Patent Application
20190362013
The present disclosure generally relates to computer technology for solving technical challenges in quickly identifying search results and presenting the search results efficiently and effectively in a user interface. More specifically, the present disclosure relates to performing automated sourcing by automatically performing searches for online records of candidates based on one or more searches already performed, and displaying these additional online records simultaneously on a screen of a user interface.
The rise of the Internet has occasioned two related phenomena: the increase in the presence of online systems with connected members that have corresponding member profiles visible to large numbers of people, and the increase in use of these online systems for job searches, by applicants, employers, social referrals, and recruiters. Employers and recruiters attempting to connect candidates and employers, or refer them to a suitable position (e.g., job title), often perform searches on the online systems to identify candidates who have relevant qualifications that make them good candidates for whatever job opening the employers or recruiters are attempting to fill. The employers or recruiters then can contact these candidates to see if they are interested in applying for the job opening.
Traditional querying of online systems for candidates involves the employer or recruiter entering one or more search terms to manually create a query. A key challenge in a search for candidates (e.g., talent search) is to translate the criteria of a hiring position into a search query that leads to desired candidates. To fulfill this goal, the searcher typically needs to understand which skills are typically required for the position (e.g., job title), what are the alternatives, which companies are likely to have such candidates, which schools the candidates are most likely to graduate from, etc. Moreover, this knowledge varies over time. Furthermore, some attributes, such as the culture of a company, are not easily entered into a search box as query terms. As a result, it is not surprising that, even for experienced recruiters, many search trials are often required in order to obtain an appropriate query that meets the recruiters' search intent. Additionally, small business owners do not have the time to navigate through and review large numbers of candidates.
Furthermore, presentation of search results in response to recruiter searches can be challenging. Typically, results are presented in a manner similar to traditional online web search results, namely, the results are ranked and results presented vertically with the viewer scrolling through results. As such, normally no more than one or two results are visible at a time. This presents a challenge to a user of the user interface, who must scroll through the screens multiple times to select candidates he or she wishes to contact.
Some embodiments of the technology are illustrated, by way of example and not limitation, in the figures of the accompanying drawings.
The present disclosure describes, among other things, methods, systems, and computer program products that individually provide various functionality. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of different embodiments of the present disclosure. It will be evident, however, to one skilled in the art, that the present disclosure may be practiced without all of the specific details.
In an example embodiment, multiple technological innovations are provided to improve automated sourcing by retrieving computer records related to job candidates automatically and presenting the results in a novel user interface that improves the efficiency of a recruiter or other user interface user to select and communicate with corresponding job candidates. In some example embodiments, this process is further improved by providing recruiters with the ability to select certain fields of candidate records as being “important” to a particular search, and the automated sourcing functionality can then utilize these important fields when retrieving additional candidate records.
An API server 114 and a web server 116 are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers 118. The application server(s) 118 host one or more applications 120. The application server(s) 118 are, in turn, shown to be coupled to one or more database servers 124 that facilitate access to one or more databases 126. While the application(s) 120 are shown in
Further, while the client-server system 100 shown in
The web client 106 accesses the various applications 120 via the web interface supported by the web server 116. Similarly, the programmatic client 108 accesses the various services and functions provided by the application(s) 120 via the programmatic interface provided by the API server 114.
In some embodiments, any website referred to herein may comprise online content that may be rendered on a variety of devices including, but not limited to, a desktop personal computer (PC), a laptop, and a mobile device (e.g., a tablet computer, smartphone, etc.). In this respect, any of these devices may be employed by a user to use the features of the present disclosure. In some embodiments, a user can use a mobile app on a mobile device (any of the client machines 110, 112 and the third-party server 130 may be a mobile device) to access and browse online content, such as any of the online content disclosed herein. A mobile server (e.g., API server 114) may communicate with the mobile app and the application server(s) 118 in order to make the features of the present disclosure available on the mobile device. In some embodiments, the networked system 102 may comprise functional components of a social networking service.
