Patent Application
20170228695
Embodiments of the present disclosure relate generally to content items presentation and, more particularly, but not by way of limitation, to marginal value based content item mixing.
Feeds, streams, content aggregators, and the like are populated with content items that can both engage users and provide monetization. For instance, a particular feed may include ‘organic’ content items (e.g., news updates, social media updates, user generated content, and so on) as well as monetized content items such as advertisements. In some instances, the monetized content items can be based on different monetization models. For instance, a particular monetized content item may be an advertisement that is pay-per-click or pay-per-impression while another monetized content item may use a flat fee scheme that is results-based or time period-based rather than specifying a number of impressions or clicks. However, determining number of impressions, placement, and other display parameters to maximize revenue in a feed that mixes content items associated with different monetization models presents a number of challenges.
Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and cannot be considered as limiting its scope.
The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail.
A number of different monetization models can be used to monetize content. For instance, advertisements are typically directly monetized based on a pay-per-click or pay-per-impression model. Some content can be indirectly monetized using a flat fee scheme that is results-based or time period-based rather than specifying a number of impressions or clicks. For example, an employer with a job opening may pay a flat rate fee for creating awareness of the job opening during a specified time period, such as one month. This type of monetization is not based on a number of impressions or a number of clicks and can be more sensible in situations where there a specific result is desired (e.g., filling a single employment position) as opposed to promoting for sale an inexhaustible or nearly inexhaustible product (e.g., a digital good). Content providers and content hosts can monetize their platform by presenting monetized content items in addition to organic content items (e.g., likes, posts, comments, new connections, suggested connections, news articles, or other content of interest to the user). However, space on a user interface to present content is a valuable and limited resource. To optimize revenue from the available space, it is desirable to present the most lucrative monetized content. However, comparing directly monetized content items (e.g., an advertisement) with indirectly monetized content items (e.g., a flat fee scheme) can be a challenge since they cannot be directly compared.
To predict a higher revenue generating content item among directly monetized and indirectly monetized content items, in various example embodiments a server system receives a request to fill a user interface position within a content list. For example, a user of a user device may be viewing a content feed and the content feed may request content items from the server system. The server system calculates a variable monetary value for positioning a particular job listing within the content list. In a various embodiments, the variable monetary value represents a monetary value for filling the position with the particular job listing and can take into account an incremental value and an average monetary value.
In these embodiments, the incremental value represents a diminishing return for subsequent presentations of the job listing. For instance, if the job listing has been presented one thousand times, there may be a reduced value, as compared to an earlier presentation of the job listing, to presenting the job listing again since there is a chance that the position has been filled and showing the job listing would be moot.
In these embodiments, the average monetary value represents a value of filling the available content position with the job listing without regard to a diminishing return. For example, if a flat fee has been paid for presenting the job listing for one month, and, based on historical data, presenting the job listing results in a certain number of user engagements with the job listing (e.g., clicking on the job listing, saving the job listing, and so), then the average monetary value for presenting the job listing may be the flat fee divided by the certain number of user engagements resulting from presenting the job listing. In some embodiments, the server system calculates the average monetary value across multiple jobs (e.g., across a job sector such as technology jobs or a particular type of job such as accountant).
Subsequent to calculating the variable monetary value, the server system may then compute a first expected value for the particular job listing. In various example embodiments, the first expected value is based on the variable monetary value and an interaction likelihood. The interaction likelihood may be a probability that a particular user will interact with the job listing (e.g., based on contextual information such as user data indicating suitability to apply to the job of the particular job listing). The server system may then compare the first expected value with a second expected value corresponding to another content item. For example, the second expected value corresponds to an advertisement. In this example, the second expected value may be a cost-per-click of the advertisement (direct monetization) multiplied by an interaction likelihood with the advertisement (e.g., probability a user will click on the advertisement). In this way, the server system can compare the indirect monetization content (e.g., a flat fee scheme) with the direct monetization content (e.g., an advertisement) on a like-for-like basis. Based on the first expected value exceeding the second expected value, in an embodiment, the server system fills the available content position with the particular job listing instead of the other content item. In this way, the server system can facilitate maximizing revenue derived from available content positions within a content feed or another content platform.
