Abstract: A document re-finding system generates embeddings for concept clips provided by a user, the concept clips defining a concept for searching for content of interest to the user in a plurality of documents previously seen by the user. The re-finding system determines semantic relationships between the concept and document clips, related to respective document among the plurality of documents, based on the concept embedding and embeddings generated for the document clips. A graphical user interface depicting the semantic relationships is rendered to the user and is operable to enable re-finding a document, among the plurality of documents, having the content of interest to the user.

Abstract: In examples, a schema augmentation system for exploratory research leverages intelligence from a machine learning model to augment such tasks by leveraging intelligence derived from machine learning capabilities. Augmenting tasks include schematization of content, such as information units and groupings of information units. Based on the schematization of such content, semantic proximities for information units are determined. The semantic proximities may be used to identify and present potentially relevant information units, for example to accelerate the exploratory research task at hand. As such, users engaged in consumption of heterogenous content (e.g., across client applications and/or content sources), may receive machine-augmented support to find potential information units.

Abstract: In examples, a schema augmentation system for exploratory research leverages intelligence from a machine learning model to augment such tasks by leveraging intelligence derived from machine learning capabilities. Augmenting tasks include schematization of content, such as information units and groupings of information units. Based on the schematization of such content, semantic proximities for information units are determined. The semantic proximities may be used to identify and present potentially relevant information units, for example to accelerate the exploratory research task at hand. As such, users engaged in consumption of heterogeneous content (e.g., across client applications and/or content sources), may receive machine-augmented support to find potential information units.

Abstract: A document re-finding system generates embeddings for concept clips provided by a user, the concept clips defining a concept for searching for content of interest to the user in a plurality of documents previously seen by the user. The re-finding system determines semantic relationships between the concept and document clips, related to respective document among the plurality of documents, based on the concept embedding and embeddings generated for the document clips. A graphical user interface depicting the semantic relationships is rendered to the user and is operable to enable re-finding a document, among the plurality of documents, having the content of interest to the user.

Abstract: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

Abstract: A document re-finding system generates embeddings for concept clips provided by a user, the concept clips defining a concept for searching for content of interest to the user in a plurality of documents previously seen by the user. The re-finding system determines semantic relationships between the concept and document clips, related to respective document among the plurality of documents, based on the concept embedding and embeddings generated for the document clips. A graphical user interface depicting the semantic relationships is rendered to the user and is operable to enable re-finding a document, among the plurality of documents, having the content of interest to the user.

Abstract: Described are systems and methods that enable users to virtually experience an environment at a destination location from their own user device, control their experience and even interact with others that are physically located within the environment. Likewise, the user's experience may even be improved with the inclusion of enhancements that are presented to the user as the user experiences the environment. Users may interact with the enhancements to obtain additional enhancements, interact with others physically located within the environment, interact with and direct a guide that is physically located within the environment, and receive near real-time video and audio that is transmitted from a guide device located within the environment.

Abstract: Various methods and apparatus relating to estimating and mitigating a stress level of a user are disclosed herein. Methods can include collecting potential stress indicator data from the user interacting with a computing device. The potential stress indicator data can include one or more of environmental data and contextual data associated with the user. Methods can include estimating the stress level of the user based on the potential stress indicator data. Methods can include performing an evaluation of whether to mitigate the stress level of the user via one or more stress mitigation interventions. Methods can include presenting the one or more stress mitigation interventions to the user via a graphical user interface (GUI) when the evaluation indicates that the stress level should be mitigated.

Abstract: In example, a natural language input is processed to generate a formal query that is executed to retrieve data from a data store. An indication as to which subparts of the natural language input were used to generate the formal query may be presented to a user. The user may debug the query, such that associations between natural language input subparts and formal query subparts may be changed, added, or removed by the user. Further, a set of steps may be generated that provide an indication to the user as to how the formal query is executed, for example using sample data from the data store. The user may manipulate the sample data, thereby causing the presented steps to be updated accordingly. Once the user has made adjustments and has finished debugging the query, the revised formal query may be executed to retrieve updated data from the data store.

