POINT IN TIME PREDICTIVE GRAPHICAL MODEL EXPLORATION

In various example embodiments, a system and methods are presented for generation and manipulation of predictive models within a user interface. The system and methods receive a view query with object data and time data and generate a user interface having a first graphical representation of a set of historical data responsive to the view query. The systems and methods generate a predictive model based on the set of historical data and generate a second graphical representation for the predictive model. The systems and methods generate and monitor a movable pivot element to automatically modify the predictive model and second graphical representation upon a change in position of the pivot element.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to user interface interactions and, more particularly, but not by way of limitation, to modeling and manipulating related predictive models within a graphical user interface.

BACKGROUND

Conventionally, systems and methods for data modeling generate models through intentional user interaction and request. These systems often require the user to be familiar with both the subject and context of the models and also with the systems, procedures, and assumptions used to generate a model. Further, the data models generated by these systems often model a single predetermined attribute of the underlying data, taking into account only the characteristics of the data directly influencing the predetermined attribute.

Display of data models is conventionally static, requiring separate generation of models prior to rendering new data on a user interface. User interfaces for display of the data models often require direct interaction with predetermined fields and an understanding of appropriate data or queries to be entered in the fields in order to refresh of an existing model or change to a differing model.

DETAILED DESCRIPTION

Systems and methodologies described herein enable generation of a user interface as well as predictive models which allow a user to rapidly explore a wide array of related predictive models. The user may identify causal factors in data presented within the user interface. The systems and methodologies may enable the user to add annotations identifying the causal factors and factors or events desired by the user to iteratively adjust or modify the predictive models. The methodologies and systems presented herein achieve a synthesis between domain expert knowledge and model predictions without the domain expert having direct access or knowledge of the underlying predictive models.

User interface elements of the user interface described by the systems and methods of the present disclosure enable pivot changes and modifications of predictive models based on positioning of a pivot element. Changes in position of the pivot element may act as a query causing a change in later predictions. The results of the predictive model may be overlaid or otherwise contemporaneously displayed with historical data. Further, each movement causes the generation of a new predictive model incorporating previous iterations of predictive models and changes to the pivot element to focus on a specified or selected aspect of the historical data presented.

With reference toFIG. 1, an example embodiment of a high-level client-server-based network architecture100is shown. A networked system102, in the example forms of a network-based predictive modeling system, provides server-side functionality via a network104(e.g., the Internet or wide area network (WAN)) to one or more client devices110.FIG. 1illustrates, for example, a web client112(e.g., a browser, such as the INTERNET EXPLORER® browser developed by Microsoft® Corporation of Redmond, Washington State), an application114, and a programmatic client116executing on client device110.

The client device110may comprise, but is not limited to, mobile phones, desktop computers, laptops, personal digital assistants (PDAs), smart phones, tablets, ultra books, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may utilize to access the networked system102. In some embodiments, the client device110may comprise a display component (not shown) to display information (e.g., in the form of user interfaces). In further embodiments, the client device110may comprise one or more of a touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and so forth.

The client device110may be a device of a user that is used to perform a transaction involving object data and predictive models within the networked system102. One or more users106may be a person, a machine, or other means of interacting with client device110. In embodiments, the user106is not part of the network architecture100, but may interact with the network architecture100via client device110or another means. For example, one or more portions of network104may 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), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a WiFi network, a WiMax network, another type of network, or a combination of two or more such networks. Each of the client device110may include one or more applications (also referred to as “apps”) such as, but not limited to, a web browser, messaging application, electronic mail (email) application, and the like.

One or more users106may be a person, a machine, or other means of interacting with the client device110. In example embodiments, the user106is not part of the network architecture100, but may interact with the network architecture100via the client device110or other means. For instance, the user provides input (e.g., touch screen input or alphanumeric input) to the client device110and the input is communicated to the networked system102via the network104. In this instance, the networked system102, in response to receiving the input from the user, communicates information to the client device110via the network104to be presented to the user. In this way, the user can interact with the networked system102using the client device110.

