Abstract: An audio stream is subjected to speech-to-text processing in order to obtain a textual representation of the audio stream. Hierarchical topic extraction is performed on the textual representation to obtain a multi-level hierarchical topic representation of the textual representation. A user interface actuator is generated, which allows a user to search through the audio stream. Different levels of the multi-level hierarchical topic representation are displayed to the user, based upon the speed of actuation of the user interface actuator.

Abstract: Systems and methods for enhanced classification of sequences of objects based on clique similarity and metadata associated with the sequences are presented. Sequences are received. Events are detected based on analyzing k-skip-n-grams included in the sequences. For each event of the detected plurality of events, a graph is generated. The graph for a particular event includes z-cliques that correspond to portions of the k-skip-n-grams that are included in the sequences that are associated with the particular event. A first sequence, which is separate from the other sequences, is received. The first sequence includes a first plurality of k-skip-n-grams. A trained classifier is employed to classify the first sequence as being associated with a first event of the detected events. Classifying the first sequence is based on a comparison between the first plurality of k-skip-n-grams and the z-cliques of the graph that is generated for the first event.

Abstract: An intrusion detection system detects when an unexpected person enters the environment of a user who is in a meeting. A privacy protection action which is an action that is to be taken in response to the detected intrusion, is identified. Audio and/or video systems are then controlled to perform the privacy protection action. Machine learning can be used, based upon user interactions, to improve intrusion detection and other parts of the system.

Abstract: A text stream source is accessed that includes a plurality of text content items. Unique word groupings are determined for the plurality of text content items. A burst detection algorithm is executed to determine word groupings that are currently bursting and that started within a specified time period. Based on the word groupings, an issue is determined based on identifying a set of texts forming at least one clique.

Abstract: An audio stream is subjected to speech-to-text processing in order to obtain a textual representation of the audio stream. Hierarchical topic extraction is performed on the textual representation to obtain a multi-level hierarchical topic representation of the textual representation. A user interface actuator is generated, which allows a user to search through the audio stream. Different levels of the multi-level hierarchical topic representation are displayed to the user, based upon the speed of actuation of the user interface actuator.

Abstract: In the present disclosure, artificial intelligence (AI) processing is trained and leveraged to learn user-specific insights that are contextually relevant to a state of a user communication. Contextual information about a state of a user communication may be collected and analyzed. That contextual information may be cross-referenced with an extensive knowledge graph that is constructed from user context data. Exemplary AI processing may further be trained to apply a relevance analysis to assist with processing described herein including generation and curation of data insights that are most relevant to a state of a user communication. In some examples, the data insight generation process may be augmented by pre-generating data insights that may be relevant to a user communication prior to occurrence of the user communication. Further technical examples pertain to the rendering and presentation of representations of data insights through a graphical user interface (GUI).

Abstract: An audio stream is subjected to speech-to-text processing in order to obtain a textual representation of the audio stream. Hierarchical topic extraction is performed on the textual representation to obtain a multi-level hierarchical topic representation of the textual representation. A user interface actuator is generated, which allows a user to search through the audio stream. Different levels of the multi-level hierarchical topic representation are displayed to the user, based upon the speed of actuation of the user interface actuator.

Abstract: An intrusion detection system detects when an unexpected person enters the environment of a user who is in a meeting. A privacy protection action which is an action that is to be taken in response to the detected intrusion, is identified. Audio and/or video systems are then controlled to perform the privacy protection action. Machine learning can be used, based upon user interactions, to improve intrusion detection and other parts of the system.

Abstract: In the present disclosure, artificial intelligence (AI) processing is trained and leveraged to learn user-specific insights that are contextually relevant to a state of a user communication. Contextual information about a state of a user communication may be collected and analyzed. That contextual information may be cross-referenced with an extensive knowledge graph that is constructed from user context data. Exemplary AI processing may further be trained to apply a relevance analysis to assist with processing described herein including generation and curation of data insights that are most relevant to a state of a user communication. In some examples, the data insight generation process may be augmented by pre-generating data insights that may be relevant to a user communication prior to occurrence of the user communication. Further technical examples pertain to the rendering and presentation of representations of data insights through a graphical user interface (GUI).

