Abstract: Systems and methods for enhancing surgical outcomes by providing generators having optimal RF output for sealing, fusing and/or cutting tissue or vessels under all dynamic conditions are described. Examples of dynamic conditions may include varying tissue impedance load due to electrosurgical operations or tissue affects, any operational conditions and commands determined by the surgeon, surgical procedure and/or device script. This is achieved by implementing a digital closed-loop control system within the electrosurgical generator to regulate voltage, current, and power of the RF output. The digital closed-loop control system may include an RF amplifier for generating RF energy, a feedback system for constantly monitoring the electrical characteristics, e.g.

Abstract: Systems and methods described herein utilize supervised machine learning to generate a model for scoring interview responses. The system may access a training response, which in one embodiment is an audiovisual recording of a person responding to an interview question. The training response may have an assigned human-determined score. The system may extract at least one delivery feature and at least one content feature from the audiovisual recording of the training response, and use the extracted features and the human-determined score to train a response scoring model for scoring interview responses. The response scoring model may be configured based on the training to automatically assign scores to audiovisual recordings of interview responses. The scores for interview responses may be used by interviewers to assess candidates.

Abstract: Systems and methods described herein utilize supervised machine learning to generate a model for scoring interview responses. The system may access a training response, which in one embodiment is an audiovisual recording of a person responding to an interview question. The training response may have an assigned human-determined score. The system may extract at least one delivery feature and at least one content feature from the audiovisual recording of the training response, and use the extracted features and the human-determined score to train a response scoring model for scoring interview responses. The response scoring model may be configured based on the training to automatically assign scores to audiovisual recordings of interview responses. The scores for interview responses may be used by interviewers to assess candidates.