The disclosed implementations relate generally to the field of social networks and in particular to a system for analyzing applicant 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 schools, 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 using interest and qualification data from a large pool of members to generate useful statistical data for a particular member. 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 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.
A social networking system stores data for millions of members of the social network. This data can be leveraged to assist members of the social network in researching and pursuing educational opportunities. In some example embodiments, the social networking system stores qualification data for a plurality of the members of the social network.
Qualification data includes any data that may be considered by a school when determining whether to admit a potential student. For example, qualification data includes, but is not limited to, test scores, employment history, demographic information, work history, education history, grade point averages, hobbies, accomplishments, volunteer history, and so on. In some example embodiments, the qualification data is explicitly supplied by the member (e.g., the member transmits test scores to the social networking system). In other example embodiments, the social networking system is able to infer some qualification information such as demographic information and work history. In other example embodiments, the social networking system verifies information submitted by a member or inferred by the system through testing agencies or educational institutions with the member's permission.
The social networking system receives an interest notification message from a client system associated with a first member. An interest notification message includes a list of one or more education institutions and informs the social networking system that the member is at least potentially interested in applying to those one or more education institutions. In some example embodiments, the interest notification message also includes a request for additional information about the one or more education institutions. The interest notification message includes, but is not limited to, the member identification number, one or more schools the member is interested in, a time frame the member is interested in (e.g., the school year the member intends to attend one of the schools), and any other relevant information. In some example embodiments, the interest notification message also includes a request for data analytics for one or more of the education institutions listed in the interest notification message.
In response to receiving an interest notification message, the social networking system determines a list of potential applicant members that have also indicated an interest in attending a respective education institution in the list of education institutions in the interest notification message received from the first member. The list of members is determined based on one or more interest notification messages from one or more other members of the social networking system. This list potentially represents a sample of the group of potential applicants that are applying to the respective education institution. The social networking system can then analyze the qualification data stored for each member in the list of potential applicant members that have indicated an interest in attending the respective education institution.
In some example embodiments, the social networking system organizes the qualification data into categories (e.g., SAT scores, high school GPA, college GPA, years of experience in field) and then generates a rank in each category for each member in the list of potential applicant members. Thus the social networking system can automatically determine where the first member ranks relative to other potential students in a variety of categories. In some example embodiments, the social networking system generates an overall candidate score or rank. The overall candidate score is based on rankings of each category of qualification data and differing weights attached to each category based on the category's importance. For example, an MBA program may give the category that measures the number of years of work experience in an appropriate field the highest weight while assigning high school GPA the lowest weight. In some example embodiments, the social networking system determines, for each education institution, what category scores are important and how they are weighted. In some example embodiments, this is determined from available acceptance data.
Each category of qualification data will then have a range of scores, from the member with the highest score in that category, to the member with the lowest score in that category. The members can be given a rank for each category and/or an overall rank. In some example embodiments, the social networking system then transmits the generated rankings and/or scores to the requesting client system for display. These rankings or scores can be represented in any suitable manner including charts and graphs that visually depict a member's relative rank or score.
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 a particular university). 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 data 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, terms beginning and ending dates, holidays, and so on), ranking information on the school, enrollment statistics, and other information.
In some example embodiments, the interest data 136 stores data received from a plurality of members that indicates the specific schools (or other educational opportunities) that a specific member is interested in pursuing. In some example embodiments, the interest data 136 for a respective member is received directly from the member. For example, a member selects one or more universities as potential educational opportunities that the member is interested in pursuing.
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 data 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 data 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 qualification analysis module 124 or an applicant ranking 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 qualification analysis module 124 and an applicant ranking module 126. As illustrated in
Generally, the qualification analysis module 124 gathers, determines, and/or generates qualification data for a member of the server system (e.g., social networking system 120 in
The server system (e.g., social networking system 120 in
Once qualification data 132 has been gathered or generated, the qualification analysis module 124 sorts the qualification data 132 into distinct categories (e.g., GPA, GMAT score, or work history). Sorting qualification data 132 into categories allow members to be compared and ranked by the applicant ranking module 126.