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Consistent with some embodiments, when a person initially registers to become a member of the social networking service, the person will be prompted to provide some personal information, such as his or her name, age (e.g., birth date), gender, interests, contact information, home town, address, the names of the member's spouse and/or family members, educational background (e.g., schools, majors, etc.), current job title, job description, industry, employment history, skills, professional organizations, interests, and so on. This information is stored, for example, as profile data in the database 128.
Once registered, a member may invite other members, or be invited by other members, to connect via the social networking service. A “connection” may specify a bi-lateral agreement by the members, such that both members acknowledge the establishment of the connection. Similarly, with some embodiments, a member may elect to “follow” another member. In contrast to establishing a connection, the concept of “following” another member typically is a unilateral operation, and at least with some embodiments, does not require acknowledgement or approval by the member that is being followed. When one member connects with or follows another member, the member who is connected to or following the other member may receive messages or updates (e.g., content items) in his or her personalized content stream about various activities undertaken by the other member. More specifically, the messages or updates presented in the content stream may be authored and/or published or shared by the other member, or may be automatically generated based on some activity or event involving the other member. In addition to following another member, a member may elect to follow a company, a topic, a conversation, a web page, or some other entity or object, which may or may not be included in the social graph maintained by the social networking system. With some embodiments, because the content selection algorithm selects content relating to or associated with the particular entities that a member is connected with or is following, as a member connects with and/or follows other entities, the universe of available content items for presentation to the member in his or her content stream increases.
As members interact with various applications, content, and user interfaces of the social networking system 120, information relating to the member's activity and behavior may be stored in a database, such as the database 132.
The social networking system 120 may provide a broad range of other applications and services that allow members the opportunity to share and receive information, often customized to the interests of the member. For example, with some embodiments, the social networking system 120 may include a photo sharing application that allows members to upload and share photos with other members. With some embodiments, members of the social networking system 120 may be able to self-organize into groups, or interest groups, organized around a subject matter or topic of interest. With some embodiments, members may subscribe to or join groups affiliated with one or more companies. For instance, with some embodiments, members of the social networking service may indicate an affiliation with a company at which they are employed, such that news and events pertaining to the company are automatically communicated to the members in their personalized activity or content streams. With some embodiments, members may be allowed to subscribe to receive information concerning companies other than the company with which they are employed. Membership in a group, a subscription or following relationship with a company or group, as well as an employment relationship with a company, are all examples of different types of relationships that may exist between different entities, as defined by the social graph and modeled with social graph data of the database 130.
The application logic layer includes various application server module(s) 124, which, in conjunction with the user interface module(s) 122, generates various user interfaces with data retrieved from various data sources or data services in the data layer. With some embodiments, individual application server modules 124 are used to implement the functionality associated with various applications, services and features of the social networking system 120. For instance, a messaging application, such as an email application, an instant messaging application, or some hybrid or variation of the two, may be implemented with one or more application server modules 124. A photo sharing application may be implemented with one or more application server modules 124. Similarly, a search engine enabling users to search for and browse member profiles may be implemented with one or more application server modules 124. Of course, other applications and services may be separately embodied in their own application server modules 124. As illustrated in
Additionally, a third party application(s) 148, executing on a third party server(s) 146, is shown as being communicatively coupled to the social networking system 120 and the client device(s) 150. The third party server(s) 146 may support one or more features or functions on a website hosted by the third party.
In some implementations, the presentation module 210 provides various presentation and user interface functionality operable to interactively present (or cause presentation) and receive information from the user. In various example embodiments, the presentation module 210 functions in conjunction with the user interface module 122 of
The communication module 220 provides various communications functionality and web services. For example, the communication module 220 provides network communication such as communicating with the social networking system 120, the client devices 150, and the third party server(s) 146. In various example embodiments, the network communication can operate over wired or wireless modalities. Web services are intended to include retrieving information from the third party server(s) 146, the database(s) 128-132, and the application server module(s) 124. In some implementations, information retrieved by the communication module 220 comprises data associated with the user (e.g., user profile information from an online account, social network service data associated with the user), data associated with one or more items listed on an e-commerce website (e.g., images of the item, reviews of the item, item price), or other data to facilitate the functionality described herein.
