Patent Application
20170221164
The disclosed example embodiments relate generally to the field of social networks and, in particular, to personalizing career recommendations.
The rise of the computer age has resulted in increased access to personalized services online. As the cost of electronics and networking services drops, many services can be provided remotely over the Internet. For example, entertainment has increasingly shifted to the online space with companies such as Netflix and Amazon streaming television shows and movies to members at home. Similarly, electronic mail (e-mail) has reduced the need for letters to be physically delivered. Instead, messages are sent over networked systems almost instantly.
Another service provided over networks is social networking services. Large social networks allow members to connect with each other and share information. One such type of information is information about members' jobs, careers, education, and goals. Social networks enable members to share and view information about their careers and skills. Using that information, a social network can provide learning and employment opportunities.
Some example embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:
Like reference numerals refer to corresponding parts throughout the drawings.
The present disclosure describes methods, systems, and computer program products for providing improved job listing information for members. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the various aspects of different example embodiments. It will be evident, however, to one skilled in the art, that any particular example embodiment may be practiced without all of the specific details and/or with variations, permutations, and combinations of the various features and elements described herein.
In some example embodiments, a server system (e.g., a server system that provides a social networking service) can provide one or more skill learning courses. In some example embodiments, the skill learning courses are network delivered video courses that are made available to members of the server system either as part of their membership with the server system or for an additional fee. However, as technology improves, demand for skill learning courses changes and increases as new or different skills become relevant in the job market.
With finite resources to produce new skill learning courses, the server system must determine which skill learning courses will bring the most value to the members of the server system. The server system thus analyzes each skill or skill cluster (e.g., a group of closely related skills) to generate a content priority score for that skill or skill cluster. The higher the content priority skill, the greater the incentive for the server system to produce additional skill learning material for that skill or skill cluster.
In determining a content priority score for a particular skill or skill cluster, the server system first determines the level of demand for that skill. The terms interest and demand may be used interchangeably in the text of this disclosure. In some example embodiments, the server system stores or has access to a large number of current job listings. Each job listing includes a list of requirements. The server system analyzes the requirements of each job listing to determine a list of required skills for that job.
Once all the job listings have been analyzed, the server system is able to determine which skills are the most commonly required for current job listings. In some example embodiments, the server system also stores data about the most required skills in the past. Using past job requirement data, the server system can determine trends in skill requirement for jobs.
In some example embodiments, the server system also determines, for each respective skill or skill cluster, the number of members who have that respective skill. In some example embodiments, the server system also analyzes the new hires or job changes to determine which skills are the most common for newly hired workers.
In some example embodiments, the server system also accesses search information for a particular time period. In some example embodiments, the accessed search information includes all the terms searched by users during a given period of time and their relative frequency. The server system uses the relative search frequency of terms associated with each skill or skill cluster to estimate member interest in each of the skills or skill clusters.
The server system also determines, for each skill or skill cluster, the number of skill learning materials currently available through the server system. Each skill or skill cluster is given a score representing the amount and quality of the skill learning material for each skill.
The server system then generates an overall content priority score for each skill or skill cluster by comparing estimated member demand (e.g., based on skill listings, member skill profiles, job change records, and member search histories) to the current available content score for that particular skill or skill cluster. Skills with more member interest or demand and less already produced skill learning material are given a high content priority score, such that producing content for learning those skills are more likely to occur.
In some example embodiments, a client system 102 is an electronic device, such as a personal computer (PC), a laptop, a smartphone, a tablet, a mobile phone, or any other electronic device capable of communication with a communication network 110. The client system 102 includes one or more client applications 104, which are executed by the client system 102. In some example embodiments, the client application(s) 104 include one or more applications from a set consisting of search applications, communication applications, productivity applications, game applications, word processing applications, or any other useful applications. The client application(s) 104 include a web browser. The client system 102 uses a web browser to send and receive requests to and from the server system 120 and displays information received from the server system 120.
