Patent Application
20180158163
The disclosed example embodiments relate generally to the field of data analytics and, in particular, to inferring appropriate courses for recommendation based on member characteristics in a social networking system.
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. Large social networks allow members to connect with each other and share information. Social networks enable members to share and view information about their careers and skills. This career and skill information can be analyzed to determine where a member of the social network is in their career and to predict or suggest next steps.
Some example embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings.
Like reference numerals refer to corresponding parts throughout the drawings.
The present disclosure describes methods, systems, and computer program products for using member profile information to match members with learning opportunities provided by a social networking system or a related service. 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, the social networking system has a plurality of members. Some of the members are interested in using the services of the social networking system to enhance or further their careers. One potential way to do that is to acquire new skills. Learning new skills can increase the number of jobs for which the member is qualified to apply for.
To this end, the social networking system can access a member profile associated with the member, including educational history, work history, current job, location, current skills, and so on. By analyzing this information, the social networking system can identify one or more skills that would be appropriate for the member to learn.
Identifying appropriate skills can be based on a number of factors. Such factors include determining which skills are currently the most popular. One example method to measure the current popularity of a skill is to calculate the number of members who have added that skill in the most recent year (or any other applicable time frame). Skills with the highest numbers of members adding them in the applicable period of time are deemed the most popular.
In other example embodiments, the social networking system can use the learning history of the member to identify new skills or courses that the member should engage with. For example, the social networking system can analyze the past courses that the member has taken and, based on that information, identify future skills to learn. For example, the social networking system can identify a particular subject or area of interest for the member and identify skills in that area that the member does not have yet.
In other example embodiments, the social networking system identifies a group of members who are similar to a particular member member of the social networking system (e.g., the server 120 in
In other example embodiments, the social networking system identifies, from historical member data, members who were similar to the first member in the past. For example, the social networking system can analyze the profiles of members as they existed 3-5 years ago and identify members whose past profiles are similar to the current profile of a particular member. Once this group of past similar members is identified, the social networking system can analyze their subsequent work histories (e.g., which jobs did they move on to, what skills did they learn) to identify one or more potential career paths for the first member. Using these potential career paths, the social networking system can then identify one or more skills associated with the career paths (e.g., based on jobs in the career path or particular skills needed).
Once a number of recommended skills are identified, the social networking system ranks them based on a confidence score assigned to each potential skill based on the social networking system's estimation of the likelihood that the member will want to learn the particular skill. In some example embodiments, the social networking system then identifies one or more courses for each skill based on skill information stored for each course. The courses associated with the most highly ranked skills can then be recommended to the member.
In some example embodiments, the 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 the 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 the web browser to send and receive requests to and from the social networking system 120 and to display information received from the social networking system 120.
In some example embodiments, 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 one or more services.
In some example embodiments, the client system 102 sends a request to the social networking system 120 for course recommendations for one or more courses. For example, a member of the social networking system 120 uses the client system 102 to log into the social networking system 120 and request one or more course recommendations. In response, the client system 102 receives the ranked list of recommended courses from the social networking system 120 and displays that ranked list of courses 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 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, 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 or is associated with member interaction data. In other example embodiments, the member interaction data is distinct from, but associated with, the member profile data 130. The member interaction data stores information detailing the various interactions each member has through the social networking system 120. In some example embodiments, interactions include posts, likes, messages, adding or removing social contacts, and adding or removing member content items (e.g., a message or like), while others are general interactions (e.g., posting a status update) and are not related to another particular member. Thus, if a given member interaction is directed towards or includes a specific member, that member is also included in the membership interaction record.
In some example embodiments, the member profile data 130 includes the skill data 132. In other example embodiments, the skill data 132 is distinct from, but associated with, the member profile data 130. The skill data 132 stores skill data for each member of the social networking system 120. The skill data 132 may include both explicit skills and implicit skills.
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 social networking system 120, and member-submitted comments. In some example embodiments, implicit skills may also be called inferred skills or skills a member may have. For example, member A lists an undergraduate degree in architecture and has a past job history that includes Project Architect for at least three different projects. The social networking system 120 determines that member A has a skill in AutoCAD even though member A has not directly reported having that skill. In some example embodiments, implicit skills are not listed on a member's public profile.