The average value module 230 provides functionality to calculate the average monetary value that, in various example embodiments, represents a value for filling an available content position with a job listing. The average value module 230 can access historical data of engagements with job listings previously presented to users of the content feed. For example, the historical data may indicate a total number of engagements resulting from presenting the job listing in the content feed and a total number of engagements resulting when the job listing is omitted from the content feed. The average value module 230 may then determine an increase in engagements resulting from presenting the job listing in the content feed. The average value module 230 may access revenue data associated with presenting job listings in the content feed. For instance, the average value module 230 accesses flat fee payment revenue for presenting the job listings (e.g., a flat fee for presenting the job listing for one month). In various embodiments, the average value module 230 calculates the average monetary value based on the revenue for presenting the job listings and increase in engagements with the job listings. In these embodiments, the average monetary value represents an average value per engagement for presenting job listings in a content feed or another content platform. The average value module 230 can filter the data across job sector, job type, geographic region, or a combination thereof (e.g., calculate an average monetary value for job in a certain job sector such as technology jobs).
The discriminative attribute module 240 provides functionality to identify significant or discriminative attributes or engagement actions. For example, the discriminative attribute module 240 accesses confirmed hire data that indicates when a particular job associated with a job listing has been filled. In some instance, the particular job associated with the job listing has been filled as a result of presenting the job listing. In an example embodiment, the discriminative attribute module 240 determines this by identifying a link between a user engagement with the job listing and an update to the user current employment that indicates the user accepted a job position corresponding to the job listing. The discriminative attribute module 240 correlates the confirmed hires with engagement actions to identify significant or discriminative engagement actions. Put another way, the discriminative attribute module 240 identifies engagement actions that drive confirmed hires.
The incremental value module 250 provides functionality to determine the incremental value representing a diminishing return for subsequent presentations or engagement actions associated with the particular job listing. For instance, if the job listing has been presented one thousand times, there may be a reduced value, as compared to an earlier presentation of the job listing, to presenting the job listing again since there is a chance that the position has been filled and showing the job listing would be moot. That is to say, one a job position is filled, there is little customer value in continuing to promote the job listing.
The variable value module 260 provides functionality to calculate the variable monetary value and the expected value for monetized content items. For example, the variable value module 260 calculates the variable monetary value for positioning a particular job listing within the content list based on the average monetary value and the incremental value. In an example embodiment, the variable value module 260 calculates the first expected value based on the variable monetary value (denoted below as rα,variable) and a likelihood of interaction (e.g., probability the user will interact with or engage with the job listing such as a click). In a specific non-limiting example, the variable value module 260 calculates the first expected value as follows:
Expected Value=Likelihood of Interaction*Variable Monetary Value
For instance, a particular expected value may be a product of an engagement likelihood and monetary value, such as a cost-per-click for an advertisement, or the variable monetary value for an indirectly monetized content item.
At operation 310, the communication module 220 receives a request to fill a position within a content list being displayed on a user interface of a user device. For example, the user of the user device (e.g., a smart phone or desktop computer) may initiate presentation of a content feed or content list and the user device communicates a request to the social networking system 120 and the content optimization system 200 to populate the content list with content items. Although a content feed or content list is described, the content optimization system 200 may perform the methods and operations described herein in response to content requests from other types of content platforms (e.g., a fixed content space on a user interface that is refreshed periodically with new content).
At operation 320, the variable value module 260 calculates the variable monetary value for positioning the particular job listing within the content list based on the average monetary value and the incremental value for the particular job listing. In these embodiments, the average monetary value, calculated by the average value module 230, represents a value for filling an available content position with a job listing. For example, the average monetary value may be a total revenue collected from presenting job listings divided by an increase in engagement actions resulting from presenting the job listings. In this example, the average monetary value indicates revenue collected per engagement for a job listing.
In various embodiments, the incremental value represents a diminishing return for subsequent presentations or engagement actions associated with the particular job listing. That is to say, a value to a customer of another presentation of the particular job listing (e.g., an employer seeking to fill a vacant position) can diminish the more the particular job listing is presented. For example, the first time the particular job listing is presented may correspond to a highest value to the customer since the job probably has not been filled; an nth time (e.g., one thousandth time) the particular job listing is presented may correspond to a lower value to the customer as compared to the first presentation as the job may have been filled since the first presentation and before the nth presentation.
