Abstract: Techniques are provided for controlling an output laser pulse signal of a medical device. A control device defines a time duration of capacitive discharge to a laser device. The time duration corresponds to an intended energy of the output laser pulse signal. The control device generates a plurality of sub-pulse control signals. The sub-pulse control signals define a series of capacitive discharge events of the capacitor bank. The control device modulates one or more of a sub-pulse control signal period or a sub-pulse time duration of the sub-pulse control signals to modify the capacitive discharge of the capacitor bank to the laser device during the time duration.

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 laser system includes a first laser cavity to output a laser light along a first path, a first mirror to receive the laser light from the first laser cavity, and redirect the laser light along a second path that is different than the first path, a second mirror to receive the laser light from the first mirror, and redirect the laser light along a third path that is different than the first path and the second path, a beam splitter located at a first position on the third path, a beam combiner located at a second position on the third path; and a coupling lens assembly, the coupling lens assembly including a lens located at a third position on the third path, wherein the coupling lens assembly moves the lens in x-, y-, and x-directions.

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 laser system includes a first laser cavity to output a laser light along a first path, a first mirror to receive the laser light from the first laser cavity, and redirect the laser light along a second path that is different than the first path, a second mirror to receive the laser light from the first mirror, and redirect the laser light along a third path that is different than the first path and the second path, a beam splitter located at a first position on the third path, a beam combiner located at a second position on the third path; and a coupling lens assembly, the coupling lens assembly including a lens located at a third position on the third path, wherein the coupling lens assembly moves the lens in x-, y-, and x-directions.

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: A medical laser system for outputting laser pulses includes at least one laser cavity configured to generate at least one laser pulse, a rotating mirror configured to receive and reflect the at least one laser pulse, a beam splitter configured to receive and reflect a portion of the at least one laser pulse received from the rotating mirror, an energy-sensing device configured to detect the portion of the at least one laser pulse, an energy measurement assembly configured to generate a feedback signal based on the portion of the at least one laser pulse detected by the energy-sensing device, and a controller configured to generate an electronic control pulse based on the feedback signal received from the energy measurement assembly to generate at least one adjusted laser pulse.

Abstract: A medical laser system for outputting laser pulses includes at least one laser cavity, a rotating mirror, a user interface, and a controller. The controller is configured to receive at least one laser parameter associated with a laser pulse output by the system. The controller is configured to determine an average power level of the laser pulse based on the at least one laser parameter associated with the laser pulse. The controller is configured to determine a pulse width modulation (PWM) control signal based on at least one laser parameter. The controller is configured to generate the laser pulse based on the average power level and the PWM control signal, the laser pulse comprising at least one of a first shape, a second shape, or a third shape. Each of the first shape, the second shape, and the third shape of the laser pulse includes different pulse widths.

Abstract: A medical laser system for outputting laser pulses includes at least one laser cavity configured to generate at least one laser pulse, a rotating mirror configured to receive and reflect the at least one laser pulse, a beam splitter configured to receive and reflect a portion of the at least one laser pulse received from the rotating mirror, an energy-sensing device configured to detect the portion of the at least one laser pulse, an energy measurement assembly configured to generate a measurement signal based on the portion of the at least one laser pulse detected by the energy-sensing device, and a controller. The controller may include a calibration module. The calibration module may be configured to generate at least one categorized calibration table, determine calibration parameters, interpolate the calibration parameters, and cause the at least one laser cavity to generate at least one calibrated laser pulse.

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 laser system includes a first laser cavity to output a laser light along a first path, a first mirror to receive the laser light from the first laser cavity, and redirect the laser light along a second path that is different than the first path, a second mirror to receive the laser light from the first mirror, and redirect the laser light along a third path that is different than the first path and the second path, a beam splitter located at a first position on the third path, a beam combiner located at a second position on the third path; and a coupling lens assembly, the coupling lens assembly including a lens located at a third position on the third path, wherein the coupling lens assembly moves the lens in x-, y-, and x-directions.

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 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 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: A method of generating an output log for analysis of a computer program, the method comprising: receiving a recording of an execution of the program; receiving an additional print instruction to print a value of a data item and an indication of a point in the program at which the additional print instruction is to be evaluated; determining a corresponding point in the recording of the execution based upon the indication of the point in the program; and evaluating the additional print instruction based upon the recording of the execution and the determined corresponding point to determine an output of the additional print instruction for insertion into the output log.