As shown in
An application logic layer may include the search engine 216 and one or more various application server module(s) 214 which, in conjunction with the user interface module(s) 212, generate various user interfaces (e.g., web pages) with data retrieved from various data sources in a data layer. In some embodiments, individual application server modules) 214 are used to implement the functionality associated with various applications 120 and/or services provided by the social networking service, Member interaction detection happens inside the user interface module(s) 212, the application server module(s) 214, and the search engine 216, each of which can fire tracking events in the online system 210. That is, upon detecting a particular member interaction, any of the user interface module(s) 212, the application server module(s) 214, and the search engine 216 can log the interaction, including the type of interaction and any metadata relating to the interaction, in a member activity and behavior database 222.
As shown in
Once registered, a member may invite other members, or be invited by other members, to connect via the social networking service. A ‘connection’ may constitute a bilateral agreement by the members, such that both members acknowledge the establishment of the connection. Similarly, in some embodiments, a member may elect to ‘follow’ another member. In contrast to establishing a connection, ‘following’ another member typically is a unilateral operation and, at least in some embodiments, does not require acknowledgement or approval by the member that is being followed. When one member follows another, the member who is following may receive status updates (e.g., in an activity or content stream) or other messages published by the member being followed, or relating to various activities undertaken by the member who is being followed. Similarly, when a member follows an organization, the member becomes eligible to receive messages or status updates published on behalf of the organization. For instance, messages or status updates published on behalf of an organization that a member is following will appear in the member's personalized data feed, commonly referred to as an activity stream or content stream. In any case, the various associations and relationships that the members establish with other members, or with other entities and objects, are stored and maintained within a social graph in a social graph database 220.
As members interact with the various applications 120, services, and content made available via the social networking service, the members' interactions and behavior (e.g., content viewed, links or buttons selected, messages responded to, etc.) may be tracked, and information concerning the members' activities and behavior may be logged or stored, for example, as indicated in
In some embodiments, the databases 218, 220, and 222 may be incorporated into the database(s) 126 shown in
Although not shown, in some embodiments, the online system 210 provides an API block via which applications 120 and services can access various data and services provided or maintained by the social networking service. For example, using an API, an application may be able to request and/or receive one or more candidate selections. Such applications 120 may be browser-based applications 120, or may be operating system specific. In particular, some applications 120 may reside and execute (at least partially) on one or more mobile devices (e.g., phone or tablet computing devices) with a mobile operating system. Furthermore, while in many cases the applications 120 or services that leverage the API may be applications 120 and services that are developed and maintained by the entity operating the social networking service. Nothing other than data privacy, concerns prevents the API from being provided to the public or to certain third parties under special arrangements, thereby making the candidate selections available to third-party applications 128 and services.
Although the search engine 216 is referred to herein as being used in the context of a social networking service, it is contemplated that it may also be employed in the context of any website or online services. Additionally, although features of the present disclosure are referred to herein as being used or presented in the context of a web page, it is contemplated that any user interface view (e.g., a user interface on a mobile device or on desktop software) is within the scope of the present disclosure.
In an example embodiment, when member profiles are indexed, forward search indexes are created and stored. The search engine 216 facilitates the indexing and searching for content within the social networking service, such as the indexing and searching for data or information contained in the data layer, such as profile data (stored, e.g., in the profile database 218), social graph data (stored, e.g., in the social graph database 220), and member activity and behavior data (stored, e.g., in the member activity and behavior database 2). The search engine 216 may collect, parse, and/or store data in an index or other similar structure to facilitate the identification and retrieval of information in response to received queries for information. This may include, but is not limited to, forward search indexes, inverted indexes, N-gram indexes, and so on.
In an example embodiment, the input from the client profile search component 302 includes an identification of one or more suggested candidates for a job opening. This identification may be accomplished in many ways. In some example embodiments, the input may be an explicit identification of one or more member profiles stored in the profile database 218. This explicit identification may be determined by the searcher, for example, browsing or otherwise locating specific suggested candidate profiles that the searcher feels match a position the searcher is currently seeking to till. For example, the searcher may know the identity of individuals on a team in which the open position is available, and may navigate to and select the profiles associated with those team individuals. In another example embodiment, the searcher may create one or more hypothetical ‘suggested candidate’ profiles and use those as the input. In another example embodiment, the searcher may browse or search profiles in the profile database 218 using traditional browsing or searching techniques. In some example embodiments, the explicit identification may be provided by the job poster.
