partial query, identifies two or more categories of data that include information satisfying the query, ranks the identified categories of data based on various selection criteria, and presents suggested search terms based on the rankings. However, the conventional techniques relate to a display of the results, not the selection in that the conventional techniques rank the results of the query on two or more identified categories and calculate a quality matrix that is used to display results. The conventional techniques do not intelligently learn to provide best alternatives when a null response may occur. 
         [0004]    That is, there is a technical problem in that the conventional techniques do not consider a cognitive way of determining a best alternative when a match does not exist and do not consider using user preferences to weigh values of features of potential results to intelligently provide a better alternative. 
       SUMMARY 
       [0005]    Thus, the inventors have realized a technical solution to the technical problem to provide significantly more than the conventional technique of question/answer interaction by configuring a cognitive analysis of requested items by extracting the requested features and values of the features by the user and intelligently providing a closest alternative based on extracting the same features of alternative items and comparing the values of the alternatives with user preferences to return the closest alternative. Thus, the technical solution improves upon the computer functionality itself by providing better results more efficiently. 
         [0006]    In an exemplary embodiment, the present invention can provide a cognitive proximate recommendation method including a database, the method including identifying a requested item based on a user request, first extracting a requested feature and a requested value of the requested feature for the requested item, and returning a return item from a plurality of return items stored in the database by: second extracting a return feature corresponding to the requested feature for each of the plurality of return items, third extracting a return value of the return feature, and calculating a proximal distance between the return value for each of the plurality of return items and the requested value of the requested item. 
         [0007]    Further, in another exemplary embodiment, the present invention can provide a non-transitory computer-readable recording medium recording a cognitive proximate recommendation program including a database, the program causing a computer to perform: identifying a requested item based on a user request, first extracting a requested feature and a requested value of the requested feature for the requested item, and returning a return item from a plurality of return items stored in the database by: second extracting a return feature corresponding to the requested feature for each of the plurality of return items, third extracting a return value of the return feature, and calculating a proximal distance between the return value for each of the plurality of return items and the requested value of the requested item. 
         [0008]    Even further, in another exemplary embodiment, the present invention can provide a cognitive proximate recommendation system, said system including a database, a processor, and a memory, the memory storing instructions to cause the processor to: identifying a requested item based on a user request, first extracting a requested feature and a requested value of the requested feature for the requested item, and returning a return item from a plurality of return items stored in the database by: second extracting a return feature corresponding to the requested feature for each of the plurality of return items, third extracting a return value of the return feature, and calculating a proximal distance between the return value for each of the plurality of return items and the requested value of the requested item. 
         [0009]    There has thus been outlined, rather broadly, an embodiment of the invention in order that the detailed description thereof herein may be better understood, and in order that the present contribution to the art may be better appreciated. There are, of course, additional exemplary embodiments of the invention that will be described below and which will form the subject matter of the claims appended hereto. 
         [0010]    It is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The invention is capable of embodiments in addition to those described and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein, as well as the abstract, are for the purpose of description and should not be regarded as limiting. 
         [0011]    As such, those skilled in the art will appreciate that the conception upon which this disclosure is based may readily be utilized as a basis for the designing of other structures, methods and systems for carrying out the several purposes of the present invention. It is important, therefore, that the claims be regarded as including such equivalent constructions insofar as they do not depart from the spirit and scope of the present invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]    The exemplary aspects of the invention will be better understood from the following detailed description of the exemplary embodiments of the invention with reference to the drawings. 
           [0013]      FIG. 1  exemplarily shows a high level flow chart for a cognitive proximate recommendation method  100 . 
           [0014]      FIG. 2  exemplarily shows a high level flow chart for at least Step  105  of the cognitive proximate recommendation method  100 . 
           [0015]      FIG. 3  exemplarily shows one embodiment of method  100 . 
           [0016]      FIG. 4  depicts a cloud computing node according to an embodiment of the present invention. 
           [0017]      FIG. 5  depicts a cloud computing environment according to another embodiment of the present invention. 
