Comcast Cable Communications, LLC

Zhao Xing

Christopher Paul Whitely

Robert Alan Bress

Landy Eng, Jr

A group of households associated with known content viewing behavior may be determined. The group of households may be compared to at least one household with unknown content viewing behavior, based on a plurality of characteristics. Based on the comparison, a subset of the group of households that is associated with characteristics most similar to characteristics associated with the at least one household with unknown viewing behavior may be determined. A predicted content viewing behavior associated with the at least one household with unknown content viewing behavior may be determined based on the known content viewing behavior of the subset of the group of households.

Systems, methods, and devices for household classification

Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Methods and systems for predicting content consumption

A time associated with last selection of a content item at a user device may be determined. A time associated with a last user interaction with the user device may be determined. During presentation of the content item at the user device, it may be determined that a supplemental content insertion opportunity is available. A request for a supplemental content item may be sent to a server, wherein the request comprises data indicating the time associated with the last selection of the content item at the user device and data indicating the time associated with the last user interaction with the user device.

Systems, methods, and devices for selecting supplemental content

Systems, methods, and devices relating to determining viewership data are described herein. In a method, viewing data associated with a household is received. A first portion of the viewing data is indicative of video programming associated with a first video device and a second portion of the viewing data is indicative of video programming associated with a second video device. One or more characteristics associated with the first and second portions of the viewing data are determined. Based on the one or more characteristics and a comparison of the respective video programming associated with the first and second video devices, it is determined that the first portion of the viewing data is duplicative, at least in part, with the second portion of the viewing data.

Systems, methods, and devices for determining viewership data

Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

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Philadelphia, PA, United States of America

Zhao Xing