The disclosed implementations relate generally to the field of social networks and in particular to a system for generating predictions based on analysis of large scale historic data.
The rise of the computer age has resulted in increased access to personalized services online. As the cost of electronics and networking services drop, many services that were previously provided in person are now 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. Similarly, online social networking sites allow members to build and maintain personal and business relationships in a much more comprehensive and manageable manner.
One important application of new computer technologies is allowing users to explore and learn. Some education tools are being moved such that they can be accessed directly over the Internet. For example, massive open online courses (MOOCs) allow users from different parts of the world to all experience the same education experiences. In addition, even non-network based education can be enhanced by improving access to information about education institutions, programs, and opportunities to interested parties. Networked computer systems can collect and process large amounts of data to streamline and enhance education opportunities.
Some implementations 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 generating an admittance prediction based on historical admittance data that predicts whether a particular member of a social networking system will be admitted to a particular education institution in accordance with some implementations. 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 implementations. It will be evident, however, to one skilled in the art that the any particular implementation 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 social networking system stores a large set of historical acceptance data for one or more educational institutions (e.g., universities and colleges). This historical acceptance data represents the admission results of thousands or millions of potential applicants, including lists of members who applied to one or more of the educational institutions, the qualification data associated with each member, and whether the member was admitted to the one or more educational institutions to which they applied.
The social networking system analyzes the historical acceptance data for one or more educational institutions to predict, for a respective member of the social networking system, whether or not that member will be admitted to one or more educational institutions. Once the social networking system generates a prediction for a particular member, the prediction is then transmitted to a client system associated with the member. In some example embodiments, the social networking system only generates a prediction in response to a request from the client system.
In some example embodiments, the social networking system can also suggest alternative education institutions to the member based on the education institution in which the member has already indicated an interest.
In some example embodiments, predictions are generated by organizing qualification data into one or more categories (e.g., work experience, education, test scores, and so on). The social networking system can then generate a score for each category for each prospective student in the historical acceptance data. A category score represents the level to which the member the category score is associated with meets the optimal qualifications for that category. For example, the category score for grade point average (GPA) is a score from 0 (representing the lowest possible GPA) to 1 (representing the highest possible GPA). The social networking system then determines for each category score, based on historical acceptance data, the minimum score needed to be accepted to a particular education institution. For example, if no applicants with a GPA score lower than 0.85 (e.g., representing approximately a 3.5 GPA) were accepted, the minimum acceptable score for the GPA score is 0.85.
In some example embodiments, the social networking system determines whether a respective member meets all the minimum acceptable scores for each category for a particular education institution. In accordance with a determination that the member meets or exceeds the minimum acceptable scores for each category, the social networking system generates a predication that the member will be accepted in to the particular education institution. The predication is then transmitted to the client system associated with the member.
In some implementations, 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 implementations, 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 106. The client system 102 uses the web browser 106 to communicate with the social networking system 120 and displays information received from the social networking system 120. In some implementations, the client system 102 includes an application specifically customized for communication with the social networking system 120 (e.g., a LinkedIn iPhone application). In some example embodiments, the social networking system 120 is a server system that is associated with a social networking service. However, the social networking system 120 and the server system that actually provides the social networking service may be completely distinct computer systems.
In some implementations, the client system 102 sends a request to the social networking system 120 for a webpage associated with the social networking system 120 (e.g., the client system 102 sends a request to the social networking system 120 for an updated web page associated with an education institution). For example, a member of the client system 102 logs onto the social networking system 120 and clicks to view educational information on a dedicated web page of the social networking system 120. In response, the client system 102 receives the requested data (e.g., information about schools and enrollment) and displays them on the client system 102.
In some implementations, as shown in
As shown in
As shown in
Consistent with some implementations, when a person initially registers to become a member of the social networking system 120, the person will be prompted to provide some personal information, such as his or her name, age (e.g., birth date), gender, interests, contact information, home town, address, educational background (e.g., schools, majors, etc.), current job title, job description, industry, employment history, skills, professional organizations, memberships with third party servers 150, and so on. This information is stored, for example, in the member profile database 130. In some example embodiments, the social networking system 120 will also prompt the person to determine whether that person is interested in attending one or more schools in the future.
In some implementations, the member profile data 130 includes qualification data 132. In other implementations, the qualification data 132 is distinct from, but associated with, the member profile data 130. The qualification data 132 stores data for at least some of the members of the social networking system 120. Qualification data includes, but is not limited to, test scores, employment history, demographic information, work history, education history, grade point averages, hobbies, accomplishments, member ratings, recommendations, and so on.
