Abstract: Performing collaborative search engine searching. The method includes receiving user input at a user interface for performing a plurality searches on a first search engine. The method further includes receiving user input at the user interface applying one or more augmentation AI models to searches in the plurality of searches. The method further includes creating a shareable, executable package executable by one or more search engines based on the plurality of searches and the applied AI models that when executed by the search engines causes the search engines to apply the AI models to searches performed at the search engines.

Abstract: Applying AI models to a search using a search engine for a user. A method includes receiving user search input at a search engine user interface. The search input is used with the search engine to obtain first search results. One or more AI models are applied to the first search results to obtain additional search data. The additional search data is searched to identify additional search results. Using the additional search results, a subset of second search results are identified from the first search results while filtering out other search results from the first search results. At least a portion of the second search results are provided to the user in the user interface while preventing the other search results that were filtered from being displayed in the user interface, such that a user at the user interface has the second search results returned as results.

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: Expanding search engine functionality using AI models. A method includes, as part of a search session, receiving user input at a search engine. One or more searches on a set of data using the user input. Search results are provided from the one or more searches to a user. Based on a history of the search session, suggestions are provided in a user interface of AI models that could be applied to expand potential search results for the search session. User input is received at the user interface selecting one or more of the suggested AI model. The one or more selected AI models are applied to expand the set of data. Search results to the user based on searching the expanded set of data.

Abstract: The automatic formulation of a plan for concurrent movement of physical entities within a physical space. A physical graph is used to formulate such a plan. The physical graph represents multiple physical entities that have been sensed in a physical space over time. A plan is then formulated based on an evaluation of that physical graph. Such plans are enabled by the semantic understanding of the physical space and its contents that the physical graph provides. The plan honors physical constraints of the physical space, and physical constraints of the physical entities that are moving within that physical space. The plan may be further orchestrated by communicating with the physical entities to provide instructions for movement. Then, movement is monitored to determine if the plan is being complied with. If the plan is not being complied with, further communications are made and/or an alternative plan is automatically constructed.

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: 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 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: 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: 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 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: 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: 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 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: The automatic formulation of a plan for concurrent movement of physical entities within a physical space. A physical graph is used to formulate such a plan. The physical graph represents multiple physical entities that have been sensed in a physical space over time. A plan is then formulated based on an evaluation of that physical graph. Such plans are enabled by the semantic understanding of the physical space and its contents that the physical graph provides. The plan honors physical constraints of the physical space, and physical constraints of the physical entities that are moving within that physical space. The plan may be further orchestrated by communicating with the physical entities to provide instructions for movement. Then, movement is monitored to determine if the plan is being complied with. If the plan is not being complied with, further communications are made and/or an alternative plan is automatically constructed.

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: Automatically identifying a route for a physical entity to take within a physical space in order to go to an event endpoint where an event happened, is happening, or is predicted to happen. Thus, instead of static routing in which routing is made to a fixed endpoint, dynamic routing is achieved relating to event(s). The identified endpoint may be an activity, a mobile physical entity or a group of mobile physical entities, or the like. A route is formulated that moves a particular physical entity from a current location to a location of the identified event within the physical space. The route formulation may occur via reference to a physical graph representing physical entities that are monitored within the physical space over time. Furthermore, the route formulation may occur in coordination with a planning component that orchestrates movements of physical entities within the physical space.

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 controlling of the size and quality of a computer-navigable graph of sensed features. The sensed features 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. Computer-navigation may occur over the graph in response to a query or computation. Thus, computations and queries may be performed over physical space, thereby extending computing to the ambient physical environment. To keep the size of the computer-navigable graph at a manageable levels nodes of the computer-navigable graph are removed or cleansed.

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.