An application program interface (API) server120and a web server122are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers140. The application servers140may host one or more publication systems142and predictive modeling systems150, each of which may comprise one or more components or applications and each of which may be embodied as hardware, software, firmware, or any combination thereof. The application servers140are, in turn, shown to be coupled to one or more database servers124that facilitate access to one or more information storage repositories or database(s)126. In an example embodiment, the databases126are storage devices that store information to be posted (e.g., publications or listings) to the publication system142. The databases126may also store object data, historical data, and predictive modeling data in accordance with example embodiments.

Additionally, a third party application132, executing on third party server(s)130, is shown as having programmatic access to the networked system102via the programmatic interface provided by the API server120. For example, the third party application132, utilizing information retrieved from the networked system102, supports one or more features or functions on a website hosted by the third party.

The publication system142may provide a number of publication, archival, and data storage functions and services to users106that access the networked system102. For example, the publication system142may gather, publish, and store object data, historical data for one or more objects, sales data for one or more objects, revenue data for one or more objects, release data for one or more objects, and competitor data for one or more objects. The publication system142may publish the object data and data related to the objects to an internal database or publicly available database to enable generation of predictive models based on the object data and data related to the objects. In some embodiments, the publication system142accesses one or more third party servers or databases (e.g., the third party server130) to retrieve, modify, and provision the object data within the database126.

The predictive modeling system150may provide functionality operable to perform various predictive model generation and manipulation functions, as well as functions for generating graphical representations of object data, data related to the objects, and predictive models. For example, the predictive modeling system150accesses sets of object data from the databases126, the third party servers130, the publication system142, the client device110, and other sources. In some example embodiments, the predictive modeling system150analyzes portions of the sets of object data to generate predictive models forecasting one or more aspects or characteristics of the object or performance of the object with respect to a predetermined or defined metric. In some example embodiments, the predictive modeling system150communicates with the publication systems142to access the sets of object data and transmit queries received by the predictive modeling system150to the publication system142. In an alternative embodiment, the predictive modeling system150may be a part of the publication system142.

Further, while the client-server-based network architecture100shown inFIG. 1employs a client-server architecture, the present inventive subject matter is of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various publication system142and predictive modeling system150could also be implemented as standalone software programs, which do not necessarily have networking capabilities.

The web client112may access the various publication and predictive modeling systems142and150via the web interface supported by the web server122. Similarly, the programmatic client116accesses the various services and functions provided by the publication and predictive modeling systems142and150via the programmatic interface provided by the API server120.

Additionally, a third party application(s)128, executing on a third party server(s)130, is shown as having programmatic access to the networked system102via the programmatic interface provided by the API server114. For example, the third party application128, utilizing information retrieved from the networked system102, may support one or more features or functions on a website hosted by the third party. The third party website may, for example, provide one or more promotional, marketplace, data repository, company interaction, or object tracking functions that are supported by the relevant applications of the networked system102.

FIG. 2is a block diagram illustrating components of the predictive modeling system150, according to some example embodiments. The predictive modeling system150is shown as including a receiver component210, an interface component220, a monitoring component230, a modeling component240, a range component250, and a presentation component260all configured to communicate with one another (e.g., via a bus, shared memory, or a switch). Any one or more of the components described herein may be implemented using hardware (e.g., one or more processors of a machine) or a combination of hardware and software. For example, any component described herein may configure a processor (e.g., among one or more processors of a machine) to perform operations for which that component is designed. Moreover, any two or more of these components may be combined into a single component, and the functions described herein for a single component may be subdivided among multiple components. Furthermore, according to various example embodiments, components described herein as being implemented within a single machine, database(s)126, or device (e.g., client device110) may be distributed across multiple machines, database(s)126, or devices.

The receiver component210receives or otherwise accesses object data for generation and provisioning of object data, historical data, and predictive modeling. In some embodiments, the receiver component210receives queries from the client device110to access specified aspects of the object data and historical data to enable generation of predictive models. The interface component220generates user interfaces and user interface elements through which the client device110, operated by the user106, accesses and interacts with the object data, historical data, and predictive models.

