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 disclosure is directed to methods and systems for monitoring and replaying user interactions with one or more interactive electronic documents. The methods generally include identifying an event comprising an interaction between a user and an interactive electronic document, determining to record the event, identifying for the event a user action, a target element, and a set of element features, and recording data for recreating the event. Generally, the methods and systems monitor a training user's interactions with a document and generate an automated replay agent capable of replaying or recreating those interactions on the document or on similar documents. In some embodiments, the replay agent is able to place a document in a desired state and extract information from the document in the desired state. In some embodiments, the replay agent is trained to recognize elements, or types of elements, in the document.

Abstract: In accordance with an embodiment, data may be automatically extracted from semi-structured web sites. Unsupervised learning may be used to analyze web sites and discover their structure. One method utilizes a set of heterogeneous “experts,” each expert being capable of identifying certain types of generic structure. Each expert represents its discoveries as “hints.” Based on these hints, the system may cluster the pages and text segments and identify semi-structured data that can be extracted. To identify a good clustering, a probabilistic model of the hint-generation process may be used.

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 disclosure is directed to methods and systems for monitoring and replaying user interactions with one or more interactive electronic documents. The methods generally include identifying an event comprising an interaction between a user and an interactive electronic document, determining to record the event, identifying for the event a user action, a target element, and a set of element features, and recording data for recreating the event. Generally, the methods and systems monitor a training user's interactions with a document and generate an automated replay agent capable of replaying or recreating those interactions on the document or on similar documents. In some embodiments, the replay agent is able to place a document in a desired state and extract information from the document in the desired state. In some embodiments, the replay agent is trained to recognize elements, or types of elements, in the document.