The variable value module 260 calculates the variable monetary value based on the average monetary value and the incremental value to determine a likely value for a current presentation of the job listing. The value of the current presentation of the job listing may be comparable to a cost-per-click or cost-per-impression of a directly monetized content item such as an advertisement.
At operation 330, the variable value module 260 computes a first expected value for the particular job listing based on the variable monetary value and an interaction likelihood for the particular job listing (e.g., first expected value=variable monetary value*interaction likelihood). The first expected value represents a prediction as to the revenue that may result from presenting the particular job listing. Put another way, for instance, since the variable monetary value represents revenue that may result from an interaction with the job listing and the interaction likelihood represents a probability that a particular user will engage with the job listing, the product of the two values yields a prediction as to revenue that may result from the presentation of the job listing.
At operation 340, the variable value module 260 compares the first expected value with a second expected value corresponding to filling the position within the content list with another content item. The second expected value can be computed similarly, by the variable value module 260, to the computation described at operation 330 above, except the variable value module 260 may use a cost-per-click or cost-per-impression value in place of the variable monetary value for advertisements or another value for content items monetized using other schemes. That is to say, in a scenario where another content item is an advertisement, the variable value module 260 may compute the second expected value based on a cost-per-click, for example, and interaction likelihood for the advertisement. The presentation module 210 or the variable value module 260 may then compare the first expected value directly with the second expected value.
At operation 350, the presentation module 210 causes presentation of the particular job listing within the content list being displayed on the user interface of the user device based on the first expected value exceeding the second expected value. In various embodiments, the presentation module 210 or the variable value module 260 orders, ranks, or sorts content items based on expected values associated with respective content items. For example, the presentation module 210 may display a particular content item corresponding to a highest expect value first, or more prominently (e.g., a more visible or conspicuous placement on the user interface), in the content list. The presentation module 210 can adjust a number of different content item parameters based on the expected value such as a frequency of impressions, a prominence of the display, a location of the display, a size of the display, a duration of the presentation, and so on.
At operation 410, the average value module 230 accesses revenue data for a plurality of engagement actions associated with a plurality of job listings. For instance, the average value module 230 accesses total revenue received from employers promoting job openings via job listings within the content item feed or content list. In some embodiments, the revenue data is over a specified time period (e.g., revenue from the last month).
At operation 420, the average value module 230 determines a change in a number of engagement actions resulting from displaying the plurality of job listings within the content list compared to omitting the plurality of job listings from the content list. If a time period is specified, the engagement actions should occur within the time period to be counted by the average value module 230. For example, the average value module 230 accesses data that indicates a number of engagement actions (e.g., clicks) for job listings that were not presented in the content item feed or content list. The average value module 230 may then access data that indicates a number of engagement actions for a same or similar job listings that were presented and promoted in the content item feed or content list. The average value module 230 may then use such data to determine how many additional engagement actions resulted from presenting the job listing in the content item feed.
At operation 430, the average value module 230 calculates the average monetary value of displaying job listings among the plurality of job listings using the revenue data and the change in the number of engagement actions. For example, the average value module 230 calculates the average monetary value by dividing the total revenue received from presenting a set of job listings by additional engagement actions resulting from presenting the set of job listings. In this example, the average monetary value represents revenue per engagement action for the set of job listings. In some embodiments, the average value module 230 calculates the average monetary value using revenue and a change in a number of engagement actions from a certain segment of the jobs (e.g., tech sector jobs), for a particular job type (e.g., engineering jobs), for a particular job (e.g., accountant jobs), or all jobs total.
In a specific, non-limiting example, the average value module 230 determines the average monetary value associated with each type of engagement action (denoted by α) such as a job impression, a job view, a saved job, an apply-clicked job, a job application, or a confirmed hire. In this example, the average value module 230 receives the inputs such as a time period, revenue during the time period (denoted by R), number of engagement actions during the time period (denoted by Nα), and a number of engagement actions expected through non-revenue driving channels (e.g., organic traffic) during the time period (denoted by Nαfree). In this example, the average value module 230 outputs the average monetary value (denoted by rα,average) for each type of engagement action. In an example embodiment, the average value module 230 computes an incremental number of engagement actions or a change in a number of engagement actions resulting from the revenue channel (e.g., number of additional engagement actions resulting from revenue channel versus merely organic engagements from search) as follows:
N
α,incremental
=N
α
−N
α,free
The average value module 230 may then compute the average monetary value (denoted by rα,average) for an engagement action as follows:
Note that in some instances engagement actions resulting from a particular revenue channel (e.g., recommended job in a feed or content list) are assumed to be equivalent to engagement actions resulting from other revenue channels.