The server profile search component 300 may contain an attribute extractor 304. The attribute extractor 304 may be implemented as a system component or block that is configured to extract one or more attributes from one or more profiles of one or more suggested candidates (i.e., one or more suggested candidate member profiles). For instance, the attribute extractor 304 may be configured to extract raw attributes, including, for example, skills, companies, titles, schools, industries, etc., from the profiles of the one or more suggested candidates. These raw attributes are then passed to a query builder 306. Notably, the query builder 306 is not configured to use attributes obtained from other data sources other than those related to the suggested candidates, such as profiles and/or usage information about the candidates (e.g., how active they are on the social networking service, whether they have been identified as an active job seeker from their usage of the social networking service, etc.). Examples of other data sources which the query builder 306 is not drawing from include the job posting or details about the potential employer. By focusing on the candidates themselves and attributes about the candidates, this enables the system to more efficiently form queries than if other sources were considered. The insight here is that the searcher knows more about the type of candidate he or she is looking for than an automated system extracting information about the job posting or employer would, and thus the searcher's selection of certain candidates is a much more reliable signal as to appropriate attributes for a candidate than information about the job posting or employer.
The query builder 306 may be implemented as a system component or block that is configured to aggregate raw attributes across one or more selected candidates, expand them to similar attributes, and then select the top attribute values that most closely represent the suggested candidates.
In an example embodiment, the query builder 306 is hard-wired to examine only particular attributes in fields of selected candidate profiles. These attributes include industry, location, current title, and one or more skills. These attributes may be called “query fields” as they are the attributes that will be used to automatically generate the query. Various embodiments are envisioned where various combinations of these particular attributes are used, whereas others may not be used. It should also be noted that, with respect to skills, in some embodiments, only the first skill in candidate profiles is used, but in other embodiments if selecting the first skill does not yield a search query producing enough results, the second skill in the candidates' profiles may be used and a new query formed and executed based on the second skill(s).
In other example embodiments, the query builder 306 is not hard-wired and instead the searcher is able to identify, in the selected candidate profiles, the attributes that the searcher finds appropriate. These attributes are then used to generate the query.
In some example embodiments, a machine learning algorithm may be used to identify the query fields, and potentially other information used to automatically generate the query. A machine learned model may be trained using a machine learning algorithm. The machine learning algorithm may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, Bayesian networks, instance-based learning, support vector machines, random forests, linear classifiers, quadratic classifiers, k-nearest neighbor, decision trees, and hidden Markov models. Examples of unsupervised learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck method. Training data may be fed into the machine learning algorithm to train weights applied to one or more features extracted from the training data. Those learned weights may then be used as part of the model on features extracted from runtime data, such as the fields of a user profile or usage information by the viewer, such as past interactions with the graphical user interface (e.g., which users the viewer previously communicated with).
Optionally, after a candidate query is generated, in an example embodiment, the generated query may be shown to the searcher via the client profile search component 302 and the searcher may have the opportunity to edit the generated query. This may include adding or removing some attributes, such as skills and companies, to or from the query. As part of this operation, a query processor 308 may perform a search on the query and present raw results to the searcher via the client profile search component 302. These raw results may be useful to the searcher in determining how to edit the generated query.
Referring back to the query builder 306, given the raw attributes from the profiles (and possibly usage data) of the suggested candidates, the query builder 306 generates a query containing similar attributes. As shown in
The social networking service may allow members to add skills to their profiles. Typical examples of skills that, for example, an information technology (IT) recruiter might search could be ‘search,’ ‘information retrieval,’ ‘machine learning,’ etc. Members may also endorse skills of other members in their network by, for example, asserting that the member does indeed have the specified skills. Thus, skills may be an important part of members' profiles that showcase their professional expertise. A technical challenge encountered, however, is that suggested candidates may not explicitly list all of the skills they have on their profiles. Additionally, some of their skills may not be relevant to their core expertise. For example, an IT professional may list ‘nonprofit fundraising’ as a skill.
