Patent Application
20160134934
This disclosure relates generally to computer-implemented methods and systems for calculating and estimating changes in audience size and more particularly to near real-time estimation of audience segment sizes over time.
Goods and services providers employ various forms of marketing to drive consumer demand for products and services. Marketing includes various techniques to expose to target audiences to brands, products, services, and so forth. For example, marketing often includes providing offers and promotions (e.g., advertisements) to an audience to encourage audience members to purchase a product or service. In some instances, promotions are provided through media outlets, such as television, radio, and the Internet via television commercials, radio commercials and webpage advertisements. In the context of webpages, marketing may include advertisements for a website and products or services associated with that website so as to encourage audience members to visit and/or use the website, purchase products and services offered via the website, and/or to otherwise interact with the website.
In marketing or other applications, data may be managed. User data management is the collection and analysis of user website interaction data. It can include collecting information about how individual audience members (e.g., visitors or consumers) interact with a given website. Prior solutions for calculating the number of web site visitors in a given audience segment typically require an ‘offline’ calculation performed as a scheduled job. As calculating an audience size for segments of consumers or visitors can be extremely computational expensive, prior solutions typically perform such calculations as part of a nightly job. Also, such calculations are limited to only calculating a current, single audience size. These prior solutions do not calculate the number of visitors that are in a given audience segment in near real-time, nor do they calculate changes in audience segment sizes over time. Existing techniques also do not predict or estimate how an audience segment size will change at different points in time in the future.
Therefore, there is a need for techniques for calculating audience segment sizes over time and estimating future changes in audience segment sizes in near real-time.
One exemplary embodiment involves receiving, at a computing device, a request for segment sizes for a defined audience segment, the request indicating one or more traits of visitors of network content and a time range. According to this embodiment, audience data is retrieved based at least in part on the one or more traits and the time range. Next, the embodiment calculates, by the computing device, based on the audience data, audience segment population sizes for a plurality of durations in the time range.
These illustrative features are mentioned not to limit or define the disclosure, but to provide examples to aid understanding thereof. Additional embodiments are discussed in the Detailed Description, and further description is provided there. Advantages offered by one or more of the various embodiments may be further understood by examining this specification or by practicing one or more embodiments presented.
These and other features, aspects, and advantages of the present disclosure are better understood when the following Detailed Description is read with reference to the accompanying drawings, where:
Generally, the embodiments described herein are directed to, among other things, allowing users to estimate audience segment size changes over time in near real-time. For example, embodiments calculate the number of visitors that are in a given audience segment (e.g., an audience segment size), and calculate changes to the audience segment size over time. For example, given an audience segment of males living in San Jose, Calif., marketers and advertisers can use embodiments to determine the number of people in that segment and how that audience segment has changed over time. Embodiments facilitate providing this information to users such as marketers and advertisers in near real-time, while the users are creating a campaign (e.g., an advertising campaign) tailored to the given audience segment. Embodiments described herein can quickly calculate a time series of how an audience segment has changed over time. In addition to calculating past, historical audience segment size changes at different points in time, some embodiments can apply predictive analytics to predict or estimate how an audience segment will change in future. Quickly calculating the past and future audience segment size is critical for determining campaign performance, advertising expenditures (e.g., ad spend), and expected return on investment (ROI).
Embodiments pull out data points for historical data, and use predictive models, such as, for example time series projection model algorithms to predict audience segment sizes over time. In certain embodiments, past sizes of an audience segment are received as a time series of audience data points, and used to graphically depict the audience segment size over time. The depiction can include both past, historical segment sizes, and estimated future segment sizes. The estimated segment sizes can be determined using a predictive model. For example, after obtaining the history of an audience segment, time series prediction algorithms can be applied to the time series in order to project futures segment sizes at different points in time in the future. Time series analysis can be performed by analyzing time series data for a segment in order to extract statistics and other characteristics of the segment size over time. Time series forecasting can include using a model to predict future segment sizes based on historic, previously observed segment sizes at points in time in the past. Time series analysis of segment size data can include comparing values of a single time series or multiple dependent time series at different points in time. Non-limiting examples of time series prediction algorithms include linear regression polynomial regression, multiplicative/additive decomposition, linear trend with multiplicative additive seasonality, wavelet forecasting, Fourier transforms, and neural networks.
Certain embodiments project and predict an audience segment size based on calculated and determined patterns of past audience sizes, which are expressed as a time series. Some embodiments utilize services such as Adobe® Audience Manager Audience Size services to calculate not only a current audience segment size, but also a time series of changes for the audience segment size. This time series is in turn by an audience size estimating service that is configured to estimate the size of an audience segment in a near real-time. According to an example embodiment, recency-and-frequency segment definitions in a tool such as Adobe® Audience Manager can be utilized to calculate audience segment sizes in near real-time. For example, a frequency segment definition can be expressed as “freq(purchased over past 7 days)>3” to correspond to a segment that includes visitors that have made a purchase more than 3 time over the past 7 days. Embodiments can calculate audience sizes over time for such segments in near real-time.
In some embodiments, audience segment sizes are calculated based on retrieved audience data. For example, if a time range of interest spans into the future, audience segment sizes for a plurality of durations in the time range can be calculated using past audience data (e.g., numbers of viewers/visitors within a defined audience segment at different points in time in the past) to predict future audience segment sizes. According to embodiments, future audience segment sizes can be predicted based on patterns (e.g., cyclical changes tied to a time of day and/or a day of week, upward and/or downward trends) exhibited in retrieved audience data. In cases where the time range of interest does not span into the future, estimates of audience segment sizes for a plurality of durations in the past can be calculated based in part on the patterns in the retrieved audience data and actual audience segment sizes in the past.
According to an embodiment, an audience size estimator user interface (UI) is generated that displays audience size estimates and graphically depicts audience size predictions. The audience size estimate UI can depict estimated historic segment sizes for past time periods as well as predicted segment sizes for future time periods (e.g., past and future durations). In some embodiments, a segment builder UI is provided for creating and editing audience segments. As a marketer creates a segment in the segment builder UI, a graph of the number of people/visitors in that segment over time can be displayed in the audience size estimator UI. By using the audience size estimator UI, the marketer can also see the predicted change in the number of people (e.g., visitors) in the segment in the future. The systems and methods described herein can calculate and present this graph in near real-time.