At operation 510, the discriminative attribute module 240 accesses confirmed hire data for a plurality of job listings. The confirmed hire data indicating a job position has been filled. In a specific example, a user that clicked on a particular job listing for a certain job that is later identified as being employed at the certain job may constitute a confirmed hire.
At operation 520, the discriminative attribute module 240 identifies discriminative engagement actions, or significant engagement actions, among a plurality of engagement actions based on the confirmed hire data. In various example embodiments, the discriminative engagement actions are engagement actions likely to result in a confirmed hire for a job listing. For example, the engagement actions may comprise a wide variety of different actions such as a job listing impression, a job listing view, a job listing save action, a job listing click, a job listing application, and so forth. Some of the engagement actions may be far more indicative of a confirmed hire than others. The discriminative attribute module 240 identifies significant or discriminative engagement actions using the confirmed hire data described at operation 510. In a specific example, engagement actions may be normalized based on confirmed hire data and ranked by the discriminative attribute module 240 to identify a highest, or several highest, engagement actions that are correlated with confirmed hires. In a specific example, the discriminative attribute module 240 may access confirmed hire data and engagement action data indicating that on average there are one hundred clicks on a job listing before a confirmed hire occurs and that there are five hundred views of the job listing before the confirmed hire occurs. In this example, the discriminative attribute module 240 may rank the engagement actions such that a click is associated with a value of one over one hundred and a view is associated with a value of one over five hundred and thus the click is more significant than a view in terms of resulting confirmed hires. In some embodiments, the discriminative attribute module 240 determines a weight vector by using a machine learning model (e.g., logistic regression, stochastic gradient descent that output weighted vector w, as a function of feature vector over all jobs) to learn the customer value as a function of the feature vector over all jobs. In still other embodiments, the discriminative attribute module 240 computes top engagement actions based on the pairwise correlation between each engagement action and the customer value (e.g., confirmed hire per engagement action) across all jobs. In various example embodiments, the discriminative attribute module 240 identifies the discriminative engagement actions offline (e.g., at a session and a time prior to the receiving the request to fill the position) and the discriminative attribute module 240 accesses the identified discriminative engagement action data when the request to fill the position is received by the content optimization system 200.
In a specific, non-limiting example, the discriminative attribute module 240, for each type of engagement action (denoted by α), such as a job impression, job view, saved job, apply-clicked job, job application, computes a normalized feature vector (denoted by xα,j) associated with each job (denoted by j) by including attributes such as number of job impressions, number of job view, number of times the job was saved, number of apply clicks for the job, number of applications for the job, and so on at expiration of the job recommendation time period (e.g., one month). Subsequently, the discriminative attribute module 240 calculates the customer value (denoted by yαj) of an engagement action for a job (denoted by j) as follows:
Then, in this example embodiment, the discriminative attribute module 240 determines the relative attribute weight vector (denoted by wα) using a machine learning algorithms applied to learn the customer value as a function of the feature vector over all jobs. The discriminative attribute module 240 then determine the top attributes based on the weight vector (denoted by wα). In some example embodiments, the discriminative attribute module 240 computes the top attributes based on a pair wise correlation between each attribute feature and the customer value across all jobs.
At operation 530, the incremental value module 250 calculates the incremental value using the identified discriminative engagement actions for the particular job listing, the incremental value indicating a value associated with filling the position within the content list with the particular job listing in response to the request. For example, the incremental value module 250 maps engagement actions to an incremental bucket (e.g., segments or separates the engagement actions into groups according to an occurrence count) such as one to ten, ten to one hundred, one hundred to five hundred, and so forth. The incremental value module 250 uses the discriminative attributes, or significant engagement actions, and the weight vector identified at operation 520 to then determine the incremental value using a machine learning model. For example, the incremental value module 250 trains a machine learning model with the discriminative engagement actions data. Once the model is trained, given a job listing and engagement data for the job listing (e.g., number of views, clicks, etc. that have occurred already), the incremental value module 250 can use the model to determine the incremental value of a next engagement action for the job listing.