In some example embodiments, the course data 134 includes data that logs or records a member's history of accessing educational material. In some example embodiments, educational material access history data includes one or more material access records, each of which details a particular instance of the member accessing a particular piece of educational material. In some example embodiments, each material access record details the member who accessed the educational materials, the time of the access, the course associated with the educational materials, and how much of the educational materials was read, watched, listened to, or completed.
In some example embodiments, the course data 134 also includes educational materials. Each piece of educational material is a media content item. Media content items include text items, video content items, audio content items, interactive content items (e.g., quizzes and so on), and any other materials that can be used in an educational course. In some example embodiments, each piece of educational material is associated with a specific educational course. In some example embodiments, the course data 134 also includes metadata about each course, such as the content covered by a course, its subject area, the skills that the course covers, and so on.
Once registered, a member may invite other members, or be invited by other members, to connect via the social networking system 120. A “connection” may include a bilateral agreement by the members, such that both members acknowledge the establishment of the connection. Similarly, in some example embodiments, a member may elect to “follow” another member. In contrast to establishing a “connection,” “following” another member typically is a unilateral action and, at least in some example embodiments, does not include acknowledgement or approval by the member who 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 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 example embodiments, the social networking system 120 may include a photo sharing application that allows members to upload and share photos with other members. As such, at least in some example embodiments, a photograph may be a property or entity included within a social graph. In some example embodiments, members of the social networking system 120 may be able to self-organize into groups, or interest groups, organized around subject matter or a 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. In some example embodiments, individual application server modules are used to implement the functionality associated with various applications, services, and features of the social networking system 120. 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.
A skill selection module 124 or a recommendation module 126 can also be included in the application logic layer. Of course, other applications or services that utilize the skill selection module 124 and the recommendation module 126 may be separately implemented in their own application server modules.
As illustrated in
Generally, the skill selection module 124 receives a request for a course recommendation. In response, the skill selection module 124 identifies one or more skills that are appropriate for the member to acquire. In some example embodiments, the skill selection module 124 analyzes the member profile for a member who has requested course recommendations.
In some example embodiments, the skill selection module 124 calculates a learning rate for all skills. A learning rate is a calculation of the number of members who have acquired the given skill during a fixed period of time. The skills then can be ranked based on the calculated learning rate. In some example embodiments, the skills with a learning rate (e.g., the number of members who have acquired the skill in a given time period) above a predetermine threshold or in a certain percentage (e.g., skills above a predetermined threshold or percentage) are selected. In other example embodiments, the skills are grouped by skill subject or skill type and only the skills within a skill topic group associated with the requesting member are considered when ranking skills.
In some example embodiments, the skill selection module 124 identifies appropriate skills by identifying members who are similar to the requesting member. In some example embodiments, identifying members includes grouping or clustering members based on one or more characteristics of the members. Any number of clustering techniques can be used. For example, the members can be represented as n-dimensional vectors, wherein the vectors represent the information associated with each member as a point in n-dimensional space.
Once the members are represented as n-dimensional vectors, a centroid-based clustering algorithm such as Lloyd's algorithm can be used to group members into a plurality of different groups. Then, members who are grouped into the same member group as the requesting member are determined to be similar members. In some example embodiments, the inputs that create the vectors (and are thus used to cluster members into groups are the members age, industry, skills, title, seniority, and so on).
In some example embodiments, the skill selection module 124 analyzes the skills associated with the determined similar members. In some example embodiments, the skill selection module 124 generates a list of skills for each member.
Using the list of skills for each similar member, the skill selection module 124 generates a ranked list of skills based on the number of similar members who have the skill (e.g., the more members in the group of similar members who possess the skill, the higher the skill is ranked). The skill selection module 124 can then analyze the ranked list of skills to identify any skills that the requesting member is missing.
In other example embodiments, the skill selection module 124 uses historical member information to identify member profiles in the past that are similar to the current member's profile. To accomplish this, the skill selection module 124 accesses historical member profiles from a particular period in the past (e.g., 3-5 years ago). The skill selection module 124 then uses a clustering algorithm on the past member profiles (and the current requesting member profile).
