Abstract: Technology is provided for generating conversation features for recorded conversations. The technology includes, receiving videos depicting a multiple-user interaction, segmenting the videos into multiple utterances based on identifying utterances from individual users, receiving label data for the utterance segments specifying conversation features, and storing the label data in association with the utterance segments.

Abstract: A gate assembly for a safety rail on an aerial work platform includes a gate pivotally connected to the safety rail via a hinge. The hinge includes a quick release pin and is configured such that removal of the quick release pin enables separation of the gate from the safety rail.

Abstract: Technology is provided for generating conversation features for recorded conversations. The technology includes, receiving videos depicting a multiple-user interaction, segmenting the videos into multiple utterances based on identifying utterances from individual users, receiving label data for the utterance segments specifying conversation features, and storing the label data in association with the utterance segments.

Abstract: Technology is provided for identifying synthesized conversation features from recorded conversations. The technology can identify, for each of one or more utterances, data for multiple modalities, such as acoustic data, video data, and text data. The technology can extract features, for each particular utterance of the one or more utterances, from each of the data for the multiple modalities associated with that particular utterance. The technology can also apply a machine learning model that receives the extracted features and/or previously synthesized conversation features and produces one or more additional synthesized conversation features.

Abstract: Technology is provided for identifying synthesized conversation features from recorded conversations. The technology can identify, for each of one or more utterances, data for multiple modalities, such as acoustic data, video data, and text data. The technology can extract features, for each particular utterance of the one or more utterances, from each of the data for the multiple modalities associated with that particular utterance. The technology can also apply a machine learning model that receives the extracted features and/or previously synthesized conversation features and produces one or more additional synthesized conversation features.

Abstract: Technology is provided for causing a computing system to extract conversation features from a multiparty conversation (e.g., between a coach and mentee), apply the conversation features to a machine learning system to generate conversation analysis indicators, and apply a mapping of conversation analysis indicators to actions and inferences to determine actions to take or inferences to make for the multiparty conversation. In various implementations, the actions and inferences can include determining scores for the multiparty conversation such as a score for progress toward a coaching goal, instant scores for various points throughout the conversation, conversation impact score, ownership scores, etc. These scores can be, e.g., surfaced in various user interfaces along with context and benchmark indicators, used to select resources for the coach or mentee, used to update coach/mentee matchings, used to provide real-time alerts to signify how the conversation is going, etc.

Abstract: Technology is provided for conversation analysis. The technology includes, receiving multiple utterance representations, where each utterance representation represents a portion of a conversation performed by at least two users, and each utterance representation is associated with video data, acoustic data, and text data. The technology further includes generating a first utterance output by applying video data, acoustic data, and text data of the first utterance representation to a respective video processing part of the machine learning system to generate video, text, and acoustic-based outputs. A second utterance output is further generated for a second user. Conversation analysis indicators are generated by applying, to a sequential machine learning system the combined speaker features and a previous state of the sequential machine learning system.

Abstract: Technology is provided for conversation analysis. The technology includes, receiving multiple utterance representations, where each utterance representation represents a portion of a conversation performed by at least two users, and each utterance representation is associated with video data, acoustic data, and text data. The technology further includes generating a first utterance output by applying video data, acoustic data, and text data of the first utterance representation to a respective video processing part of the machine learning system to generate video, text, and acoustic-based outputs. A second utterance output is further generated for a second user. Conversation analysis indicators are generated by applying, to a sequential machine learning system the combined speaker features and a previous state of the sequential machine learning system.

Abstract: Technology is provided for causing a computing system to extract conversation features from a multiparty conversation (e.g., between a coach and mentee), apply the conversation features to a machine learning system to generate conversation analysis indicators, and apply a mapping of conversation analysis indicators to actions and inferences to determine actions to take or inferences to make for the multiparty conversation. In various implementations, the actions and inferences can include determining scores for the multiparty conversation such as a score for progress toward a coaching goal, instant scores for various points throughout the conversation, conversation impact score, ownership scores, etc. These scores can be, e.g., surfaced in various user interfaces along with context and benchmark indicators, used to select resources for the coach or mentee, used to update coach/mentee matchings, used to provide real-time alerts to signify how the conversation is going, etc.

