Patent Application
20170255906
The present disclosure generally relates to computer technology for solving technical challenges in electronic communications. More specifically, the present disclosure relates to the ranking of job search results using candidate selection features.
Online social and professional networking services are becoming increasingly popular, with many such services boasting millions of active members. In particular, the professional networking website Linkedln has become successful at least in part because it allows members to actively search for jobs.
Some embodiments of the technology are illustrated, by way of example and not limitation, in the figures of the accompanying drawings.
The present disclosure describes, among other things, methods, systems, and computer program products that individually provide various functionality. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of different embodiments of the present disclosure. It will be evident, however, to one skilled in the art, that the present disclosure may be practiced without all of the specific details.
In a typical online system that searches through documents in a database, for example an online social networking system having a job posting functionality, the system retrieves documents based on a query, scores or ranks the retrieved documents, and then displays a portion of the retrieved documents based on the scoring or ranking. The system will score all documents that were retrieved based on the search, and this generates a sorted top hits list. For selective or narrow queries wherein the retrieved result set is relatively small, scoring all of the documents is not an issue. However, for expansive or broad queries that retrieve a million or more documents, the scoring of all of these documents can cause performance problems in the online system. Indeed, an attempt to score all documents could cause the online system to time-out, and no documents would be returned to the user in response to the user's query. This of course is confusing to a user, and leads to reduced viewings of job postings and reduced applications for jobs. This situation is exacerbated when complex and sophisticated scoring models are used that require computation of expensive features.
To address this issue, a system can employ what can be referred to as early termination with static rankings. With this approach, a static rank is calculated for every document retrieved in a search. Then, during the scoring phase, only a top percentage of documents by static rank are scored. Although this approach works well in improving performance, the relevance of the returned search results will only be unaffected if the static rank of a document is somewhat correlated with the final score attributed to the document. If this is not the case however, then documents with low static ranks that are highly relevant to the user's query will not surface and therefore will not be returned to the user. This can be a particular issue in relation to job search applications. That is, due to the personalized nature of a job search, a scoring model normally heavily weights query related features and search homophily features. Thus, for every user-query pair, it is not possible to train a global static ranking model with high recall of relevant documents after early termination.
Consequently, in another embodiment, the system can employ what can be referred to a candidate selection of retrieved documents before a scoring phase is executed. In this approach, a separate first scoring model is applied online to rank all retrieved documents. A top percentage of documents from this first scoring model are considered as candidate documents to be passed to a second scoring model. As such, the system has a two pass scoring pipeline. In the first scoring model, documents are coarsely ranked, then early terminated by taking a certain top percentage of these documents or a top number of these documents, and then finely ranked by the second scoring model. Since the first model is only a coarse ranking, a very cheap scoring model can be used, thereby optimizing for the recall of relevant documents. The second scoring model is then a more expensive scoring model that results in a fine, precise ranking of the candidate documents. The overall performance of the system is greatly improved by applying the second more expensive ranking model to only a subset of the retrieved documents. In an embodiment, when the first model is applied online, query- and searcher-related features can be used in the ranking (which are generally unavailable in systems using the aforementioned static ranks). Consequently, this results in a superior ranking function over a static ranking system and a better recall of relevant documents when applying early termination using those rankings.
In an embodiment, the first cheap scoring model can simulate static ranking by using only document quality features such as how recently a job has been posted and historical click through rates of a job posting. These document quality features can be weighted. It is noted that even with a simple first scoring model with a small selection of features and un-optimized feature weights, an improved search system is realized with the two pass scoring.
In an embodiment, the feature set of the first scoring model can be expanded by adding more document quality features, and also adding query features such as similarity between the user query and job title. With a more complex feature set, rankings resulting from the first scoring model will be more accurate. This reduces the number of documents that are considered for candidate selection and the second model, which further increases system performance.
