Patent Application
20140164064
This invention relates to computer systems and data processing. In particular, a system and methods are provided for serving electronic content items.
Many types of electronic content are served and presented to users of communication and computing devices such as smart phones, laptop computers, desktop computers, etc. The content may include images, video, audio, text, graphics and so on.
Depending on the application that is operated by the user and to/for which the content is served, such as a web browser, the content may include an entire page or screen of content (e.g., a web page) or just one or more components of a page or screen. Such components may include discrete content items such as advertisements, news articles, status updates of friends or associates of the user, announcements issued by a provider of the application or of a web site visited by the user, etc.
Unfortunately, some existing systems for serving content to users may present a single content item to one user multiple times, to the point that it becomes annoying and the user ignores it and any message it conveys. These types of systems may make no attempt to avoid over-exposure of a given content item.
On the other hand, however, a user may be less likely to act on a particular content item (e.g., by requesting more information, by viewing a larger or better version of the item) until he or she has seen or experienced it multiple times. For this reason, some other systems adhere to an “all-or-none” principle. These systems may stop serving a particular item to a given user after it is served to that user some threshold number of times, but then resume serving the item after some period of time. Thus, for some period of time, the item is completely barred from being served to the user, even if it is the most relevant item for the user and/or would generate the most revenue for the system.
In some embodiments of the invention, a system and methods are provided for controlling the serving of electronic content items for presentation to a particular user, such that a single content item is preferably served a sufficient number of times to elicit some user action, but not so often or frequently that the user becomes annoyed with it. The system may comprise a social networking service, a web server, a portal and/or some other type of service that serves content, and the content served from the system may be of one or more types (e.g., advertisements, résumés, status updates, job listings).
In these embodiments, the serving of content may be considered probabilistic in nature, in that content items considered for serving for presentation to a user are ranked based in part on probabilities that the user (or a generic user) will act on the content items. More specifically, probabilities that the items would be acted on (e.g., clicked on, selected, investigated), if served, are used in the calculation of estimated revenues to be earned by serving the items, and the items are ranked based on those estimated revenues. The highest ranked item(s) may then be served.
In some implementations, a user data store is maintained to record serving activity of the system. For example, the system may record, for each user and for each serving of content to the user (or for presentation to the user), which content item was served and when it was served. Thus, the system is able to track how many times any content item was served or presented to any user, when it was most recently served, etc. Each serving or presentation of a content item for a user may be considered an “impression.”
Base probabilities that a given user may act on particular content items may be calculated from stored data with varying degrees of granularity. For example, by considering all tracking data, encompassing all action on all content items by all users, a generic probability may be calculated that can be applied to any user and any content item.
As one alternative, users may be categorized into various segments or groups based on one or more attributes they have in common. Then, a probability that a generic user will act on a certain content item can be determined, based on actions by other users that are in the same group(s) as the generic user. As another alternative, instead of users, content items may be characterized based on one or more attributes they have in common (or based on common attributes of their target audiences), and data relating to each group of items can be analyzed to determine probabilities that a generic user will act on a content item belonging to a group, based on actions of that user on other items in the group (and/or actions of other users belonging to the same user group(s)). As yet another alternative, a base probability may be calculated by a third party and used by the system.
In some embodiments of the invention, a base probability of a given user acting on a particular content item is modified to reflect the given user's context or history with that item (e.g., how often it has been served or presented to him, how recently it has been served or presented to him). In particular, a base probability may be modified a little or a lot, depending on how many times the item was previously presented to him (and, therefore, which ordinal impression the next presentation would be).
Values for modifying base probabilities are derived through additional analysis of data recorded during system operation. More specifically, when a user acts on a content item the system records which ordinal impression of the content item to the user elicited the action (e.g., 3rd, 6th). Over time, it may be observed that users that act on a content item usually do so within some range of impressions, such as between the 4th and the 7th impression, for example.
Data regarding users' reactions to different impressions of content items are analyzed to determine corresponding weights or modifiers for each of multiple ordinal impressions. An ordinal impression's weight or modifier proportionally reflects users' actions in response to that impression as opposed to other impressions. Thus, in the example above, modifiers for impressions 4 through 7 would be higher than modifiers for impressions before number 4 and after number 7.