           [0018]      FIG. 6  depicts abstraction model layers according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]    The invention will now be described with reference to  FIGS. 1-6 , in which like reference numerals refer to like parts throughout. It is emphasized that, according to common practice, the various features of the drawing are not necessarily to scale. On the contrary, the dimensions of the various features can be arbitrarily expanded or reduced for clarity. Exemplary embodiments are provided below for illustration purposes and do not limit the claims. 
         [0020]    With reference now to  FIG. 1 , the cognitive proximate recommendation method  100  includes various steps to provide a user with a closest item having a lowest (smallest) proximal distance from the user request. Moreover, the method (system) can benefit from “learning” from past preferences of the user. As shown in at least  FIG. 4 , one or more computers of a computer system  12  can include a memory  28  having instructions stored in a storage system to perform the steps of  FIG. 1 . 
         [0021]    With the use of these various steps and instructions, the cognitive proximate recommendation method  100  may act in a more sophisticated and useful fashion, and in a cognitive manner while giving the impression of mental abilities and processes related to knowledge, attention, memory, judgment and evaluation, reasoning, and advanced computation. That is, a system is said to be “cognitive” if it possesses macro-scale properties—perception, goal-oriented behavior, learning/memory and action—that characterize systems (i.e., humans) that all agree are cognitive. 
         [0022]    Although as shown in  FIGS. 4-6  and as described later, the computer system/server  12  is exemplarily shown in cloud computing node  10  as a general-purpose computing circuit which may execute in a layer the cognitive proximate recommendation system method ( FIG. 5 ), it is noted that the present invention can be implemented outside of the cloud environment. 
         [0023]    Step  101  receives a user request of a question, query, input, search or the like that the user would like a return. Step  101  can receive the user input by, for example, a Graphical User Interface (GUI)-based interface enabling the CRUD (Create, Read, Update, Delete) processes and extension of an industry specific proximate framework. The user request  101  includes a description of the item (e.g., item description  140 ). 
         [0024]    Based on the user request of Step  101 , Step  102  identifies the requested item based on the domain-specific taxonomy database  130  and item description  140 . 
         [0025]    The domain specific taxonomy  130  includes information on lexical relations between words or ontological relations between concepts such that the item description  140  can be used to identify the item requested in Step  102  and identify features and values of the requested item and potential return items in Steps  103  and  104  as described later. The domain-specific taxonomy  130  can be cognitive in that the domain-specific taxonomy  130  can learn new lexical relations between words based on the user choosing a response not ranked the highest. 
         [0026]    For example, if the user request of Step  101  is “I want a red car with a 2.0-cylinder engine under 20,000 dollars” (e.g., the item description), Step  102  identifies that the “item” is “a car” that the user is requesting. 
         [0027]    After the item is identified by Step  102 , Step  103  extracts the features (attributes) of the requested item and the values of the features input by the user. For example, Step  103  can extract from the user input of “I want a red car with a 2.0-cylinder engine under 20,000 dollars” the feature of “color” having a value of “red”, the feature of “cost” having a value of “under 20,000 dollars”, and the feature of “engine size” having a value of “2.0-cylinder”. 
         [0028]    That is, each feature comprises one or more values. Moreover, each feature (or value of a feature) can be dependent or independent of other features (or values of the same feature, respectively). For example, a feature of “taste” in food would be dependent on the feature of “ingredients” and the values thereof. Alternatively, the engine size (“feature”) of a car and the color (“feature”) of the car can be independent from each other but the price (“feature”) of the car can be dependent on both the engine size and the color. Thus, Step  103  can define inter-relationships of the feature values or define a primary or anchor value for the linked features (e.g., for “taste” having a value of “sour”, a primary anchor value can be “Tamarind”). 
         [0029]    Accordingly, Step  103  has the ability to define a proximal model of items depending upon the features and cognitive entity phrasing maps for each attribute. 
         [0030]    Step  104  determines if there is a direct (e.g., exact) match of an item to the user based on the values of the features extracted by Step  103 . That is, Step  104  would attempt to find a car (“item”) having a color, engine size and cost (“features”) of red, 2.0-cylinder, under 20,000 dollars (“values”), respectively. 
         [0031]    If “YES”, Step  106  returns the direct matched item to the user. 