The applicant ranking module 126 uses the qualification data 132 to generate ranks or scores for members relative to other members. In some example embodiments, the rankings are made for particular schools or educational opportunities. For example, the applicant ranking module 126 receives a request for applicant ranking data for school A in school year B. In response, the applicant ranking module 126 determines a list of members that have indicated interest in attending school A during school year B.
In some example embodiments, the applicant ranking module 126 uses the list of interested members for a particular school to generate relative ranks for each member in the list. The applicant ranking module 126 generates, for each category of qualification data 132, a score for each member. In some example embodiments, the scores are from 0 to 1 wherein 1 represents the highest score in the list of members and 0 represents the lowest. All other scores can then be scaled proportionally to fit within the score range. For example, if the category is LSAT scores a score of 180 is 1 and a score of 143 is 0, and all other scores are between these two points.
In some example embodiments, a category score for certain types of categories is generated based on reputation scores of one or more entities involved in the category score. For example, if the selected category is work experience, the category score is generated, at least in part, on the reputational score of the organizations that has employed the respective member. Thus, five years of work experience at an organization with a high reputation score will result in a higher category score than five years of work experience at an organization with a low reputations core. In some example embodiments, a reputation score for an organization or an education institution can be obtained by an outside ranking system or generated internally by an analysis of educational or career outcomes for members.
In some example embodiments, the applicant ranking module 126 generates a ranking for each member in each category based on the generated scores. In some example embodiments, the applicant ranking module 126 also generates an overall applicant ranking that combines the relative ranks of each category. In this case, the various categories can have different weights to emphasize more important categories and de-emphasize unimportant categories. The relative ranking can be customized for different schools or based on other variables.
In some example embodiments, the applicant ranking module 126 receives a request for the generated ranking information and transmits it to the requesting client system (e.g., client system 102 in
In some example embodiments, the third party server 150 is a server system that is located remotely from the social networking system 120 and connects to the social networking system 120 via the communication network 110. In some example embodiments, the third party server 150 is associated with an education institution (e.g., a university) and stores educational institution data 152 that can be requested by the social networking system 120 to be stored as education institution profile 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 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:
The user interface 400 also includes information in side sections of the interface 400 including a contact recommendation section 408, profile viewership statistic section 410, and a social graph statistic section 412.
In some example embodiments, the social networking system (e.g., social networking system 120 of
In some example embodiments, the social networking system then receives (504) a data analytics request from a first member (associated with a client system (e.g., client system 102 in
In some example embodiments, the data analytics request is generated in response to a web page request for a web page associated with a specific education institution. In other example embodiments, the data analytics request is generated based on a specific selection of the user interface component (e.g., a button or link) that specifically results in sending a data analytics request. For example, in some embodiments, merely visiting the webpage associated with an education institution will cause a data analytics request to be generated and sent to 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 social networking system (e.g., social networking system 120 of
Once the scores are generated, 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, the social networking system (e.g., social networking system 120 of
In some example embodiments, the education institution interest indication is stored in a database of education institution interest data. The database of education institution interest data includes interest information from a plurality of other members 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, 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, for a respective category in the one or more categories (616), 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
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 format 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 graphic 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 that may execute instructions 825. The term “processor” is intended to include a 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.
Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.
In some embodiments, a hardware module may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an ASIC. A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software encompassed within a general-purpose processor or other programmable processor. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.
Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information).
The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors.
Similarly, the methods described herein may be at least partially processor-implemented, a processor being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an application program interface (API)).
The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.
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 bio signals (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), 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 (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 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 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 code, multi-dimensional bar codes such as 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 metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network 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 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 847 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.
This application claims priority to U.S. Provisional Patent Application Ser. No. 62/073,712, filed Oct. 31, 2014, which is incorporated herein by reference in its entirety.
Number | Date | Country | |
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62073712 | Oct 2014 | US |