As used herein, the term “electronic content” refers to any type of media that can be rendered for display or played on computing devices. Computing devices include client and server devices such as, but not limited to, servers, desktop computers, laptop computers, smart phones, video game consoles, smart televisions, tablet computers, portable gaming devices, personal digital assistants (PDAs), digital video recorders (DVRs), remote-storage DVRs, interactive TV systems, and other systems capable of receiving and displaying electronic content and/or utilizing a network connection such as the Internet. An exemplary interactive TV system can include a television communicatively coupled to set top box (STB). Electronic content can be streamed to, downloaded by, and/or uploaded from computing devices. Electronic content can include multimedia hosted on websites, such as web television, Internet television, standard web pages, or mobile web pages specifically formatted for display on computing devices. Electronic content can also include application software developed for computing devices that is designed to perform one or more specific tasks at the computing device.
Unless specifically stated differently, a “user” is interchangeably used herein to identify a user account, a human user, or a software agent. Besides a human user who accesses and uses electronic content such as web pages and online advertisements (e.g., ads), a software application or agent sometimes accesses electronic content. Accordingly, unless specifically stated, the term “user” as used herein does not necessarily pertain to a human being.
As used herein, the term “audience” refers to any set or segment of past, current, or potential users of electronic content. An audience can be visitors to a web site. As used herein, the term “segment” refers to any class of users of electronic content. For example, a segment can be a class of web site visitors. A class can be defined by a specific set of criteria or attributes. For example, a class of users of electronic content who are English-speaking male over age 30 can be a segment. An audience can comprise a group of consumers or visitors having profiles associated with them. The profiles can include user characteristics based on prior visits to web sites, a current visit to a particular web site, and/or a user identifier (i.e., a user ID). A cookie can be used to identify a user, consumer, or web site visitor as a member of an audience segment. Users having at least one common attribute, such as, for example a demographic attribute, can be part of an audience. For example, a segment of users sharing one or more demographic attributes, such as females over age 30, can be included in a ‘female-over 30’ audience. Audiences can be defined in terms of demographic data such as, for example, nationalities, spoken languages, residence addresses, and/or business addresses associated with users. For example, an audience segment of ‘young men in San Jose’ can be defined for users who are males between 18 and 25 years old who live and/or work in San Jose, Calif. Audiences can also be based on time-based attributes such as, for example, a time of day electronic content is accessed, a range of dates content is accessed, a day of week of access, a month during which content is accessed, and/or a season during which access occurs. An audience can be an intersection of two or more segments, such as, for example, males under age 26 in San Jose who access electronic content on a Sunday evening.
As used herein, a “campaign” refers to a collection of components, such as ads, and rules related to a marketing initiative or promotional effort. A campaign can be defined by an advertiser or ad agency based on who the advertiser wants to reach in terms of certain categories or types of viewers (e.g., audience segments), within a certain time period (e.g., durations, time ranges, date ranges, seasons), within a certain geographic region (e.g., cities, states, countries), a and/or a certain number of impressions for ads (e.g., a number of times ads in the campaign are served up to video players). Campaigns can include linear ads, overlay ads, and other ads such as, for example, hypervideo ads. Hypervideo advertisements can be embodied as displayed video streams containing embedded, selectable or actionable (e.g., user-clickable) anchors. Ad playback information for such hypervideo advertisements can include events corresponding to a selection or click of an anchor. Marketers and advertisers may be interested in knowing a predicted or estimated number of ad impressions for certain audience segments over time. For example, such audience size estimates can be used to tailor campaigns so that they are targeted in part to geographic regions included in certain time zones where video content and ads inserted into the video content will be placed. A campaign can include a set of rules defining what instances of components should be shown to whom, and when to show the instances. An exemplary campaign can include a plurality of instances of ads embodied as a set of alternative ads. In certain embodiments, for ads included in a campaign, all ad instances and offers are stored in that campaign along with the rules as campaign data. Various embodiments enable each offer in a campaign to be associated with a specific segment of users of electronic content (e.g., an audience segment). According to these embodiments, when a visitor arrives at a web page containing a targeted ad, the ad dynamically selects and displays an appropriate offer according to segment information for the visitor in accordance with rules defined for a campaign including the component. A given set of electronic content can include one or more ad that could be targeted (i.e., targetable ads). In certain embodiments, multiple campaigns can be applicable to that electronic content. According to these embodiments, each of these campaigns can include respective data for all of the variants of the targetable ads and actual content such as image, video and text assets comprising the ads. Campaign rules determine what will be displayed inside electronic content, such as a web page. Campaign data can include logic (i.e., targeting rules) for determining which ads are to be displayed to certain segments or audiences of users. Campaign data indicates target segments and audiences a campaign is interested in.
As used herein, the term “advertiser” refers to any potential purchaser or buyer of items from an inventory (i.e., ad slots) of a publisher. Advertisers can be ad agencies, marketers, or ad networks that supply and deliver ads. Advertisers may also be the originators of media buys, campaigns, and creatives. As shown in
As used herein, the term “ad server” refers to an actual ad delivery system configured to deliver ads to visitors to a website. In various embodiments, an ad server delivers ads to be viewed on a publisher's website, such as publisher website 114 shown in
“First,” “Second,” etc. As used herein, these terms are used as labels for nouns that they precede, and do not imply any type of sequencing or ordering (e.g., spatial, temporal, logical, etc.). For example, for a segment analysis module evaluating the qualification of a visitor according to a segment rule that includes first and second visitor traits, the terms “first” and “second” visitor traits can be used to refer to any two visitor traits that are part of the segment rule. That is, the “first” and “second” traits are not limited to logical traits 0 and 1.
Referring now to the drawings,
While certain embodiments are described in terms of online advertising and marketing, similar techniques may apply in customer segmentation, content customization, and/or variable pricing of products and services.