In an embodiment, the first scoring models are trained with an expanded feature set. Since the goal of the first scoring model is to optimize the recall of relevant documents in a top percentage or top number of documents, traditional learning algorithms are superfluous. Instead, the system can reduce the scoring to a classification problem. Specifically, given a query-document tuple, the document is classified as being relevant or not relevant to the searcher's query. Training data can be labeled by considering all clicked jobs as relevant and vice-versa. Logistic regression can be used to train weights. Logistic regression is well understood by those skilled in the art, and will not be described in further detail herein, in order to avoid occluding various aspects of this disclosure. Additionally, the system may use various other modelling techniques understood by those skilled in the art. For example, other modelling techniques may include other machine learning models such as a Naïve Bayes model, a support vector machines (SVM) model, a decision trees model, and a neural network model, all of which are understood by those skilled in the art.
Consequently, in light of the foregoing, in an example embodiment, on online social networking system receives a job search query from a member of the online social networking system. The system retrieves job postings from a database in the online social networking system using the job search query, and applies a first scoring model to the retrieved job postings, thereby generating a first coarse ranking of the retrieved job postings. The system identifies a top percentage or top number of job postings from the first coarse ranking, and then applies a second scoring model to the top percentage or top number of job postings, thereby generating a second fine ranking of the retrieved job postings. The system finally displays the second fine ranking of the retrieved job postings on a computer display device.
Any of the above-discussed embodiments can be implemented on a client-server system such as the system illustrated in
An application program interface (API) server 114 and a web server 116 are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers 118. The application server(s) 118 host one or more applications 120. The application server(s) 118 are, in turn, shown to be coupled to one or more database servers 124 that facilitate access to one or more databases 126. While the application(s) 120 are shown in
Further, while the client-server system 100 shown in
The web client 106 accesses the various applications 120 via the web interface supported by the web server 116. Similarly, the programmatic client 108 accesses the various services and functions provided by the application(s) 120 via the programmatic interface provided by the API server 114.
In some embodiments, any website referred to herein may comprise online content that may be rendered on a variety of devices including, but not limited to, a desktop personal computer (PC), a laptop, and a mobile device (e.g., a tablet computer, smartphone, etc.). In this respect, any of these devices may be employed by a user to use the features of the present disclosure. In some embodiments, a user can use a mobile app on a mobile device (any of the machines 110, 112, and the third party server 130 may be a mobile device) to access and browse online content, such as any of the online content disclosed herein. A mobile server (e.g., API server 114) may communicate with the mobile app and the application server(s) 118 in order to make the features of the present disclosure available on the mobile device.
In some embodiments, the networked system 102 may comprise functional components of a social networking service.
As shown in
An application logic layer may include one or more various application server modules 214, which, in conjunction with the user interface module(s) 212, generate various user interfaces (e.g., web pages) with data retrieved from various data sources in a data layer. In some embodiments, individual application server modules 214 are used to implement the functionality associated with various applications 120 and/or services provided by the social networking service.
As shown in
As members interact with the various applications 120, services, and content made available via the social networking service, the members' interactions and behavior (e.g., content viewed, links or buttons selected, messages responded to, etc.) may be tracked, and information concerning the members' activities and behavior may be logged or stored, for example, as indicated in
In some embodiments, the databases 218, 220, and 222 may be incorporated into the database(s) 126 in
Although not shown, in some embodiments, the social networking service system 210 provides an API module via which applications 120 and services can access various data and services provided or maintained by the social networking service. For example, using an API, an application may be able to request and/or receive one or more navigation recommendations. Such applications 120 may be browser-based applications 120, or may be operating system-specific. In particular, some applications 120 may reside and execute (at least partially) on one or more mobile devices (e.g., phone or tablet computing devices) with a mobile operating system. Furthermore, while in many cases the applications 120 or services that leverage the API may be applications 120 and services that are developed and maintained by the organization operating the social networking service, nothing other than data privacy concerns prevents the API from being provided to the public or to certain third parties under special arrangements, thereby making the navigation recommendations available to third party applications 128 and services.