         [0032]    If “NO”, Step  105  calculates a closest item to the requested item by the user having a lowest (e.g., smallest) proximal distance from the requested item (the details by Step  105  are shown in  FIG. 2 ). That is, Step  105   a  extracts the features of interest identified in Step  103  from each potential return item from a database  160  and Step  105   b  extracts the values of interest corresponding to the extracted features of interest. In other words, Step  105   a  extracts color, engine size, and cost of all potential cars that can be a return item and Step  105   b  extracts the values of each of the extracted features. The database  160  includes potential return items. 
         [0033]    It is noted that Step  105   a  does not extract features not extracted by Step  103 . That is, a car, for example, can be described by a plethora of features but Step  105   a  extracts the features of interest (i.e., in this present example, features of interest would be color, engine size, and cost) corresponding to the user request to provide the closest item. Thus, features not of interest (e.g., type of seats, type of transmission, type of tires, etc.) and values thereof are not extracted by Step  105   a / 105   b.    
         [0034]    Step  105   c  optionally sorts the features based on a distinct user preference or input weights to the features  150 . That is, the user ranks (preferences) the features according to importance of the returned item matching the value of that feature. The distinct user preference or input weights to the features  150  can include a pre-configured preference of the user for the features, an additional query to the user from the GUI for the user to weight each identified feature by Step  103 , a learned preference based on past user selections of the returned item (e.g., user always picks a car returned that matches cost instead of color), etc. For example, the user can assign that the feature of color is three times as important as cost and twice as important as engine size. It is noted that Step  105   c  is optional and absent an affirmative weight, by default, Step  105  ranks each feature equally. 
         [0035]    Step  105   d  queries for items fulfilling the value condition of the features and ranks the returned items based on the lowest proximal distance between the potential return item and the requested item. 
         [0036]    Step  105   e  continuously causes Step  105   d  to loop to return potential items to the user as the user response  106 . Also, if no potential return items are found within a threshold proximal distance, Step  105   e  causes Step  105   d  to find potential return items based on a similarity of values of the potential return items to the request item. For example, if the extracted feature is color with a value of “red” for a car (item) and no red cars are in the database  160 , Step  105   d  can return items to the user having a somewhat similar color such as “metallic red” (e.g., Step  105   d  can use a color scale to calculate similarity between red and a value of a potential return result). Thus, if the metallic red car is under 20,000 dollars and has a 2.0-cylinder engine, Step  105   d  can return the metallic red car to the user over a car that is, for example, green based on the preferences of the user. That is, Step  105   e  dynamically varies (overrides) the proximal distance driven by priority factors such that, for example, if a preferred flavor was “sour” and no primary ingredient to give the taste of sour was in stock (e.g., available), then Step  105   e  can traverse to the next closest ingredient and anchor it (e.g., from tamarind to lemon). Also, since  105   e  is a feedback loop, the user preferences (e.g., weights) are re-examined by Step  105   d  such that if the closest alternative was unavailable as in the above example and “lemon” was suggested, if the user was allergic to lemon, Step  105   e  would again cause the next closest alternative to be suggested such as “lime juice”. 
         [0037]    Thereby, Step  106  gives a ranked list of return items to the user ranked according to the proximal distance from the requested item. 
         [0038]    In an exemplary use case of the method  100  as shown in  FIG. 3 , the method  100  can recommend alternative meals for a user based on user preference at a restaurant that does not have the requested meal. 
         [0039]    For example, if a user normally orders “Sambar” (e.g., identified by Step  102 ), the method  100  via Step  103  breaks down the ingredients as features and determines the values of the ingredients for “Sambar”. As shown in  FIG. 3 , Sambar is broken to its atomic and composite ingredients and identifies its Anchor entity as Dhal. It is noted that some items, e.g. Hing Powder and Water are atomic and could not be broken down further. 
         [0040]    When Step  104  determines that no match exists, Step  105  searches for other available dishes and Step  105   a / 105   b  breaks down their ingredients and performs mapping of values to ingredients. In this example, Step  105   d  picks up Dhal Tadkha and Dhal Tomato curry, as closest dishes based on their Dhal content and the similarity of other ingredients. As shown in  FIG. 3 , proximates are flagged as H (high), M (medium) or L (low) based on their ability to map, e.g. H when the exact ingredient was found in another dish, L when it was not found, and M when a similar ingredient (e.g., from the same family) was found. However, a finer measure of similarity could be calculated, e.g. as percentage based on ontology tree. 
