Patent Application
20130054355
In a computing environment, a user may interact with an abundance of content while online (e.g., while connected to one or more networks, such as the Internet). A user may indicate an interest in an online topic in a variety of ways, such as by searching for the topic using a search website, navigating to an article about the topic, viewing a webpage comprising the topic, “liking” the topic on a social network site, blogging/micro-blogging about the topic, saving online content about the topic, and many more.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Currently, advertisements may be presented to a user when the user interacts with online content. However, such advertisements are not believed to be customized for a particular user based upon a level of interest of the user in a topic or sub-topic (e.g., aspect of a topic). That is, while some advertising services may provide advertisements targeted to a particular audience based on identified preferences and/or cookie information, these advertisements are not believed to be individually customized to a particular user based upon a level of interest of the user in an aspect of a topic.
Accordingly, one or more techniques and/or systems are disclosed for providing an online advertisement customized for a particular user. The advertisement may be customized for the user based upon one or more topics of interest to the user and/or aspects of such topics (e.g., as gleaned from user interaction with online content).
In one embodiment of providing a customized advertisement for a user, a request is received that comprises a user topic, such as a topic comprised in an advertisement intended to be shown to the user. Further, an impact factor can be determined for one or more user aspects that are identified in the user topic. Additionally, a ranking of at least one of the one or more user aspects can be returned to a sender of the request, in response to the request, where the ranking may be based at least upon the impact factor. At least some of the ranking can be used to customize the advertisement that may be shown to the user.
To the accomplishment of the foregoing and related ends, the following description and annexed drawings set forth certain illustrative aspects and implementations. These are indicative of but a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following detailed description when considered in conjunction with the annexed drawings.
The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are generally used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to facilitate describing the claimed subject matter.
As provided herein, an online advertisement may be customized for a particular user. As an example, interactions of a user with online content and/or entities may yield commonalities that may provide insight into how to customize an advertisement for a user. Weighting factors may be used to indicate a preference for some information over other information, which may allow aspects of topics to be ranked, which in turn may provide an indication of how to customize an advertisement.
At 106, respective impact factors are determined for one or more user aspects that are identified in a user topic received in the request (e.g., first impact factor for first user aspect, second impact factor for second user aspect, etc.). As an example, the advertisement associated with the request may be identified as comprising one or more user topics, where respective user topics comprise one or more aspects (e.g., sub-topics). By way of example and not limitation, a user topic may comprise (the categories of) movies, TV shows, theater, music, books, news, apps, places, travel, events, sports, lifestyle, celebrities, food, restaurants, consumer goods, shopping, social graph, and more. Aspects for the user topic “movies” may comprise, for example, genre, language, artists, director, studios, producer, writer, music, etc., where respective aspects can comprise a set of entities. For example, action, drama, comedy, animated, family, etc. may be entities for the “genre” aspect, whereas “Johnny Depp”, “Penelope Cruz”, “Geoffrey Rush”, and “Ian McShane” may be entities for the “actors” aspect.
In one embodiment, one or more user aspects of the user topic, comprised in the request, can be identified and respective impact factors for the one or more user aspects can be determined (e.g., based upon respective sets of entities for the one or more user aspects). An impact factor may, for example, comprise a type of “weighting” factor that indicates a user's level of interest in a particular aspect (e.g., and/or in one or more elements of the set of entities for the aspect). In one embodiment, a level of user interest in an aspect may be determined from previous user online interactions, such as from search query terms, indications of the user “liking” the aspect, navigating to websites related to the aspect, etc.
At 108 in the exemplary method 100, the one or more user aspects of the user topic are ranked, based at least in part upon the respective impact factors determined for the user aspects. Further, the ranking of the user aspects is returned in response to the request, where the ranking is used to customize an advertisement for the user, at 110.