Some embodiments can include a means for real-time and back end segment qualification and/or a means for audience size estimation using audience data shared between back end and real-time systems. For example, a segment analysis module (e.g., segment analysis modules 120, 218, and 313 of
In some embodiments, these techniques may be used in measuring the size of a segment audience, and in qualifying visitors in a segment. The segment audience size and visitor qualifications can then be used in an advertising campaign, email campaign, content customization, or for analytics purposes, among other example applications. Although certain embodiments and applications are described in terms of segment qualification, audience size estimation, and online advertising, it should be noted that the same or similar principles may be applied in other fields.
Moreover, although certain embodiments are described with respect to a webpage and/or website, it will be appreciated that the techniques disclosed herein may be employed with other forms of network content sites, such as documents with a traversable tree-like hierarchy (e.g., XML and HTML documents).
Advertisers/agencies 102 may be the buyers of ad impression opportunities for online advertising. They may also be the originators of media buys, campaigns, and creatives. In the example shown, advertisers/agencies 102 may be communicatively coupled (e.g., over a network) to demand side platform (DSP) 104 and data management platform (DMP) 108. Coupling to DMP 108 may be via advertiser website 106. In one embodiment, advertisers/agencies 102 may provide product content to advertiser website 106, where the product content may be presented for display and/or to generate a display. Advertiser website 106 may be network content owned by or otherwise associated with advertisers/agencies. Advertiser website 106 may collect data regarding visitors to the website and provide such data to DMP 108.
DMP 108 can include segment analysis module 120. Segment analysis module 120 may collect, aggregate, store, combine, and/or provide insights on audience behavioral and demographic statistics and data to advertisers/agencies 102 and publishers/customers 112. For example, segment analysis module 120 may determine real-time and back end segment qualification, and perform audience size estimation. The audience size estimation can be performed for a time series across multiple points of time in the past. DMP 108 can facilitate customers 112 easily ingesting data from multiple online and/or offline data sources. As shown, DMP 108 may receive offline audience data (also referred to as network content data) from advertisers/agencies 102 and/or publishers 112. Additionally, audience data may be collected at advertiser web site 106 and delivered to DMP 108. In some embodiments, DMP 108 may additionally or alternatively collect audience data from publisher website 114. Not shown in
DSP 104 may be an advertising campaign management application which allows advertisers to manage their campaign/creative bidding rules, use audience data at scale and/or bid on available display advertising inventory. In one embodiment, advertisers/agencies 102 may provide ad campaigns, creative, and bid rules to DSP 104. In some embodiments, DSP 104 may be integrated with SSP 110 and perform ad exchanges via real-time bidding (RTB) server-to-server pipes. As shown in
In one embodiment, SSP 110 is a platform configured to aggregate publisher ad inventory supply and allow publishers 112 to leverage audience data for revenue optimization. As noted above, SSP 110 may be integrated with DSP 104 via RTB interfaces. In some embodiments, SSP 110 may receive a get ad request from publisher website 114 and in response, provide an ad, or facilitate provision of an ad, to publisher website 114 so that publisher website 114 can present the ad to end consumer/visitor 118.
Onsite ad server 116 may manage guaranteed advertising buys and ad insertions onto publisher pages. Examples include DoubleClick for Publishers (DFP) and Open Ad Stream (OAS). Onsite ad server 116 may receive a get ad request from publisher website 114 and provide an ad, or facilitate providing of an ad, to publisher website 114 to present to end consumer/visitor 118.
Publisher(s) 112 may include supplier(s) of ad inventory (e.g., available ad slots on pages). Publisher(s) 112 may send audience data in real-time to DMP 108 as end consumers/visitors 118 browse publisher web site 114. As described above, publisher(s) 112 may also provide offline audience data to DMP 108. In one embodiment, such offline audience data may be provided in bulk feed form.
Publisher website 114 may include network content, such as an automobile shopping website, a webmail website, etc. Publisher website 114 may be executable by a client device of end consumer/visitor 118. For example, the client device may include an application (e.g., an Internet web-browser application) that can be used to generate a request for content, to render the requested content, and/or to communicate requests to various devices on the network. For instance, in response to receiving a selection of a website link on a webpage displayed to consumer/visitor 118 or responsive to receiving a request to navigate to a uniform resource locator (URL) in a browser application, the browser application may submit a request for the corresponding webpage/content associated with the URL to a content server (not shown), and content server may provide corresponding content, including an HyperText Markup Language (HTML) file. The HTML file can then be parsed and executed by the browser application to render the requested website for display to the consumer/visitor 118. In some instances, execution of the HTML file may cause the corresponding webpage/content, such as publisher website 114 to provide real-time audience data regarding the web browsing of the consumer/visitor 118 to segment analysis module 120. While a webpage is discussed as an example of the network content available for use with the embodiments described herein, as would be readily apparent to one of ordinary skill in the art, other forms of electronic content, such as audio, image, or video files, may be used without departing from the scope and content herein disclosed. Likewise, while references herein to HTML and the hypertext transfer protocol (HTTP) protocol are discussed as an example of the languages and protocols available for use with the embodiments described herein, one of skill in the art will readily realize that other forms of languages and protocols, such as, but not limited to Extensible Markup Language (XML), file transfer protocol (FTP), Internet Protocol television (IPTV), real-time messaging protocol (RTMP), HTTP dynamic streaming (HDS), HTTP Live Streaming (HLS), and Dynamic Adaptive Streaming over HTTP (MPEG-DASH) may be used without departing from the scope and content herein disclosed.
Each of DSP 104, advertiser website 106, DMP 108, SSP 110, onsite ad server 116, and publisher website 114 may be communicatively coupled to one another via a network (not shown). The network may include any channel for providing communication between each of the entities of system 100. In an embodiment, the network may be a data communications network such as the Internet. In one or more embodiments, the network can be one of or a combination of networks such as Hybrid Fiber Coax, Fiber To The Home, Data Over Cable Service Interface Specification (DOCSIS), a Wide Area Network (WAN), WiFi, a Local Area Network (LAN), or any other wired or wireless network. The network may include a single network or combination of networks that facilitate communication between each of the entities (e.g., advertiser website 106, DMP 108, SSP 110, onsite ad server 116, and publisher website 114) of system 100. In some embodiments, various components of system 100 may be collocated (e.g., DSP 104 and SSP 110) as part of the same computing device or devices. In one embodiment, one or more of the components of system 100 may be remote from each other (e.g., hosted on or located on different computing devices connected over the network). In some embodiments, various components of system 100 may be cloud based. For example, one or more components of system 100 may be a virtual server implemented using multiple computing systems or servers connected in a grid or cloud computing topology. One or more of the servers in system 100 may have a single processor in a multi-core/multiprocessor system. Such a system can be configured to operate alone with a single server, such as server 116 or 122, or in a cluster of computing devices operating in a cluster or server farm. For example, one or more of DMP 108, DSP 104, and/or SSP 110 can be implemented on multiple computing devices. According to one such embodiment, DMP 108 may be implemented as a distributed system on a number of cloud nodes and on a number of servers.