In a specific, non-limiting example, the incremental value module 250 determines the relative customer value (the incremental value) of a particular job recommendation by mapping engagement actions to buckets (e.g., 1-10, 11-50, 51-100, etc.) of reach type of engagement action (e.g., job impression, job view, saved job, apply-clicked job, job application). The incremental value module 250 then computers a weighted combination of most discriminative attribute features for each job (denoted by j) using weight vector wα (although in a special case, the incremental value module 250 uses one attribute at a time). The incremental value module 250 returns the incremental customer value (denoted by cα(j)) of a particular engagement action for job, j as follows:
In this instance, the incremental value module 250 calculates the derivative with respect to the ‘bucketized’ versions.
Subsequently, the variable value module 260 calculates the variable monetary value using rα,average, the identified discriminative engagement actions, the incremental value (denoted by cα(j)) for engagement actions for job (denoted by j) based on a combination of one or more most discriminative engagement actions. That is to say, in some example embodiments, the variable value module 260 computes the variable monetary value (denoted by rα,variable) for engagement actions of a job j as follows:
r
α,variable
=c
α(j)*rα,average
At operation 610, the discriminative attribute module 240 accesses profile data (e.g., profile data stored in databases 128 of
At operation 620, the discriminative attribute module 240 infers a confirmed hire for the plurality of job listings by comparing the current employment description with job description data of the plurality of job listings. For example, if the user engages with the job listing (e.g., a click, a job save, or another engagement action) and profile data of the user on the social networking service indicates that the user is currently employed at the job associated with the job listing, the discriminative attribute module 240 may infer a confirmed hire for that job listing. Other schemes may be employed to determine confirmed hires such as user self-reporting or employer reporting.
Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules can constitute either software modules (e.g., code embodied on a machine-readable medium) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and can be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) can be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.
In some embodiments, a hardware module can be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module can include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module can be a special-purpose processor, such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC). A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module can include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware modules become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) can be driven by cost and time considerations.
Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.
Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules can be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications can be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module can perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module can then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules can also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information).
The various operations of example methods described herein can be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors.
Similarly, the methods described herein can be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method can be performed by one or more processors or processor-implemented modules. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API)).
The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented modules can be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented modules are distributed across a number of geographic locations.
The modules, methods, applications and so forth described in conjunction with
Software architectures are used in conjunction with hardware architectures to create devices and machines tailored to particular purposes. For example, a particular hardware architecture coupled with a particular software architecture will create a mobile device, such as a mobile phone, tablet device, and the like, while yet another combination produces a server computer for use within a cloud computing architecture. Not all combinations of such software and hardware architectures are presented here as those of skill in the art can readily understand how to implement the inventive subject matter in different contexts from the disclosure contained herein.
In the example architecture of
The operating system 814 may manage hardware resources and provide common services. The operating system 814 may include, for example, a kernel 828, services 830, and drivers 832. The kernel 828 may act as an abstraction layer between the hardware and the other software layers. For example, the kernel 828 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services 830 may provide other common services for the other software layers. The drivers 832 may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 832 may include display drivers, camera drivers, BLUETOOTH® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration.
The libraries 816 may provide a common infrastructure that may be utilized by the applications 820 or other components or layers. The libraries 816 typically provide functionality that allows other software modules to perform tasks in an easier fashion than to interface directly with the underlying operating system 814 functionality (e.g., kernel 828, services 830 or drivers 832). The libraries 816 may include system libraries 834 (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 816 may include API libraries 836 such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPREG4, H.264, MP3, AAC, AMR, JPG, or PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries 816 may also include a wide variety of other libraries 838 to provide many other APIs to the applications 820 and other software components/modules.
The frameworks/middleware 818 (also sometimes referred to as middleware) may provide a higher-level common infrastructure that may be utilized by the applications 820 or other software components/modules. For example, the frameworks/middleware 818 may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware 818 may provide a broad spectrum of other APIs that may be utilized by the applications 820 or other software components/modules, some of which may be specific to a particular operating system or platform.