In some example embodiments, the client system 102 includes an application specifically customized for communication with the server system 120 (e.g., a LinkedIn iPhone application). In some example embodiments, the server system 120 is a server system that is associated with one or more services.
In some example embodiments, the client system 102 sends a request to the server system 120 for a webpage associated with the server system 120. For example, a member uses a client system 102 to log into the server system 120 and clicks a link to view a job listing for a job they are interested in from server system 120. In response, the client system 102 receives the requested job listing data (e.g., data describing the position, the associated organization, the job requirements, and responsibilities) and displays that data in a user interface on the client system 102.
In some example embodiments, as shown in
As shown in
As shown in
Consistent with some example embodiments, when a person initially registers to become a member of the server system 120, the person will be prompted to provide some personal information, such as his or her name, age (e.g., birth date), gender, contact information, home town, address, educational background (e.g., schools, majors, etc.), current job title, job description, industry, employment history, skills, professional organizations, memberships with other online service systems, and so on. This information is stored, for example, in the member profile data 130.
In some example embodiments, the member profile data 130 includes skill data 132. In other example embodiments, the member skill database 132 is distinct from, but associated with, the member profile database 130. The member skill database 132 stores skill data for each member of the server system (e.g., system 120 in
In some example embodiments, explicit skills are skills that the member is determined to have based on skill information directly received from the member. For example, a member reports that they have skills in using the C++, Java, PHP, CSS, and Python programming languages. Because the member directly reported these skills they are considered explicit skills. In some example embodiments, explicit skills are listed on a member's public profile.
In some example embodiments, one or more skills are determined based on an analysis of the non-skill data stored in a member profile. Skills determined in this way are considered implicit skills. Implicit skills are determined or inferred by analyzing data stored in a member profile, including but not limited to education, job history, hobbies, friends, skill ratings, interests, projects a member has worked on, activity on the server system (e.g., system 120 in
The course data 134 includes a listing of all available training materials or courses available through the server system 120. In some example embodiments, each course is a series of videos demonstrating or explaining skills and concepts associated with a particular topic or skill. For example, a course on Java programming includes video, audio, web-based, or written materials that explain concepts related to Java programming and demonstrate those skills. In some example embodiments, the course materials also include one or more interactive learning opportunities.
The course skill data 136 includes a listing of which skills or skill clusters are taught by which course or training material. In some example embodiments, the skills associated with particular courses are determined when a course is created. For example, the user who creates a course can list the skills or skills groups taught by the course. In other example embodiments, the content of a course is analyzed to determine which skills are taught by the course.
Once registered, a member may invite other members, or be invited by other members, to connect via the network service. A “connection” may include a bi-lateral agreement by the members, such that both members acknowledge the establishment of the connection. Similarly, with some example embodiments, a member may elect to “follow” another member. In contrast to establishing a “connection,” the concept of “following” another member typically is a unilateral operation and, at least with some example embodiments, does not include acknowledgement or approval by the member that is being followed. When one member follows another, the member who is following may receive automatic notifications about various interactions undertaken by the member being followed. In addition to following another member, a member may elect to follow a company, a topic, a conversation, or some other entity, which may or may not be included in the social graph. Various other types of relationships may exist between different entities and are represented in the social graph data 138.
The server system 120 may provide a broad range of other applications and services that allow members the opportunity to share and receive information, often customized to the interests of the member. In some example embodiments, the server system 120 may include a photo sharing application that allows members to upload and share photos with other members. As such, at least with some example embodiments, a photograph may be a property or entity included within a social graph. With some example embodiments, members of a server system 120 may be able to self-organize into groups, or interest groups, organized around a subject matter or topic of interest. In some example embodiments, the data for a group may be stored in a database. When a member joins a group, his or her membership in the group will be reflected in the member profile data 130 and the social graph data 138.