The monitoring component230monitors one or more user interface elements generated by the interface component220to trigger automated operations with respect to generating or modifying predictive models and displaying data underlying the generated predictive models. The modeling component240generates one or more predictive models based on retrieved or accessed object data and historical data. In some embodiments, the modeling component240generates and modifies the predictive models automatically based on indirect user interaction with the user interface or user interface elements, without the user106or the client device110directly specifying or requesting generation or modification of the predictive models. The range component250identifies time data related to the object data and historical data and communicates specified characteristics of the time data to the interface component for generation of portions of the user interface and the one or more user interface elements.

The presentation component260causes presentation of the user interface and user interface elements generated by the interface component220to the client device110. In some embodiments, the presentation component260causes presentation by transmitting the use interfaces and user interface elements to the client device110over the network104. The presentation component260may operate in cooperation with one or more of the receiver component210and the monitoring component230to monitor interaction with the user interface and user interface elements at the client device110.

FIG. 3is a flowchart of operations of the predictive modeling system150in performing a method300of generating and manipulating related predictive models within a graphical user interface, according to some example embodiments. Operations in the method300may be performed by the predictive modeling system150, using components described herein.

In operation310, the receiver component210receives a view query comprising object data and time data. The object data and the time data are associated with a set of historical information for a specified object. The view query may also include multivariate time series modeling input. In some embodiments, the view query is received by the receiver component210through a request entered in a user interface generated by the interface component220and presented at the client device110. For example, the interface component220may generate a user interface with one or more data entry fields. In some instances, the view query, as received by the receiver component210through the user interface, may be formatted to query the database126to retrieve a set of historical data representative of an object. In some embodiments, the receiver component210may configure or otherwise format the view query to query the database126.

The view query may be passed to the database126by the receiver component210to access a knowledge base within the database126to surface contextually relevant objects and information relating to the objects. For example, the object may be a product, and the database126provisions product information, events, news articles, sales information, profitability information, competing products, competitor companies, related products or product lines, dates for release of the product, dates for release of related products, dates for termination of a product line, and other object information. In some embodiments, where the view query includes time data, the contextually relevant objects and information may be determined in part based on the time data. For example, the objects and information may be determined based on a start date, an end date, a time range, or other time data included in the query view.

In operation320, the interface component220generates a user interface with a first graphical representation and a second graphical representation. The first graphical representation represents a set of historical data and the second graphical representation represents a predictive model based on at least a portion of the set of historical data. In some embodiments, the second graphical representation extends outwardly from the first graphical representation at a point along the first graphical representation. The second graphical representation may extend outwardly from the first graphical representation as a continuation of the first graphical representation. For example, where the first graphical representation is a graphed line, the second graphical representation may be a portion of the graphed line continuing past an ending point of the first graphical representation. In some instances, the first graphical representation and the second graphical representation may be differentiated based on a change in color, a change in line type (e.g., solid or dotted), a change in direction of the first graphical representation to the second graphical representation, or other change in appearance. In some instances, as shown inFIG. 4, the first graphical representation may be a first graphical plot of a set of historical data. The second graphical representation may be a second graphical plot of a set of predicted data. The first graphical plot and the second graphical plot may form a combined graphical plot or line representing both the historical data and predicted data.

In some instances, the set of historical data may be presented as a graph, as shown inFIG. 4. For example, the user interface may be generated as a set of time series data, displayed with time along an x axis and quantity or quantities along a y axis. As shown, the set of historical data may include the quantity or quantities displayed along the y axis. The set of historical data may include actual available data or smoothed interpolations from it. The set of historical data may be understood as ground truth of the object associated with the set of historical data. InFIG. 4, the user interface400is depicted as a graph with the first graphical representation410for the historical data extending across the x and y axes of the graph and the second graphical representation420extending across the x and y axes of the graph.