         [0041]    Based on user preferences factored in Step  105   c , Step  105   d  eliminates Dhal Tadkha because the user (i.e., Jack) is allergic to Citric juice, and therefore suggests Dhal Tomato curry as the best proximate to replace the initial Sambar order. The allergy to citric juice could be know either directly from the user (e.g. as a note/input at the time of order), or from the history of the user&#39;s orders, if he was a regular customer, the method  100  could have learned that some ingredients are systematically avoided by the user and therefore would exclude them from the suggestions. 
         [0042]    Exemplary Hardware Aspects, Using a Cloud Computing Environment 
         [0043]    It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
         [0044]    Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
         [0045]    Characteristics are as follows: 
         [0046]    On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
         [0047]    Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
         [0048]    Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
         [0049]    Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
         [0050]    Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service. 
         [0051]    Service Models are as follows: 
         [0052]    Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client circuits through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
         [0053]    Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
         [0054]    Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
         [0055]    Deployment Models are as follows: 
         [0056]    Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
         [0057]    Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
         [0058]    Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
         [0059]    Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
         [0060]    A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes. 
         [0061]    Referring now to  FIG. 4 , a schematic of an example of a cloud computing node is shown. Cloud computing node  10  is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node  10  is capable of being implemented and/or performing any of the functionality set forth hereinabove. 
         [0062]    In cloud computing node  10  there is a computer system/server  12 , which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server  12  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop circuits, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or circuits, and the like. 
         [0063]    Computer system/server  12  may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server  12  may be practiced in distributed cloud computing environments where tasks are performed by remote processing circuits that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage circuits. 
         [0064]    As shown in  FIG. 4 , computer system/server  12  in cloud computing node  10  is shown in the form of a general-purpose computing circuit. The components of computer system/server  12  may include, but are not limited to, one or more processors or processing units  16 , a system memory  28 , and a bus  18  that couples various system components including system memory  28  to processor  16 . 
         [0065]    Bus  18  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
         [0066]    Computer system/server  12  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server  12 , and it includes both volatile and non-volatile media, removable and non-removable media. 
         [0067]    System memory  28  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  30  and/or cache memory  32 . Computer system/server  12  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  34  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  18  by one or more data media interfaces. As will be further depicted and described below, memory  28  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention. 
         [0068]    Program/utility  40 , having a set (at least one) of program modules  42 , may be stored in memory  28  by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules  42  generally carry out the functions and/or methodologies of embodiments of the invention as described herein. 
         [0069]    Computer system/server  12  may also communicate with one or more external circuits  14  such as a keyboard, a pointing circuit, a display  24 , etc.; one or more circuits that enable a user to interact with computer system/server  12 ; and/or any circuits (e.g., network card, modem, etc.) that enable computer system/server  12  to communicate with one or more other computing circuits. Such communication can occur via Input/Output (I/O) interfaces  22 . Still yet, computer system/server  12  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter  20 . As depicted, network adapter  20  communicates with the other components of computer system/server  12  via bus  18 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server  12 . Examples, include, but are not limited to: microcode, circuit drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
         [0070]    Referring now to  FIG. 5 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  comprises one or more cloud computing nodes  10  with which local computing circuits used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing circuit. It is understood that the types of computing circuits  54 A-N shown in  FIG. 5  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized circuit over any type of network and/or network addressable connection (e.g., using a web browser). 
         [0071]    Referring now to  FIG. 6 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 5 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 6  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
         [0072]    Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage circuits  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
         [0073]    Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
         [0074]    In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
         [0075]    Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and, more particularly relative to the present invention, the anti-counterfeiting system  100  and the anti-counterfeiting system  600  described herein. 
         [0076]    The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
         [0077]    Further, Applicant&#39;s intent is to encompass the equivalents of all claim elements, and no amendment to any claim of the present application should be construed as a disclaimer of any interest in or right to an equivalent of any element or feature of the amended claim.