In some example embodiments, the application logic layer includes various application server modules, which, in conjunction with the user interface module(s) 122, generate various user interfaces (e.g., web pages) with data retrieved from various data sources in the data layer. With some example embodiments, individual application server modules are used to implement the functionality associated with various applications, services, and features of the server service. For instance, a messaging application, such as an email application, an instant messaging application, or some hybrid or variation of the two, may be implemented with one or more application server modules. Similarly, a search engine enabling members to search for and browse member profiles may be implemented with one or more application server modules. An interest determination module 124 or a scoring module 126 can also be included in the application logic layer. Of course, other applications or services that utilize the interest determination module 124 or the scoring module 126 may be separately implemented in their own application server modules.
As illustrated in
Generally, the interest determination module 124 is accessed when evaluating the amount of interest in leaning a particular skill or skill cluster. In some example embodiments, both member interest (e.g., interest as determined based on member actions and data) and potential employer interest (e.g., interest from potential employers in hiring members with the skill or skill cluster.).
In some example embodiments, the interest determination module 124 first determines employer interest by evaluating a plurality of job listings that are currently available. The interest determination module 124 analyzes the text of the job listings and, based on which skills are required, determines a frequency that each skill or skill cluster is required. In some example embodiments, each skill can be ranked based on how often it is required.
The interest determination module 124 can also determine, for each skill or skill cluster, how many members already have that skill. In some example embodiments, the number of members who have a skill can be represented as a percentage of all members or all members in a particular field. In some example embodiments, the interest determination module 124 compares the number of members who have a particular skill with how commonly that skill is required by job listings. For example, skills that are required by a large number of open job listings but are not very common among the members of the server system 120 are determined to be more highly in demand than skills that are commonly held by members of the server system 120.
In some example embodiments, the interest determination module 124 also analyzes data regarding recent job changes among the members of the server system 120. In some example embodiments, the interest determination module 124 determines the volume of new hires who have a particular skill or skill cluster. This is then compared to the number of jobs that require the skill and the number of members that have the skill.
In some example embodiments, the interest determination module 124 also determines the amount of search volume for keywords associated with each skill. In some example embodiments, skills associated with keywords that have higher than average search volume can be determined to be in demand.
In some example embodiments, the interest determination module 124 also determines the number of courses or other learning material that teach each skill in the list of skills.
In some example embodiments, the scoring module 126 uses the determined demand for each skill and the determined supply for each skill to generate a content priority score for each skill. In some example embodiments, the content priority skill can then be used to generate recommendations as to what content would be most beneficial to add to the course data 134.
Memory 212 includes high-speed random access memory, such as dynamic random-access memory (DRAM), static random access memory (SRAM), double data rate random access memory (DDR RAM) or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 212 may optionally include one or more storage devices remotely located from the CPU(s) 202. memory 212, or alternately, the non-volatile memory device(s) within memory 212, comprise(s) a non-transitory computer-readable storage medium.
In some example embodiments, memory 212, or the computer-readable storage medium of memory 212, stores the following programs, modules, and data structures, or a subset thereof:
Memory 306, or alternately the non-volatile memory device(s) within memory 306, comprises a non-transitory computer-readable storage medium. In some example embodiments, memory 306, or the computer-readable storage medium of memory 306, stores the following programs, modules, and data structures, or a subset thereof:
In some example embodiments, a respective member profile 402 stores a unique member ID 404 for the member profile 402, the overall member rating 430 for the member, a name 406 for the member (e.g., the member's legal name), member interests 408, member education history 410 (e.g., the high school and universities the member attended and the subjects studied), employment history 412 (e.g., member's past and present work history with job titles), social graph data 414 (e.g., a listing of the member's relationships as tracked by the social network system (
In some example embodiments, a member profile 402 includes a list of skills (422-1 to 422-Q) and associated skill ratings (424-1 to 424-T). Each skill 422 represents a skill or ability that the member associated with the member profile 402 has. For example, a computer programmer might list FORTRAN as a skill 422. In addition, each skill 422 has an associated skill rating 424. In some example embodiments, a skill rating 424 represents the server system's (
In some example embodiments, a server system (e.g., the server system 120 in
In some example embodiments, member interest data 502 includes data describing the amount of members that already have a given skill 422. In some example embodiments, this is determined based on an analysis of the member profiles 402. In general, skills 422 that are already possessed by a large number of members are less likely to be in high demand than skills 422 possessed by a relatively small number of members.