As an illustrative example, a client of the online advertising service may have an advertisement for an upcoming release of the movie “Fantastic Mr. Fox” in retail stores. In this example, the service can send a request comprising the user topic “Fantastic Mr. Fox movie” in response to the user navigating to a website on which the online advertising service expects to display the advertisement for the movie. The user topic may comprise the identified aspects of: actors (George Clooney, Meryl Streep, Bill Murray, etc.), genre (animation, adventure, comedy, family), director (Wes Anderson), writers (Roald Dahl, Wes Anderson), etc. In this example, the user may have previously indicated an interest in Roald Dahl (e.g., by looking for his books online, previewing movie trailers for other Roald Dahl movies, etc.), and animated movies (e.g., by purchasing other animated movies online); and these interests can be used to determine respective impact factors for “writer: Roald Dahl”, “genre: Animation”, and the other aspects of the user topic “Fantastic Mr. Fox movie”. In this example, the respective user aspects of the user topic can be ranked according to their respective impact factors, and the rankings returned to the online advertising service that sent the request.
The online advertising service may utilize the ranking of the user aspects, for example, to customize the advertisement on the website for display to the user. As an illustrative example,
In the example embodiment 400, a first customized advertisement 404 may highlight (e.g., bold, change font, color, move to a beginning of the advertisement, etc.) “Roald Dahl” as a writer of the movie (e.g., given that the user previously indicated an interest in Roald Dahl). A second customized advertisement 406 may highlight the fact that the movie is animated (e.g., given that the user previously indicated an interest in animated movies). As another example, a third customized advertisement may highlight a combination of ranked aspects (e.g., highly ranked aspects), such as “The animated “Fantastic Mr. Fox”, based on a book by Roald Dahl, is coming to stores next month.”
As an example, the user may navigate to an online retailer or shopping service that sells shoes, where the navigation to the site can comprise an online content interaction. As an illustrative example, the user may directly enter a URL for the shoe retailer, select the retailer from search results, select a link on another page, select a link in an email, or some other way to reach the shoe retailer/shopping service site. Information about the navigation and/or resulting landing page may be extracted, for example, to indicate a base topic.
As another example, query terms entered into a search site may comprise an online user interaction from which a base topic can be identified (e.g., the query term(s) can comprise the base topic). Once identified, the base topic may be categorized into one or more topic categories, such as movies, TV shows, theater, music, books, news, apps, places, travel, events, sports, lifestyle, celebrities, food, restaurants, consumer goods, shopping, social graph, and more. In this example, the base topic “shoes”, for example, may be categorized into topic categories “consumer goods” and “shopping”; and/or if the user entered a query term for “Nike shoes” the base topic “Nike shoes”, for example, may be categorized into the topic category of “sports”, as Nike typically sells athletic shoes.
At 210 in the example embodiment 200, one or more base aspects can be identified for the base topic. In one embodiment, identifying the one or more base aspects can based upon metadata associated with the base topic where at least some of this metadata may be regarded as base aspect metadata when used to identify a particular base aspect for the base topic.
As an illustrative example, the user may watch trailers for the animated movies “Toy Story” and “Up” using an online video hosting service. A base topic “movies” can be identified and categorized for the respective online user interactions with the movie trailers. Further, in this example, the respective interactions can be examined to identify metadata, such as base aspect metadata for an “animated - genre” aspect, base aspect metadata for a “Disney - Producers” aspect, base aspect metadata for a “Pixar—Producers” aspect, base aspect metadata for a “Pete Docter - writer” aspect, base aspect metadata for a “winner Academy Award—awards” aspect, and others.
It will be appreciated that an “aspect” of a “topic” is not limited to any particular embodiment, described herein. The aspects of the topic, for example, can comprise and/or be based on any metadata that may be identified for the topic, based on the online user interaction. In one embodiment, base aspect metadata can be identified by crawling one or more online networks (e.g., the Internet) for information about the base topic. For example, for a base topic “Adam Sandler” (e.g., categorized into a “celebrity” topic category), crawling the Internet may identify metadata for base aspects such as “actor,” “comedian,” all the various movies and TV shows he has appeared in, “the Hanukah Song”, his date of birth, place of residence, and more.
At 212, the base aspect metadata for the one or more base aspects 250 can be selectively stored in a corresponding aspect data store. For example, a database may comprise the base topic “Adam Sandler” that is linked to the various base aspect metadata identified by crawling the Internet. In one embodiment, the aspect data store may comprise remote (e.g., cloud-based) storage, for example, connected to a service that may provide advertisement customization services, for example.