While segment analysis module 120 is shown in
Audience data, whether real-time or offline audience data (e.g., first party or from a third party data provider), may include a variety of information, such as cookies, hits, page views, visits, sessions, downloads, first visits, first sessions, visitors, unique visitors, unique users, repeat visitors, new visitors, impressions, singletons, bounce rates, exit percentages, visibility time, session duration, page view duration, time on page, active time, engagement time, page depth, page views per session, frequency, session per unique, click path, click, site overlay, behavioral traits, user intents, user interests, demographic data, etc. The various data may describe usage and visitation patterns for websites (e.g., publisher website 114, advertiser website 106, etc.) and/or individual webpages within the website. The various data may include information relating to the activity and interactions of one or more users/visitors with a given website or webpage. For example, audience data may include historic and/or current website browsing information for one or more website visitors, including, but not limited to identification of links selected, identification of web pages viewed, related content topics, and other data that may help gauge user interactions with webpages/websites.
In some embodiments, audience data may include information indicative of a location associated with audience members. For example, audience data may include location data indicative of a geographic location of the client device of consumer/visitor 118. Non-limiting examples of these embodiments are shown in
In some embodiments, audience data is accumulated over time to generate a set of audience data (e.g., offline audience data) that is representative of activity and interactions of one or more users with a given website or webpage. In one embodiment, such an accumulation may be performed by various publishers/customers with respect to audience data generated through web sites/network content that the publishers/customers own. In one embodiment, such an accumulation may be performed by third party data providers.
The data (e.g., real-time and/or offline) may be used to qualify visitors in a segment, according to the disclosed techniques, and/or to perform real-time audience size estimation for a segment. Such a qualification of a visitor may be usable to select an ad to display to consumer/visitor 118. Audience size estimation may be usable to evaluate a worth of a segment. For example, if the segment is sufficiently large, then it may be worth commencing a new advertising campaign or continuing an existing one. Conversely, if the segment is small, then it may not be worth commencing a new advertising campaign directed toward that segment or it may be determined that a new outreach campaign should be commenced to increase the size of the segment.
In some embodiments, segment analysis module 120 may include computer executable code (e.g., executable software modules) stored on a computer readable storage medium that is executable by a computer to provide associated processing. For example, segment analysis module 120 may process real-time and/or offline audience data to perform the techniques described herein.
In some embodiments, a customer/publisher/content provider 112 may log-in to a website or some other user interface/network portal, for example, hosted by DMP 108, and may interact with audience data to create, modify, and/or apply a segment rule. The segment rule may then be usable to determine an audience size and/or qualify visitors in real-time or as an offline back end qualification. Such determinations may be usable to select and place an online ad for one or more visitors.
Segment analysis module 218 can receive, via user interface/application programming interface (UI/API) portal 220 from publisher/customer 202, input that defines a segment rule. Such input may be provided via touch-screen, mouse, keyboard, or other suitable device. Such input can comprise selection of one or more visitor traits, Boolean operator(s), recency/frequency requirements, and/or destination rules, among other inputs. A visitor trait may correspond to a single data collection event from received audience data that is descriptive of a visitor of network content. Example visitor traits can include, but are not limited to a gender (e.g., male or female), a geographic area (e.g., living or working in San Jose), an age range (e.g., 18-25 years old), an expensive camera shopper/buyer, high-end car shopper/buyer, laptop shopper/buyer, etc. For example, the expensive camera shopper trait may correspond to data collection events indicative of browsing for a camera over $500. Based on the received input, a segment rule may be created. The inputs and/or segment rule may persist in customer/control data database 222. Customer/control data database 222 may provide customer segment and destination rules as an asynchronous feed to data collection server(s) 226 of edge servers 230 and segment rule processor 224. Segment rule processor 224 may interface with both real-time component 228 and back end component 232 such that the same segment rule may be used by either or both components. In the exemplary embodiment shown in
Segment analysis module 218 may then perform the techniques described below with reference to
Segment analysis module 312 may receive input, via UI/API portal 314, from publisher/customer 302. Such input may be provided via touchscreen, mouse, keyboard, or other suitable device. As described herein, the input may include one or more traits, Boolean operator(s), recency/frequency requirements, and/or destination rules, time ranges, among other inputs. Based on such input, a segment rule may be created and/or an audience may be defined. In an audience size determination application, a synchronous query for audience size may be sent from UI/API portal 314 to audience size estimator 316 and a real-time determination of the audience size may be provided from audience size estimator 316 to UI/API Portal 314. A similar synchronous query and result can also take place between audience size estimator 316 and audience indexes/database 218.
Back end component 322 may receive, via an asynchronous data aggregation feed from data collection servers 320, first party data via HTTP data collection requests from client browser 310, which may be executed on a client device of consumers/visitors 306. Back end component 322 may also receive third party data from data provider servers 326 of data partners 324. Third party data may be received as a bulk asynchronous feed. Back end component 322 may then provide visitor/trait data from the first and third party data in an asynchronous feed to audience indexes/database 318, upon which the data may be used to determine audience segment size, as described herein. In an embodiment, the audience indexes/database 318 can be implemented as an in-memory database for performance reasons.
Segment analysis module 312 may then perform the methods described below with reference to
With continued reference to
The architecture further includes a master cluster 438 of servers 440A-440N. As shown in
Periodically (e.g., daily), a controller 446 sub-samples a set of visitors and inserts their traits into master cluster 438. The visitor traits are indexed by the time/date they occurred. A periodic ingest job 448 is used to insert the data into a cluster 450. As shown, controller 446 and cluster 450 can be implemented using an Apache Hadoop framework. The Hadoop framework can facilitate storage and large-scale processing of data sets on clusters of servers. After the trait data is inserted into cluster 450, master cluster 438 can do an import 444 to import the data.
