Abstract: A flashlight includes a light powered by a battery, and a circuit having: a memory for storing a battery life information and voltage output information versus time for the battery to power the light, a controller operating a count down timer to accumulate an amount of time that the battery powers the light, a voltage measure circuit for monitoring the voltage output by the battery while the battery powers the light and supply the voltage output to the controller. The controller determines: a first remaining battery life by comparing the accumulated time to the battery life, a second remaining battery life by comparing the voltage output to the voltage output information, and the lesser of the first and second remaining battery lives. The flashlight includes a display for receiving a command from the controller to display the lesser of the first remaining battery life and the second remaining battery life.

Abstract: A flashlight includes a light powered by a battery, and a circuit having: a memory for storing a battery life information and voltage output information versus time for the battery to power the light, a controller operating a count down timer to accumulate an amount of time that the battery powers the light, a voltage measure circuit for monitoring the voltage output by the battery while the battery powers the light and supply the voltage output to the controller. The controller determines: a first remaining battery life by comparing the accumulated time to the battery life, a second remaining battery life by comparing the voltage output to the voltage output information, and the lesser of the first and second remaining battery lives. The flashlight includes a display for receiving a command from the controller to display the lesser of the first remaining battery life and the second remaining battery life.

Abstract: A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received.

Abstract: A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received.

Abstract: A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received.

Abstract: A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received.

Abstract: An arrangement for capturing and transmitting a series of images, as well as associated sound clips, from a wireless digital camera to a network-based server for viewing at a web site by designated individuals. Text and/or audio messages from the viewers can be transmitted through the wireless packet network back to the digital camera, resulting in an interactive, bidirectional communication system between the camera user and the image viewers.