Abstract: The subject disclosure is directed towards a web service that maintains a set of models used to generate plans, such as vacation plans, in which the set of models includes models that are authored by crowd contributors via the service. The models include rules, constraints and/or equations, and may be text based and declarative such that any author can edit an existing model or combination of existing models into a new model. Users can access the models to generate a plan according to user parameters, view a presentation of that plan, and interact to provide new parameters to the model and/or with objects in the plan to modify the plan and view a presentation of the modified plan.

Abstract: The present invention extends to methods, systems, and computer program products for using cinematic techniques to present data. Embodiments of the invention can be used to infer and generate cinematic techniques or combinations thereof based on a model and user action. Cinematic techniques can be used to meet the data exploration and analysis requirements of a user. As such, embodiments of the invention permit users (including non-programmers) to employ cinematic techniques (possibly in combination with other techniques) to gain insights into their data and also convey appropriate emotional messages.

Abstract: The improved exercise of artificial intelligence. Raw output data is obtained by applying an input data set to an artificial intelligence (AI). Such raw output data is sometimes difficult to interpret. The principles defined herein provide a systematic way to refine the output for a wide variety of AI models. An AI model collection characterization structure is utilized for purpose of refining AI model output so as to be more useful. The characterization structure represents, for each of multiple and perhaps numerous AI models, a refinement of output data that resulted from application of an AI model to input data. Upon obtaining output data from the AI model, the appropriate refinement may then be applied. The refined data may then be semantically indexed to provide a semantic index. The characterization structure may also provide tailored information to allow for intuitive querying against the semantic index.

Abstract: Providing an improved user interface to a user for facilitating data management produced by artificial intelligence. A method includes receiving user input adding an input dataset to an active area of a user interface. The method further includes receiving user input adding an artificial intelligence model to the active area of the user interface. The method further includes, based on the user adding the artificial intelligence model to the active area of the user interface, providing feedback on the user interface to the user indicating an effect of adding the artificial intelligence model to the active area of the user interface.

Abstract: The improved exercise of artificial intelligence by providing a systematic way fora computing system to interface with output from AI models. To do this, the computing system obtains results of an input data set being applied to an AI model. The results are then refined based characteristic(s) of the AI model and perhaps the input data set. Based upon characteristic(s) of the AI model and perhaps the input data set, interface element(s) are identified that can be used to interface with the refined results. The interface element(s) are then communicated to an interface element that interfaces with the refined results. The interface element(s) may include, for instance, operator(s) or term(s) that may be used to query against the refined results and/or an identification of visualization(s) that may be used to present to a user results of queries against the refined results.

Abstract: The improved exercise of artificial intelligence by systematically refining and semantically indexing the output from AI models, so that the semantic index is highly relevant. To do this, the computing system obtains results of an input data set being applied to an AI model. The computing system then determines a refinement to apply to the obtained results. This determination may be based on one or more characteristics of the AI model and/or input data set. The determination may also be based on hints associated with that AI model, and/or learned behavior regarding how that AI model is typically used. The obtained results are then refined using the determined refinement. It is then this more relevant refined results that are semantically indexed to generate the semantic index. Thus, the semantic index represents, the more useful output from an AI model, which is semantically exposed so as to provide meaning.

Abstract: The managing of sensed signals used to sense features of physical entities over time. A computer-navigable graph of sensed features is generated. For each sensed feature, a signal segment that was used to sense that feature is computer-associated with the sensed feature. Later, the graph of sensed features may be navigated to that features. The resulting signal segment(s) may then be access allowing for rendering of the signal evidence that resulted in the sensed feature. Accordingly, the principles described herein allow for sophisticated and organized navigation to sensed features of physical entities in the physical world, and allow for rapid rendering of the signals that evidence that sensed features.

Abstract: Facilitating navigation of a computer-navigable graph of sensed features that were gathered by recognition or estimation of one or more features of physical entities that were sensed over time by sensor(s) within a physical space. The computer-navigable graph includes multiple nodes including multiple signal segment nodes and multiple feature nodes. Each of at least some of the signal segment nodes evidence feature(s) represented in feature node(s). Navigation is facilitated by, for each of multiple signal segment nodes, identifying relationship(s) between the corresponding signal segment node and other node(s) of the graph. At navigation time, when a user navigates to the corresponding signal segment nodes, a visualization of any detected relationship may be rendered to the user. When the user interacts with the rendering in such a way as to select the relationship, the user is navigated to the other node.

Abstract: A data marketplace infrastructure allows a data source company or a third party to associate to a data feed with a customizable preview application having analytic and visual features (e.g., business logic and a user interface). A potential customer can use discovery services of the data marketplace infrastructure to identify one or more data feeds offering sample data in a domain of interest of the potential customer. The data feed can transmit the customizable preview application to allow the potential customer to interact with the data from the data feed before purchasing the data. The potential customer may also customize the preview application so as to change the default application behavior in a manner that improves the potential customer's understanding and appreciation of the data feed.

Abstract: The facilitating of output on an output device that receives output from an application that actually redefines during use. An initial output of information to be presented is caused to be rendered on the output device. Then, upon encountering that the application itself is redefined, the output is altered, and the output device updated. Furthermore, there may be other triggers that change the output on the output device including a change in allocation of output between multiple output devices, a change in users of the output device, a change in position of one or more users with respect to the output device, a change in control of one or more users with respect to the output device, a change in authorization of one or more users with respect to the output device or the information outputted. Accordingly, the output device may be updated appropriately even in a dynamic computing environment.