Each of the predictive models, generated by the modeling component240, may be a machine learning model that is multiply cross-validated on the available data. Cross-validation may be performed in varying ways based on a size of a data set and a distribution of individual values within the data set. In some instances, the modeling component240may generate N folds of the available data and use N-1 of the folds to set parameters and train the machine learning model. The modeling component240may then evaluate the machine learning model and cross-validation based on the remaining fold of the available data. The folds may be understood as subsamples of the available data, divided for use in cross-validation operations. In some instances, the folds represent equal sized subsamples of the available data. In some instances, the folds may represent subsamples divided into a distribution of available data based on relationships determined among the available data.

In some embodiments, the available data may be the set of historical data provided by the database126in response to the view query. The available data may also include additional information supplied by the database126in response to the view query which is not presented within the user interface. The available data may also include additional information not supplied for presentation within the user interface but included within the database126and identified in response to the view query. In some instances, additional information may include external financial data relating to the current analysis or broader economic climate, dated events falling into predetermined classes (e.g., a recall or a product launch), and data sets relating to entities in the database being examined.

The process of cross-validation may set a number of hyper-parameters to the model. The hyper-parameters may be set or otherwise determined by grid search, Bayesian parameter search, and other suitable methods. In some embodiments, the hyper-parameters may have a range of potential values. Grid search methods may be used to systematically search through combinations of all potential values for all of the hyper-parameters. Bayesian methods may ignore certain combinations that are determined to provide theoretically sub-optimal given results in favor of other combinations providing differing results. The hyper-parameters control one or more aspects of the analysis, including which features of the data are kept in the model, the relative weights of those features, the relative importance of each input data point, the choice of learning objective, and the choice of optimization algorithm to use. The hyper-parameters of the model may be understood to be parameters which are determined and set during a learning phase of generating the machine learning models.

The process of generating and modifying the predictive models may avoid over-fitting to the available data. For example, in some instances, the process is repeated with different subsets of the available data and repeatedly assessed on held-out portions of the data (i.e., cross-validated) to avoid over-fitting In some embodiments, the predictive models enable inference and generalization of the set of historical data.

Referring again toFIG. 3, in operation330, the interface component220generates a movable pivot element. In some embodiments, the pivot element is a user interface element initially depicted in a first position on the first graphical representation. The movable pivot element may intersect the first graphical representation and is movable along the first graphical representation. As shown inFIG. 4, a movable pivot element430may be a draggable pivot line intersecting the first graphical representation. The movable pivot element may dynamically set a pivot time. In some embodiments, the pivot time marks a point in time acting as a bound for generating the predictive model. The pivot time may be an end bound (e.g., an end time) for generating the predictive model. Where the pivot time is an end bound440, a start bound450may be a starting time for the set of historical data. In some embodiments, the starting time is the earliest time available for the set of historical data on the database126. The starting time may be the earliest time depicted within the user interface for the set of historical data (e.g., the time range of the view query). The pivot time may also be a point on the first graphical representation at which the second graphical representation begins and from which the second graphical representation may extend. Depicting the first graphical representation and the second representation within the same user interface enables direct comparison of the ground truth with a generated predictive model.

In some embodiments, the predictive model generated in operation320is generated based on a portion of the set of historical data represented by the first graphical representation extending from a first end of the first graphical representation to the first position of the pivot element. In these embodiments, the second graphical representation extends outwardly from the first graphical representation at the position of the pivot element on the first graphical representation. Adjusting the pivot element enables exploration of a plurality of predictive models within a given time period (e.g., hundreds of different models in any given second).

In operation340, the monitoring component230monitors the pivot element within the user interface to detect a change in position of the pivot element from the first position to a second position. In some embodiments, the monitoring component230uses one or more JavaScript operations or functions for identifying interaction with the user interface such as mouse movements or clicks, touches, keyboard events, and other suitable user interface interactions. The monitoring component230may monitor the pivot element based on the position of the pivot element with respect to a pixel position of the displayed user interface, a position on the first graphical representation, a change in position along a Cartesian coordinate system, or any other user interface element tracking method.