The education institution profile data 134 also stores data related to schools represented on the social networking system 120 and their students. Thus, members of the social networking system 120 may be associated with schools. In addition, education institution profile data 134 includes information that describes the location of the school, the programs it offers, the demographic information of its students, the costs of school, scholarship programs offered by the school, important school dates (e.g., deadlines, term beginning and ending dates, holidays, and so on), ranking information on the school, enrollment statistics, and other information.
In some example embodiments, the historical acceptance data 136 stores data that describes, for a respective education institution in a plurality of education institutions, a list of applicants to the respective education institution, their qualification data, and, for each respective applicant in the list of applicants, whether the respective education institution accepted the respective applicant or not. In some example embodiments, the historical acceptance data 136 is data provided by the education institutions directly. In other example embodiments, the historical acceptance data 136 is derived from information stored in the member profile data 130 including a list of education institutions in which a particular member is interested (or has applied to), the qualifications the particular member has, and whether the respective member later updated their education history to include one or more education institutions. For example, a member with an interest in school A for school year B who later updates their member profile to include attending school A will be determined as having been accepted to school A. Conversely, if no education institution or school is then listed in the member's profile, then the member is determined not to be accepted to school A.
In other example embodiments, the historical acceptance data 136 is received from one or more education institutions. The education institutions transmit a list of applicants to the social networking system (e.g., social networking system 120 of
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 implementations, 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 implementations, 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 activities 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 database 138.
The social networking system 120 may provide a broad range of other applications and services that allow members the opportunity to share and receive information, often customized to the interests of the member. In some implementations, the social networking service may include a photo sharing application that allows members to upload and share photos with other members. As such, at least with some implementations, a photograph may be a property or entity included within a social graph. With some implementations, members of a social networking service may be able to self-organize into groups, or interest groups, organized around a subject matter or topic of interest. In some implementations, 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 organization activity data, the member activity data, and the social graph data stored in the social graph database 138.
In some implementations, the application logic layer includes various application server modules, which, in conjunction with the user interface module(s) 122, generates various user interfaces (e.g., web pages) with data retrieved from various data sources in the data layer. With some implementations, individual application server modules are used to implement the functionality associated with various applications, services, and features of the social networking 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. Of course, other applications or services that utilize a historical data analysis module 124 or a prediction module 126 may be separately implemented in their own application server modules.
In addition to the various application server modules, the application logic layer includes a historical data analysis module 124 and a prediction module 126. As illustrated in
Generally, the historical data analysis module 124 gathers and analyzes historical acceptance data for a plurality of education institutions. Each respective education institution has an associated list of applicants (e.g., members who applied to attend the respective education institution), the qualification data 132 associated with each applicant, and a determination concerning whether the member were accepted into the respective education institution. In some example embodiments, any historical acceptance data stored without any personal identifying information (PII). Thus all the historical acceptance data is stored anonymously. In other example embodiments, the historical data analysis module 124 infers whether each applicant was accepted based on updates to the applicant's member profile.
In some example embodiments, the historical data analysis module 124 creates a profile of a minimum accepted applicant for each respective education institution by creating one or more categories from the qualification data 132. A profile of a minimum accepted applicant includes an estimated minimum level of qualification data needed to be accepted to the respective education institution.
The historical data analysis module 124 generates a category score for each category for each respective applicant in the list of applicants. The category score represents the degree to which the qualification data in that category makes the applicant a good candidate. For example, in work history, the type of employment (based on employer and title) and the number of year of experience determine how high the category score is.
In some example embodiments, the historical data analysis module 124 then determines, for each category, a minimum acceptable score (e.g., the lowest score for that category that had an applicant be admitted) for a respective school. Once minimum acceptable scores have been determined for each category, the historical data analysis module 124 sends the list of minimum acceptable scores to the prediction module 126. In some example embodiments, the historical data analysis module 124 also determines an average acceptable score (the average (or median) score for all accepted applicants) and transmits it to the prediction module 126.
In some example embodiments, the prediction module 126 uses the list of minimum acceptable scores to predict whether a particular member will be accepted into a respective education institution. The prediction module 126 determines whether any of the category scores are below the minimum acceptable score. If any of the category scores are below the minimum acceptable score for that category, the prediction module 126 determines that the particular member is unlikely to be admitted.
In some example embodiments, in accordance with a determination that none of the particular member's category scores are below the minimum acceptable score, the prediction module 126 compares the particular member's category scores to average acceptance scores for the categories. For example, if the average acceptance score for LSAT scores at a law school is 170, the prediction module 126 determines whether a particular member's LSAT is above 170.
