Methods and apparatus to generate audience metrics using third-party privacy-protected cloud environments

Abstract

Methods and apparatus to generate audience metrics using third-party privacy-protected cloud environments. In some examples, an apparatus comprising a model generator to generate an individualization model based on truth data indicating first true users exposed to media via first panelist client devices, a model analyzer to produce user probabilities for second panelist client devices based on the individualization model, the user probabilities indicating likelihoods of second true users being exposed to media via the second panelist client devices, a data modifier to: select a user probability from the user probabilities based on an impression, the impression associated with at least one feature and a selected device from the second panelist client devices, the user probability associated with the at least one feature and the selected device, the user probability indicating a likelihood of a set of second true users being exposed to media corresponding to the impression via the selected device, and assign the impression to the set of second true users.

What is claimed is: 
     
         1 . An apparatus comprising:
 a model generator to generate an individualization model based on truth data indicating first true users exposed to media via first panelist client devices;   a model analyzer to produce user probabilities for second panelist client devices based on the individualization model, the user probabilities indicating likelihoods of second true users being exposed to media via the second panelist client devices;   a data modifier to:
 select a user probability from the user probabilities based on an impression, the impression associated with at least one feature and a selected device from the second panelist client devices, the user probability associated with the at least one feature and the selected device, the user probability indicating a likelihood of a set of second true users being exposed to media corresponding to the impression via the selected device; and 
 assign the impression to the set of second true users. 
   
     
     
         2 . The apparatus of  claim 1 , wherein the user probabilities are first user probabilities, wherein the user probability is a first user probability, wherein the impression is a first impression, wherein the selected device is a first selected device,
 wherein the model analyzer is to further produce second user probabilities for the first panelist client devices based on the truth data, and   wherein the data modifier is to further:
 select a second user probability from the second user probabilities based on a second impression associated with at least one feature and a second selected device from the first panelist client devices, the second user probability associated with the at least one feature and the second selected device, the second user probability indicating a likelihood of one or more first true users being exposed to media corresponding to the impression via the second selected device; and 
 assign the second impression to the one or more first true users. 
   
     
     
         3 . The apparatus of  claim 1 , wherein the model generator to generate the individualization model is based on training and validating the individualization model, the model generator to further train and validate the individualization model based on the truth data. 
     
     
         4 . The apparatus of  claim 1 , wherein the model analyzer to produce the user probabilities is further based on predicting demographic probabilities, the model analyzer to further predict demographic probabilities associated with the second panelist client devices based on the individualization model, the demographic probabilities indicating likelihoods of demographics corresponding to the second true users being exposed to the media via the second panelist client devices. 
     
     
         5 . The apparatus of  claim 4 , wherein the demographics include at least one of age, gender, race, income, home location, or occupation. 
     
     
         6 . The apparatus of  claim 1 , wherein at least one feature includes at least one of type of content, a time, or a type of device. 
     
     
         7 . The apparatus of  claim 1 , wherein the truth data is from survey responses indicating at least one of a type of content, a time of day, or a type of device, associated with the first true users. 
     
     
         8 . The apparatus of  claim 1 , wherein the model analyzer to produce the user probabilities is further based on known information including at least one of primary users, type of devices, or demographic composition corresponding to the second panelist client devices. 
     
     
         9 . The apparatus of  claim 1 , wherein the set of second true users include more than one second true users, wherein data modifier is to further assign fractional impressions to the set of second true users to assign the impression to the set of second true users. 
     
     
         10 . The apparatus of  claim 1 , wherein the model analyzer is to further store the individualization model to at least one memory. 
     
     
         11 . The apparatus of  claim 1 , wherein the data modifier is to further store the user probabilities to at least one memory. 
     
     
         12 . An apparatus comprising:
 at least one memory;   instructions; and   processor circuitry to execute the instructions to at least:   generate an individualization model based on truth data indicating first true users exposed to media via first panelist client devices;   produce user probabilities for second panelist client devices based on the individualization model, the user probabilities indicating likelihoods of second true users being exposed to media via the second panelist client devices;   select a user probability from the user probabilities based on an impression, the impression associated with at least one feature and a selected device from the second panelist client devices, the user probability associated with the at least one feature and the selected device, the user probability indicating the likelihood of a set of second true users being exposed to media corresponding to the impression via the selected device; and   assign the impression to the set of second true users.   
     
     
         13 . The apparatus of  claim 12 , wherein the user probabilities are first user probabilities, wherein the user probability is a first user probability, wherein the impression is a first impression, wherein the selected device is a first selected device, wherein the processor circuitry is to further:
 produce second user probabilities for the first panelist client devices based on the truth data;   select a second user probability from the second user probabilities based on a second impression associated with at least one feature and a second selected device from the first panelist client devices, the second user probability associated with the at least one feature and the second selected device, the second user probability indicating the likelihood of one or more first true users being exposed to media corresponding to the impression via the second selected device; and   assign the second impression to the one or more first true users.   
     
     
         14 . The apparatus of  claim 12 , wherein the processor circuitry to generate the individualization model is based on training and validating the individualization model, the processor circuitry to further train and validate the individualization model based on the truth data. 
     
     
         15 . The apparatus of  claim 12 , wherein the processor circuitry to produce the user probabilities is further based on predicting demographic probabilities, the processor circuitry to further predict demographic probabilities associated with the second panelist client devices based on the individualization model, the demographic probabilities indicating likelihoods of demographics corresponding to the second true users being exposed to the media via the second panelist client devices. 
     
     
         16 . The apparatus of  claim 15 , wherein the demographics include at least one of age, gender, race, income, home location, or occupation. 
     
     
         17 . The apparatus of  claim 12 , wherein at least one feature includes at least one of type of content, a time, or a type of device. 
     
     
         18 . The apparatus of  claim 12 , wherein the truth data is from survey responses indicating at least one of a type of content, a time of day, or a type of device, associated with the first true users. 
     
     
         19 . The apparatus of  claim 12 , wherein the processor circuitry to produce the user probabilities is further based on known information including at least one of primary users, type of devices, or demographic composition corresponding to the second panelist client devices. 
     
     
         20 . The apparatus of  claim 12 , wherein the set of second true users include more than one second true users, wherein the processor circuitry is to further assign fractional impressions to the set of second true users to assign the impression to the set of second true users. 
     
     
         21 . The apparatus of  claim 12 , wherein the processor circuitry is to further store the individualization model to the at least one memory. 
     
     
         22 . The apparatus of  claim 12 , wherein the processor circuitry is to further store the user probabilities to at least one memory.