Abstract: Technologies are provided for the monitoring, detection, and notification of emerging, related issues within a system, which may indicate a problem. Within a computing-security system, a sudden increase in the frequency of events associated with unauthorized logon attempts signal a real-time and ongoing security risk. A method monitors system-related events and generates a vector representation for each event based on event features. Clusters of related events are determined, and a state automaton is employed to determine a strength of temporal “bursty” activity for each cluster. Hypothesis testing is performed on each cluster to determine a likelihood that the cluster is a temporally emergent cluster. Clusters with a bursting likelihood above a threshold are determined to be an emergent cluster associated with an anomalous issue. A notification regarding the detected anomaly is provided. A remedial action addressing the anomaly is performed.

Abstract: An audio stream is subjected to speech-to-text processing in order to obtain a textual representation of the audio stream. Hierarchical topic extraction is performed on the textual representation to obtain a multi-level hierarchical topic representation of the textual representation. A user interface actuator is generated, which allows a user to search through the audio stream. Different levels of the multi-level hierarchical topic representation are displayed to the user, based upon the speed of actuation of the user interface actuator.

Abstract: A text stream source is accessed that includes a plurality of text content items. Unique word groupings are determined for the plurality of text content items. A burst detection algorithm is executed to determine word groupings that are currently bursting and that started within a specified time period. Based on the word groupings, an issue is determined based on identifying a set of texts forming at least one clique.

Abstract: A system for extracting differential topics from a dataset including a user interface, a memory for storing executable program code, and one or more electronic processors coupled to the memory and the user interface. The electronics processors are configured to receive a dataset from one or more servers, wherein the dataset comprises user feedback data associated with a software program. The electronic processors are also configured to extract text from the dataset, convert the extracted text to vector data, and determine anomalous data clusters associated with the vector data using statistical analysis. The electronic processors are also configured to differentiate overlapping anomalous data clusters using a classification algorithm, wherein the differentiated overlapping anomalous data clusters are associated with specific topics, and export each specific topic associated with the differentiated overlapping data cluster.

Abstract: A text stream source is accessed that includes a plurality of text content items. Unique word groupings are determined for the plurality of text content items. A burst detection algorithm is executed to determine word groupings that are currently bursting and that started within a specified time period. Based on the word groupings, an issue is determined based on identifying a set of texts forming at least one clique.

Abstract: Technologies are provided for the monitoring, detection, and notification of emerging, related issues within a system, which may indicate a problem. Within a computing-security system, a sudden increase in the frequency of events associated with unauthorized logon attempts signal a real-time and ongoing security risk. A method monitors system-related events and generates a vector representation for each event based on event features. Clusters of related events are determined, and a state automaton is employed to determine a strength of temporal “bursty” activity for each cluster. Hypothesis testing is performed on each cluster to determine a likelihood that the cluster is a temporally emergent cluster. Clusters with a bursting likelihood above a threshold are determined to be an emergent cluster associated with an anomalous issue. A notification regarding the detected anomaly is provided. A remedial action addressing the anomaly is performed.

Abstract: Systems and methods for enhanced classification of sequences of objects based on clique similarity and metadata associated with the sequences are presented. Sequences are received. Events are detected based on analyzing k-skip-n-grams included in the sequences. For each event of the detected plurality of events, a graph is generated. The graph for a particular event includes z-cliques that correspond to portions of the k-skip-n-grams that are included in the sequences that are associated with the particular event. A first sequence, which is separate from the other sequences, is received. The first sequence includes a first plurality of k-skip-n-grams. A trained classifier is employed to classify the first sequence as being associated with a first event of the detected events. Classifying the first sequence is based on a comparison between the first plurality of k-skip-n-grams and the z-cliques of the graph that is generated for the first event.

Abstract: A text stream source is accessed that includes a plurality of text content items. Unique word groupings are determined for the plurality of text content items. A burst detection algorithm is executed to determine word groupings that are currently bursting and that started within a specified time period. Based on the word groupings, an issue is determined based on identifying a set of texts forming at least one clique.

Abstract: A surgical drill aimer (1, 70, 100) is provided that is configured to be secured about a bone with a drill guide (8, 82, 108) on one side and a drill target (4, 88, 114) on an opposite side. A suture passage (40, 92, 120) is provided which is shaped to receive a suture (44, 94, 130) and has an exit (46, 90, 12) at or near the drill target and configured such that a suture extending therefrom is accessible through a drill hole formed using the drill aimer and opening in line with the drill target.