In operation350, the modeling component240automatically modifies the predictive model to generate a modified predictive model. In some embodiments, in response to the generation of the modified predictive model, the interface component220modifies the second graphical representation to represent the modified predictive model. The modeling component240may modify a previously generated predictive model based on feedback loops to circle back to previous models in differing ways based on a selected feedback loop or a specified input of one or more differing feedback loops.

FIG. 5is a flow chart of operations of the predictive modeling system150in performing operations of a method500of generating and manipulating related predictive models within a graphical user interface, according to some example embodiments. The operations depicted inFIG. 5may be performed by the predictive modeling system150, using components described herein. As shown inFIG. 5, in some embodiments, the method500may be performed as a part of or sub-operations of the method300, described above.

In operation510, the receiver component210receives a selection of a candidate cause point605within the user interface600shown inFIG. 6. In some embodiments, the candidate cause point605is a point on the first graphical representation after which the second graphical representation deviates from the first graphical representation when the pivot element is located at the candidate cause point605. The candidate cause point605may be selected by the receiver component210receiving a user interaction with the user interface, such as a mouse click, a tap of a touchscreen, or other interaction. In some instances, if a predictive model from a pivot time becomes inaccurate or deviates from at least a portion of the first graphical representation (e.g., the set of historical data) beyond a suitable threshold, selection of the candidate cause point605may indicate a cause of the deviation.

In operation520, the modeling component240generates a modified predictive model based on the second position of the pivot element and the candidate cause point605. The modeling component240may incorporate an event, change in the set of historical data, or other aspect of the first graphical representation indicated by the candidate cause point605into generating the modified model to enhance accuracy or better fit the modified predictive model to the portion of the set of historical data from which it previously deviated. Selection of the candidate cause point605may increase the relevance and accuracy of a given model and enable inclusion of similar events into future predictive models without subsequent user interaction. The candidate cause represented by the candidate cause point605may be used as an additional predictor in the model and may be incorporated into the model free fit using one or more techniques described in the present disclosure.

In operation530, the receiver component210receives a request for second object data. In some embodiments, the second object data is rendered and replaces previously received object data, predictive models, and graphical representations. As shown inFIG. 6, the second object data is rendered on a user interface600with the first object data, predictive model, the first graphical representation410, and the second graphical representation420. The second object may be related to the first object and be represented within the database126by object identifiers, historical data for one or more objects, sales data for one or more objects, revenue data for one or more objects, release data for one or more objects, and competitor data for one or more objects. The second object may be identified as related to the first object based on matching categories of the first object and the second object, matching a temporal component (e.g., time data) between the first object and the second object, or determining one or more other similarities among the object data of the first object and the second object. In some embodiments, the second object may be identified based on inferences drawn on the first graphical representation and the second graphical representation within the user interface. The inferences may be drawn by the predictive modeling system150based on the generated user interface presented to the client device110. The predictive modeling system150may query the database126(e.g., the knowledge base) to surface objects determined to be relevant to the product and the current model. The predictive modeling system150may additionally surface results for the second object by queries received from the client device110based on one or more search engine relevance determinations.

Where the second object is related to the first object, the related object (e.g., a related product or product group) may be displayed within the graphical user interface as described below. Related objects may include products, product groups, events (e.g., system generated events, manually added events, events sourced from publicly available databases), accounts, and manually created adjustments. Objects, object representations, and historical data points of the set of historical data may be hyperlinked to one or more resource locations (e.g., an address within the publication system142). In some embodiments, the hyperlinks enable users to change the context of a generated user interface (e.g., user modified or system generated context changes) and drill into details of displayed objects or displayed object data.

In operation540, the interface component220generates a third graphical representation610of a subsequent set of historical data (e.g., a set of historical data of the second object) and a fourth graphical representation620of a subsequent predictive model (e.g., a predictive model for the second object). As described herein, the third graphical representation610and the fourth graphical representation620may be associated with the second object. In some embodiments, the operation540may be performed similarly to or the same as the operation320.