Abstract: Displaying application output in a manner that is not predetermined by the application, nor by the display. The computing system associated with the display accesses a set of information output by an application and to be displayed on a display. However, rather than explicit instructions on how to display being received from the application itself, the computing system instead accesses a set of one or more display rules that are associated with the set of information. The computing system then uses the set of one or more display rules to determine how to layout the set of information on the display. If the set of information were to change, the rules may be reevaluated to determine how the new information should be laid out. If the associated set of display rules were to also changed, the reevaluation would occur thereby resulting in a potentially different layout on the display.

Abstract: The splitting of an application in response to detected environmental events (such as user input). Such splitting may be performed for purposes of sharing the application. The application is a transformation chain instance. From the detected environmental event(s), it is determined that a portion transformation chain class is to be created from the larger transformation chain class of the application. In response, the portion transformation chain class is created, instantiated and operated. A sharing mechanism may be used to allow the split portion of the application to be shared with other entities without losing control.

Abstract: The present invention extends to methods, systems, and computer program products for using cinematic techniques to present data. Embodiments of the invention can be used to infer and generate cinematic techniques or combinations thereof based on a model and user action. Cinematic techniques can be used to meet the data exploration and analysis requirements of a user. As such, embodiments of the invention permit users (including non-programmers) to employ cinematic techniques (possibly in combination with other techniques) to gain insights into their data and also convey appropriate emotional messages.

Abstract: The automated binding of a data source to a compound control. The properties of the compound control are identified. Furthermore, a particular data source is associated with the compound control. For each of one or more of the properties of the compound control, information regarding the compound control and the data source are used to identify a set of one or more fields of the particular data source that may be bound to the corresponding property of the compound control. If the corresponding property of the compound control has bound thereto an output parameter of a declarative transformation, the nature of the transformation may also be taken into consideration when identifying the set of one or more fields that may be used in order to bind to the corresponding property of the compound control.

Abstract: Operating an application in response to spatial status of objects within a physical environment in which the application operates. A system detects the spatial status (e.g., orientation, movement, position, and so forth) of multiple environmental objects that operate within a physical environment from which input is provided to, and to which output is provided from, an application. The relationship between at least some different environmental objects result in altered a behavior of the application. The spatial status of at least some of the environmental objects is subject to change. Thus, a mechanism is described that allows an application to adjust in a manner that is highly responsive to the physical environment in which the application interfaces.

Abstract: The sharing of signal segments using a semantic understanding of what is included within the signal segment. Upon selection of a physical entity (or portion thereof) that is rendered within a signal segment, the system then extracts portion(s) of signal segment(s) in which that selected physical entity appears. A representation of the extracted signal segment portion are then dispatched to recipients. Thus, portions of signal segments may be shared at the granularity of the content of what the signal segment depicts. There is wide flexibility in what representations of those signal segment portion(s) are shared.

Abstract: Automatically training an actor upon the occurrence of a physical condition with respect to that actor. Upon detecting that the actor has the physical condition (e.g., is engaging in or is about to engage in a physical activity), the system determines that training is to be provided for that activity. Upon determining that training is to be provided, the system automatically dispatches training. For instance, the system might cause a human or robot to be dispatched to the actor to show the actor how to perform the activity. Alternatively or instead, a representation of a signal segment may be dispatched to the actor. The representation providing the training to the actor may include a similar target of work to what the actor is presently targeting by the activity. The representation may also include a representation of a person that engaged in the activity properly previously.

Abstract: The setting of physical condition(s) upon which to perform one or more identified actions. This is done via the use of a physical graph that represents state of one or more physical entities within a physical space and observed by sensors. The system monitors the physical graph for the occurrence of the physical condition. If the physical condition occurs, then one or more identified actions are performed. The identification of such actions may occur in advance of the detection of the physical condition and/or may be deferred until the physical condition occurs. The physical condition may be a single physical condition or may be a more complex set of physical conditions. Such actions could include actions such as presenting information to the user, and sending communications out to others. However, the actions could even include physical actions.

Abstract: Routing of communications to group member(s) where group membership is identified by physical status. A computing system detects a communication that identifies targets of the communication at least in part by physical status. The system responds by identifying at least partial membership in a group that is identified by the physical status identified in the communication, and then dispatching the communication to at least one member of the members of that group. The identity of the members of the group may change dynamically as the physical status of particular physical entities changes over time. Accordingly, a user may communicate to individuals based on physical status, rather than identify any particular individual or status group. The communication may be directed to all members of the group. Alternatively, the communication may be initially directed towards a subset of the group, with the communication being conditionally later broadened.

Abstract: Automatic generation of a narration of what is happening in a signal segment (live or recorded). The signal segment that is to be narrated is accessed from a physical graph. In the physical graph, the signal segment evidences state of physical entities, and thus has a semantic understanding of what is depicted in the signal segment. The system then automatically determines how the physical entities are acting within the signal segment based on that semantic understanding, and builds a narration of the activities based on the determined actions. The system may determine what is interesting for narration based on a wide variety of criteria. The system could use machine learning to determine what will be interesting to narrate.