The TV, using a camera attached to it, first recognizes the viewer using “Face Recognition” techniques. It then relays the statistics about the time the viewer views a particular program and his facial expressions (happy, sad, interested, disinterested, etc) while watching it to a central hub.
The central hub (which may be on the cloud or in a self managed datacenter) then collates this data with the ontology of the program. [This ontology is built by data mining and supervised machine learning using a distributed cluster of computers.] Thus, over time, the central hub builds a profile of the viewer according to the viewer’s viewing pattern.
In the meanwhile, the TV also recognizes the facial expression of the viewer and looks for the signs of boredom (drowsy eyes, long frowns, vacant stares, etc). As soon as it recognizes a “disinterested pattern”, it calls a web-service running on the central hub.
This web-service searches the ontology of programs currently on air and sorts it according to the viewer’s profile. Then it predicts a list of programs which will most likely interest the viewer and sends it back to the TV.
The TV then suggests the viewer about the programs (s)he can watch if he is not interested in the current one. If the viewer changes the program then the TV sends the information to the central hub about the program he switched to. The Central hub adds this information to the profile of the viewer.
In case of conflicts, i.e. more than one viewer, the central hub decides on the predicted list of interesting programs using a priority list of the viewers based on previous history as to whose choice prevailed last time such a conflict arose.
This figure describes the architecture in a simple yet concise manner:
Note: If anyone wants to use this idea or wants further clarification then please contact me @ paritosh (dot) gunjan (at) gmail (dot) com.
