Abstract: A headset computer device provides user voice indication of location of the device. The user may implicitly or explicitly present by voice input his and hence the HSC device location. A voice driven location module is coupled to the voice recognition engine, a map database and GPS of the HSC device. Based on user voiced indications of 3D space location, the voice driven location module determines device location and resets 3D space location accordingly.

Abstract: A speech processing engine of an HSC device transcribes user utterance input and displays resulting text on a microdisplay. The speech processing engine further provides to the user text editing functions and auto suggestions. The user interface utilizes head gesture control and natural language for text editing. Standard language dictionaries, user's dictionary and user's address book contacts data are utilized.

Abstract: A speech processing engine of an HSC device transcribes user utterance input and displays resulting text on a microdisplay. The speech processing engine further provides to the user text editing functions and auto suggestions. The user interface utilizes head gesture control and natural language for text editing. Standard language dictionaries, user's dictionary and user's address book contacts data are utilized.

Abstract: A headset computer device provides user voice indication of location of the device. The user may implicitly or explicitly present by voice input his and hence the HSC device location. A voice driven location module is coupled to the voice recognition engine, a map database and GPS of the HSC device. Based on user voiced indications of 3D space location, the voice driven location module determines device location and resets 3D space location accordingly.

Abstract: A method for generating an natural language processing (NLP) model including obtaining tags, obtaining actions to be implemented by a third party application, obtaining a training corpus including sentences, where at least one word in each of the sentences is associated with one of the tags, and wherein each of the sentences is associated with one of the actions. The method further includes generating features for the NLP model for the third party application using the tags, the actions, and the training corpus, training the NLP model using the features and the training corpus to obtain a trained NLP model, and generating an APIKey for use by the third party application, where the API provides the third party application access to the trained NLP model.

Abstract: A method for generating an natural language processing (NLP) model including obtaining tags, obtaining actions to be implemented by a third party application, obtaining a training corpus including sentences, where at least one word in each of the sentences is associated with one of the tags, and wherein each of the sentences is associated with one of the actions. The method further includes generating features for the NLP model for the third party application using the tags, the actions, and the training corpus, training the NLP model using the features and the training corpus to obtain a trained NLP model, and generating an APIKey for use by the third party application, where the API provides the third party application access to the trained NLP model.