Once a group of past member profiles are identified as being similar to the current requesting member's profile, the skill selection module 124 analyzes the subsequent history of those member to identify the most common jobs that those members moved to and the most common skills those members learned subsequently. The skill selection module 124 then uses these jobs and skills as potential future career paths for the requesting member. Each potential future career path includes one or more jobs and associated skills. For each path, the skill selection module 124 selects a skill to recommend to the member.
In some example embodiments, the recommendation module 126 receives a list of skills from the skill selection module 124 that are appropriate for the requesting member. The recommendation module 126 then matches each skill in the list of skills with one or more courses based on metadata about the courses. For example, each course has a list of skills that are taught by the course. The recommendation module 126 then ranks each matching course based on one of: the popularity of the skills taught by the course, the preferences of the member, and member reviews after taking the course In some example embodiments, the top-ranked course recommendations are transmitted to the requesting member for display.
The 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. The memory 212 may optionally include one or more storage devices remotely located from the CPU(s) 202. The memory 212, or alternatively, the non-volatile memory device(s) within the memory 212, comprise(s) a non-transitory computer-readable storage medium.
In some example embodiments, the memory 212, or the computer-readable storage medium of the memory 212, stores the following programs, modules, and data structures, or a subset thereof:
The memory 306, or alternatively the non-volatile memory device(s) within the memory 306, comprises a non-transitory computer-readable storage medium. In some example embodiments, the memory 306, or the computer-readable storage medium of the 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, a location 406 associated with the member (e.g., the location that the member indicated was their location), a name 408 for the member (e.g., the member's legal name), member interests 410, member education history 412 (e.g., the high school and universities the member attended and the subjects studied, online courses or certifications, licenses, and so on), employment history 414 (e.g., member's past and present work history with job titles), social graph data 416 (e.g., a listing of the member's relationships as tracked by the social networking system 120), occupation 418, skills 420, experience 426 (for listing experiences that don't fit under other categories, such as community service or serving on the board of a professional organization), and a detailed course viewing history 428 (e.g., a list of all courses taken through the social networking system 120 or associated educational sites).
In some example embodiments, a member profile 402 includes a list of skills 422-1 to 422-Q. 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.
As can be seen, a recommendations tab 506 has been selected and a page of relevant course recommendations 504 is displayed. The course recommendations 504 are determined based on the skills possessed by the requesting member and members similar to the requesting member. Specifically, courses that teach skills that the requesting member does not have but that are possessed by members who are or were similar to the requesting member are more likely to be recommended. Each course recommendation 502-1 to 502-8 displays a link to additional information about the course, including information about the course contents, the course prerequisites, and how to access the course or enroll in the course. In some example embodiments, the course recommendations also display information as to why that particular course is being recommended to the member (not shown in FIG.). For example, if a course is being recommended because it will help the member qualify for a particular job or type of job that can be displayed to the member on the course commendation page.
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the recommendation request 602 is received by a similarity measurement module 614. Using information in the recommendation request 602 (e.g., member ID of the first member and any specific course content requests that the first member may have), the similarity measurement module 614 accesses the member profile data 130 and identifies a group of members who are similar to the first member.
In some example embodiments, the similarity measurement module 614 first plots each member in an n-dimensional vector space based on information included in the member profile. For example, information such as demographic information, location information, work history, educational history, and member activity can be used as input to generate a particular n-dimensional point in the n-dimensional vector space. In some example embodiments, this mapping is done using a model created by a deep learning algorithm.
In some example embodiments, the model is created using a deep learning or neural network learning method. In some example embodiments, the social networking system (e.g., the server 120 in
In another example embodiment, the model is trained to generate appropriate weights using a neural network using a set training data. The training data has all the input data as will be used in a live example, as well as ground truth data (e.g., data that represents the ideal output from the model). In this example, the neural network takes inputs (e.g., member profile data, message data, social graph data, work profile data, title information). Each of these inputs is given a weight and passed to a plurality of hidden nodes. The hidden nodes exchange information, also given weights, to produce an output (in this case one or more factor weights). In some example embodiments, there are several layers of hidden nodes. The model is compared to the ideal output and the weights used by the models are updated until the model produces accurate data. Once the model is trained, the model is tested using a test set of data. The model can then be used to generate the weights used in the decision maker score calculations.
