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公开(公告)号：US11049409B1
公开(公告)日：2021-06-29
申请号：US14974721
申请日：2015-12-18
摘要： Systems and methods are provided for automatically scoring a response and statistically revaluating whether it can be considered as aberrant. In one embodiment, a constructed response is evaluated via a pre-screening stage and a post-hoc screening stage. The pre-screening stage attempts to determine whether the constructed response is aberrant based on a variety of aberration metrics and criteria. If the constructed response is deemed not to be aberrant, then the post-hoc screening stage attempts to predict a discrepancy between what score an automated scoring system would assigned and what score a human rater would assign to the response. If the discrepancy is sufficiently low, then the constructed response may be scored by an automated scoring engine. On the other hand, if the constructed response failed to pass either of the two stages, then a flag may be raised to indicate that additional human review may be needed.
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公开(公告)号：US10607188B2
公开(公告)日：2020-03-31
申请号：US14667006
申请日：2015-03-24
发明人： Patrick Charles Kyllonen , Lei Chen , Michelle Paulette Martin , Isaac Bejar , Chee Wee Leong , Joanna Gorin , David Michael Williamson
摘要： 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.
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公开(公告)号：US20150269529A1
公开(公告)日：2015-09-24
申请号：US14667006
申请日：2015-03-24
发明人： Patrick Charles Kyllonen , Lei Chen , Michelle Paulette Martin , Isaac Bejar , Chee Wee Leong , Joanna Gorin , David Michael Williamson
CPC分类号： G06Q10/1053 , G10L15/26 , G10L25/63
摘要： 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.
摘要翻译： 本文所述的系统和方法利用监督机器学习来生成用于评分面试响应的模型。 该系统可以访问训练响应，其在一个实施例中是响应面试问题的人的视听记录。 训练反应可能具有指定的人为因素。 系统可以从训练响应的视听记录中提取至少一个递送特征和至少一个内容特征，并且使用所提取的特征和人为评分来训练用于评分访问响应的响应评分模型。 响应评分模型可以基于训练来配置，以便自动将分数分配给面试响应的视听记录。 采访者可以使用面试回答的评分来评估候选人。
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