Abstract: A method of processing a video file to generate a modified video file, the modified video file including a translated audio content of the video file, the method comprising: receiving the video file; accessing a facial model or a speech model for a specific speaker, wherein the facial model maps speech to facial expressions, and the speech model maps text to speech; receiving a reference content for the originating video file for the specific speaker; generating modified audio content for the specific speaker and/or modified facial expression for the specific speaker; and modifying the video file in accordance with the modified content and/or the modified expression to generate the modified video file.

Abstract: A method of generating matching metadata vectors for identifying content items in a store searchable by input vectors, the method comprising: receiving multiple training inputs, each training input comprising a content identifier indicative of a content item, and at least one natural language description of the content item; for each training input: converting the natural language description into at least one text component; generating at least one vector, each vector corresponding to one text component; generating a set of component parts for each vector, each component part corresponding to a coordinate initialized with a random value; adjusting each random coordinate based on the relationship of each component part to other vectors; determining a weighting for each vector with respect to the item; and defining a metadata vector for each item comprising the vectors containing the adjusted coordinates for that item and the weighting for each vector.

Abstract: A method includes comparing each textless video clip to a plurality of portions of the video file corresponding to the full length video file; determining each textless video clip being similar to only one portion of the video file as being a matched pair; for each matched pair identifying if their text content is different, wherein identification of a different text content dictates that the textless video clip corresponds to a portion of the video file having overlaid text; training a classifier to predict whether an area of text detected in the full length video is overlaid text; determining the probability of each portion of the full-length video having overlaid text; determining each textless video clip being similar to more than one portion of the video file as being a potential matched pair; and resolving the potential matched pairs with the determined probability.

Abstract: A method of processing a video file to generate a modified video file, the modified video file including a translated audio content of the video file, the method comprising: receiving the video file; accessing a facial model or a speech model for a specific speaker, wherein the facial model maps speech to facial expressions, and the speech model maps text to speech; receiving a reference content for the originating video file for the specific speaker; generating modified audio content for the specific speaker and/or modified facial expression for the specific speaker; and modifying the video file in accordance with the modified content and/or the modified expression to generate the modified video file.

Abstract: A method of generating matching metadata vectors for identifying content items in a store searchable by input vectors, the method comprising: receiving multiple training inputs, each training input comprising a content identifier indicative of a content item, and at least one natural language description of the content item; for each training input: converting the natural language description into at least one text component; generating at least one vector, each vector corresponding to one text component; generating a set of component parts for each vector, each component part corresponding to a coordinate initialized with a random value; adjusting each random coordinate based on the relationship of each component part to other vectors; determining a weighting for each vector with respect to the item; and defining a metadata vector for each item comprising the vectors containing the adjusted coordinates for that item and the weighting for each vector.

Abstract: A method for dynamically interlinking documents within a collection, comprising downloading documents within said collection; generating a reverse index and a content signature database of said collection, selecting for each document within said collection, a list of words within to convert into search links, based on the said content signature database, and displaying search results based on said reverse index.

Abstract: A method for resolving ambiguities in natural language by organizing the task into multiple iterations of analysis done in successive levels of depth. We refer to this method as Adaptive Language Processing (ALP), in that the processing done is adaptive to the users' need for accuracy and efficiency. At each level of the ALP model the most accurate disambiguation is made based on the available information. As more analysis is done, additional knowledge is incorporated in a systematic manner to improve disambiguation accuracy. This multi-level approach allows for time-consuming steps to be parlayed or omitted, based on the needs of the users. Associated with each level of processing is a measure of confidence, used to gauge the confidence of a process in its disambiguation accuracy. An overall confidence measure is also used to reflect the level of the analysis done, the particular NLP techniques used in the disambiguation, and the amount of training data available.