Abstract: A mining vehicle monitoring and control system includes at least one sensor that is located in the vehicle. The sensor collects real-time data for at least one condition of the vehicle. A server is located remote from the vehicle and a first transmission means transmits the collected data to the server. At least one control parameter is loaded onto the server and a comparison means in the server compares the collected data to the control parameter and generates at least one vehicle condition recommendation based on the comparison. A display device is located in the vehicle and is visible to a driver of the vehicle. A second transmission means transmits the vehicle condition recommendation from the server to the display device. The driver of the vehicle is able to see the vehicle condition recommendation and adjust the operating conditions of the vehicle according to the recommendation.

Abstract: Information extraction methods for use in extracting values from unstructured documents for predetermined or user-specified attributes into structured databases are provided herein. Methods include (a) automatically training machine learning models for extracting values from unstructured documents such that the values of the attributes are known for those training documents but the locations of the values in the documents are not known, (b) making a sustained connection between structured databases and unstructured documents so that the data across those two types of data stores can be cross-referred by the users any time, (c) a graphical interface specialized for rich user feedback to rapidly adapt and improve the machine learning models.

Abstract: A mining vehicle monitoring and control system includes at least one sensor that is located in the vehicle. The sensor collects real-time data for at least one condition of the vehicle. A server is located remote from the vehicle and a first transmission means transmits the collected data to the server. At least one control parameter is loaded onto the server and a comparison means in the server compares the collected data to the control parameter and generates at least one vehicle condition recommendation based on the comparison. A display device is located in the vehicle and is visible to a driver of the vehicle. A second transmission means transmits the vehicle condition recommendation from the server to the display device. The driver of the vehicle is able to see the vehicle condition recommendation and adjust the operating conditions of the vehicle according to the recommendation.

Abstract: Information extraction methods for use in extracting values from unstructured documents for predetermined or user-specified attributes into structured databases are provided herein. Methods include (a) automatically training machine learning models for extracting values from unstructured documents such that the values of the attributes are known for those training documents but the locations of the values in the documents are not known, (b) making a sustained connection between structured databases and unstructured documents so that the data across those two types of data stores can be cross-referred by the users any time, (c) a graphical interface specialized for rich user feedback to rapidly adapt and improve the machine learning models.

Abstract: A probabilistic framework for acoustic-phonetic automatic speech recognition organizes a set of phonetic features into a hierarchy consisting of a broad manner feature sub-hierarchy and a fine phonetic feature sub-hierarchy. Each phonetic feature of said hierarchy corresponds to a set of acoustic correlates and each broad manner feature of said broad manner feature sub-hierarchy is further associated with a corresponding set of acoustic landmarks. A pattern recognizer is trained from a knowledge base of phonetic features and corresponding acoustic correlates. Acoustic correlates are extracted from a speech signal and are presented to the pattern recognizer. Acoustic landmarks are identified and located from broad manner classes classified by the pattern recognizer. Fine phonetic features are determined by the pattern recognizer at and around the acoustic landmarks. The determination of fine phonetic features may be constrained by a pronunciation model.

Abstract: A probabilistic framework for acoustic-phonetic automatic speech recognition organizes a set of phonetic features into a hierarchy consisting of a broad manner feature sub-hierarchy and a fine phonetic feature sub-hierarchy. Each phonetic feature of said hierarchy corresponds to a set of acoustic correlates and each broad manner feature of said broad manner feature sub-hierarchy is further associated with a corresponding set of acoustic landmarks. A pattern recognizer is trained from a knowledge base of phonetic features and corresponding acoustic correlates. Acoustic correlates are extracted from a speech signal and are presented to the pattern recognizer. Acoustic landmarks are identified and located from broad manner classes classified by the pattern recognizer. Fine phonetic features are determined by the pattern recognizer at and around the acoustic landmarks. The determination of fine phonetic features may be constrained by a pronunciation model.