Embodiments using Solr servers as slave servers 436A-N and/or master servers 440A-N can utilize an expressive query language supported by Solr servers. According to these embodiments, web server 432 can be configured to translate the audience segment definitions indicated in segment size request 428 into a set of Solr queries. For example, the audience segment (genderMale AND livesInSanJose) can be translated into the Solr query: (genderMale_<1dayAgo>:* or genderMale_<2daysAgo>:* or genderMale_<3daysAgo>:*) and (livesInSanJose_<1 dayAgo>:* or livesInSanJose_<2daysAgo>:* or livesInSanJose:<3daysAgo>). When this query is sent to slave cluster 434, the architecture will return the number of visitors that are in the segment for a given period (e.g., a given day). To obtain the time series, multiple queries are submitted.
Web server 432 is used to convert the audience segment indicated in request 428 into multiple queries (e.g., multiple Solr queries or multiple queries for an in-memory database). Although only a single web server 432 is depicted in
Exemplary Methods
The method begins at block 500 when it is determined that a visitor qualifies to be included in an audience segment according to a segment rule. This step can include evaluating network content data. Network content data may include audience data, such as, but not limited to, real-time audience data and offline audience data. In certain embodiments, at least a portion of the network content data can be received from a third party data provider. For example, various network content sites can provide data about visitors to the sites to a third party data provider, who in turn can aggregate that data and provide it to the segment analysis module.
In one embodiment, at least a portion of the network content data that is received from the third party data provider may be matched with at least some other of the network content data that is associated with the visitor's visit to the first network content. The result of the matching may be matched data. Determining that the visitor qualifies in the segment may be based on the matched data. As an example of matched data, a visitor may have logged into network content under a profile associated with the visitor (e.g., logged into a social networking website, shopping website, forum, etc.) or may have entered data into an online form. Data created during the visit and logging in under a profile or entering information into an online forum may include demographic information (e.g., name, age, gender, occupation, etc. Data created during that visit may also include a visitor identifier (e.g., an IP address, a MAC address, a unique visitor ID, etc.) associated with the visitor. The data from the third party provider may also include the same visitor identifier thereby allowing data having the same visitor identifier to be matched. As an example, it may be known that visitor A is male because he logged in under his profile to a social networking site. Third party provider may have data from months ago that includes the visitor's IP address that matches the visitor's IP address from where the visitor logged in to the social networking website. Therefore, the data from the third party provider may then be determined as corresponding to a male visitor.
As shown in
Evaluating network content data by the backend component may include performing a full table scan on an HBase cluster (or comparable data store) that stores billions of visitor profiles. Each profile includes network content data associated with one of the visitors. Moreover, evaluating network content data (by the real-time and/or back end) may include evaluating the network content data for all of the active segment rules. Note that many segment rules may exist at once but for ease of explanation, much of the description focuses on a single segment rule. In various embodiments, evaluating network content data may be performed periodically (e.g., hourly, daily, weekly, etc.) by the back end component. Periodic evaluations may result in updated visitor qualifications based on a new, updated, or changed segment rule, or based on new data that allows a visitor to be qualified under the segment rule.
In some embodiments, at least a portion of the network content data, which is evaluated according to the segment rule, may be associated with the visitor's visit to first network content.
In certain embodiments, determining that a visitor qualifies in the segment may be performed after or in response to a change to a previous segment rule. For example, the previous segment rule may have defined a male, high-end car segment as a visitor who is male and has visited a site related to a high-end car (e.g., a car with a manufacturer suggested retail price/MSRP>$50,000), and has visited such a web site (or distinct webpages) five times in the previous three days. The updated segment rule may lower the threshold of high-end car to MSRP>$40,000. Accordingly, qualification of the visitor in the segment may be determined automatically in response to the update to the segment rule or it may require user input to determine qualification under the updated segment rule.
In some embodiments, the segment rule may be created after the visitor has completed visiting the first network content. For example, similar to the example above, a segment rule for a male, high-end car segment may be created after the visitor has already visited a high-end car web site the requisite number of times to qualify in the segment. Moreover, the segment rule may be created after the visitor has left the first network content such that the visitor is not actively browsing the first network content. Accordingly, the visitor may be qualified, for example, by the back end component, after such a visit to the network content is complete by using the newly created segment rule and offline data (e.g., first party from the customer who owns the first network content or from a third party data provider, etc.).
In some embodiments, a visitor may qualify in a segment based entirely on third party provided data. Such embodiments may permit a customer to target an ad to a visitor who has never visited a website associated with the customer. In other embodiments, first party data (e.g., from the visitor's visit to a website), third party data, or some combination of first and third party data may be used to qualify the visitor in a segment.
In one embodiment, the same segment rule and same code libraries may be shared across both the real-time component as well as the back end component application. The code libraries may exist on each of the data collection servers of the real-time components and also on a plurality of nodes of the back end components. In one embodiment, the back end component application may be a Map-Reduce application implemented in Java. The same core logic may be used in both the real-time and back end components. The data storage layer for the real-time component, though, may be different than the data storage layer for the back end component. In the case of the real-time component, the data storage layer may be a combination of visitor cookie data as well as behavioral trait data stored in the real-time profile cache server (PCS) machines. In one embodiment, the data storage layer for the back end component may be a distributed data store implemented in the cloud, such as an HBase cluster of open source, non-relational, distributed databases.
At 510, an indication of the visitor's segment qualification may be stored in the real-time component. In an embodiment where determining that the visitor qualifies in the segment is performed by the back end component, storing the indication may include the back end component providing the indication to the real-time component. Note that an indication of each segment that the visitor qualifies for may be stored in the real-time component. For example, a visitor may qualify for multiple segments, each of which may be represented in the real-time component by an indication of the qualification. After the visitor's segment qualification is stored, control is passed to block 520.
