Abstract: A method for providing a user interface (UI) in an electronic device having a background includes presenting a first keyword in the background by animation in response to an operation performed by a user on the electronic device, wherein the first keyword enables the user to start the electronic device for executing a first function.

Abstract: A method for providing a user interface (UI) in an electronic device having a background includes presenting a first keyword in the background by animation in response to an operation performed by a user on the electronic device, wherein the first keyword enables the user to start the electronic device for executing a first function.

Abstract: A method for providing a user interface (UI) in an electronic device includes determining at least a starting keyword from a given target database, and associating each said starting keyword with a function of the electronic device; determining at least a linking keyword from a user profile database; creating a keyword tree from the at least a starting keyword and the at least a linking keyword, wherein the keyword tree has at least two layers, and the at least a linking keyword is placed in an upper layer relative to the at least a starting keyword; and providing the keyword tree on the electronic device for a user to browse and thereby select at least a said starting keyword by at least a said linking keyword on the keyword tree, thus starting the function.

Abstract: A method for providing a user interface (UI) in an electronic device having a background includes presenting a first keyword in the background by animation in response to an operation performed by a user on the electronic device, wherein the first keyword enables the user to start the electronic device for executing a first function.

Abstract: A health check evaluation indicator building method includes the following steps. A visualization process is performed to produce source graphic data on a plurality of features and attributes according to a time series, wherein each of the source graphic data includes a group number. Then, a similarity comparison is performed between the source graphic data and a plurality of target graphic data with the same group number to produce a plurality of similarity scores, where the minimum thereof corresponds to role model graphic data. Based on the role model graphic data, one of a best proposal and an alternative proposal is selected and a result thereof is fed back after execution. A health check evaluation indicator building system and a computer readable medium to embody the above method are also disclosed.

Abstract: A health check path evaluation indicator building system and a method thereof, a device therewith and a computer program product thereof are provided. The health check evaluation indicator building method includes the following steps. A visualization process is performed to produce source graphic data on a plurality of features and attributes according to a time series, wherein each of the source graphic data includes a group number. Then, a similarity comparison is performed between the source graphic data and a plurality of target graphic data with the same group number to produce a plurality of similarity scores, where the minimum thereof corresponds to role model graphic data. Based on the role model graphic data, one of a best proposal and an alternative proposal is selected and a result thereof is fed back after execution. The disclosure also includes the health check evaluation indicator building system to embody the above method.

Abstract: A method for providing a user interface (UI) in an electronic device having a background includes presenting a first keyword in the background by animation in response to an operation performed by a user on the electronic device, wherein the first keyword enables the user to start the electronic device for executing a first function.

Abstract: A method for providing a user interface (UI) in an electronic device includes determining at least a starting keyword from a given target database, and associating each said starting keyword with a function of the electronic device; determining at least a linking keyword from a user profile database; creating a keyword tree from the at least a starting keyword and the at least a linking keyword, wherein the keyword tree has at least two layers, and the at least a linking keyword is placed in an upper layer relative to the at least a starting keyword; and providing the keyword tree on the electronic device for a user to browse and thereby select at least a said starting keyword by at least a said linking keyword on the keyword tree, thus starting the function.

Abstract: A self-learning method for keyword based human machine interaction is disclosed. At least one of a plurality of keywords predetermined in a database is selected and a priority score of the selected keyword is updated and a weighted factor is generated for the selected keyword. A weighted score and the weighted factor of the selected keyword are transmitted to the keywords related to the selected keyword. The selected keyword is pushed to a keyword buffer and linkage strengths between the keywords in the keyword buffer are strengthened. When a keyword has been stored in the keyword buffer for over a predetermined reset time period, a reset operation is performed to remove the keyword from the keyword buffer.

Abstract: A processing method for a portable navigation device equipped with a conversation system is disclosed. When the portable navigation device is operating in a dumb mode, events are displayed on a screen of the device according to preset conditions and parameters. In a normal mode, events scheduled in the 1st to Xth order are pushed to the user according to preset conditions, and, in a hype mode, events are displayed on the screen based on preset conditions and parameters while events residing at the top layer of the screen are actively and repeatedly pushed to the user.

