Abstract: The present disclosure relate to highlighting audience members with reactions to a presenter of an online meeting. Unlike physical, fact-to-face meeting that enables spontaneous interactions among the presenter and the audiences that are collocated with the presenter, presenting materials during an online meeting raises an issue of the present not being able to see real-time reactions or feedback by the audience members. The present disclosure addresses the issue by dynamically determining one or more audience members who indicate reactions during the online meeting or presentation and displaying faces of the one or more audience members under spotlight to the presenter. The presenter sees faces of the audience members with reactions during the online presentation and responds to the audience members and keep the audience engaged. The spotlight audience server analyzes video frames and determines types of reactions of the audience members.

Abstract: The present disclosure relate to highlighting audience members with reactions to a presenter of an online meeting. Unlike physical, fact-to-face meeting that enables spontaneous interactions among the presenter and the audiences that are collocated with the presenter, presenting materials during an online meeting raises an issue of the present not being able to see real-time reactions or feedback by the audience members. The present disclosure addresses the issue by dynamically determining one or more audience members who indicate reactions during the online meeting or presentation and displaying faces of the one or more audience members under spotlight to the presenter. The presenter sees faces of the audience members with reactions during the online presentation and responds to the audience members and keep the audience engaged. The spotlight audience server analyzes video frames and determines types of reactions of the audience members.

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: 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: Systems and methods for providing a user characteristic to a service provider for a virtual conference with a user are provided. In particular, a computing device may collect raw media data associated with the user during the virtual conference between the user and the service provider. During the virtual conference, the computing device may perform a first processing of the raw media data to extract intermediate user data, wherein the intermediate user data comprises one or more of a physiological signal and a behavioral signal associated the user. The computing device may further transform the raw media data into transformed media data and transmit the transformed media data with the intermediate user data to a server for second processing of the intermediate user data.

Abstract: The present disclosure relate to highlighting audience members with reactions to a presenter of an online meeting. Unlike physical, fact-to-face meeting that enables spontaneous interactions among the presenter and the audiences that are collocated with the presenter, presenting materials during an online meeting raises an issue of the present not being able to see real-time reactions or feedback by the audience members. The present disclosure addresses the issue by dynamically determining one or more audience members who indicate reactions during the online meeting or presentation and displaying faces of the one or more audience members under spotlight to the presenter. The presenter sees faces of the audience members with reactions during the online presentation and responds to the audience members and keep the audience engaged. The spotlight audience server analyzes video frames and determines types of reactions of the audience members.

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: 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: 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: Disclosed herein are technologies directed to a feature ideator. The feature ideator can initiate a classifier that analyzes a training set of data in a classification process. The feature ideator can generate one or more suggested features relating to errors generated during the classification process. The feature ideator can generate an output to cause the errors to be rendered in a format that provides for an interaction with a user. A user can review the summary of the errors or the individual errors and select one or more features to increase the accuracy of the classifier.

Abstract: Disclosed herein are technologies directed to a feature ideator. The feature ideator can initiate a classifier that analyzes a training set of data in a classification process. The feature ideator can generate one or more suggested features relating to errors generated during the classification process. The feature ideator can generate an output to cause the errors to be rendered in a format that provides for an interaction with a user. A user can review the summary of the errors or the individual errors and select one or more features to increase the accuracy of the classifier.