As with base probabilities, these modifiers may be different for different groups/types of users and/or content items. Specifically, different probability modifiers may be derived for different types of content items (e.g., based on their attributes and/or attributes of their target audiences) and/or different types of users (e.g., based on their attributes), as well as for different ordinal impressions, different times of day, different manners of presentation of the content item (e.g., on a smart phone, on a computer, location of the content impression within a page of content) and/or other factors.
The following description is presented to enable any person skilled in the art to make and use the invention. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown.
In some embodiments of the invention, a system and method are provided for serving electronic content. The content may be any type of electronic data formatted for presentation via a web browser or some other application program or user interface. The content may include complete compositions presented individually, such as web pages, documents or videos, or may be components that can be presented as part of a web page or other composition, such as advertisements, job listings, notifications, status updates, news, documents, sports information, images, videos and so on. In short, electronic content items that are served in embodiments of the invention may include any type of content that can be presented to a user on a communications or computing device.
Although a “content item” may refer to a discrete component or composition of content in some embodiments, in other embodiments it may refer to a collection of components or compositions. In particular, one type of content item is a “campaign” associated with a product, a service, a person, an organization or other thing. A campaign may encompass multiple graphics, videos, textual compilations or other entities that may, in and of themselves, be deemed “content items.”
As used herein, the term “impression” may refer to any serving or presentation of a content item. Thus, an impression of a given content item may refer to the serving of that content item (e.g., to a web browser, to another application) in response to a request for content (or a content query) and/or the resulting presentation of that content item to a user (e.g., by the web browser or other application).
Embodiments of the invention may be implemented as part of virtually any online system or service that serves data, whether it is a social network service, a web server, a portal site, a search engine, etc.
In a system described herein that serves electronic content to users and/or for presentation to users, some information about a target user to whom a content item is to be served or presented is received at the system as part of a query or request for content. For example, when a user of a social network service connects to the service's site and navigates to a particular page, a web browser or other display engine generates a query to a data server operated by the site, to identify and/or obtain content to present to the user. The content query may include or be accompanied by one or more attributes or characteristics of the user (e.g., gender, age, location, employment status).
Similarly, content items that have been stored and that are to be served to users of the system may have associated attributes that identify target audiences of the content items. For example, an advertisement designed to promote sales of a particular product, a job listing describing a new job opening, or some other item, may be received with information identifying types of users to whom the item should be presented (e.g., gender, age, location, employment status).
In response to a query or request for content for a particular user, the system searches for appropriate content, by comparing known attributes of the user to recorded attributes of the target audiences of the stored content items. One or more appropriate content items are identified and served for presentation to the user.
System 110 may host a social networking service, a portal site, a search engine or some other service, which a user accesses via client software application 102. As part of the service, the system serves content for presentation to users via the client application. Client application 102 may be or may comprise a web browser or other application program capable of presenting content items to a user, and may execute on a portable or stationary computing or communication device operated by a user.
Profile server 114 maintains profiles, in profile database 124, of users or members of a service hosted by system 110. A user's profile may reflect any number of attributes or characteristics of the user, including personal (e.g., gender, age range), professional (e.g., job title, employer), social (e.g., organizations the user is a member of, geographic area of residence), education (e.g., degree, university attended), etc.
When a service connection or content request is received at front-end server 112 from or on behalf of a user, the system retrieves some or all data constituting the user's profile from the profile server. The profile data may be shared throughout system 110 to accompany various actions or communications, such as when content is requested from content server 118, when a record of activity is stored at tracking server 116 and/or user store 128, and so on.
Tracking server 116 monitors and records activity of system 110 (e.g., in tracking database(s) 126). For example, whenever content is served from front-end server 112 (e.g., to a client software), the tracking server records what is served, to whom (e.g., which user), and when. Similarly, the tracking server also records user actions regarding content items presented to the users (e.g., clicks, follow-on requests), to include identities of the user and the content item acted on, what action was taken, etc.
Content server 118 maintains one or more repositories of content items for serving to users (e.g., content store 130), an index of the content items, and user store 128. An illustrative means for indexing content items to facilitate their selection and serving to users is described in U.S. patent application Ser. No. 13/705,115, which is entitled “Apparatus and Method for Indexing Electronic Content” and is incorporated herein by reference.
User store 128 stores, for each user of system 110, a record of content items served to the user, or served for presentation to the user, and when they were served. In particular, user store 128 may be configured to allow the content server (and/or other components of system 110) to quickly determine how many previous impressions of a given content item were presented to a given user, when they were presented, how they were presented (e.g., how prominently or where they were presented within a web page or other page of content), and/or other information. Although some of this data may duplicate what is stored by tracking server 116, contents of user store 128 are rapidly accessible to the content server, and may be used (as described below) to help select content items to serve in response to a current content request.