In some example embodiments, the prediction module 126 determines the percentage of category scores for the particular member that are above the respective average acceptance score. For example, the prediction module 126 determines that four of the category scores (out of a total of ten category scores) associated with Member A are above the average category scores for their respective category. Thus the percentage of category scores above average is forty percent. The prediction module 126 then generates an acceptance prediction for the respective member based on one or more of the category scores, the percentage of category scores above average, the number of category scores below the minimum acceptable score, and so on.
In some example embodiments, the higher the percentage of category scores above the average score the more likely that the prediction module 126 will return a prediction that the respective member will be accepted. In some example embodiments, different category scores have different weights, such that a high category score for a particular category is more important than other category scores. The prediction module 126 then transmits the prediction to the client system 102 associated with the particular member.
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 implementations, 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 implementations, memory 306 or the computer readable storage medium of memory 306 stores the following programs, modules, and data structures, or a subset thereof:
In addition, the user interface 400 also includes a list of suggested education institutions 418. These are education institutions that the social networking system (e.g., social networking system 120 of
The user interface 400 also includes information in side sections of the interface including a contact recommendation section 408, profile viewership statistic section 410, and a social graph statistic section 412.
In some implementations, the method is performed at a social networking system (e.g., system 120 in
The social networking system (e.g., system 120 in
In some example embodiments, the social networking system (e.g., social networking system 120 of
The social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some implementations the method is performed at a social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, historic admittance data for a respective education institution is received from the respective education institution directly. For example, a university contracts with the social networking system (e.g., social networking system 120 of
In some example embodiments, the historic admittance data includes one or more potential students who were accepted by the respective education institution but did not attend the educational institution.
In some example embodiments, historic admittance data for a respective school includes data that identifies a plurality of potential students who applied and were accepted and a plurality of students who applied to the respective school and were rejected. In other example embodiments, historic admittance data includes, for each respective potential student, qualification data associated with the respective potential student.
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, for a respective education institution in the determined one or more education institutions, the social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, in accordance with a determination that the first member is likely to be admitted to the respective education institution, the social networking system (e.g., social networking system 120 of
In some implementations the method is performed at a social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, for a respective category in the one or more qualification data categories, the social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, admittance prediction is represented as a percentage representing the likelihood that the member will be accepted into the respective education institution. For example, the chance of admission is 75%, which represents the likelihood that the member will be accepted into the education institution if the member applies. In some example embodiments, the admittance prediction is represented by one of a plurality of discrete states. For example, all potential applicants are given a “high,” “medium,” or “low” chance of admittance to a respective education institution based on the analysis of the social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, generating an admittance prediction based on the comparison with historic admittance data further comprises the social networking system (e.g., social networking system 120 of
In some example embodiments, for each category in the one or more categories, the social networking system (e.g., social networking system 120 of
In some implementations, the method is performed at a social networking system (e.g., social networking system 120 of
In some example embodiments, when generating an admittance prediction based on the comparison with historic admittance data comprises, the social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system (e.g., social networking system 120 of
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 708. 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 MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries 704 may also include a wide variety of other libraries 734 to provide many other APIs to the applications 708.
The frameworks 706 may provide a high-level common infrastructure that may be utilized by the applications 708. 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 708, some of which may be specific to a particular operating system or platform.
The applications 708 include a home application 750, a contacts application 752, a browser application 754, a book reader application 756, a location application 758, a media application 760, a messaging application 762, a game application 764, and a broad assortment of other applications such as 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 such as iOS™, Android™ Windows® Phone, or other mobile operating systems. 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, processor 815 and processor 820, which may execute instructions 825. The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (also referred to as “cores”) that may execute instructions 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, static memory 840, and the processors 810 may be considered as 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 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, 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 coupling 882 and coupling 872, respectively. For example, the communication components 864 may include a network interface component or other suitable device to interface with the network 880. In further examples, 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 and/or any of a wide variety of peripheral devices (e.g., a peripheral device couple 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 additional, 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 a 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 other 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 devices 870. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions 825 for execution by the machine 800, and includes digital or analog communications signals or other intangible medium 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 purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the possible implementations to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations 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 implementations 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 implementations. 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 implementations herein is for the purpose of describing particular implementations only and is not intended to be limiting. As used in the description of the implementations 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.
This application claims the priority benefit of U.S. Provisional Patent Application Ser. No. 62/073,845, filed Oct. 31, 2014, which is incorporated herein by reference in its entirety.
Number | Date | Country | |
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62073845 | Oct 2014 | US |