Systems and methods for effectively and efficiently receiving, analyzing, and using subscriber content choice information are described. One embodiment of the present invention includes a subscriber-action database, such as a clickstream database, a media-content database, and a merge processor electronically connected to the two databases for merging data in the subscriber-action database with data in the media-content database to create a subscriber content-choice database. The media-content database may include programming and/or advertising data. The media-content database may also include category and context information to provide the capability to comprehensively analyze data in the subscriber content-choice database.

Examples relate to a method, a computer program, an apparatus and a system for determining information related to an audience of an audio-visual content program. The method comprises monitoring at least a subset of the audience. The method further comprises determining the information related to the audience based on the monitored subset of the audience. The information related to the audience comprises measured metrics for the subset of the audience. The method further comprises determining at least one conversion factor based on a relationship between a property value measured within at least one previously monitored audience subsets and the corresponding property value of the previous audience. The at least one conversion factor indicates a relationship between a property measured within the monitored subset of the audience and at least one property of the audience. The method further comprises estimating the at least one property of the audience based on the at least one conversion factor.

A method and system may input telemetry into a data structure. The telemetry may comprise a plurality of measurement results or other data collected from remote network nodes. Subscriber use metrics may be determined from the telemetry. From the use metrics and habits of the subscriber, user characteristics may be determined. A likelihood of service cancellation, based on the user characteristics, may be estimated in an embodiment. In another embodiment, a likelihood of receiving an inquiry from the subscriber at a subscriber support center may be estimated. Service enhancements may be proactively provided to the subscriber, based on the telemetry, the subscriber use metrics and the user characteristics in advance of service cancellation or receiving the inquiry. Last mile equipment may provide telemetry. The last mile equipment may include set top boxes, cellular phones, tablets and the like.

An example apparatus to monitor media that is presented via a mobile device includes: an application monitor to determine when media presentation software is being executed by the mobile device; and a metering information collector to collect at least one of a signature or a code of the media presented by the media presentation software based on the media presentation software being executed by the mobile device.

An example apparatus to monitor media that is presented via a mobile device includes: an application monitor to determine when media presentation software is being executed by the mobile device; and a metering information collector to collect at least one of a signature or a code of the media presented by the media presentation software based on the media presentation software being executed by the mobile device.

Methods and apparatus to calibrate media ratings based on return path data are disclosed. An apparatus includes a processor and memory including instructions that, when executed, cause the processor to: determine an initial rating for the media provided in a first geographic area based on return path data (RPD) tuning information obtained from RPD devices in subscriber households in the first geographic area; determine a first panelist rating for the media provided in a second geographic area based on first panel tuning information obtained from first metering devices in a first subset of panelist households in the second geographic area; determine a nonsubscriber calibration factor based on the first panelist rating; and determine a final rating for the media in the first geographic area by modifying the initial rating based on the nonsubscriber calibration factor.

In some examples, one or more processors configured by executable instructions may receive an audio signal and data to embed in the audio signal. For example, the data may be received dynamically from one or more data sources. The one or more processors may embed the received data in real time into the audio signal as embedded data based at least in part on controlling a phase angle of a selected frequency component of the audio signal. For instance, the embedded data may include at least one of text, a bar code, a quick response code, an image, a uniform resource locator (URL), or multimedia content. Additionally, the one or more processors may send the audio signal with the embedded data over a network to a plurality of electronic devices that include respective decoders for extracting the embedded data from the audio signal.

In some examples, one or more processors configured by executable instructions may receive an audio signal and data to embed in the audio signal. For example, the data may be received dynamically from one or more data sources. The one or more processors may embed the received data in real time into the audio signal as embedded data based at least in part on controlling a phase angle of a selected frequency component of the audio signal. For instance, the embedded data may include at least one of text, a bar code, a quick response code, an image, a uniform resource locator (URL), or multimedia content. Additionally, the one or more processors may send the audio signal with the embedded data over a network to a plurality of electronic devices that include respective decoders for extracting the embedded data from the audio signal.

Systems and methods are provided for determining a media cost equivalent to attribute to the appearance of a sponsor's logo or other object in a media item, such as an image or video. This determination may be based on factors such as clarity, prominence, visibility, size, or placement of a sponsor logo or other object related to the sponsor within at least one media item. One or more machine learning models may be utilized to automatically identify objects within, and otherwise analyze, image data in order to determine sponsor value.

The present invention relates generally to a sophisticated system and software to assist users deciding what video programing to watch. Based on analysis of viewing by users and their friends (which are the most reliable and accurate influencers on peoples viewing decisions), the system provides tools, ratings, recommendations and alerts. Specifically, the system leverages various forms of unique software tools and artificial intelligence processing to produce accurate and relevant information for users deciding what to watch. Given the huge and expanding amounts of video content and multiple viewing platforms, finding out what to watch is a crucial problem for a very high percentage (over 80%) of all video viewers, and the current invention represents a crucial and timely solution to this problem.