When content is stored at content server 118, it may be stored with attributes, indications, characteristics and/or other information describing one or more target audiences of the content. For example, a provider of an advertisement to be served to users may identify relevant demographic attribute and desired values of target users, a provider of a job listing may identify characteristics of users that should be informed of the opening, and so on.
System 110 may include other components not illustrated in
For example, the functionality may be distributed among the illustrated components in an alternative manner, such as by merging or further dividing functions of one or more components, or may be distributed among a different collection of components. Yet further, while implemented as separate hardware components (e.g., computer servers) in
In some embodiments of the invention, a system such as system 110 of
In either scenario, when the system receives a request or query for content to be presented to a user, the system receives (or retrieves) an identity and/or a set of attributes of the user for use in selecting appropriate content. For example, the user may first login to system 110 (e.g., via front-end server 112) before requests are issued for content for the user. In this case, the requests identify the user by name, user identifier or some other indicia understood by the system.
As one alternative, a token may be stored on the user's communication or computing device and may be delivered to the system with a content request or query. Such a token may identify the user in some way (e.g., with a user identifier assigned by or known to system 110). As another alternative, when a request or query for content for presentation to the user is received at system 110, it may be accompanied by a set of attributes of the user (e.g., age range, gender, location).
In some embodiments of the invention, a system for serving content is operated in a manner that attempts to avoid serving the same content item to the same user on such a frequent basis that the user develops a negative opinion of the item, a subject or feature of the item, and/or a provider or sponsor of the item. In these embodiments, the system serves sponsored content for presentation to users—content that a provider pays to have served—and a goal is to have a sponsored content item served often enough to elicit a response from a user, but not so often that the user develops an aversion to it.
With reference to system 110 of
Users' behavior regarding content items served/presented to them is also recorded. A user's behavior may include clicking on a content item, requesting more information regarding a subject of the content item, requesting re-presentation of the item, and/or other activity that reflects an interest in the content item, a theme or subject of the content item or a provider of the item.
Thus, the system maintains historical data regarding how often and when a particular content item was served, to whom it was served, and what action a user that received the content item did in response. For example, if a user acts on a content item, the system may record that action, which content item was acted upon, which impression of the content item resulted in the action (e.g., 1st, 4th, 7th), and/or other details.
The system may not only record what content items were served to a user in response to a particular request, but may also record where/how they were presented to the user. In particular, content items served by the system may be presented at specific locations within pages of content navigated by a user or otherwise displayed to the user. Those locations may be identified in the content requests, in which case the system may note where each item is presented.
Presentation locations may illustratively comprise x/y coordinates or relative positions/orientations within a page, such as “top” (e.g., at the top of the page), “right margin,” “lower left” and so on. The coordinates, general locations, ratings of the locations (e.g., indicating their relative values or desirability) and/or other indicia of the content item locations may be used to determine a prominence with which a content item is presented, the likelihood that a user will see the content item, and/or other factors.
For example, a content item presented in a large, rectangular, banner-type slot at the top of a page is more prominent and therefore more likely to be observed and viewed by a user (and can be more easily acted on) than another content item presented in a small, square slot at the lower right-hand margin of the page. As will be discussed below, the prominence with which a content item is presented to a user may be considered when determining whether the user is likely to act upon the content item (e.g., by clicking on it) or whether the user may be becoming fatigued by the content item.
The estimated values may be calculated in part on a probability or probabilities that the user or a generic that will receive the content item(s) would act upon the item(s) (e.g., by clicking on them). Such a probability may be modified based on the number of, and/or recency with which, previous impressions of the content item(s) were served to the user.
In operation 202, data are collected over time regarding the serving of multiple content items to multiple users. As indicated above, each serving of each content item may be recorded, along with a time/date of the serving, a location at which the item was presented (e.g., a position within a page of content), the user to whom the content item was presented, and any action taken by the user regarding the item.
Data may continue to be collected during execution of the illustrated method and afterward. In particular, the data may be continually updated as content items continue to be served by the content-serving system and as new content items are received and stored for serving.
In operation 204, collected data are analyzed to determine when users have historically acted upon or been most receptive to the content items that were served. Different analyses or operations may be performed in different embodiments of the invention.
