Method and apparatus for neuropsychological modeling of human experience and purchasing behavior

Abstract

A system for accurately modeling of buyer/purchaser psychology and ranking of content objects within a channel for user initiated browsing and presentation contains a neuropsychological modeling engine, a ranking application, and a behavior modeler which communicate with each other and a presentation system over communication networks. The neuropsychological modeling engine utilizes metafiles associated with content objects, a purchaser/viewer model and a channel model to derive a value ψ representing an individual's mood and a value m representing an individual's motivational strength to select a content object. If the value ψ is within an acceptable predetermined range, the value m is used to determine a ranking for the content object relative to other content objects associated with the channel model. Also disclosed are a system and technique for simultaneously presenting multiple, s content object data streams on the user interface in a manner which encourages multidimensional browsing using traditional navigation commands.

What is claimed is: 
     
         1 . A recommendation system operatively coupled to a network accessible source of indexed content objects and a viewer system, the recommendation system capable of modeling of buyer/purchaser psychology, the recommendation system comprising:
 A) a neuropsychological modeling engine operatively coupled to the network accessible source of indexed content objects;   B) a behavior modeler operatively coupled to the viewer system;   C) a ranking application operatively coupled to the neuropsychological modeling engine;   D) a first memory operatively coupled to the ranking application, the neuropsychological modeling engine, and the behavior modeler for storing a plurality of viewer models;   E) a second memory operatively coupled to the ranking application, the neuropsychological modeling engine, and the behavior modeler for storing a plurality of channel models and rankings of content objects relative to a channel model;   wherein the neuropsychological modeling engine is configured to:
 i) compare metadata associated with a content object received from the source of indexed content objects to metadata associated with a viewer model and at least one viewer channel associated with the viewer, and 
 ii) determine if the received content object is eligible for ranking amongst other content objects associated with viewer channel in accordance with the viewer's emotional motivation to select the content object. 
   
     
     
         2 . The system of  claim 1  wherein the recommendation system further comprises:
 D) a mathematical model of human emotion stored in memory and accessible by the neuropsychological modeling engine. 
 
     
     
         3 . The system of  claim 1  wherein neuropsychological modeling engine is further configured to:
 iii) generate a fear vector value representing an individual's fear to select or purchase the content object; 
 iv) generate a desire vector value representing the individual's desire to select or purchase the offered item; 
 v) derive, from the desire vector value and the fear vector value, a value ψ representing an individuals mood; 
 vi) derive, from the desire vector value and the fear vector value, a value m representing an individuals motivational strength to select the content object; and 
 vii) if the value ψ representing an individuals mood is within an acceptable predetermined range, providing the value m to ranking application. 
 
     
     
         4 . The system of  claim 3  wherein the ranking application is configured to, using the value in received from the neuropsychological modeling engine, determine a ranking for the content object relative to other content objects associated with the channel model. 
     
     
         5 . A method for modeling of buyer/purchaser psychology comprising:
 A) comparing metadata associated with a content object to metadata associated with a channel model;   B) generating a fear vector value representing an individual's fear to select or purchase the content object;   C) generating a desire vector value representing the individual's desire to select or purchase the offered item; and   D) deriving from the value for the desire vector and the value for the fear vector a ranking for the content object relative to other content objects associated with the channel model.   
     
     
         6 . The method of  claim 5  wherein D) comprises:
 D1) deriving, from the desire vector value and the fear vector value, a value ψ representing an individuals mood. 
 
     
     
         7 . The method of  claim 6  wherein D) further comprises:
 D2) deriving, from the desire vector value and the fear vector value, a value m representing an individuals motivational strength to select or purchase the content object. 
 
     
     
         8 . The method of  claim 7  wherein D) further comprises:
 D3) if the value ψ representing an individuals mood is within an acceptable predetermined range, using the value m representing an individuals motivational strength to select or purchase the content object to determine a ranking for the content object relative to other content objects associated with the channel model. 
 
     
     
         9 . The method of  claim 5  further comprising:
 E) maintaining the channel model and a viewer model associated with the individual in a network accessible memory. 
 
     
     
         10 . A system for modeling of buyer/purchaser psychology comprising:
 A) a first network accessible memory for storing at least one channel model;   B) a modeling engine operably coupled to the network accessible memory and configured to compare metadata associated with a content object to metadata associated with the channel model and for generating:
 i) a fear vector value representing an individual's fear (reluctance) to select or purchase the content object; and 
 ii) a desire vector value representing the individual's desire to select the content object. 
   
     
     
         11 . The system of  claim 10  wherein the modeling engine is further configured to generate:
 iv) a value ψ representing an individuals mood, the value ψ being derived from the desire vector value and the fear vector value. 
 
     
     
         12 . The system of  claim 11  wherein the modeling engine is further configured to generate:
 v) a value m representing an individuals motivational strength to select or purchase the content object, the value m being derived from the desire vector value and the fear vector value. 
 
     
     
         13 . The system of  claim 10  further comprising:
 C) a ranking module, responsive to the value m generated by the modeling engine, for deriving a ranking for the content object relative to other content objects associated with the channel model, if the value ψ generated by the modeling engine is within an acceptable predetermined range. 
 
     
     
         14 . The system of  claim 10  further comprising:
 C) a second network accessible memory for storing at least a portion of the content object and the other content objects. 
 
     
     
         15 . The system of  claim 10  further comprising:
 C) a network accessible a data structure stored in memory, the data structure comprising any of: 
 channel identification data; 
 viewer identification data; 
 at least one dominant preference variable; 
 at least one sub-dominant preference variable; or 
 memory reference identifying the location of a data structure representing rankings of content objects associated with the channel. 
 
     
     
         16 . A method for modeling of buyer/purchaser psychology comprising:
 A) receiving data associated with a viewing event;   B) comparing the data associated with the viewing even to metadata associated with one of a channel model and viewer model associated with a viewer that generated the viewing event data; and   C) modifying one of the channel model and viewer model to account for the viewing event.   
     
     
         17 . The method of  claim 16  wherein B) further comprises:
 B1) comparing metadata associated with the channel model to data associated with a viewer model. 
 
     
     
         18 . The method of  claim 16  wherein B) comprises:
 B1) comparing the data associated with the viewing event to metadata associated with the channel model; and 
 wherein C) comprises: 
 C1) modifying the channel model to account for the viewing event. 
 
     
     
         19 . The method of  claim 16  wherein B) comprises:
 B1) comparing the data associated with the viewing event to metadata associated with the channel model; and 
 wherein C) comprises; 
 C1) modifying the viewer model to account for the viewing event. 
 
     
     
         20 . The method of  claim 16  wherein B) comprises:
 B1) comparing the data associated with the viewing event to metadata associated with the viewer model; and 
 wherein C) comprises: 
 C1) modifying the viewer model to account for the viewing event.