To overcome these challenges, in some example embodiments, expertise scores for the suggested candidate may be estimated based on explicit skills (skills the suggested candidate has explicitly listed in a member profile or resume) as well as implicit skills (skills the suggested candidate is likely to have, but has not explicitly linked).
Certain embodiments determine a candidate's skill strength by determining the candidate's strongest skills. These embodiments determine a given candidate's skill strength based on member profile attributes such as, but not limited to, numbers of endorsements for skills, inferences based on related skills (e.g., if a candidate knows Enterprise JavaBeans/EJB, JUnit, Eclipse, and Java 2 Platform, Enterprise Edition (J2EE), then an inference may be made that the candidate has strong Java skills), the member's profile text, and the member's interactions with a social networking service (e.g., an online professional network), and an expertise score.
In some embodiments, probabilities of occurrences of clusters of skills may be determined for suggested candidates. The suggested candidates can be conceptualized as a training dataset used to determine probabilities of occurrences of skills amongst suggested candidates for a given organization. In an example, such skills possessed by suggested candidates may be correlated with titles (e.g., software developer or software engineer). For instance, for a given title, (e.g., software developer) and its related titles (e.g., software engineer), skills can be clustered. For the given title, clusters of the skills may follow a power law distribution, with few of the skills being highly prevalent (i.e., having relatively higher probabilities of occurrences) amongst the population of suggested candidates, followed by a heavy tail of less prevalent skills with lower probabilities of occurrences.
Some embodiments may identify distributions of numbers of unique explicit skills observed among suggested candidates. For example, on a per member basis, the average number of explicit skills for a member may be identified, and the distribution may show that about 50% of the member profiles have more than a certain number of skills (e.g., 20 skills), In an embodiment, a skill reputation score can be used to identify relevant and important skills amongst those associated with a member's profile and a given title. In an embodiment, a user interface can present coverage of regional data for a given title identifier (title ID) in standardized data.
In response to this reaching out, the system has determined that this candidate has been selected. As such, attributes from the corresponding candidate's profile are extracted and used to perform an automated sourcing search, producing a plurality of search results in the form of candidate records 408A-408C, Notably, more than one of these candidate records 408A-408C are displayed in the additional candidates portion 404. Here, candidate records 408A-408C are depicted. Notably, these candidate records 408A-408C are condensed versions of the profiles of the corresponding candidates, formatted in a manner that makes them ideal for horizontal display as opposed to vertical display. Thus, candidate records 408A-408C are displayed side-by-side. One advantage to this is that more candidate records 408A-408C can be viewed on the screen at a time than if they were displayed vertically. Another advantage is that this means that corresponding fields of the candidate records 408A-408C are displayed on or near the same row in all the candidate records 408A-408C in a manner that allows the user to scan from side to side and easily compare similar fields. Yet another advantage of this horizontal display is that it also becomes easier to compare which attributes produced the match between 408A-408C. Another advantage is that this horizontal display makes it easier to reach out to multiple candidates at a time.
Specifically, in an example embodiment, display of candidate search results involves not just the display of candidates and candidate attributes but also the display of the reasons why they are considered a match. In
In an example embodiment, prior to display of the candidate records 408A-408C the candidate records 408A-408C returned from the arch may be filtered to, for example, remove candidate records 408A-408C that the user has previously seen or rated, or candidates whose current position is already with a hiring company for which the search is being performed.
Additionally, in the example embodiment in
It should be noted that in cases where there are too many matched candidate records to display simultaneously (e.g., more than three), a subset of the matched candidate records can be dynamically determined based on various criteria, such as screen size, relevance of candidate records, size of important fields of the candidate records, etc.
At operation 508, a query is automatically generated using the one or more query fields. In some example embodiments, the query may be automatically, generated using only the one or more query fields (excluding, for example, information about a corresponding job posting where the user profiles are profiles of candidates who may have interest in applying for a job specified by the job posting.