In one analysis, for each user click on an impression of a content item (or some other user action), it is determined which ordinal presentation of that content item to that user resulted in the action (e.g., 3rd impression, 5th impression). This may be repeated for any number of content items or all of them.
The results may be graphed or otherwise aggregated. In an illustrative graph, ordinal numbers of impressions of content items may comprise one axis (e.g., the x-axis), while the other axis (e.g., the y-axis) represents numbers of users who acted on a content item. As one alternative, instead of absolute numbers of users, the other axis may represent, out of all users who acted on a content item, percentages of those users who acted upon a particular ordinal impression of the content item.
In different embodiments of the invention, and as introduced above, data for different combinations of users and/or content items may be graphed and/or analyzed separately. For example, the graph of
Returning to
This probability may be termed pCTR for probable (or predicted) Click-Through-Rate. In some embodiments of the invention, different pCTR values are calculated for different types or groups of users.
In particular, for users matching one set of attributes (e.g., between 26 and 30, male, working as software engineers), one probability may be calculated indicating their likelihood of acting on a content item having a particular set of attributes (e.g., target audience attributes). The probability may be calculated by considering out of all users having the first set of attributes that were presented a content item having the particular set of attributes, how many of the users acted on the content item. This type of calculation may be performed for any number of groups/classifications of users and for any number of groups/classifications of content items.
Also, however, in operation 206 a probability modifier is generated to modify a pCTR to account for the frequency (and/or recency) with which impressions of a particular content item were presented to a particular user. In the graph of
Generation of probability modifiers is illustrated in
As seen in
In
In operation 208 of the method of
In response to the request, the specified user's attributes and/or other information (e.g., attributes identifying target audiences of stored content items) are used to identify multiple candidate content items that could be served in response to the request.
Illustratively, attributes of target audiences of content items available for serving by the system may be compared to attributes of the specified user. Depending on how specific or narrow the attributes are, any number of candidate content items considered suitable for the user may be identified.
In operation 210, for some or all of the candidate content items, information indicating how many times each candidate content item has previously been served for presentation to the user is retrieved, such as from user store 128 of system 110 of
In operation 212, estimated values or revenues of the candidate content items are calculated, and the items are ranked according to those values. Content items' estimated values, or revenues, may be calculated differently in different implementations, but will apply modified probabilities of user action as described above.
In some embodiments of the invention, a content item's estimated value V is calculated as
V=bid*pCTR*modifier
In these embodiments, a content item's “bid” is the amount that a sponsor or provider of the item will pay to the content-serving system (or an operator of the system) in return for serving/presenting the content item. The bid may be based on CPC (cost-per-click), CPM (cost-per-mil) or some other measure, which may require monitoring after the content is served (e.g., to determine whether the user clicked on it).
As described previously, a content item's pCTR is its probable or predicted click-through-rate, which may be defined as a simplistic probability that the user to whom the content item is presented will act on it. The pCTR value may be generated within or outside of the content-serving system, and may be calculated simply by determining a ratio (or percentage) of (a) users that acted on the content item to (b) all users to whom the content item was presented. In some implementations, only the actions of users that are similar to the target user of the current query (i.e., the specified user) may be considered in calculating or determining a pCTR, or when retrieving a stored pCTR, and/or only actions on content items similar to the candidate content item currently being valued.
One user may be considered similar to another if they share a threshold number of common attributes (e.g., age ranges, job titles, gender). Similarly, one content item may be considered similar to another if they (or their target audiences) share a threshold number of common attributes. Thus, in these implementations, when pCTRs are needed for calculating estimated values of candidate content items, the pCTRs may (or may not) be specific a particular groups or types of users and/or content items.
For example, if 1.3% of all users between the ages of 21 and 25 to whom an advertisement for a vacation package has been presented have clicked on the ad (or an announcement of a job opening for a reporter, or some other item), the corresponding pCTR (e.g., 0.013) may be used to calculate the estimated value of that item (and/or similar items) whenever it is a candidate to be served to a user having that attribute, even if the current user has seen the ad fifty times.
It may be noted that one problem with a pCTR is that it may be calculated without regard to how recently and/or frequently the target user was served an impression of the content item. In particular, the same pCTR may be used if the specified user has never received an impression of a particular content item before, or if he has received 30 impressions of the same content item in the last 2 hours.