At 214, a common aspect can be identified, from the one or more base aspects, from one or more base topics. In one embodiment, a common aspect can comprise a first base aspect from a first base topic that matches a second base aspect from a second base topic. As an example, a first base topic comprising “Toy Story” (e.g., categorized into a “movie” topic category) and a second base topic “Up” (e.g., also categorized into a “movie” topic category) can respectively comprise the common base aspects: genre—animated, producers—Disney, and writer—Pete Docter. That is, in this example, both movies have the genre, producers, and writer in common. As another example, a user's search queries may comprise “Manchester United” (a soccer team), “Lionel Messi” (a soccer player), and “World Cup 2010” (a soccer tournament). In this example, respective base topics: Manchester United (e.g., categorized into a “sports” topic category), Lionel Messi (e.g., categorized into a “sports” topic category), and World Cup 2010 (e.g., categorized into an “events” topic category) may comprise a common base aspect “soccer”.
At 216 in the example embodiment 200, respective impact factors are determined for one or more base aspects, based at least upon the identified common aspect. The impact factors 252 for the one or more base aspects can be stored in an impact factor data store, at 218. As an example, the impact factor can comprise a type of weighting for a base aspect that indicates a level of interest the user may have in the base aspect.
As an illustrative example, a review of base topics for the user (e.g., identified from search queries, articles saved, social network “likes”, etc.) may indicate that the base topics comprise a first common aspect “Nike” for shoe-related topics, athletic-related topics, clothing-related topics. In this example, the first common aspect “Nike” may appear a greater number times in the base topics than a second common aspect “Adidas.” In one embodiment, the common aspect comprising a higher number of appearances (e.g., the first common aspect) may have a higher impact factor that the common aspect comprising a lower number of appearances (e.g., the second common aspect), and such impact factors can be stored in an impact factor data store.
It will be appreciated that determining the impact factor for a base aspect is not limited to the embodiments described herein. The impact factor, for example, can comprise a representation of the users level of interest in a particular aspect of a topic, as determined by the user's online interaction with content that may comprise the topic, and/or the aspect. As an example, those skilled in the art may devise a formula for determining the impact factor that comprises variables that account for the user's level of interest in the aspect.
At 308, the user topic can be matched to a base topic stored in a topic data store (e.g., 612 of
At 310, one or more user aspects can be identified for the user topic. For example, metadata associated with the user topic may be identified, such as from a topic aspect data base, and/or from information obtained by crawling online networks for the metadata associated with the user topic, and this metadata or portions thereof (e.g., base aspect metadata) may be examined to identify one or more user aspects for the user topic. At 312, the one or more user aspects can be matched to one or more corresponding base aspects 350 associated with the matched base topic, stored in an aspect data store, for example. Further, at 314, an impact factor 352 corresponding to a matched (e.g., first) base aspect can be retrieved from an impact factor data store, for example, for the corresponding (e.g., first) user aspect (e.g., the user aspect that matched the (e.g., first) base aspect).
In one embodiment, instead of identifying the user aspects and matching them to the base aspects, merely the base aspects of the matched base topic may be identified for the user topic. For example, for the user topic “shoes” that is matched to the base topic “shoes”, the base aspects: shoe brands (e.g., Nike, Adidas, Reebok, etc.), shoe types (e.g., running shoes, dress shoes, casual shoes, etc.), which were identified for the base topic “shoes” may be used to retrieve corresponding impact factors for user aspects of the user topic.
As an example, impact factors may have been determined for respective base aspects, from one or more base topics identified from the user's online content interactions. In this example, a stored impact factor may be linked to a corresponding base factor, such as in a database. Further, the base aspect matching the user aspect (e.g., or from the base topic matching the user topic) may be identified in the database, and the linked impact factor can be retrieved and used for the user aspect.
At 316 in the example embodiment 300, the one or more user aspects can be ranked based at least upon the corresponding impact factors. As an illustrative example, the user may enter a query for “sporting goods” on a search website. In this example, a search result (e.g., a sponsored search result) may comprise a website for a national sporting goods provider, for example. Typically, below a search result title, the search website may place some text from a snippet of the associated webpage. In this example, the website may comprise pages for a variety of sports, such as basketball, football, and also soccer. In this example, “soccer” may comprise a higher impact factor than basketball and football, due to the users previous online interactions with soccer related content. Therefore, the “soccer” user aspect may comprise a higher ranking than the other user aspects of the user topic “sporting good,” and may be included in the snippet below the website title in the search results (e.g., sponsored search results).