In this example, the similarity measurement module 614 then groups members based on their position in the n-dimensional vector space. In some example embodiments, the members are clustered into groups based on all the data contained in their member profiles. Clustering can be accomplished with a wide variety of clustering algorithms. One example algorithm includes k-means clustering. To use k-means clustering for members, each member is assigned a position in n-dimensional Euclidean space (based on courses accessed). Each member is assigned to a cluster whose center point is the closest using an equation such as:
S
i
(t)
={x
p
:∥x
p
−m
i
(t)∥2≤∥xp−mj(t)∥2∀j,1≤j≤k}
where each member (x) is assigned to one cluster S at time t, based on which center point (m with coordinates i, j) is closest to the position of the member in the space.
Once members have been assigned to clusters, the central points of the clusters are updated with a formula such as:
Once new central points are determined, the members are clustered again. Once the members stop shifting between clusters, the clusters are determined to have settled.
In this way, members can be grouped into a plurality of groups based on their skills, work history, education, and so on. Once the first member is grouped into a settled group of members, a list is created of the other members in the group (e.g., members who were determined to be similar to the first member during the grouping process). That list of similar members 604 is then transferred to the skill selection module 124.
The skill selection module 124 then determines, for the list of similar members 604, a list of skills that are commonly held by the members based on skill data 132. Skills on this list of skills can be ranked based on a list of factors, including, but not limited to, the frequency of the skills in the group of similar members, how recently the skills were acquired on average (e.g., skills that were acquired recently being ranked higher than skill that were acquired further in the past), a correlation of skills to earnings (e.g., skills associated with higher pay being ranked higher), and so on.
In some example embodiments, each factor is given a weight based on the relative importance of each factor (based on existing metrics or member preferences). For example, a skill ranking score could be using a formula such as:
SRS=f1*w1+f2*w2+f3*w3+f4*w4
In some example embodiments, this example, each factor (e.g., factors f1-f4) has an associated weight (e.g., a value between 0 to 1 such that all the weights add up to 1). The skill ranking score (SRS) is then used as the bases for ranking each skill.
Once the skills have been identified and ranked, the skill selection module 124 identifies at least one skill in the list of skills that the first member does not possess based on the skill rankings. For example, the skill selection module 124 might identify the five most highly ranked skills that the first member does not possess. In other example embodiments, the skill selection module 124 selects all skills that are above a predetermined threshold.
The one or more selected skills are transmitted to a course selection module 616 as skill data 606. The course selection module 616 then accesses the course data 134 to identify one or more courses that teach one of the skills in the skill data 606 based on information about the courses. For example, each course has associated metadata that lists skills taught or improved by the course. In some example embodiments, the course selection module 616 ranks prospective courses based on member feedback data (e.g., data from members rating the course by quality), course prerequisites, the level of member that the course is aimed at (e.g., a beginner vs. an experienced programmer), and so on.
The course selection module 616 then transmits course list data 610, which includes a list of all potential courses that could be recommended to the first member, including data about each course, such as ranking and content. The recommendation module 126 receives the course list data 610 and selects one or more courses based on the rankings (e.g., the four highest-ranked courses). The recommended courses 612 are transmitted to the client system (e.g., the client system 102 in
In some embodiments, the method is performed by a social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In response to receiving the request, the social networking system (e.g., the social networking system 120 in
In some example embodiments, once a group of similar members is identified, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
The social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some embodiments, the method is performed by a social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, identifying the group of members who are similar to the first member includes the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, accessing member profiles for a plurality of other members of the social networking system further comprises the social networking system (e.g., the social networking system 120 in
In some example embodiments, the member profiles include a change log and the past member profile data is calculated by reconstructing member profiles using the change log.