Although the search engine 216 is referred to herein as being used in the context of a social networking service, it is contemplated that it may also be employed in the context of any website or online services. Additionally, although features of the present disclosure are referred to herein as being used or presented in the context of a web page, it is contemplated that any user interface view (e.g., a user interface on a mobile device or on desktop software) is within the scope of the present disclosure.
In an example embodiment, when member profiles are indexed, forward search indexes are created and stored. The search engine 216 facilitates the indexing and searching for content within the social networking service, such as the indexing and searching for data or information contained in the data layer, such as profile data (stored, e.g., in the profile database 218), social graph data (stored, e.g., in the social graph database 220), and member activity and behavior data (stored, e.g., in the member activity and behavior database 222). The search engine 216 may collect, parse, and/or store data in an index or other similar structure to facilitate the identification and retrieval of information in response to received queries for information. This may include, but is not limited to, forward search indexes, inverted indexes, N-gram indexes, and so on.
Companies, recruiters, or other individuals or organizations may then post job postings to the social networking system. These job postings may be stored in job posting database 224 and may be available to members of the social networking service system 210 for search, perusal, and application.
The next step is to map the segments into specific entities. An entity mapper 604 may match the segments against a dictionary of corresponding types. Some segments may be ambiguous—Cambridge, for example, may refer to Cambridge, Mass. or Cambridge, England. A profile extracted for the searcher may be used to resolve ambiguities in a personalized way. For example, if the searcher is currently residing in the United States, the entity mapper 604 will be more likely to map Cambridge to Cambridge, Mass. than Cambridge, England. Likewise, the skills of the searcher (as denoted in the searcher's member profile) can be used to resolve a particularly ambiguous job title (e.g., “engineer” refers to “Software engineer” because the searcher has many software-related skills as opposed to a “structural engineer,” of which the searcher has no related skills).
In the social network, a node tends to be connected or interact with other nodes that are similar to it. In the context of a job search, in an example embodiment a job searcher tends to be interested in the jobs that require similar expertise as his or her skills. Members of a social network may be permitted to add skills to their profiles. These skills may be among thousands of standardized skills. Members can also endorse skills of other members in their network.
Learning to rank, also known as machine-learned ranking, is an application of machine learning, typically supervised, semi-supervised, or reinforcement learning. Training data comprises lists of items with some partial order specified between items in each list. This order is typically induced by giving numerical or ordinal score or a binary judgement for each item. The ranking model's purpose is to rank, e.g., produce a permutation of items in lists in a way which is similar to the rankings in the training data in some sense.
In an example embodiment, existing features are generally divided into three categories: textural features, geographic features, and social features. The most traditional type of features is textural features. These features match the keywords in queries with different sections of a job description.
Geographic features relate to the location of the searcher/job opening. Social features indicate how the results socially relate to the searcher, based on factors such as how the searcher socially connects with the company posting the job (e.g., if he or she follows the company or has friends working at the company).
A traditional way to obtain training data is to use human experts to label the results. However, given a large training data set for a personal search, it is expensive to use human experts. At the same time, it is very hard for people other than the searcher to know the true relevance of the results. For example, for the query of “software engineer,” a new college graduate in the U.S. and an experienced candidate in Canada could be interested in very different results. In an example embodiment, log data is used as implicit feedback from searchers to generate training data. Log data comprises information about how users interact with results, such as which results they click on and which of the underlying jobs associated with the job postings they apply for.
One problem with log data is something known as “position bias,” as users tend to interact with top results. Thus, labels inferred from user actions may be biased towards the ranking function generating the data. In order to counter the position bias, in an example embodiment, search results are randomized and shown to a small percentage of traffic. Additionally, log data may include not just information such as which documents the searcher clicked on but also which job positions the searcher applied for. Applying is a stronger signal of relevance than clicking, and thus a higher label may be assigned to applied results (considered as perfect results) and a lower label to clicked results (considered as good results). Results that received no interaction at all are considered as bad results, although for results shown below the last interacted one it cannot be determined whether the searcher deliberately did not interact with these results or whether the searcher did not look at them. In an example embodiment, results shown below the last result to be interacted with are discarded.