Abstract: In examples, a schema augmentation system for exploratory research leverages intelligence from a machine learning model to augment such tasks by leveraging intelligence derived from machine learning capabilities. Augmenting tasks include schematization of content, such as information units and groupings of information units. Based on the schematization of such content, semantic proximities for information units are determined. The semantic proximities may be used to identify and present potentially relevant information units, for example to accelerate the exploratory research task at hand. As such, users engaged in consumption of heterogeneous content (e.g., across client applications and/or content sources), may receive machine-augmented support to find potential information units.

Abstract: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations may of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

Abstract: Described are systems and methods that enable users to virtually experience an environment at a destination location from their own user device, control their experience and even interact with others that are physically located within the environment. Likewise, the user's experience may even be improved with the inclusion of enhancements that are presented to the user as the user experiences the environment. Users may interact with the enhancements to obtain additional enhancements, interact with others physically located within the environment, interact with and direct a guide that is physically located within the environment, and receive near real-time video and audio that is transmitted from a guide device located within the environment.

Abstract: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations may of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

Abstract: Described are systems and methods that enable users to virtually experience an environment at a destination location from their own user device, control their experience and even interact with others that are physically located within the environment. Likewise, the user's experience may even be improved with the inclusion of enhancements that are presented to the user as the user experiences the environment. User's may interact with the enhancements to obtain additional enhancements, interact with others physically located within the environment, interact with and direct a guide that is physically located within the environment, and receive near real-time video and audio that is transmitted from a guide device located within the environment.

Abstract: Described are systems and methods that enable users to virtually experience an environment at a destination location from their own user device, control their experience and even interact with others that are physically located within the environment. Likewise, the user's experience may even be improved with the inclusion of enhancements that are presented to the user as the user experiences the environment. User's may interact with the enhancements to obtain additional enhancements, interact with others physically located within the environment, interact with and direct a guide that is physically located within the environment, and receive near real-time video and audio that is transmitted from a guide device located within the environment.

Abstract: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations may of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

Abstract: Various embodiments enable, in a mapping context, various visual entities to be clustered into groups that do not occlude one another. In at least some embodiments, individual clusters are represented on a map by a puddle defined by a computed contour line. Users can interact with the puddle to acquire more information about the puddle's content. In at least some embodiments, user interaction can include zooming operations, clicking operations, hovering operations and the like.

Abstract: Information may be presented to a user in a way that reflects an awareness of the user's current situation. The relationship between user's situation, and various people and things, may be analyzed to determine the user's proximity to those people and things. (Proximity may refer not only to geographic proximity, but also temporal proximity, relevance proximity, etc.) A user interface may show people and things at different levels of proximity to the user's current situation, with the level of proximity being represented visually. The user may reposition the center of focus to one of the people or things depicted. When the center is repositioned, the level of proximity of people and things may be shown relative to the new center of focus, filtered based on existing relationships of those people and things to the user.

Abstract: Search results may be rendered on a map utilizing one or more points of interest. An individual point of interest may include a casing layer and a core layer. Casing layers and/or core layers may be configured to change in size and color at different levels of detail associated with zoom operations. Zoom operations may further include de-clustering overlapping points of interest to eliminate overlap. A hover input associated with a cluster may render a numerical value associated with a corresponding number of points of interest that appear in the cluster. Various embodiments provide a visual language that enables a continuous representation, at different levels of detail, that range from a single discrete point to multiple points that are near each other or clustered together to unify the look-and-feel of represented elements and provide interactive consistency across various map properties and platforms.

Abstract: Methods are provided for displaying image results responsive to a search query. In addition to displaying responsive results for a query, responsive results are also provided for related queries. The results are ordered along a plurality of display axes, including at least one axis corresponding to the ordering of the various search queries. The results can be displayed in an aligned or non-aligned manner. The results can then be translated along one or more of the display axes to allow a user to browse the various results.