The applications 820 include built-in applications 840 or third party applications 842. Examples of representative built-in applications 840 may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, or a game application. Third party applications 842 may include any of the built-in applications 840 as well as a broad assortment of other applications. In a specific example, the third party application 842 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. In this example, the third party application 842 may invoke the API calls 824 provided by the mobile operating system such as operating system 814 to facilitate functionality described herein. In an example embodiment, the applications 820) include a social networking service application that provides various social networking service features such as a content feed, aggregator, or list.
The applications 820 may utilize built-in operating system functions (e.g., kernel 828, services 830 or drivers 832), libraries (e.g., system libraries 834, API libraries 836, and other libraries 838), frameworks/middleware 818 to create user interfaces to interact with users of the system. Alternatively, or additionally, in some systems interactions with a user may occur through a presentation layer, such as presentation layer 844. In these systems, the application/module “logic” can be separated from the aspects of the application/module that interact with a user.
Some software architectures utilize virtual machines. In the example of
The machine 900 can include processors 910, memory/storage 930, and I/O components 950, which can be configured to communicate with each other such as via a bus 902. In an example embodiment, the processors 910 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) can include, for example, processor 912 and processor 914 that may execute instructions 916. The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as “cores”) that can execute instructions contemporaneously. Although
The memory/storage 930 can include a memory 932, such as a main memory, or other memory storage, and a storage unit 936, both accessible to the processors 910 such as via the bus 902. The storage unit 936 and memory 932 store the instructions 916 embodying any one or more of the methodologies or functions described herein. The instructions 916 can also reside, completely or partially, within the memory 932, within the storage unit 936, within at least one of the processors 910 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 900. Accordingly, the memory 932, the storage unit 936, and the memory of the processors 910 are examples of machine-readable media.
As used herein, the term “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions 916. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions 916) for execution by a machine (e.g., machine 900), such that the instructions, when executed by one or more processors of the machine 900 (e.g., processors 910), cause the machine 900 to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se.
The I/O components 950 can include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 950 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 950 can include many other components that are not shown in
In further example embodiments, the I/O components 950 can include biometric components 956, motion components 958, environmental components 960, or position components 962 among a wide array of other components. For example, the biometric components 956 can include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components 958 can include acceleration sensor components (e.g., an accelerometer), gravitation sensor components, rotation sensor components (e.g., a gyroscope), and so forth. The environmental components 960 can include, for example, illumination sensor components (e.g., a photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., a barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensor components (e.g., machine olfaction detection sensors, gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 962 can include location sensor components (e.g., a Global Positioning System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.
Communication can be implemented using a wide variety of technologies. The I/O components 950 may include communication components 964 operable to couple the machine 900 to a network 980 or devices 970 via a coupling 982 and a coupling 972, respectively. For example, the communication components 964 include a network interface component or other suitable device to interface with the network 980. In further examples, communication components 964 include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, BLUETOOTH® components (e.g., BLUETOOTH® Low Energy), WI-FI® components, and other communication components to provide communication via other modalities. The devices 970 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)).
Moreover, the communication components 964 can detect identifiers or include components operable to detect identifiers. For example, the communication components 964 can include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as a Universal Product Code (UPC) bar code, multi-dimensional bar codes such as a Quick Response (QR) code, Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes, and other optical codes), acoustic detection components (e.g., microphones to identify tagged audio signals), or any suitable combination thereof. In addition, a variety of information can be derived via the communication components 964, such as location via Internet Protocol (IP) geo-location, location via WI-FI® signal triangulation, location via detecting a BLUETOOTH® or NFC beacon signal that may indicate a particular location, and so forth.
In various example embodiments, one or more portions of the network 980 can be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a WI-FI® network, another type of network, or a combination of two or more such networks. For example, the network 980 or a portion of the network 980 may include a wireless or cellular network, and the coupling 982 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, the coupling 982 can implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology.
The instructions 916 can be transmitted or received over the network 980 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 964) and utilizing any one of a number of well-known transfer protocols (e.g., Hypertext Transfer Protocol (HTTP)). Similarly, the instructions 916 can be transmitted or received using a transmission medium via the coupling 972 (e.g., a peer-to-peer coupling) to devices 970. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 916 for execution by the machine 900, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.
Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed.
The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