In some embodiments, as shown inFIG. 6, once the third graphical representation610and the fourth graphical representation620have been rendered, the pivot element430intersects one or more graphical representations associated with the first object and one or more graphical representations associated with the second object. As shown inFIG. 6, the third graphical representation610and the fourth graphical620representation may be presented along with the first graphical410representation and the second graphical representation420. In some instances, the third graphical representation610and the fourth graphical representation620may be presented in a separate user interface (e.g., a separate tab, a separate window, a separate graph positioned proximate to a graph presenting the first graphical representation and the second graphical representation). Where presented in a separate user interface, the pivot element430may be configured to be synchronized between the separate user interfaces.

FIG. 7is a flow chart of operations of the predictive modeling system150in performing operations of a method700of generating and manipulating related predictive models within a graphical user interface, according to some example embodiments. The operations depicted inFIG. 7may be performed by the predictive modeling system150, using components described herein. In some embodiments, as shown inFIG. 7, the method700may be performed as part of or as sub-operations of the method300, described above.

In operation710, the range component250identifies a time range630, as shown inFIG. 6, for the set of historical data. In some embodiments, the time range may be identified from the view query, the set of historical data, available object data from the database126, publicly available databases, or any other suitable data source. Once identified by the range component250, the range component250passes the identified time range to the interface component220.

In operation720, the interface component220generates a range element640representing the time range. The range element640may be a user interface element indicating or positioned proximate to an indication of the time range. For example, as shown inFIG. 6, the range element640is positioned proximate to an x axis of a graph (e.g., the time range630), where the x axis provides a time measurement indicating intervals of time. InFIG. 6, the range element640is a track for a slider. In some embodiments, the range element640extends a length of the graph including the graphical representations of one or more objects. In some instances, the range element640extends along a portion of the graph, based on the set of historical data and the graphical representations of the set of historical data. In these embodiments where the range element640is based on the set of historical data, the range may extend a distance such that display of the graph depicts a portion beyond a terminating edge of the graphical representation of the set of historical data sufficient to portray a portion of a graphical representation of a predictive model. In some embodiments, the portion of the graph extending beyond the set of historical data may be predetermined based on the predictive model or the object data. In some embodiments, the portion of the graph extending beyond the set of historical data may be dynamically determined based on the set of historical data. For example, the portion of the graph beyond the set of historical data may be proportionate to the graph containing the graphical representation of the set of historical data.

In operation730, the interface component220generates a time interface element650movable along the range element640. Movement of the time interface element650causes presentation of differing portions of the set of historical data. The range element640enables selection of a selected time range (e.g., a subset) from the time range identified in the operation710. In some embodiments, the time interface element650may be a slider or tab within a slider bar or scroll bar. The time interface may be sized based on the time range identified in710and the range element720.

In some embodiments, the time interface element comprises an indicator portion. The indicator portion may include one or more visual indicators660. A portion of the one or more visual indicators660may be configured to provide quick reference data for at least a portion of the time range. The visual indicators660of the time interface element650may contain markers for events (e.g., peak sales regions or product introduction and discontinuation), sub-ranges, time ranges of one or more sets of historical data of one or more objects, candidate cause elements, or any other suitable information.

In some embodiments, the predictive modeling system150may monitor the one or more visual indicators660and/or interaction with the one or more visual indicators (e.g., a click of a mouse, a tap of a touchscreen, or hovering of a cursor). In response to the interaction with the one or more visual indicators, the interface component220generates and causes display of additional information. For example, the interface component220may generate a window670, overlay, pop-up, new window, or other user interface portion containing the information represented by the selected visual portion. In some embodiments, regardless of interaction with the one or more visual indicators660, the user interface600may display an event window680providing information relating to the one or more visual indicators660.

According to various example embodiments, one or more of the methodologies described herein may facilitate generation and manipulation of predictive models based on a complex set of historical data. Methodologies for generating and modifying the predictive models and user interface elements automatically refresh or modify underlying data and models to determine contextually relevant data and relationships among data stored within the database126of the publication system142. Accordingly, one or more of the methodologies described herein may have the effect of allowing a user to navigate through varying predictive models and assumptions based on historical data, thereby increasing visibility of trends and causal and correlating factors within the historical data.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” 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 components 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 component that operates to perform certain operations as described herein.