Referring specifically to
The details of the first scoring model are as follows. As indicated at 931, the first scoring model includes a processor-inexpensive filtering of the retrieved job postings. A processor-inexpensive filtering involves any type of algorithm that does not require extensive processor cycles to filter and generate a list of job postings based either on job posting quality features or on the search query from the member. In a particular embodiment, as indicated at 932, the first scoring model involves the use of job posting quality features. Examples of job posting quality features include the age of a particular job posting, click through rates for a particular job posting, a job title of a particular job posting, and a premium status of the particular job posting. A job posting can be given premium status, for example, when the employer who posts the job pays a fee to the online social networking service. The use of job posting quality features optimizes the retrieval of relevant job postings. For example, when the click through rate of a job posting is high, that job posting has generated a good level of interest from other job seekers. Therefore, there is a greater possibility that that job posting will also be of interest to other job seekers as compared to a job posting that has a lower click through rate.
At 933, a weighting is applied to the job posting quality features. For example, the job title and job premium may be weighted more heavily than the age of a job posting, so that the job title and job premium will have a greater effect on whether a job posting is presented to a job seeker. At 933A, the job posting quality features are weighted using a logistical regression. Specifically, at 933B, job search query and job posting tuples are constructed. At 933C, for each job search query and job posting tuple, the relevancy between the job search query and job posting is determined. For example, a percentage of terms from the job search query that match or have equivalents with terms from the job posting is determined. At 933D, training data is identified based on the click through rate for the job posting in the job posting tuple. That is, data or terms from a job posting that has a high click through rate will more likely be used as training data than data or terms from a job posting that has a lower click through rate. At 933E, the logistic regression is used for the training.
In an embodiment, as indicated at 934, the first scoring model, in addition to including job posting quality features, can also include query-related features. For example, in this particular embodiment, the first scoring model can analyse a similarity between the job search query from the member and the titles of the jobs in the job postings, and/or a similarity between a profile of the member and the job posting.
As indicated at 951, the second scoring model comprises a processor-expensive filtering of the top percentage or number of job postings. A processor-expensive filtering involves any type of algorithm that requires extensive processor cycles to filter and generate a list of job postings based on the search query from the member. At 952, the processor-expensive filtering of the second scoring model includes a comparison of the job search query to the job posting and a comparison of a profile of the member and the job posting.
As indicated at 935, the first scoring model is an online process. As briefly discussed above, and as indicated in more detail at 935A, the first scoring model includes both searcher-related features and query-related features. More specifically, as indicated at 935B, the searcher-related features and query-related features can include a matching percentage between search query terms and job posting terms and/or a matching percentage between terms from a user's profile and job posting terms.
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 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 Application Specific Integrated Circuit (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 executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware modules become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. 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 accordingly configures a particular processor or processors, 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, with a particular processor or processors 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 API).
The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the 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 processors or processor-implemented modules may be distributed across a number of geographic locations.
The modules, methods, applications, and so forth described in conjunction with
Software architectures are used in conjunction with hardware architectures to create devices and machines tailored to particular purposes. For example, a particular hardware architecture coupled with a particular software architecture will create a mobile device, such as a mobile phone, tablet device, or so forth. A slightly different hardware and software architecture may yield a smart device for use in the “internet of things,” while yet another combination produces a server computer for use within a cloud computing architecture. Not all combinations of such software and hardware architectures are presented here, as those of skill in the art can readily understand how to implement the inventive subject matter in different contexts from the disclosure contained herein.
In the example architecture of
The operating system 1014 may manage hardware resources and provide common services. The operating system 1014 may include, for example, a kernel 1028, services 1030, and drivers 1032. The kernel 1028 may act as an abstraction layer between the hardware and the other software layers. For example, the kernel 1028 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services 1030 may provide other common services for the other software layers. The drivers 1032 may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 1032 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 depending on the hardware configuration.