Finally, the “modifier” for calculating a content item's estimated value is a probability modifier described above, which serves to modify or adjust the pCTR (and the estimated value) to account for how many times and/or how recently the content item has been presented to the specified user. The range of values of probability modifiers may vary from one implementation to another, such as 0.0 to 1.0, as shown in
By using a suitable modifier, if the specified user hasn't seen enough impressions yet to take note of the content item (or of something featured in the item), or if has seen so many impressions that they now annoy him, the resulting estimated value will reflect the fact that he is not very likely to act on a new impression, regardless of what the pCTR reports as the probability that the user will take action.
In sum, the probability represented by a pCTR may be derived from a correlation between attributes of a group of users and attributes of a content item considered for serving to a target user (or attributes of a target audience of the item), and may have no personal relation to the target user. In contrast, the proper probability modifier dynamically adjusts the pCTR (and estimated value) based on the user's current context, to account for how frequently (and/or recently) the content item was served to the user.
In an illustrative calculation of a content item's estimated value, the item's bid, such as $16.00 CPC, is multiplied by the appropriate pCTR, such as 0.015 (representing 1.5%), and the appropriate probability modifier, such as 0.9. These values may be dynamically computed or may be retrieved from a content server, content index, content repository, a tracking server, a tracking database or some other component. Any or all of them may reside in memory to avoid the delay in retrieving them from permanent storage.
The estimated value V=$16.00*0.0015*0.9=$0.0216 (or 2.16¢). Thus, by modifying the content item's pCTR with the probability modifier according to the illustrated embodiment of the invention, a more realistic probability that the user will act on a new impression of the item is determined, and a more accurate estimated value can be derived.
In operation 214, after the candidate content items have been ranked according to their estimated values/revenues, the top N items are served in response to the content request, where N may be determined by the number of items requested to be served.
After operation 214, the illustrated method ends.
In the method of
In methods of serving a content item according to other embodiments of the invention, impressions of a content item may be weighted differently based on the prominence of the impression and/or other factors, such as size, permanence (e.g., how long it is presented), color, behavior, the application or service that presented the page, etc.
In one implementation, presentation of a content item in the most prominent location of a page may be treated as a single impression, while presentations of the content item in less prominent locations may be treated as less than a full impression. For example, a banner location at the top of a page may equate to 1 impression, while an impression at the right-hand edge may be treated as 0.75 impression or 0.5 impression depending on whether it is closer to the top or bottom of the page, and an impression at the bottom of the page (which possibly is seen only if the user scrolls the page down), may be treated as 0.25 impression. In other implementations, weights may be assigned using some other scheme, such as awarding a value of 1 to impressions placed in the least prominent position and awarding to impressions in other positions values that are integer or decimal multiples of 1.
These weights may be applied or employed when assembling historical data, when analyzing the data, when calculating probability modifiers and/or when calculating estimated values of content items that are candidates for serving to a user.
For example, when collecting historical data regarding user actions on content item impressions, and/or determining probability modifiers, weights may be applied such that the Ntth time a particular content item is presented to a particular user only counts as the Mth time (M<N), because some of the impressions were not in the most prominent location of a page.
In one implementation, each presentation of a content item may be represented by the weight corresponding to the position in which it was presented (e.g., 1, 0.75, 0.25). In this implementation, multiple presentations may therefore be required before the 1 ordinal impression (or other ordinal impression) is counted.
Similarly, and as discussed above, when calculating one candidate content item's estimated value, in order to rank it for possible serving to a specified user, previous presentations of the item to the user are used to consider the current context and to calculate or determine a probability modifier. Some or all of those previous impressions may be weighted.
Thus, if four impressions had been presented, all at the top of pages of content, they may count as four full impressions and a probability multiplier corresponding to the 4th ordinal impression may be read from a graph or from other stored data. However, if all four had been presented at the bottom of the pages, at locations having weights of 0.25, then the four impressions together may only count as one impression, and the modifier corresponding to the 1st ordinal impression may be applied.
In some embodiments of the invention, probability modifiers are stored in a matrix, array or other data structure for easy retrieval, and may be derived from the frequency and/or recency of presentation of a set of content items—e.g., the total number of impressions presented, and how recently it was last presented. The data structure may be retained in memory to expedite the process of valuing content items and ranking them for selection in response to a content request, and may be continually or regularly updated as tracking data are collected and analyzed.
For example, after a request for content for a specified user is received, and when each candidate content item is being evaluated for serving in response to the request, the “modifier” for the estimated value equation above may be read directly from the table and applied to a pCTR to give it context.