At operation 510, a search for additional user profiles is performed using the automatically generated query, resulting in one or more additional user profile results. At operation 512, summaries of a plurality of the additional user profile results are displayed horizontally across the display in the graphical user interface. At operation 514, a button 412 is rendered in the graphical user interface that, when selected by the viewer of the graphical user interface, causes a communication to be generated and sent to each of the displayed additional user profile results. This button allows the viewer to reach out to all similar candidates in one click.
In various implementations, the operating system 604 manages hardware resources and provides common services. The operating system 604 includes, for example, a kernel 620, services 622, and drivers 624. The kernel 620 acts as an abstraction layer between the hardware and the other software layers, consistent with some embodiments. For example, the kernel 620 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services 622 can provide other common services for the other software layers. The drivers 624 are responsible for controlling or interfacing with the underlying hardware, according to some embodiments. For instance, the drivers 624 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy 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.
In some embodiments, the libraries 606 provide a low-level common infrastructure utilized by the applications 610. The libraries 606 can include system libraries 630 (e.g., C standard library) that can provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 606 can include API libraries 632 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic context on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 606 can also include a wide variety of other libraries 834 to provide many other APIs to the applications 610.
The frameworks 608 provide a high-level common infrastructure that can be utilized by the applications 610, according to some embodiments. For example, the frameworks 608 provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks 608 can provide a broad spectrum of other APIs that can be utilized by the applications 610, some of which may be specific to a particular operating system 604 or platform.
In an example embodiment, the applications 610 include a home application 650, a contacts application 652, a browser application 654, a book reader application 656, a location application 658, a media application 660, a messaging application 662, a game application 664, and a broad assortment of other applications such as a third-party application 666. According to some embodiments, the applications 610 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 610, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C. Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 666 (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 another mobile operating system. In this example, the third-party application 666 can invoke the API calls 612 provided by the operating system 604 to facilitate functionality described herein.
The machine 700 may include processors 710, memory 730, and 110 components 750, which may be configured to communicate with each other such as via a bus 702. In an example embodiment, the processors 710 (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) may include, for example, a processor 712 and a processor 714 that may execute the instructions 716. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions 716 contemporaneously. Although
The memory 730 may include a main memory 732, a static memory 734, and a storage unit 736, all accessible to the processors 710 such as via the bus 702. The main memory 732, the static memory 734, and the storage unit 736 store the instructions 716 embodying any one or more of the methodologies or functions described herein. The instructions 716 may also reside, completely or partially, within the main memory 732, within the static memory 734, within the storage unit 736, within at least one of the processors 710 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 700.
The I/O components 750 may 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 750 that are included in a particular machine 700 will depend on the type of machine 700. 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 750 may include many other components that are not shown in
In further example embodiments, the I/O components 750 may include biometric components 756, motion components 757, environmental components 760, or position components 762, among a wide array of other components. For example; the biometric components 756 may 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 757 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 760 may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components infrared sensors that detect nearby objects), gas sensors (e.g., 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 762 may 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 may be implemented using a wide variety of technologies. The I/O components 750 may include communication components 764 operable to couple the machine 700 to a network 780 or devices 770 via a coupling 782 and a coupling 772, respectively. For example, the communication components 764 may include a network interface component or another suitable device to interface with the network 780. In further examples, the communication components 764 may 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 770 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).
Moreover, the communication components 764 may detect identifiers or include components operable to detect identifiers. For example, the communication components 764 may 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 Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes); or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 764, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.
The various memories i.e., 730, 732, 734, and/or memory of the processor(s) 710) and/or the storage unit 736 may store one or more sets of instructions 716 and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 716), when executed by the processor(s) 710, cause various operations to implement the disclosed embodiments.
As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions 716 and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to the processors 710. Specific examples of machine-storage media, computer-storage media, and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), field-programmable gate array (FPGA), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.
In various example embodiments, one or more portions of the network 780 may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the 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 780 or a portion of the network 780 may include a wireless or cellular network, and the coupling 782 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling 782 may 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 716 may be transmitted or received over the network 780 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 764) and utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Similarly, the instructions 716 may be transmitted or received using a transmission medium via the coupling 772 (e.g., a peer-to-peer coupling) to the devices 770. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 716 for execution by the machine 700, and include digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals.