In block 520, the visitor is included in audience data for the segment. This inclusion is based at least in part on the visitor's qualification in the segment and a time of the qualification. As shown, this step can be accomplished by performing a periodic ingest job (e.g., a daily job) and importing the ingested data into the audience data (e.g., via import 444 into a cluster). For example, block 520 can comprise periodically using controller 446 to sub-sample a set of visitors and inserting their traits into master cluster 438. The visitor traits can then be indexed by the time and date they occurred. Periodic ingest job 448 can be run as part of block 520 to insert the data into cluster 450. After the trait data is inserted into cluster 450, master cluster 438 can do an import 444 to import the data.
Next, at block 530, a request for segment size(s) is received. As shown in
At block 540, audience data is retrieved. As shown, this step can comprise translating request received at block 530 into queries for audience data and then submitting the queries. In an embodiment, Web server 432 is used at block 540 to convert the audience segment indicated in the request into multiple queries (e.g., multiple Solr queries). In an additional or alternative embodiment, web server 432 can also be used in block 540 to concatenate all individual requests received at block 530 into a time series.
At this point, in block 550, audience segment sizes over time are calculated based on the audience data retrieved at block 540. As depicted in
Turning now to
As shown at block 600, a selection of a visitor trait may be received. As described herein, the visitor trait may be descriptive of visitors of network content. Example traits can include, but are not limited to, male, car buyer, between ages 18 and 25, living in San Jose, interested in laptops, etc. Selection of a visitor trait may be received via a user interface or portal, for example, from a customer associated with network content. Example user interfaces are shown in
At block 610, a segment rule may be created that is usable to evaluate a combined recency and frequency of one or more qualification events together. The qualification events may indicate qualification of a visitor according to a trait. The one or more qualification events may be based on collected network content data (e.g., audience data) associated with a plurality of visitors. Each of the one or more qualification events may correspond to qualification of a separate portion of the collected network content data according to the trait.
In contrast to a system that separately evaluates recency and separately evaluates frequency, evaluating a combined recency and frequency of one or more qualification events together may include evaluating a number of qualification events over a defined period of time (e.g., the visitor qualified x times over the past y days). A system that separately evaluates recency and frequency may evaluate a most recent event and separately evaluate a frequency counter of the event (e.g., the visitor qualified x times total and qualified once in the past y days). The following example illustrates the difference. Consider a scenario in which visitor A has visited a high-end car website once a month for the past five months with the most recent visit occurring two days ago. At block 610, a segment rule may be created that defines a high-end car buyer segment as someone who has visited a high-end car website five times in the past three days. Visitor A, who has visited such a website once a month for the past five months with the most recent visit being two days ago, would not qualify according to the five times in the past three days combined recency and frequency segment rule. However, in a system that considers the recency and frequency separately, visitor A would qualify under frequency as having visited a high-end car website five times total and would also qualify under recency because they visited a high-end car website two days ago. Marketers are often interested in the intensity with which visitors are associated with a visitor trait. Evaluating the combined recency and frequency may reflect the intensity, whereas separate recency and separate frequency does not reflect such intensity. In the preceding example, visitor A is not associated with very much intensity, and therefore may be viewed, by marketers and advertisers, as a casual browser of the high-end car website rather than a serious shopper for or consumer of high-end cars.
In accordance with an embodiment, each of the one or more qualification events may be associated with a time of occurrence of the respective qualification event. Each time of occurrence may be stored with an indication of the respective qualification event, as opposed to a system that only stores the most recent timestamp of a qualification event. Such times of occurrence may be usable in generating a time series of audience data for a segment. Such times of occurrence may also be usable in evaluating the combined recency and frequency according to the segment rule.
In certain embodiments, an indication of each of the one or more qualification events may be stored (e.g., in a data store). In some embodiments, the data store may be local to the real-time component, such as in a data store that is quickly accessible (e.g., an in-memory database, a GPU database, or a data cache). In some embodiments, the indication may be stored in the back end component in addition to or instead of storing the indication local to the real-time component.
In an embodiment in which multiple visitor traits are selected, a selection of an operator for the segment rule may be received. The operator may be for a Boolean expression that includes the trait and another trait. For instance, the segment rule may include one or more instances of the operators AND, OR, NOT AND, or NOT OR. For example, a segment rule may be male AND 18-25 years of age AND living in San Jose AND a high-end camera buyer. The combined recency and frequency discussed above may be applied across one or more of the multiple visitor traits, such as, for example, the high-end camera buyer trait. Moreover, an audience segment rule having Boolean expressions may have such Boolean expressions in addition to or instead of combined recency and frequency. Thus, in certain embodiments, a segment rule may not use the described combined recency and frequency. Instead, these embodiments may use one or more Boolean expressions, such as, for example, male AND 18-25 years of age AND living in San Jose. Further, for example, an audience segment rule can be defined for a high-end camera buyer AND high-end car buyer. The combined recency and frequency can be applied to either high-end camera buyer, high-end car buyer, or both traits. According to some embodiments, the segment rule may be an n-level deep nested Boolean expression. Such an example is illustrated in trait code view 1902 of
As illustrated at 620, the combined recency and frequency of the one or more qualification events can be evaluated according to the segment rule. Evaluating the qualification events according to the segment rule may include reading the qualification events from the data store, which may include reading a respective time of occurrence of the respective qualification events. In one embodiment, evaluating the qualification events according to the segment rule may include estimating the segment population size based on the stored indications of each of the one or more qualification events. Such estimating can be performed as part of block 650, which is described below.
In accordance with embodiments, an ad to be provided to the visitor of network content may be determined as part of block 620. The specific ad chosen can be selected by an ad server (e.g., onsite ad server 116). In other instances, the segment analysis module may select the ad or simply provide an instruction that indicates which segments a visitor qualifies for. That instruction may be accompanied with indications of which ads a particular visitor has already seen such that any ad viewing limits may be respected. The evaluating of block 620 may result in the visitor qualifying for a segment being associated with a particular ad.
After the combined recency and frequency of the qualification events are evaluated, control is passed to block 630.