At 318, the ranking of the one or more user aspects for the user topic can be returned to the online site (e.g., website or online service), and the advertisement can be customized using the ranking. As an illustrative example,
In this example embodiment 450, a first customized advertisement 454 may highlight (e.g., bold, include text, move text, change text color, etc.) the “Nike shoes” angle for the user, which may entice the user to click on the advertisement or interact with the online retailer. Further, a second customized advertisement 546 may highlight the “discount” angle of the advertisement by putting the “20% off”, bolded at the beginning of the advertisement. Additionally, in one embodiment, more than one user aspect may be utilized in the customization of the advertisement. For example, a customization of the base advertisement 452 may comprise “Get 20% of Nike shoes on your next shoe purchase.”
Returning to
As an illustrative example, the user may navigate to a website that provides reviews of software, hardware, and other electronic devices. In this example, based on the users previous interactions with the website, the advertiser may know that the user may be interested in buying a new camera (e.g., based on site based searches and navigation). The advertiser can request a ranking for user aspects of the user topic “camera” (e.g., brands, types, costs, preferred retailers, specs, etc.), and based on the user aspects matched to the users base aspects, the impact factors may be updated for the base topic camera (e.g., increased due to additional interest indicated for cameras by the user).
A system may be devised that can examine metadata associated with a user's online interactions with content, identify commonalities, and provide a customization scheme to an advertiser to customize advertisements shown to the user based on the commonalities. For example, by identifying the common aspects of the user's online interactions, subsequent user interactions with online content, such as for advertisements, may be customized to cater more to the user. By comparing aspects of user topics from advertisements, for example, with aspects of base topics identified from previous interaction, the advertiser may be able to better “show the user what they want to see”.
Further, in the exemplary system 500, an aspect ranking component 506 is operably coupled with the impact factor determination component 504. The aspect ranking component 506 is configured to return a ranking of at least one of the one or more user aspects in response 552 to the request 550. The ranking is based at least upon the impact factors, and at least some of the ranking is used to customize an advertisement for the user.
In one embodiment, the user engagement component 602 can be configured to forward the base topic to a categorization component 614. The categorization component may be configured to determine a base topic category for a base topic, and/or store the base topic in a corresponding base topic category data store (e.g., movies, people, products, news). For example, a base topic identified by the user engagement component 602 may be categorized into one or more categories associated with the topic, such as movies, people, products, news, and many more. The base topic can then be stored in a corresponding portion of the topic data store 612, for example, thereby storing one or more user interests based on the user's online content interactions.
In one embodiment, the categorization component 614 can be configured to determine a user topic category for the user topic, such as received in a request 650 for an advertising service (e.g., or advertiser). Further, in one embodiment, the categorization component 614 can be configured to match the user topic category to a base topic stored in the topic data store. As an example, when the request 650, comprising the user topic, is received, the user topic can be categorized, and compared to one or more stored base topics, to identify a match, if present.
In the example embodiment 600, an aspect determination component 618 can be configured to identify one or more base aspects for the base topic, and/or store the identified one or more base aspects in an aspect data store 660. Further, the aspect determination component 618 can be configured to identify one or more user aspects for the user topic. For example, the aspect determination component 618 may connect to one or more online networks (e.g., the Internet 658) to identify metadata associated with a base topic, and store the metadata indicative of a base aspect in a corresponding portion of the aspect data store 650.
For example, a topic comprising a city name may comprise metadata such as location, weather, population, language, government type, attractions, cost-of-living, demographics, entertainment, dining, sports, etc. In this example, the base aspects, and/or the user aspects may comprise the respective metadata associated with the corresponding base topic, and/or user topic. The base aspects of the city base topic can be stored in the aspect data store 660, for example, while the user aspects may be compared to the stored base aspects in order to identify a corresponding impact factor (e.g., stored in relation to a matched base aspect).