In some example embodiments, member interest data 502 reflects the number of job listings that require the skill 422. In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the member interest data 502 is determined based on new job placement data. In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
The server system (e.g., the server system 120 in
In other example embodiments, server system (e.g., the server system 120 in
Once the search terms have been identified, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the recommendation generation module 508 uses both demand information (e.g., member interest data 502 and search volume data 504) and supply information (e.g., course supply data 506) to determine an overall content priority score, wherein the content priority score represents the degree that demand outpaces current supply.
In some example embodiments, the recommendation generation module 508 ranks the skills 422 based on their content priority score. In some example embodiments, the recommendation generation module 508 selects one or more skills 422 based on the rankings to recommend for additional content generation (e.g., more courses).
In some embodiments, the method is performed at a server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, for a respective skill 422 in a list of skills 422, the server system (e.g., the server system 120 in
Determining a member interest score for the respective skill 422 includes the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the ratio also includes the number of jobs that require the respective skill 422 as follows:
In some example embodiments, the calculated ratio serves as the member interest score. In some example embodiments, the skills 422 can be ranked based on the determined member interests score to identify the one or more skills 422 that are the most in demand from members.
In some example embodiments, for each respective skill 422, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some embodiments, the method is performed at a server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, employer interest is associated with the ratio of all job listings that require the respective skill to the total number of job listings. For example, if there are 500 job listings that require Skill A and 200 job listings that require Skill B, with 3000 total job listings, Skill A will have a higher employer interest score than Skill B.
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, for each respective skill 422 in the list of skills 422, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some embodiments, the method is performed at a server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, generating a content priority score for a particular skill 422 includes determining the current member interest score and determining an employer interest score. The server system (e.g., the server system 120 in
The server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
In some example embodiments, the server system (e.g., the server system 120 in
The operating system 702 may manage hardware resources and provide common services. The operating system 702 may include, for example, a kernel 720, services 722, and drivers 724. The kernel 720 may act as an abstraction layer between the hardware and the other software layers. For example, the kernel 720 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services 722 may provide other common services for the other software layers. The drivers 724 may be responsible for controlling and/or interfacing with the underlying hardware. For instance, the drivers 724 may include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth.
The libraries 704 may provide a low-level common infrastructure that may be utilized by the applications 709. The libraries 704 may include system libraries 730 (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 704 may include API libraries 732 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries 704 may also include a wide variety of other libraries 734 to provide many other APIs to the applications 709.
The frameworks 706 may provide a high-level common infrastructure that may be utilized by the applications 709. For example, the frameworks 706 may provide various graphical user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks 706 may provide a broad spectrum of other APIs that may be utilized by the applications 709, some of which may be specific to a particular operating system 702 or platform.
The applications 709 include a home application 750, a contacts application 752, a browser application 754, a book reader application 756, a location application 759, a media application 760, a messaging application 762, a game application 764, and a broad assortment of other applications such as a third party application 766. In a specific example, the third party application 766 (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 702 such as iOS™, Android™ Windows® Phone, or other mobile operating systems 702. In this example, the third party application 766 may invoke the API calls 710 provided by the mobile operating system 702 to facilitate functionality described herein.
The machine 800 may include processors 810, memory 830, and I/O components 850, which may be configured to communicate with each other via a bus 805. In an example embodiment, the processors 810 (e.g., a 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 815 and a processor 820, which may execute the instructions 825. The term “processor” is intended to include multi-core processors 810 that may comprise two or more independent processors 815, 820 (also referred to as “cores”) that may execute the instructions 825 contemporaneously. Although
The memory 830 may include a main memory 835, a static memory 840, and a storage unit 845 accessible to the processors 810 via the bus 805. The storage unit 845 may include a machine-readable medium 847 on which are stored the instructions 825 embodying any one or more of the methodologies or functions described herein. The instructions 825 may also reside, completely or at least partially, within the main memory 835, within the static memory 840, within at least one of the processors 810 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 800. Accordingly, the main memory 835, the static memory 840, and the processors 810 may be considered machine-readable media 847.
