Abstract: A system for prospectively identifying media characteristics for inclusion in media content is disclosed. A neural network database including media characteristic information and feature information may associate relationships among the media characteristic information and feature information. Personal characteristic information associated with target media consumers may be used to select a subset of the neural network database. A first set of nodes, representing selected feature information, may be activated. The node interactions may be calculated to detect the activation of a second set of nodes, the second set of nodes representing media characteristic information. Generally, a node is activated when an activation value of the node exceeds a threshold value. Media characteristic information may be identified for inclusion in media content based on the second set of nodes.

Abstract: The present invention relates to a method of checking data gathered from a content source comprising: receiving initial data from the content source; training a data profiler to generate a set of trusted constraint modules, said training comprising (1) selecting constraint modules having parameters that are applicable to the initial data, (2) adjusting the parameters of the applicable constraint modules to conform with new data from the content source, (3) identifying non-stable constraint modules, and (4) generating a set of trusted constraint modules by removing the non-stable constraint modules; applying the set of trusted constraint modules to subsequently received data from the content source to determine whether the subsequently received data meets the parameters of the set of trusted constraint modules; and signaling a failure upon the subsequently received data failing to meet the parameters of the set of trusted constraint modules.

Abstract: The present invention relates to a method of checking data gathered from a content source comprising: receiving initial data from the content source; training a data profiler to generate a set of trusted constraint modules, said training comprising (1) selecting constraint modules having parameters that are applicable to the initial data, (2) adjusting the parameters of the applicable constraint modules to conform with new data from the content source, (3) identifying non-stable constraint modules, and (4) generating a set of trusted constraint modules by removing the non-stable constraint modules; applying the set of trusted constraint modules to subsequently received data from the content source to determine whether the subsequently received data meets the parameters of the set of trusted constraint modules; and signaling a failure upon the subsequently received data failing to meet the parameters of the set of trusted constraint modules.

Abstract: The present invention relates to a method of checking data gathered from a content source comprising: receiving initial data from the content source; training a data profiler to generate a set of trusted constraint modules, said training comprising (1) selecting constraint modules having parameters that are applicable to the initial data, (2) adjusting the parameters of the applicable constraint modules to conform with new data from the content source, (3) identifying non-stable constraint modules, and (4) generating a set of trusted constraint modules by removing the non-stable constraint modules; applying the set of trusted constraint modules to subsequently received data from the content source to determine whether the subsequently received data meets the parameters of the set of trusted constraint modules; and signaling a failure upon the subsequently received data failing to meet the parameters of the set of trusted constraint modules.