The libraries 1016 may provide a common infrastructure that may be utilized by the applications 1020 and/or other components and/or layers. The libraries 1016 typically provide functionality that allows other software modules to perform tasks in an easier fashion than by interfacing directly with the underlying operating system 1014 functionality (e.g., kernel 1028, services 1030, and/or drivers 1032). The libraries 1016 may include system 1034 libraries (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 1016 may include API 1036 libraries 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 1016 may also include a wide variety of other libraries 1038 to provide many other APIs to the applications 1020 and other software components/modules.
The frameworks 1018 (also sometimes referred to as middleware) may provide a higher-level common infrastructure that may be utilized by the applications 1020 and/or other software components/modules. For example, the frameworks 1018 may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks 1018 may provide a broad spectrum of other APIs that may be utilized by the applications 1020 and/or other software components/modules, some of which may be specific to a particular operating system or platform.
The applications 1020 include built-in applications 1040 and/or third party applications 1042. Examples of representative built-in applications 1040 may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. The third party applications 1042 may include any of the built-in applications as well as a broad assortment of other applications. In a specific example, the third party application 1042 (e.g., an application developed using the AndroidTM or iOSTM 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 1042 may invoke the API calls 1024 provided by the mobile operating system, such as the operating system 1014, to facilitate functionality described herein.
The applications 1020 may utilize built-in operating system 1014 functions (e.g., kernel 1028, services 1030, and/or drivers 1032), libraries 1016 (e.g., system 1034, APIs 1036, and other libraries 1038), and frameworks/middleware 1018 to create user interfaces to interact with users of the system. Alternatively, or additionally, in some systems, interactions with a user may occur through a presentation layer, such as the presentation layer 1044. In these systems, the application/module “logic” can be separated from the aspects of the application/module that interact with a user.
Some software architectures utilize virtual machines. In the example of
The machine 1100 may include processors 1110, memory/storage 1130, and I/O components 1150, which may be configured to communicate with each other such as via a bus 1102. In an example embodiment, the processors 1110 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1112 and a processor 1114 that may execute the instructions 1116. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although
The memory/storage 1130 may include a memory 1132, such as a main memory, or other memory storage, and a storage unit 1136, both accessible to the processors 1110 such as via the bus 1102. The storage unit 1136 and memory 1132 store the instructions 1116 embodying any one or more of the methodologies or functions described herein. The instructions 1116 may also reside, completely or partially, within the memory 1132, within the storage unit 1136, within at least one of the processors 1110 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1100. Accordingly, the memory 1132, the storage unit 1136, and the memory of the processors 1110 are examples of machine-readable media.
As used herein, “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)), and/or any suitable combination thereof. 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 1116. 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 1116) for execution by a machine (e.g., machine 1100), such that the instructions, when executed by one or more processors of the machine (e.g., processors 1110), cause the machine 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” excludes signals per se.
The I/O components 1150 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1150 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1150 may include many other components that are not shown in
In further example embodiments, the I/O components 1150 may include biometric components 1156, motion components 1158, environmental components 1160, or position components 1162, among a wide array of other components. For example, the biometric components 1156 may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components 1158 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 1160 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), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 1162 may include location sensor components (e.g., a Global Position System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.
Communication may be implemented using a wide variety of technologies. The I/O components 1150 may include communication components 1164 operable to couple the machine 1100 to a network 1180 or devices 1170 via a coupling 1182 and a coupling 1172, respectively. For example, the communication components 1164 may include a network interface component or other suitable device to interface with the network 1180. In further examples, the communication components 1164 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 1170 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).
Moreover, the communication components 1164 may detect identifiers or include components operable to detect identifiers. For example, the communication components 1164 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 1164, 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 1180 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (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 1180 or a portion of the network 1180 may include a wireless or cellular network and the coupling 1182 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 1182 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), 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 1116 may be transmitted or received over the network 1180 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 1164) and utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Similarly, the instructions 1116 may be transmitted or received using a transmission medium via the coupling 1172 (e.g., a peer-to-peer coupling) to the devices 1170. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 1116 for execution by the machine 1100, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.
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.