In matrix 400, one dimension is populated with total numbers of impressions 402 (e.g., 0, 2, 4) presented during a default time period (e.g., 90 days), while the other is populated with multiple time spans 412-420 during which a most recent impression was presented. The default time period may match the time period during which tracking data were collected and analyzed in order to yield the probability modifiers, or some other time period (e.g., 30 days, 60 days). The indicated index values may vary from implementation to implementation, and are not limited to those indicated in
As described above, in some implementations, the data may be derived from all user actions (e.g., clicks) on all content items during the default time period. In other implementations, the data may reflect just action taken by a group of users having a set of common attributes, and/or user actions on content items having a set of common attributes or having target audiences that have a set of common attributes. Yet further, an implementation of the matrix may be narrowed or tailored to capture just one user's experience.
The scope of a probability modifier may therefore differ from the scope of a pCTR that it is used to modify. While the pCTR may apply to a collection of users (and/or content items) having particular attributes, the probability modifier may be derived from a larger (or smaller) set of users and/or a larger (or smaller) set of content items.
In some embodiments of the invention, to retrieve the appropriate probability modifier, the cell is read that corresponds to the total number of previous impressions of the content item presented to that user and to the time-frame in which the most recent impression was served. Thus, if the content item had been served a total of 4 times to the user, with the most recent impression being in the last six hours, the appropriate modifier or action likelihood read from matrix 400 is 0.7.
Content-serving system 500 of
Memory 504 stores probability modifiers, pCTRs, bids, other data, and/or logic manipulated during operation of system 500.
Storage 506 of the content-serving system stores content for serving to/for users, user data, tracking data and/or other information. Storage 506 also stores logic that may be loaded into memory 504 for execution by processor 502. Such logic includes tracking logic 522, analysis logic 524, ranking logic 526 and serving logic 528. In other embodiments of the invention, any or all of these logic modules or other content may be combined or divided to aggregate or separate their functionality as desired.
Tracking logic 522 comprises processor-executable instructions for tracking content requests received at system 500, tracking the serving of content items to users, tracking user actions on or regarding content items, and/or other behavior. Logic 522 may include, be accompanied by, or used to assemble data reflecting users' activity, content providers' activity and/or other aspects of the system.
Analysis logic 524 comprises processor-executable instructions for analyzing user activity, generating probabilities (e.g., pCTRs), probability modifiers and/or other data, and may be used to test newly generated data.
Ranking logic 526 comprises processor-executable instructions for ranking content items being considered for serving in response to a content request. Logic 526 may therefore retrieve content item bids, pCTRs, probability modifiers and/or other values, use them to compute content items' estimated values or revenues, and rank the items by the results.
Serving logic 528 comprises processor-executable instructions for handling and responding to content requests. Serving logic may thus process a new request, search for content items suitable for serving in response and serve the selected (e.g., top-ranking) items.
The environment in which some embodiments of the invention are executed may incorporate a general-purpose computer or a special-purpose device such as a hand-held computer or communication device. Details of such devices (e.g., processor, memory, data storage, display) may be omitted for the sake of clarity.
Data structures and code described in this detailed description are typically stored on a non-transitory computer-readable storage medium, which may be any device or medium that can store code and/or data for use by a computer system. Non-transitory computer-readable storage media includes, but is not limited to, volatile memory, non-volatile memory, magnetic and optical storage devices such as disk drives, magnetic tape, CDs (compact discs), DVDs (digital versatile discs or digital video discs), or other non-transitory computer-readable media now known or later developed.
The methods and processes described in the detailed description can be embodied as code and/or data, which can be stored in a non-transitory computer-readable storage medium as described above. When a processor or computer system reads and executes the code and/or data stored on the medium, the processor or computer system performs the methods and processes embodied as data structures and code and stored within the medium.
Furthermore, the methods and processes described below can be included in hardware modules. For example, the hardware modules may include, but are not limited to, application-specific integrated circuit (ASIC) chips, field-programmable gate arrays (FPGAs) and other programmable-logic devices now known or later developed. When the hardware modules are activated, the hardware modules perform the methods and processes included within the hardware modules.
The foregoing descriptions of embodiments of the invention have been presented for purposes of illustration and description only. They are not intended to be exhaustive or to limit the invention to the forms disclosed. Accordingly, many modifications and variations will be apparent to practitioners skilled in the art. The scope of the invention is defined by the appended claims, not the preceding disclosure.
