Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A database includes a repository of data segments to be searched, called standard streams. But rather than searching all possible segments of each standard stream, the database includes a set of index files that reference a number of different segments in the repository. Each index file provides information about whether various data segments in the repository are likely to match a given test stream, although in the presence of noise there may be multiple possible matches. By consulting a number of the index files, a searching algorithm identifies a set of candidate data segments to test and thus reduces the number of streams that must be tested.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database may contain a number of these characteristic thumbprints, and the database can be searched for a particular thumbprint.

Abstract: A characteristic thumbprint is extracted from a data signal, the thumbprint based on statistics relating to the data signal. The data signal can be compared indirectly by matching this thumbprint against one or more reference thumbprints. The data signal may be any type of signal, including streaming digitized audio or obtained from static files. A database for containing a number of such thumbprints and methods for searching the database are also described.

Abstract: A database includes a repository of data segments to be searched, called standard streams. But rather than searching all possible segments of each standard stream, the database includes a set of index files that reference a number of different segments in the repository. Each index file provides information about whether various data segments in the repository are likely to match a given test stream, although in the presence of noise there may be multiple possible matches. By consulting a number of the index files, a searching algorithm identifies a set of candidate data segments to test and thus reduces the number of streams that must be tested.

Abstract: A computerized system lays out document templates represented as a tree of text and shape elements, including variable elements. The user can define a shape element to have a maximize or minimize property in one or more dimensions. The layout makes the minimized dimensions of a shape as small as its contents will allow; and makes the maximized dimensions expand as much as available space allows. Such maximization or minimization can be performed within a horizontal or vertical sequence box. Variable values mapped into variable shape elements can include sub-trees of text and/or shape elements, including shape elements which have the maximize or minimize property, and elements which are themselves variable elements. An anchor point can be fixed at a selected point on a shape, causing the anchor point to remain fixed as the rest of the shape expands or contract. Variable Image elements can maintain the aspect ratios of images mapped into them as those images are scaled.

Abstract: A word processor including a screen display unit for displaying characters, etc. on a screen, a key input unit for inputting the characters, etc., a print unit for printing document data of documents, a document producing unit for producing the documents from data inputted from the key input unit, and a document control unit for inputting document control data to a recording medium or outputting data stored in the recording medium to the screen display unit. The key input unit includes a command device for giving a command relevant to automatic production of a title of an untitled document, and the document control means includes a title producing device for automatically producing the title of the untitled document in response to the command from the command device.