Accordingly, the phrase “hardware component” 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 component” refers to a hardware component. Considering embodiments in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component 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 components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time.

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 components 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 components may be distributed across a number of geographic locations.

Machine and Software Architecture

The components, methods, applications and so forth described in conjunction withFIGS. 2-7are implemented in some embodiments in the context of a machine and an associated software architecture. In various embodiments, the components, methods, applications and so forth described above are implemented in the context of a plurality of machines, distributed across and communicating via a network, and one or more associated software architectures. The sections below describe representative software architecture(s) and machine (e.g., hardware) architecture that are suitable for use with the disclosed embodiments.

Software Architecture

FIG. 8is a block diagram800illustrating a representative software architecture802, which may be used in conjunction with various hardware architectures herein described.FIG. 8is merely a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture802may be executing on hardware such as machine900ofFIG. 9that includes, among other things, processors910, memory930, and Input/Output (I/O) components950. A representative hardware layer804is illustrated and can represent, for example, the machine800ofFIG. 8. The representative hardware layer804comprises one or more processing units806having associated executable instructions808. Executable instructions808represent the executable instructions of the software architecture802, including implementation of the methods, components, and so forth ofFIGS. 2-4. Hardware layer804also includes memory and/or storage components810, which also have executable instructions808. Hardware layer804may also comprise other hardware as indicated by812, which represents any other hardware of the hardware layer804, such as the other hardware illustrated as part of machine900.

In the example architecture ofFIG. 8, the software802may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software802may include layers such as an operating system814, libraries816, frameworks/middleware818, applications820, and presentation layer822. Operationally, the applications820and/or other components within the layers may invoke API calls824through the software stack and receive a response, returned values, and so forth, illustrated as messages826in response to the API calls824. The layers illustrated are representative in nature and not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware layer818, while others may provide such a layer. Other software architectures may include additional or different layers.

The operating system814may manage hardware resources and provide common services. The operating system814may include, for example, a kernel828, services830, and drivers832. The kernel828may act as an abstraction layer between the hardware and the other software layers. For example, the kernel828may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services830may provide other common services for the other software layers. The drivers832may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers832may 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 libraries816may provide a common infrastructure that may be utilized by the applications820and/or other components and/or layers. The libraries816typically provide functionality that allows other software components to perform tasks in an easier fashion than to interface directly with the underlying operating system814functionality (e.g., kernel828, services830and/or drivers832). The libraries816may include system834libraries (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 libraries816may include API libraries836such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as Moving Pictures Experts Group 4 (MPEG4), H.264, MP3, Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR), Joint Photographic Experts Group (JPEG), Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework that may be used to render two dimensions and three dimensions in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries816may also include a wide variety of other libraries838to provide many other APIs to the applications820and other software components/modules.

The frameworks818(also sometimes referred to as middleware) may provide a higher-level common infrastructure that may be utilized by the applications820and/or other software components/modules. For example, the frameworks818may provide various graphical user interface functions, high-level resource management, high-level location services, and so forth. The frameworks818may provide a broad spectrum of other APIs that may be utilized by the applications820and/or other software components/modules, some of which may be specific to a particular operating system or platform. In some example embodiments, predictive modeling components819(e.g., one or more components of the predictive modeling system150) may be implemented at least in part within the middleware/frameworks818. For example, in some instances, at least a portion of the interface component220and the presentation component260, providing graphical and non-graphical user interface functions, may be implemented in the middleware/frameworks818. Similarly, in some example embodiments, portions of one or more of the receiver component210, the monitoring component230, the modeling component240, and the range component250may be implemented in the middleware/frameworks818.

