Abstract: A computer-implemented method obtains information related to a business process. The computer-implemented method may include (1) capturing business event information for a business event that occurs during the business process; (2) generating a unique identifier configured to identify the business event; (3) translating the business event information into a final event record including the unique identifier, the final event record being configured according to a common data structure; and (4) storing the final event record in a database for analysis. As such, the computer-implemented method permits business information to be captured from disparate business processes and to be stored in a common format to facilitate efficient and productive analysis.

Abstract: A computer-implemented method obtains information related to a business process. The computer-implemented method may include (1) capturing business event information for a business event that occurs during the business process; (2) generating a unique identifier configured to identify the business event; (3) translating the business event information into a final event record including the unique identifier, the final event record being configured according to a common data structure; and (4) storing the final event record in a database for analysis. As such, the computer-implemented method permits business information to be captured from disparate business processes and to be stored in a common format to facilitate efficient and productive analysis.

Abstract: A computer-implemented conversion engine can automatically determine source file formats of a set of source files, and invoke corresponding file converters to convert the source files into output files that are formatted according to the same target file format. If an individual source file is larger than a file size threshold, the conversion engine can divide the source file into smaller component files, separately convert the individual component files into the target file format, and assemble the converted component files into an output file that corresponds to the source file.

Abstract: A wearable, wireless and battery-free continuous health monitor includes an RFID tag used as an On-Off Keying Device to measure the heart rate. The tag's returned signal strength variations are used to calculate the respiration rate. Machine learning algorithms are employed to determine the health parameters.

Abstract: The disclosure features wireless power transmitters configured to detect a radio frequency identification (RFID) tag, the transmitters including a transmitter resonator, a transmitter impedance matching network coupled to the transmitter resonator, an amplifier coupled to the transmitter impedance matching network, a detection subsystem connected to the transmitter resonator, and a controller coupled to the amplifier and to the detection subsystem and configured so that during operation of the transmitter, the controller is configured to control the transmitter resonator, the amplifier, and the impedance matching network to cycle the transmitter between a power transmission mode and a RFID tag detection mode.

Abstract: The disclosure features wireless power transmitters configured to detect a radio frequency identification (RFID) tag, the transmitters including a transmitter resonator, a transmitter impedance matching network coupled to the transmitter resonator, an amplifier coupled to the transmitter impedance matching network, a detection subsystem connected to the transmitter resonator, and a controller coupled to the amplifier and to the detection subsystem and configured so that during operation of the transmitter, the controller is configured to control the transmitter resonator, the amplifier, and the impedance matching network to cycle the transmitter between a power transmission mode and a RFID tag detection mode.

Abstract: A wearable, wireless and battery-free continuous health monitor includes an RFID tag used as an On-Off Keying Device to measure the heart rate. The tag's returned signal strength variations are used to calculate the respiration rate. Machine learning algorithms are employed to determine the health parameters.