Abstract: A self-learning method for keyword based human machine interaction is disclosed. At least one of a plurality of keywords predetermined in a database is selected and a priority score of the selected keyword is updated and a weighted factor is generated for the selected keyword. A weighted score and the weighted factor of the selected keyword are transmitted to the keywords related to the selected keyword. The selected keyword is pushed to a keyword buffer and linkage strengths between the keywords in the keyword buffer are strengthened. When a keyword has been stored in the keyword buffer for over a predetermined reset time period, a reset operation is performed to remove the keyword from the keyword buffer.

Abstract: A processing method for a portable navigation device equipped with a conversation system is disclosed. When the portable navigation device is operating in a dumb mode, events are displayed on a screen of the device according to preset conditions and parameters. In a normal mode, events scheduled in the 1st to Xth order are pushed to the user according to preset conditions, and, in a hype mode, events are displayed on the screen based on preset conditions and parameters while events residing at the top layer of the screen are actively and repeatedly pushed to the user.

Abstract: A configurable distributed speech recognition system comprises a configurable distributed speech recognition protocol, and a configurable distributed speech recognition server. Herein, the configurable distributed speech recognition protocol is used to establish data transmitting format, for a client speech mobile device to packet the speech data and configuration data become a message packet. The configurable distributed speech recognition system receives the message packet from the client speech mobile device, configures its own speech recognition modules and resources according to the configuration data, and then returns a result to the client speech mobile device after completing the speech recognition.

Abstract: A speech recognition method includes receiving signals derived from indices of a codebook corresponding to recognition feature vectors extracted from speech to be recognized. The signals include an indication of the number of bits per codebook index. The method also includes obtaining the string of indices from the received signals, obtaining the corresponding recognition feature vectors from the string of indices, and applying the recognition feature vectors to a word-level recognition process. To conserve network capacity, the size of the codebook and the corresponding number of bits per codebook index, are adapted on a dialogue-by-dialogue basis. The adaptation accomplishes a tradeoff between expected recognition rate and expected bitrate by optimizing a metric which is a function of both.

Abstract: In a mobile wireless communication system automatic speech recognition is performed in a distributed manner using a mobile station based near or front end stage which extracts and vector quantizes recognition feature parameters from frames of an utterance and an infrastructure based back or far end stage which reverses the vector quantization to recover the feature parameters and subjects the feature parameters to a Hidden Markov Model (HMM) evaluation to obtain a recognition decision for the utterance. In order to conserve network capacity, the size (Sz) of the codebook used for the vector quantization, and the corresponding number of bits (B) per codebook index B, are adapted on a dialogue-by-dialogue basis in relation to the vocabulary size |V| for the dialogue. The adaptation, which is performed at the front end, accomplishes a tradeoff between expected recognition rate RR and expected bitrate RR by optimizing a metric which is a function of both.

Abstract: In a mobile wireless communication system automatic speech recognition is performed in a distributed manner using a mobile station based near or front end stage which extracts and vector quantizes recognition feature parameters from frames of an utterance and an infrastructure based back or far end stage which reverses the vector quantization to recover the feature parameters and subjects the feature parameters to a Hidden Markov Model (HMM) evaluation to obtain a recognition decision for the utterance. In order to conserve network capacity, the size (Sz) of the codebook used for the vector quantization, and the corresponding number of bits (B) per codebook index B, are adapted on a dialogue-by dialogue basis in relation to the vocabulary size |V| for the dialogue. The adaptation is performed at the front end, accomplishes a tradeoff between expected recognition rate RR and expected bitrate BR by optimizing a metric which is a function of both.

Abstract: A configurable distributed speech recognition system comprises a configurable distributed speech recognition protocol, and a configurable distributed speech recognition server. Herein, the configurable distributed speech recognition protocol is used to establish data transmitting format, for a client speech mobile device to packet the speech data and configuration data become a message packet. The configurable distributed speech recognition system receives the message packet from the client speech mobile device, configures its own speech recognition modules and resources according to the configuration data, and then returns a result to the client speech mobile device after completing the speech recognition.