Abstract: A method and system for classifying malicious locators where a processor is trained on a set of known malicious locators using a non-supervised learning procedure. Once trained, the processor may classify new locators as being generated by a particular generation kit.

Abstract: Technology is provided for generating conversation features for recorded conversations. The technology includes, receiving videos depicting a multiple-user interaction, segmenting the videos into multiple utterances based on identifying utterances from individual users, receiving label data for the utterance segments specifying conversation features, and storing the label data in association with the utterance segments.

Abstract: Technology is provided for causing a computing system to extract conversation features from a multiparty conversation (e.g., between a coach and mentee), apply the conversation features to a machine learning system to generate conversation analysis indicators, and apply a mapping of conversation analysis indicators to actions and inferences to determine actions to take or inferences to make for the multiparty conversation. In various implementations, the actions and inferences can include determining scores for the multiparty conversation such as a score for progress toward a coaching goal, instant scores for various points throughout the conversation, conversation impact score, ownership scores, etc. These scores can be, e.g., surfaced in various user interfaces along with context and benchmark indicators, used to select resources for the coach or mentee, used to update coach/mentee matchings, used to provide real-time alerts to signify how the conversation is going, etc.

Abstract: Technology is provided for conversation analysis. The technology includes, receiving multiple utterance representations, where each utterance representation represents a portion of a conversation performed by at least two users, and each utterance representation is associated with video data, acoustic data, and text data. The technology further includes generating a first utterance output by applying video data, acoustic data, and text data of the first utterance representation to a respective video processing part of the machine learning system to generate video, text, and acoustic-based outputs. A second utterance output is further generated for a second user. Conversation analysis indicators are generated by applying, to a sequential machine learning system the combined speaker features and a previous state of the sequential machine learning system.

Abstract: Technology is provided for identifying synthesized conversation features from recorded conversations. The technology can identify, for each of one or more utterances, data for multiple modalities, such as acoustic data, video data, and text data. The technology can extract features, for each particular utterance of the one or more utterances, from each of the data for the multiple modalities associated with that particular utterance. The technology can also apply a machine learning model that receives the extracted features and/or previously synthesized conversation features and produces one or more additional synthesized conversation features.

Abstract: Methods and systems for neutralizing malicious locators. Threat actors may shut down their web pages or applications (i.e., resources) that serve malicious content upon receiving request(s) configured to be perceived by the resource as non-browser requests. Therefore, initiating (large-scale) non-browser requests, or requests that are at least perceived as non-browser requests, may effectively act to inhibit, or even nullify, intended attack vectors.

Abstract: Methods and systems for neutralizing malicious locators. Threat actors may shut down their web pages or applications (i.e., resources) that serve malicious content upon receiving request(s) configured to be perceived by the resource as non-browser requests. Therefore, initiating (large-scale) non-browser requests, or requests that are at least perceived as non-browser requests, may effectively act to inhibit, or even nullify, intended attack vectors.

Abstract: Methods and systems for classifying malicious locators. A processor is trained on a set of known malicious locators using a non-supervised learning procedure. Once trained, the processor may classify new locators as being generated by a particular generation kit.

Abstract: Methods and systems for classifying malicious locators. A processor is trained on a set of known malicious locators using a non-supervised learning procedure. Once trained, the processor may classify new locators as being generated by a particular generation kit.

Abstract: Methods and systems for neutralizing malicious locators. Threat actors may shut down their web pages or applications (i.e., resources) that serve malicious content upon receiving request(s) configured to be perceived by the resource as non-browser requests. Therefore, initiating (large-scale) non-browser requests, or requests that are at least perceived as non-browser requests, may effectively act to inhibit, or even nullify, intended attack vectors.

Abstract: Methods and systems for classifying malicious locators. A processor is trained on a set of known malicious locators using a non-supervised learning procedure. Once trained, the processor may classify new locators as being generated by a particular generation kit.