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system clusters (814) the current member profile of the first member with historical member profiles for the other members to identify members who were similar to the current first member at a given point in the past. Thus, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some embodiments, the method is performed by a social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, for a particular skill in the list of skills, the social networking system (e.g., the social networking system 120 in
In accordance with a determination that the first member does not possess the particular skill, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
In some example embodiments, the social networking system (e.g., the social networking system 120 in
The operating system 902 may manage hardware resources and provide common services. The operating system 902 may include, for example, a kernel 920, services 922, and drivers 924. The kernel 920 may act as an abstraction layer between the hardware and the other software layers. For example, the kernel 920 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services 922 may provide other common services for the other software layers. The drivers 924 may be responsible for controlling and/or interfacing with the underlying hardware. For instance, the drivers 924 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 904 may provide a low-level common infrastructure that may be utilized by the applications 908. The libraries 904 may include system libraries 930 (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 904 may include API libraries 932 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 904 may also include a wide variety of other libraries 934 to provide many other APIs to the applications 908.
The frameworks 906 may provide a high-level common infrastructure that may be utilized by the applications 908. For example, the frameworks 906 may provide various graphical user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks 906 may provide a broad spectrum of other APIs that may be utilized by the applications 908, some of which may be specific to a particular operating system 902 or platform.
The applications 908 include a home application 950, a contacts application 952, a browser application 954, a book reader application 956, a location application 958, a media application 960, a messaging application 962, a game application 964, and a broad assortment of other applications, such as a third-party application 966. In a specific example, the third-party application 966 (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 966 may invoke the API calls 910 provided by the mobile operating system, such as the operating system 902, to facilitate functionality described herein.
The machine 1000 may include processors 1010, memory 1030, and I/O components 1050, which may be configured to communicate with each other via a bus 1005. In an example embodiment, the processors 1010 (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 1015 and a processor 1020, which may execute the instructions 1025. The term “processor” is intended to include multi-core processors 1010 that may comprise two or more independent processors 1015, 1020 (also referred to as “cores”) that may execute the instructions 1025 contemporaneously. Although
The memory 1030 may include a main memory 1035, a static memory 1040, and a storage unit 1045 accessible to the processors 1010 via the bus 1005. The storage unit 1045 may include a machine-readable medium 1047 on which are stored the instructions 1025 embodying any one or more of the methodologies or functions described herein. The instructions 1025 may also reside, completely or at least partially, within the main memory 1035, within the static memory 1040, within at least one of the processors 1010 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1000. Accordingly, the main memory 1035, the static memory 1040, and the processors 1010 may be considered machine-readable media 1047.
As used herein, the term “memory” refers to a machine-readable medium 1047 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 1047 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 1025. 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 1025) for execution by a machine (e.g., machine 1000), such that the instructions 1025, when executed by one or more processors of the machine 1000 (e.g., processors 1010), cause the machine 1000 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 1050 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 1050 may include many other components that are not shown in
In further example embodiments, the I/O components 1050 may include biometric components 1056, motion components 1058, environmental components 1060, and/or position components 1062, among a wide array of other components. For example, the biometric components 1056 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 1058 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 1060 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 1062 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 1050 may include communication components 1064 operable to couple the machine 1000 to a network 1080 and/or devices 1070 via a coupling 1082 and a coupling 1072, respectively. For example, the communication components 1064 may include a network interface component or another suitable device to interface with the network 1080. In further examples, the communication components 1064 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 1070 may be another machine 1000 and/or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).
Moreover, the communication components 1064 may detect identifiers and/or include components operable to detect identifiers. For example, the communication components 1064 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 1064, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.
In various example embodiments, one or more portions of the network 1080 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 1080 or a portion of the network 1080 may include a wireless or cellular network and the coupling 1082 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 1082 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 1025 may be transmitted and/or received over the network 1080 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 1064) and utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Similarly, the instructions 1025 may be transmitted and/or received using a transmission medium via the coupling 1072 (e.g., a peer-to-peer coupling) to the devices 1070. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 1025 for execution by the machine 1000, and includes digital or analog communications signals or other intangible media to facilitate communication of such software 900.
Furthermore, the machine-readable medium 1047 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 1047 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 1047 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.
