Abstract: Disclosed are various embodiments for segmenting and classifying video content using sounds. In one embodiment, a plurality of segments of a video content item are generated by analyzing audio accompanying the video content item. A subset of the plurality of segments that correspond to music segments is selected based at least in part on an audio characteristic of the subset of the plurality of segments. Individual segments of the subset of the plurality of segments are processed to determine whether a classification applies to the individual segments. A list of segments of the video content item to which the classification applies is generated.

Abstract: Disclosed are various embodiments for segmenting and classifying video content using conversation. In one embodiment, a plurality of segments of a video content item are generated by analyzing audio accompanying the video content item. A subset of the plurality of segments that correspond to conversation segments are selected. Individual segments of the subset of the plurality of segments are processed to determine whether a classification applies to the individual segments. A list of segments of the video content item to which the classification applies is generated.

Abstract: Techniques for automated up-sampling of media files are provided. In some examples, a title associated with a media file, a metadata file associated with the title, and the media file may be received. The media file may be partitioned into one or more scene files, each scene file including a plurality of frame images in a sequence. One or more up-sampled scene files may be generated, each corresponding to a scene file of the one or more scene files. An up-sampled media file may be generated by combining at least a subset of the one or more up-sampled scene files. Generating one or more up-sampled scene files may include identifying one or more characters in a frame image of the plurality of frame images, based at least in part on implementation of a facial recognition algorithm including deep learning features in a neural network.

Abstract: A pulsed-survey service may be provided by obtaining a question sequence including a number of survey questions, and determining a base survey-pulse rate, such as once per week, but no more frequent than once per day. During each pulse period, for each of a number of subscriber entities, a pulsed survey is performed. Each survey pulse poses and collects responses for at least one, but no more than ten percent of the survey questions. Both intra- and inter-entity benchmark statistics are determined for the pulsed-survey responses, and the statistics and survey results are provided for presentation to the subscriber entity via a survey-results interface.

Abstract: A pulsed-survey service may be provided by obtaining a question sequence including a number of survey questions, and determining a base survey-pulse rate, such as once per week, but no more frequent than once per day. During each pulse period, for each of a number of subscriber entities, a pulsed survey is performed. Each survey pulse poses and collects responses for at least one, but no more than ten percent of the survey questions. Both intra- and inter-entity benchmark statistics are determined for the pulsed-survey responses, and the statistics and survey results are provided for presentation to the subscriber entity via a survey-results interface.

Abstract: A system and method for generating real-time promotions to a visitor of an electronic commerce (e-commerce) World Wide website to increase the likelihood of purchase on the website by the visitor. The system and method receive and store clickstream data provided by the visitor, and calculate the probability that the visitor will leave the website or will make a purchase on the website based upon this clickstream data. The system and method then utilize the calculated probabilities, as well as the frequency of visits to the website by the visitor, and the time of the visit to the website, to decide whether or not real-time promotions should be generated on the website. If it is decided that promotions should be generated, then the system and method automatically calculate what promotions to send, when to send them, and how to send them.