Subscriber Characterization System with Filters

Abstract

A subscriber characterization system with filters in which the subscriber's selections are monitored, including monitoring of the time duration programming is watched, the volume at which the programming is listened to, and any available information regarding the type of programming, including category and sub-category of the programming. The raw subscriber selection data is then processed to eliminate data associated with irrelevant activities such as channel surfing, channel jumping, or extended periods of inactivity. The actual subscriber selection data is used to form program characteristics vectors. The programming characteristics vectors can be used in combination with the actual subscriber selection data to form a subscriber profile. Heuristic rules indicating the relationships between programming choices and demographics can be applied to generate additional probabilistic subscriber profiles regarding demographics and programming and product interests.

1 . A computer-implemented method of determining at least one characteristic of a video or audio presentation being presented to a viewer, the method comprising: 
 (a) monitoring viewer interactions with a multimedia device;    (b) processing the viewer interactions to obtain viewer interaction data corresponding to the viewer interactions;    (c) retrieving one or more previously developed heuristic rules, wherein the previously developed heuristic rules relate at least one aspect of the viewer interaction data to the at least one characteristic of the video or audio presentation, and wherein the previously developed heuristic rules have been previously developed through the application of at least one heuristic process;    (d) applying one or more of the previously developed heuristic rules to at least a subset of the viewer interaction data; and    (e) inferring the at least one characteristic of the video or audio presentation being presented to the viewer based on the application of the previously developed heuristic rules.    
   
   
       2 . The method of  claim 1 , wherein the heuristic rules are probabilistic in nature.  
   
   
       3 . The method of  claim 1 , wherein the at least one inferred characteristic is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.  
   
   
       4 . The method of  claim 1 , wherein the viewer interaction data includes at least some subset of: 
 (a) viewing time per channel, category, or network;    (b) channel changes per time;    (c) average volume per time period, channel, category, or network; and    (d) dwell time per channel, category, or network.    
   
   
       5 . The method of  claim 1 , wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.  
   
   
       6 . The method of  claim 1 , further comprising: 
 (f) reporting the at least one inferred characteristic, wherein the report includes predictive values of the at least one inferred characteristic and at least one other characteristic, and wherein the predictive values are assigned by the heuristic rules based on the viewer interaction data.    
   
   
       7 . A computer-implemented method of determining at least one demographic attribute of a video or audio presentation being presented to a viewer, the method comprising: 
 (a) monitoring viewer interactions with a multimedia device;    (b) processing the viewer interactions to obtain viewer interaction data corresponding to the viewer interactions;    (c) retrieving one or more previously developed heuristic rules, wherein the previously developed heuristic rules relate at least one aspect of the viewer interaction data to the at least one demographic attribute of the video or audio presentation, and wherein the previously developed heuristic rules have been previously developed through the application of at least one heuristic process;    (d) applying one or more of the previously developed heuristic rules to at least a subset of the viewer interaction data; and    (e) inferring the at least one demographic attribute of the video or audio presentation being watched by a viewer based on the application of the previously developed heuristic rules.    
   
   
       8 . The method of  claim 7 , wherein the heuristic rules are probabilistic in nature.  
   
   
       9 . The method of  claim 7 , wherein the at least one demographic attribute is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.  
   
   
       10 . The method of  claim 7 , wherein the viewer interaction data includes at least some subset of: 
 (a) viewing time per channel, category, or network;    (b) channel changes per time;    (c) average volume per time period, channel, category, or network; and    (d) dwell time per channel, category, or network.    
   
   
       11 . The method of  claim 7 , wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.  
   
   
       12 . The method of  claim 7 , further comprising: 
 (f) reporting the at least one demographic attribute, wherein the report includes predictive values of the at least one demographic attribute and at least one other demographic attribute, and wherein the predictive values are assigned by the heuristic rules based on the viewer interaction data.    
   
   
       13 . A computer-implemented method of determining which viewer or viewers of an audience of a video or audio presentation are viewing the video or audio presentation, the method comprising: 
 (a) monitoring viewer interactions with a multimedia device;    (b) processing the viewer interactions to obtain viewer interaction data corresponding to the viewer interactions of at least one viewer;    (c) retrieving one or more previously developed heuristic rules, wherein the previously developed heuristic rules relate at least one aspect of the viewer interaction data to at least one characteristic of a viewer, wherein the previously developed heuristic rules have been previously developed through the application of at least one heuristic process;    (d) applying one or more of the previously developed heuristic rules to at least a subset of the viewer interaction data; and    (e) inferring which viewer or viewers of a plurality of viewers of the audience are viewing the video or audio presentation based on the application of the previously developed heuristic rules.    
   
   
       14 . The method of  claim 13 , wherein the heuristic rules of step (d) are applied at least to a subset of previously stored viewer interaction data.  
   
   
       15 . The method of  claim 14 , wherein the inferring of step (e) utilizes the application of the heuristic rules to the previously stored viewer interaction data.  
   
   
       16 . The method of  claim 13 , wherein the heuristic rules are probabilistic in nature.  
   
   
       17 . The method of  claim 13 , wherein the at least on inferred characteristic is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.  
   
   
       18 . The method of  claim 13 , wherein the viewer interaction data includes at least some subset of: 
 (a) viewing time per channel, category, or network;    (b) channel changes per time;    (c) average volume per time period, channel, category, or network; and    (d) dwell time per channel, category, or network.    
   
   
       19 . The method of  claim 13 , wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.  
   
   
       20 . A computer-implemented method of determining which viewer or viewers in a household are viewing a video or audio presentation, the method comprising: 
 (a) monitoring viewer interactions with a multimedia device;    (b) processing the viewer interactions to obtain viewer interaction data corresponding to the viewer interactions of the household;    (c) retrieving on or more previously developed heuristic rules, wherein the previously developed heuristic rules relate viewer interaction data to at least one characteristic of a viewer, wherein the previously developed heuristic rules have been previously developed through the application of at least one heuristic process;    (d) applying one or more of the previously developed heuristic rules to at least a subset of the viewer interaction data; and    (e) inferring which viewer or viewers in the household are viewing the video or audio presentation based on the application of heuristic rules.    
   
   
       21 . The method of  claim 20 , wherein the heuristic rules of step (d) are applied at least to a subset of previously stored viewer interaction data.  
   
   
       22 . The method of  claim 21 , wherein the inferring of step (e) utilizes the application of the heuristic rules to the previously stored viewer interaction data.  
   
   
       23 . The method of  claim 20 , wherein the heuristic rules are probabilistic in nature.  
   
   
       24 . The method of  claim 20 , wherein the at least on inferred characteristic is expressed as a probability assigned by the heuristic rules based on the viewer interaction data.  
   
   
       25 . The method of  claim 20 , wherein the viewer interaction data includes at least some subset of: 
 (a) viewing time per channel, category, or network;    (b)channel changes per time;    (c) average volume per time period, channel, category, or network; and    (d) dwell time per channel, category, or network.    
   
   
       26 . The method of  claim 20 , wherein the at least one heuristic process incorporates at least two types of analysis selected from the group consisting of exploratory problem-solving, self-learning, discovery, experiments, trial and error, inferences, educated guesses, market studies, human knowledge and experience.