The applications820include built-in applications840, third party applications842, and/or predictive modeling components843(e.g., user facing portions of one or more of the components of the predictive modeling system150). Examples of representative built-in applications840may 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. Third party applications842may include any of the built in applications as well as a broad assortment of other applications. In a specific example, the third party application842(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 application842may invoke the API calls824provided by the mobile operating system such as operating system814to facilitate functionality described herein. In various example embodiments, the user facing portions of the predictive modeling components843may include one or more components or portions of components described with respect toFIG. 2. For example, in some instances, portions of the receiver component210, the interface component220, the monitoring component230, the modeling component240, the range component250, and the presentation component260associated with user interface elements (e.g., data entry and data output functions) may be implemented in the form of an application.

The applications820may utilize built in operating system functions (e.g., kernel828, services830and/or drivers832), libraries (e.g., system834, APIs836, and other libraries838), frameworks/middleware818to 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 presentation layer844. In these systems, the application/component “logic” can be separated from the aspects of the application/component that interact with a user.

Some software architectures utilize virtual machines. In the example ofFIG. 8, this is illustrated by virtual machine848. A virtual machine creates a software environment where applications/components can execute as if they were executing on a hardware machine (such as the machine ofFIG. 9, for example). A virtual machine is hosted by a host operating system (operating system814inFIG. 8) and typically, although not always, has a virtual machine monitor846, which manages the operation of the virtual machine as well as the interface with the host operating system (i.e., operating system814). A software architecture executes within the virtual machine such as an operating system850, libraries852, frameworks/middleware854, applications856and/or presentation layer858. These layers of software architecture executing within the virtual machine848can be the same as corresponding layers previously described or may be different.

Example Machine Architecture and Machine-Readable Medium

FIG. 9is a block diagram illustrating components of a machine900, according to some example embodiments, able to read instructions (e.g., processor executable instructions) from a machine-readable medium (e.g., a non-transitory machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,FIG. 9shows a diagrammatic representation of the machine900in the example form of a computer system, within which instructions916(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine900to perform any one or more of the methodologies discussed herein may be executed. For example the instructions may cause the machine to execute the flow diagrams ofFIGS. 3, 5, and 7. Additionally, or alternatively, the instructions may implement the receiver component210, the interface component220, the monitoring component230, the modeling component240, the range component250, and the presentation component260ofFIGS. 2-7, and so forth. The instructions transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described.

In alternative embodiments, the machine900operates as a standalone device or may be coupled (e.g., networked) to other machines in a networked system. In a networked deployment, the machine900may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine900may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box, an entertainment media system, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions916, sequentially or otherwise, that specify actions to be taken by machine900. In some example embodiments, in the networked deployment, one or more machines may implement at least a portion of the components described above. The one or more machines interacting with the machine900may comprise, but not be limited to a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), and other smart devices. Further, while only a single machine900is illustrated, the term “machine” shall also be taken to include a collection of machines900that individually or jointly execute the instructions916to perform any one or more of the methodologies discussed herein.

The machine900may include processors910, memory930, and I/O components950, which may be configured to communicate with each other such as via a bus902. In an example embodiment, the processors910(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, processor912and processor914that may execute instructions916. The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. AlthoughFIG. 9shows multiple processors, the machine900may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core process), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory/storage930may include a memory932, such as a main memory, or other memory storage, and a storage unit936, both accessible to the processors910such as via the bus902. The storage unit936and memory932store the instructions916embodying any one or more of the methodologies or functions described herein. The instructions916may also reside, completely or partially, within the memory932, within the storage unit936, within at least one of the processors910(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine900. Accordingly, the memory932, the storage unit936, and the memory of processors910are examples of machine-readable media.

Communication may be implemented using a wide variety of technologies. The I/O components950may include communication components964operable to couple the machine900to a network980or devices970via coupling982and coupling972, respectively. For example, the communication components964may include a network interface component or other suitable device to interface with the network980. In further examples, communication components964may 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 devices970may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

Transmission Medium

The instructions916may be transmitted or received over the network980using a transmission medium via a network interface device (e.g., a network interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916may be transmitted or received using a transmission medium via the coupling972(e.g., a peer-to-peer coupling) to devices970. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions916for execution by the machine900, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

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