As used herein, the term “memory” refers to a machine-readable medium 847 able to store data temporarily or permanently and may be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium 847 is shown, in an example embodiment, to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store the instructions 825. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions 825) for execution by a machine (e.g., machine 800), such that the instructions 825, when executed by one or more processors of the machine 800 (e.g., processors 810), cause the machine 800 to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, one or more data repositories in the form of a solid-state memory (e.g., flash memory), an optical medium, a magnetic medium, other non-volatile memory (e.g., erasable programmable read-only memory (EPROM)), or any suitable combination thereof. The term “machine-readable medium” specifically excludes non-statutory signals per se.
The I/O components 850 may include a wide variety of components to receive input, provide and/or produce output, transmit information, exchange information, capture measurements, and so on. It will be appreciated that the I/O components 850 may include many other components that are not shown in
In further example embodiments, the I/O components 850 may include biometric components 856, motion components 858, environmental components 860, and/or position components 862, among a wide array of other components. For example, the biometric components 856 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, finger print identification, or electroencephalogram based identification), and the like. The motion components 858 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 860 may include, for example, illumination sensor components (e.g., photometer), acoustic sensor components (e.g., one or more microphones that detect background noise), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), proximity sensor components (e.g., infrared sensors that detect nearby objects), and/or other components that may provide indications, measurements, and/or signals corresponding to a surrounding physical environment. The position components 862 may include location sensor components (e.g., a Global Position System (GPS) receiver component), altitude sensor components (e.g., altimeters and/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 850 may include communication components 864 operable to couple the machine 800 to a network 880 and/or devices 870 via a coupling 882 and a coupling 872, respectively. For example, the communication components 864 may include a network interface component or another suitable device to interface with the network 880. In further examples, the communication components 864 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 870 may be another machine 800 and/or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).
Moreover, the communication components 864 may detect identifiers and/or include components operable to detect identifiers. For example, the communication components 864 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 codes, multi-dimensional bar codes such as a Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF48, Ultra Code, UCC RSS-2D bar code, and other optical codes), acoustic detection components (e.g., microphones to identify tagged audio signals), and so on. In addition, a variety of information may be derived via the communication components 864 such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.
In various example embodiments, one or more portions of the network 880 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a LAN, a wireless LAN (WLAN), a WAN, a wireless WAN (WWAN), a MAN, the Internet, a portion of the Internet, a portion of the public switched telephone network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network 880 or a portion of the network 880 may include a wireless or cellular network and the coupling 882 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 882 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 825 may be transmitted and/or received over the network 880 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 864) and utilizing any one of a number of well-known transfer protocols (e.g., HyperText Transfer Protocol (HTTP)) Similarly, the instructions 825 may be transmitted and/or received using a transmission medium via the coupling 872 (e.g., a peer-to-peer coupling) to the devices 870. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 825 for execution by the machine 800, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.
Furthermore, the machine-readable medium 847 is non-transitory (in other words, not having any transitory signals) in that it does not embody a propagating signal. However, labeling the machine-readable medium 847 as “non-transitory” should not be construed to mean that the medium is incapable of movement; the medium should be considered as being transportable from one physical location to another. Additionally, since the machine-readable medium 847 is tangible, the medium may be considered to be a machine-readable device.
Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.
Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed.
The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
The foregoing description, for the purpose of explanation, has been described with reference to specific example embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the possible example embodiments to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The example embodiments were chosen and described in order to best explain the principles involved and their practical applications, to thereby enable others skilled in the art to best utilize the various example embodiments with various modifications as are suited to the particular use contemplated.
It will also be understood that, although the terms “first,” “second,” and so forth may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the present example embodiments. The first contact and the second contact are both contacts, but they are not the same contact.
The terminology used in the description of the example embodiments herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used in the description of the example embodiments and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or”, as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