At block 630, a request for segment size(s) is received. As depicted in
At block 640, audience data is retrieved. As shown, this block can comprise translating request received at block 530 into queries for audience data and then executing the queries to obtain a time series of audience data. In one embodiment, Web server 432 can be used at block 640 to convert the audience segment indicated in the request into multiple queries (e.g., multiple queries to be executed by an in-memory database). In an additional or alternative embodiment, web server 432 can also be used at block 640 to concatenate all individual requests received at block 630 into the time series.
Next, at block 650, audience segment sizes over time are calculated based on the audience data retrieved at block 640. As illustrated in
The segment population size may indicate the number of unique visitors that qualify under a segment according to the segment rule. The segment population size may be performed for one or more time ranges. For example, the segment population size may be for the last 7 days, last 30 days, last 60 days, etc. Estimating the audience segment size (e.g., population) for historic time periods and time ranges may include evaluating the combined recency and frequency of each qualification event (e.g., including a time of occurrence associated with each respective qualification event) within the time periods and ranges of interest.
In one embodiment, estimating the segment population size may include executing a search query on a data store of the back end component that searches millions of visitor profiles (from first and third party data) nearly immediately. The search service may be referred to as a distributed search cluster service with the result being a real-time estimation of the segment population size. In some embodiments, the search service may be implemented as an Apache Solr cluster of the segment analysis module or a comparable open source search tool/platform of the segment analysis module. For example, all the visitor profiles and traits may be stored in a Solr cluster. Upon receiving an audience definition/segment rule (e.g., via a UI/portal), a query to search the Solr cluster may be executed. The Solr cluster may then return the number of visitors qualifying under the audience definition. Other embodiments may use a traditional database.
The segment population size for the time period (e.g., for one time period) and other respective segment population sizes (e.g., for other time periods) may be provided for display. The segment population size and each of the other respective segment population sizes may each correspond to a respective time range of a plurality of time ranges. Example displays of such segment population sizes are shown in
In one embodiment, future segment population sizes can be estimated based on the estimated segment population sizes. For example, the system may determine that the estimated segment population size has a trend of a 10% increase for each 60-day period. Therefore, the system may estimate that the future segment population size may continue to increase by 10% for subsequent 60-day periods.
After the audience segment sizes are calculated, control is passed to block 660.
In block 660, a report is generated that indicates the estimated historic segment sizes and predicted future segment sizes across the time range. The report generated in this step can be a bar chart graphically depicting daily estimated historic segment sizes in the past as well as daily predicted future segment sizes. Non-limiting examples of such reports are depicted in
The disclosed techniques may have the speed advantages of real-time segment qualification along with the data storage scale advantages of back end segment qualification. The back end segment qualification additionally permits qualification of visitors even without ever seeing that visitor again. Moreover, by using real-time qualification in conjunction with backend qualification, there may be less discrepancy rates as opposed to simply using real-time qualification and waiting for the visitor to stick around. Also, the disclosed techniques may facilitate qualifying visitors as members of audience segments in real-time and support near real-time audience segment size estimation. Additionally, the disclosed techniques may provide real-time feedback to a customer on segment population size as the segment rule is being defined, something that otherwise may not be achieved with a traditional relational database system.
Data about the visitor's visit and/or about the visitor may be collected through customer web site 710 and provided to edge data centers 718, which maintain the traits and segments. The DCS may store trait qualification activity to PCS, which may be a low latency edge cache database (e.g., implemented on top of a Not Only SQL/NoSQL database technology). The DCS may read times of trait qualifications of visitors from the PCS in real-time to determine segment qualification. Note that back end ingested trait data may also be stored at edge data centers 718 to take advantage of real-time speed with greater flexibility in segment qualification. Log files that include information about the visitor and what the visitor did during the visit may then be sent to cluster (visitor database) 740, which is implemented in the cloud in the illustrated embodiment. Sending of the log files may be periodic (e.g., every 10 minutes). Cluster (visitor database) 740 may store a large number of visitor profiles (e.g., more than 7 billion). Once that data is written to cluster (visitor database) 740, which may be part of the back end component, it may be written out to analytics cluster (raw logs) 738. Analytics cluster (raw logs) 738 allows for an association between visitor and trait. Periodically (e.g., once a day, twice a day, etc.), analytics cluster 738 may then write its stored data to Solr 732 to update the Solr indexes. That data may include a trait/time combination for each visitor for each qualification event. Estimates of segment size may then be performed according to the techniques disclosed herein. Such estimates may be provided as custom reports 708 and/or as data for display at UI portal and APIs 704 for customer 702.
From the viewpoint of customer 702, UI Portal and APIs 704 may call a customer application programming interface (API) that interacts with a cluster, such as, for example, a Solr cluster, to generate queries on the dataset and return results and a confidence interval based on the data set result size. This can result in customer 702 receiving nearly instantaneous feedback, even while creating and using extremely complex segments. For example, customer 702 may define an audience segment rule by inputting visitor traits, destination rules, and/or Routing Information Field (RIF) operators, among other inputs. That rule is stored and persisted in control database 720. Via configurator 722, tag code, visitor traits, audience segments, and destinations can be shared between the real-time component (including edge data centers 718) and the back end component (including cluster 740 and analytics cluster 738). TIM Tags, Destination Publishing, and Data Collection Scripts may then be pushed through a cloud-based server 716 to the DIL/TIM container of customer site 710. The destination IFRAME may be used for performing ID sync calls via ID sync 750. For example, the destination IFRAME may be used when trying to match a visitor ID with an ID from third party data for server-to-server file transfers. The IFRAME is a separate frame that can have a different source from the current domain that the visitor's browser is in. The IFRAME may be hidden from the visitor such that the visitor never sees it. Therefore, data transfers and data synchronization may be performed without affecting the end user/visitor. Additionally, the IFRAME is boxed off from a security standpoint. Whatever happens in the IFRAME cannot affect the customer domain. Using the IFRAME and the call to the DCS, a URL destination can be sent to send information to a third party.
Data provider partner 754 represents a third party data provider. Third party data may be matched to other data via ID sync 750. Third party data (and first party data from edge data centers 718) may be processed by inbound feed converter 744 before being stored in the backend (e.g., cluster 740). Outbound feed converter 742 of the back end component may likewise process data from the backend before sending it to SFTP publishers 746 and HTTP/HTTPS feed submitter 748. Such data from the back end may be provided to data provider partner 754.
