A system and method for conducting a refractive examination of an eye of a patient, has a communication device with a communication module that connects to the internet, a processor that is programmed to connect to a remote computer via the communication module and which has a display screen, a microphone and a speaker. The remote computer has a data storage device that stores images of eye charts. The communication device is mounted in a virtual reality headset configured to be worn by the patient and has at least one screen through which the display screen of the communication device is viewable. The communication device displays images in the form of the eye charts to the patient, who communicates through the communication to a remote examiner who conducts the refractive examination using multiple different eye charts to determine the prescription of the patient.

Example methods and systems for modifying the playback of content using pre-processed profile information are described. Example instructions, when executed, cause at least one processor to access a media stream that includes media and a profile of equalization parameters, the media stream provided to a device via a network, the profile of equalization parameters included in the media stream selected based on a comparison of a reference fingerprint to a query fingerprint generated based on the media, the profile of equalization parameters including an equalization parameter for the media; and modify playback of the media based on the equalization parameter specified in the accessed profile.

Example methods and systems for modifying the playback of content using pre-processed profile information are described. Example instructions, when executed, cause at least one processor to access a media stream that includes media and a profile of equalization parameters, the media stream provided to a device via a network, the profile of equalization parameters included in the media stream selected based on a comparison of a reference fingerprint to a query fingerprint generated based on the media, the profile of equalization parameters including an equalization parameter for the media; and modify playback of the media based on the equalization parameter specified in the accessed profile.

A consumption probability metric may be generated for a media asset. An aggregated forecast predicting user consumption of a media asset is received. A plurality of probabilities, each corresponding to a user of a plurality of users, is received, each indicating how likely a respective user is to consume the media asset. A weight for the plurality of users is calculated representing a ratio of the total number of users to a number of users in the plurality of users. A disaggregated forecast predicting user consumption of a media asset is determined based on the weight for the plurality of users and the plurality of probabilities. A modification factor is computed based on the aggregated forecast and the disaggregated forecast. A metric is generated that includes a plurality of user identifiers associated with the plurality of users and a plurality of modified probabilities each modified by the modification factor.

A consumption probability metric may be generated for a media asset. An aggregated forecast predicting user consumption of a media asset is received. A plurality of probabilities, each corresponding to a user of a plurality of users, is received, each indicating how likely a respective user is to consume the media asset. A weight for the plurality of users is calculated representing a ratio of the total number of users to a number of users in the plurality of users. A disaggregated forecast predicting user consumption of a media asset is determined based on the weight for the plurality of users and the plurality of probabilities. A modification factor is computed based on the aggregated forecast and the disaggregated forecast. A metric is generated that includes a plurality of user identifiers associated with the plurality of users and a plurality of modified probabilities each modified by the modification factor.

An estimation device includes: an obtainer that obtains first content associated with a first time and second content associated with a second time, the second time preceding the first time by a predetermined amount of time; a determiner that, by applying first processing for determining a type of content to each of the first content and the second content, obtains first type information indicating a type of the first content and second type information indicating a type of the second content; a calculator that, using the first type information and the second type information, calculates confidence level information indicating a confidence level of the first type information; and an outputter that, using the confidence level information calculated by the calculator, outputs specifying information specifying the type of the first content derived from the first type information.

Methods, systems, and apparatuses for audience metric determination are described herein. An audience segment may be targeted for delivery of content. A clustering algorithm may be used to categorize a quantity of users or devices into subsets based on a propensity to consume, present or output a particular type of content, and a quantity of time to output the particular type of content. A weight may be assigned to each subset based on its relevance to other subsets, such as based on data variance, e.g., on a distance to a midpoint of a specific subset of the subsets. An index parameter may be determined for the datasets, e.g., based on each weight for each subset, and data may be generated that reflects a ranking of content delivery spots for delivery of content to the audience segment.

A scientific system and methods are disclosed for optimizing television (e.g., “CTV” and “OTT”) advertising and related expenditure to maximize efficiency and return on investment (“ROI’) for advertisers. The scientific system comprises an initial-feedback engine that develops and refines creatives or outcomes by creating and using an artificial intelligence (“AI”) engine that creates an initial feedback loop from social media platforms and subsequently uses an intelligent advertisement-selection engine that takes the highest performing advertising on the social media platforms and directs or imports them for connected television or over-the-top advertising. The system includes a performance engine that optimizes performance of the connected television and over-the-top advertising and then moves the winning combination of a creative or outcome resulting from the application inventory, the audience segment, the part of day, the frequency or the like to linear television purchase actions.

A scientific system and methods are disclosed for optimizing television (e.g., “CTV” and “OTT”) advertising and related expenditure to maximize efficiency and return on investment (“ROI’) for advertisers. The scientific system comprises an initial-feedback engine that develops and refines creatives or outcomes by creating and using an artificial intelligence (“AI”) engine that creates an initial feedback loop from social media platforms and subsequently uses an intelligent advertisement-selection engine that takes the highest performing advertising on the social media platforms and directs or imports them for connected television or over-the-top advertising. The system includes a performance engine that optimizes performance of the connected television and over-the-top advertising and then moves the winning combination of a creative or outcome resulting from the application inventory, the audience segment, the part of day, the frequency or the like to linear television purchase actions.

A scientific system and methods are disclosed for optimizing television (e.g., “CTV” and “OTT”) advertising and related expenditure to maximize efficiency and return on investment (“ROI’) for advertisers. The scientific system comprises an initial-feedback engine that develops and refines creatives or outcomes by creating and using an artificial intelligence (“AI”) engine that creates an initial feedback loop from social media platforms and subsequently uses an intelligent advertisement-selection engine that takes the highest performing advertising on the social media platforms and directs or imports them for connected television or over-the-top advertising. The system includes a performance engine that optimizes performance of the connected television and over-the-top advertising and then moves the winning combination of a creative or outcome resulting from the application inventory, the audience segment, the part of day, the frequency or the like to linear television purchase actions.