Abstract: A system and method for learning user preferences operates by posing topics in a manner similar to a human-to-human conversation. The system learns which topics to present to a human user from an initially seeded response database containing natural language phrases. The system then records user responses into the same response database or a connected response database. The system assigns user responses into categories, such as positive, negative, request for information, null, and potentially others. The system then bases future topics on what it learns during the interaction, including user responses, user response categories, time of data, location, how busy the human user typically is at difference times of day or certain days, and the like.

Abstract: A method of searching data streams arriving over a network in order to look for a predetermined reserved set of indicia first identifies and then tags the data per the found information in a reserved indicia set. The tags are applied by “StreamTagApps” (STAs), which are small, quickly executing software applications that are each focused on the tagging of data that includes a particular indicium in order to make the identification process rapid. The method thus allows very quick action to be taken in order to maximize the usefulness of the data. Multiple STAs in one or both of horizontal and vertical configurations may be constructed in order to provide any necessary level of speed or take full advantage of an available hardware configuration.

Abstract: A method of searching data being directed into a data heap looks for a predetermined element and then tags the data per the found element. The tags are applied by data heap tagging applications or “HeapTagApps” (HTAs), which are small, quickly executing software applications that are each focused on the tagging of data that includes a particular element in order to make the identification process rapid. Multiple HTAs in one or both of horizontal and vertical configurations may be constructed in order to provide any necessary level of speed or take full advantage of an available hardware configuration.

Abstract: A method of searching data being directed into a data heap looks for the presence of multiple tags on data elements and then applies a group tag to those data elements if all tags of the group are identified. The tags are applied by group tagging applications or “GroupTagApps” (GTAs), which are small, quickly executing software applications that are each focused on the tagging of data that includes a particular set of tags in order to make the identification process rapid. Multiple GTAs in one or both of horizontal and vertical configurations may be constructed in order to provide any necessary level of speed or take full advantage of an available hardware configuration.

Abstract: A method of searching data being directed into a data heap looks for a predetermined element and then tags the data per the found element. The tags are applied by data heap tagging applications or “HeapTagApps” (HTAs), which are small, quickly executing software applications that are each focused on the tagging of data that includes a particular element in order to make the identification process rapid. Multiple HTAs in one or both of horizontal and vertical configurations may be constructed in order to provide any necessary level of speed or take full advantage of an available hardware configuration.

Abstract: A method of searching data being directed into a data heap looks for the presence of multiple tags on data elements and then applies a group tag to those data elements if all tags of the group are identified. The tags are applied by group tagging applications or “GroupTagApps” (GTAs), which are small, quickly executing software applications that are each focused on the tagging of data that includes a particular set of tags in order to make the identification process rapid. Multiple GTAs in one or both of horizontal and vertical configurations may be constructed in order to provide any necessary level of speed or take full advantage of an available hardware configuration.

Abstract: A method of searching data streams arriving over a network in order to look for a predetermined reserved set of indicia first identifies and then tags the data per the found information in a reserved indicia set. The tags are applied by “StreamTagApps” (STAs), which are small, quickly executing software applications that are each focused on the tagging of data that includes a particular indicium in order to make the identification process rapid. The method thus allows very quick action to be taken in order to maximize the usefulness of the data. Multiple STAs in one or both of horizontal and vertical configurations may be constructed in order to provide any necessary level of speed or take full advantage of an available hardware configuration.