As shown in the right side of
In
In one or more embodiments, the user interfaces and reports shown in
In response to receiving a selection of a hyperlinked visitor trait name in segment builder 902 (e.g., visitor trait name 910 of
As shown in example interface 1400 of
Exemplary Computer System Implementation
Although exemplary embodiments have been described in terms of apparatuses, systems, services, and methods, it is contemplated that certain functionality described herein may be implemented in software on microprocessors, such as a microprocessor chip included in computing devices such as the computer system 2100 illustrated in
Embodiments of a segment analysis module and/or of the various disclosed techniques described herein may be executed on one or more computer systems, which may interact with various other devices. One such computer system is illustrated by
In different embodiments, computer system 2100 may be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, a consumer device, video game console, handheld video game device, application server, storage device, one or more servers, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device.
To implement the various features and functions described above, some or all elements of the computing devices, platforms, databases (e.g., databases 222 and 313 of
Aspects of the present invention shown in
If programmable logic is used, such logic may execute on a commercially available processing platform or a special purpose device. One of ordinary skill in the art may appreciate that embodiments of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device.
For instance, at least one processor device and a memory may be used to implement the above described embodiments. A processor device may be a single processor, a plurality of processors, or combinations thereof. Processor devices may have one or more processor “cores.”
Various embodiments of the invention are described in terms of this example computer system 2100. After reading this description, it will become apparent to a person skilled in the relevant art how to implement the invention using other computer systems and/or computer architectures. Although operations may be described as a sequential process, some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multiprocessor machines. In addition, in some embodiments the order of operations may be rearranged without departing from the spirit of the disclosed subject matter.
Processor device 2104 may be a special purpose or a general purpose processor device. As will be appreciated by persons skilled in the relevant art, processor device 2104 may also be a single processor in a multi-core/multiprocessor system, such system operating alone, or in a cluster of computing devices operating in a cluster or server farm. Processor device 2104 is connected to a communication infrastructure 2106, for example, a bus, message queue, network, or multi-core message-passing scheme. In certain embodiments, a processor of one or more of the computing devices, platforms, and servers 116, 122, 214, 216, 320, 326, 432 described above with reference to
Computer system 2100 also includes a main memory 2108, for example, random access memory (RAM), and may also include a secondary memory 2110. Secondary memory 2110 may include, for example, a hard disk drive 2112, removable storage drive 2114. Removable storage drive 2114 may comprise a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, or the like. In non-limiting embodiments, one or more of the memories of the computing devices, platforms, and servers 116, 122, 214, 216, 320, 326, 432 of
The removable storage drive 2114 reads from and/or writes to a removable storage unit 2118 in a well known manner. Removable storage unit 2118 may comprise a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 2114. As will be appreciated by persons skilled in the relevant art, removable storage unit 2118 includes a non-transitory computer readable storage medium having stored therein computer software and/or data.
In alternative implementations, secondary memory 2110 may include other similar means for allowing computer programs or other instructions to be loaded into computer system 2100. Such means may include, for example, a removable storage unit 2122 and an interface 2120. Examples of such means may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or EEPROM) and associated socket, and other removable storage units 2122 and interfaces 2120 which allow software and data to be transferred from the removable storage unit 2122 to computer system 2100.
Computer system 2100 may also include a communications interface 2124. Communications interface 2124 allows software and data to be transferred between computer system 2100 and external devices. Communications interface 2124 may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, or the like. Software and data transferred via communications interface 2124 may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals capable of being received by communications interface 2124. These signals may be provided to communications interface 2124 via a communications path 2126. Communications path 2126 carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link or other communications channels.
As used herein the terms “computer readable medium” and “non-transitory computer readable medium” are used to generally refer to media such as memories, such as main memory 2108 and secondary memory 2110, which can be memory semiconductors (e.g., DRAMs, etc.). Computer readable medium and non-transitory computer readable medium can also refer to removable storage unit 2118, removable storage unit 2122, and a hard disk installed in hard disk drive 2112. Signals carried over communications path 2126 can also embody the logic described herein. These computer program products are means for providing software to computer system 2100.
Computer programs (also called computer control logic) are stored in main memory 2108 and/or secondary memory 2110. Computer programs may also be received via communications interface 2124. Such computer programs, when executed, enable computer system 2100 to implement the present invention as discussed herein. In particular, the computer programs, when executed, enable processor device 2104 to implement the processes of the present invention, such as the steps in the methods illustrated by the flowcharts of
In an embodiment, display devices used to display interfaces of servers 116, 122, 214, 216, 320, 326, 432 of
Embodiments of the invention also may be directed to computer program products comprising software stored on any computer useable medium. Such software, when executed in one or more data processing device, causes a data processing device(s) to operate as described herein. Embodiments of the invention employ any computer useable or readable medium. Examples of computer useable mediums include, but are not limited to, primary storage devices (e.g., any type of random access memory), secondary storage devices (e.g., hard drives, floppy disks, CD ROMS, ZIP disks, tapes, magnetic storage devices, and optical storage devices, MEMS, nanotechnological storage device, etc.), and communication mediums (e.g., wired and wireless communications networks, local area networks, wide area networks, intranets, etc.).
While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude inclusion of such modifications, variations, and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. Indeed, the methods and systems described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the methods and systems described herein may be made without departing from the spirit of the present disclosure. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the present disclosure.
Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform.
The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general-purpose computing device to a specialized computing device implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device.
Embodiments of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied —for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel.
Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain examples include, while other examples do not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more examples or that one or more examples necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular example.
The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. The use of “adapted to” or “configured to” herein is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Similarly, the use of “based at least in part on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based at least in part on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting.
The various features and processes described above may be used independently of one another, or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of the present disclosure. In addition, certain method or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states relating thereto can be performed in other sequences that are appropriate. For example, described blocks or states may be performed in an order other than that specifically disclosed, or multiple blocks or states may be combined in a single block or state. The example blocks or states may be performed in serial, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed examples. Similarly, the example systems and components described herein may be configured differently than described. For example, elements may be added to, removed from, or rearranged compared to the disclosed examples.
