Abstract: A method for delivering media to a playback device including outputting first test media to be viewed by a first user. The method further includes receiving a first user input related to a first perception of the first test media by the first user and indicating a first personalized quality of experience of the first user with respect to the first test media. The method further includes generating a first personalized sensitivity profile including one or more viewing characteristics of the first user based on the first user input, and determining, based at least in part on the first personalized sensitivity profile, a first media parameter. The first media parameter is determined in order to increase an efficiency of media delivery to the first playback device over a network while preserving the first personalized quality of experience of the first user.

Abstract: An insight engine that takes into account a wide variety of information from a wide variety of sources for predicting a sales insight is generated. The insight engine is generated using machine learning. Historical customer-specific information, product-specific information, and environmental information are aggregated, based on customer, product, and/or time period, into historical customer profiles. The historical customer profiles are labeled with historical sales insights to form a training set. A machine learning algorithm is applied to the training set to generate an insight engine. The insight engine is applied to a target customer profile to determine a predicted sales insight for a target entity.

Abstract: An insight engine that takes into account a wide variety of information from a wide variety of sources for predicting a sales insight is generated. The insight engine is generated using machine learning. Historical customer-specific information, product-specific information, and environmental information are aggregated, based on customer, product, and/or time period, into historical customer profiles. The historical customer profiles are labeled with historical sales insights to form a training set. A machine learning algorithm is applied to the training set to generate an insight engine. The insight engine is applied to a target customer profile to determine a predicted sales insight for a target entity.