A commonality component 620 can be configured to identify a common aspect, from the one or more base aspects, from one or more base topics. For example, the book “Charlie and the Chocolate Factory” as a base topic, comprise a same writer base aspect “Roald Dahl” as the movie “Fantastic Mr. Fox”. Therefore, in this example, the commonality component 620 may identify that the writer “Roald Dahl” comprises a common base aspect between the book base topic “Charlie and the Chocolate Factory” and the movie base topic “Fantastic Mr. Fox”.
Further, the commonality component 620 can also be configured to forward a base aspect corresponding to a common aspect to an impact factor data store 622, and/or match the respective one or more user aspects to a corresponding base aspect, in the aspect data store 660. The impact factor data store 622 can be configured to identify an impact factor for a base aspect corresponding to a common aspect, store the impact factors for the base aspects, and/or provide the impact factor corresponding to a particular (e.g., first) base aspect to the impact factor determination component 504 when one or more user aspects (e.g., a first user aspect) match the particular base aspect.
For example, when the request 650 is received from a sender 654, the respective impact factors for the one or more user aspects obtained from base aspects to which the user aspects matched can be forwarded to the impact factor determination component 504. In this example, the aspect ranking component 506 can rank the one or more user aspects using the corresponding impact factors, and the ranking can be returned in a response 652 to the sender 654, which may use the ranking to customize the advertisement to the user 656.
In the example embodiment 600, an impact factor updating component 616 configured to update the impact factor of a base aspect based at least upon the corresponding user aspect, and or additional instances of the base aspect being identified in a new online user interaction. In this example, the user engagement component 602 can be configured to forward the base topic to the impact factor updating component 616. For example, when the user 656 infracts with online content on the Internet, the one or more new base aspects identified in a new base topic (e.g., comprised in the interaction) can be forwarded to the impact factor updating component 616, which can update the one or more impact factors corresponding to a base aspect stored in the aspect data store 660. Further, the updated impact factors may be provided to the impact factor data store 622 and linked to the corresponding base aspect. In this way, for example, the impact factors may be continually updated based on new and/or ongoing user interactions with online content.
Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to implement one or more of the techniques presented herein. An exemplary computer-readable medium that may be devised in these ways is illustrated in
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
As used in this application, the terms “component,” “module,” “system”, “interface”, and the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.
Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.
Although not required, embodiments are described in the general context of “computer readable instructions” being executed by one or more computing devices. Computer readable instructions may be distributed via computer readable media (discussed below). Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. Typically, the functionality of the computer readable instructions may be combined or distributed as desired in various environments.
In other embodiments, device 812 may include additional features and/or functionality. For example, device 812 may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in
The term “computer readable media” as used herein includes computer storage 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 or other data. Memory 818 and storage 820 are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by device 812. Any such computer storage media may be part of device 812.
Device 812 may also include communication connection(s) 826 that allows device 812 to communicate with other devices. Communication connection(s) 826 may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting computing device 812 to other computing devices. Communication connection(s) 826 may include a wired connection or a wireless connection. Communication connection(s) 826 may transmit and/or receive communication media.
The term “computer readable media” may include communication media. Communication media typically embodies computer readable instructions or other data in a “modulated data signal” such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may include a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
Device 812 may include input device(s) 824 such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output device(s) 822 such as one or more displays, speakers, printers, and/or any other output device may also be included in device 812. Input device(s) 824 and output device(s) 822 may be connected to device 812 via a wired connection, wireless connection, or any combination thereof. In one embodiment, an input device or an output device from another computing device may be used as input device(s) 824 or output device(s) 822 for computing device 812.
Components of computing device 812 may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), firewire (IEEE 1394), an optical bus structure, and the like. In another embodiment, components of computing device 812 may be interconnected by a network. For example, memory 818 may be comprised of multiple physical memory units located in different physical locations interconnected by a network.
Those skilled in the art will realize that storage devices utilized to store computer readable instructions may be distributed across a network. For example, a computing device 830 accessible via network 828 may store computer readable instructions to implement one or more embodiments provided herein. Computing device 812 may access computing device 830 and download a part or all of the computer readable instructions for execution. Alternatively, computing device 812 may download pieces of the computer readable instructions, as needed, or some instructions may be executed at computing device 812 and some at computing device 830.
Various operations of embodiments are provided herein. In one embodiment, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein.
Moreover, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Further, At least one of A and B and/or the like generally means A or B or both A and B. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.
Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”
