Abstract: A multi-modal meter of a vehicle obtains information from multiple sources to determine the most accurate values of motion parameters of the vehicle. The multi-modal meter obtains data describing motion of a vehicle from various sources including an on-board diagnostics (OBD) and global positioning system (GPS.) The dynamically evaluates the signal sources for their accuracy as the vehicle travels. The multi-modal meter selects different signal sources for different portions of a ride and uses the data from the selected signal sources to determine the most accurate motion parameters. The multi-modal meter use machine learning techniques to generate metadata used by an engine configured to determine the most accurate values of motion parameters of the vehicle.

Abstract: A multi-modal meter of a vehicle obtains information from multiple sources to determine the most accurate values of motion parameters of the vehicle. The multi-modal meter obtains data describing motion of a vehicle from various sources including an on-board diagnostics (OBD) and global positioning system (GPS.) The dynamically evaluates the signal sources for their accuracy as the vehicle travels. The multi-modal meter selects different signal sources for different portions of a ride and uses the data from the selected signal sources to determine the most accurate motion parameters. The multi-modal meter use machine learning techniques to generate metadata used by an engine configured to determine the most accurate values of motion parameters of the vehicle.

Abstract: A multi-modal meter of a vehicle obtains information from multiple sources to determine the most accurate values of motion parameters of the vehicle. The multi-modal meter obtains data describing motion of a vehicle from various sources including an on-board diagnostics (OBD) and global positioning system (GPS.) The dynamically evaluates the signal sources for their accuracy as the vehicle travels. The multi-modal meter selects different signal sources for different portions of a ride and uses the data from the selected signal sources to determine the most accurate motion parameters. The multi-modal meter use machine learning techniques to generate metadata used by an engine configured to determine the most accurate values of motion parameters of the vehicle.

Abstract: The present disclosure provides a method for exchanging contact information, comprising detecting a first exchange event on a first mobile electronic device of a first user and initiating the exchange of contact information between the first mobile electronic device and a second mobile electronic device of another user that has detected a second exchange event. Next, contact information of the first user is transmitted from the first mobile electronic device to the second mobile electronic device and contact information of the second user is received at the first mobile electronic device.

Abstract: A method for contact management comprises retrieving, using a mobile electronic device of a user, contact information from multiple external sources. The method next comprises storing said contact information in a memory location of said mobile electronic device. Next, the method comprises determining with a computer processor whether contact information from a portion of said multiple sources at least partially overlaps with contact information from a remainder of said multiple sources. The method next comprises either merging the contact information to generate a consolidated contact record or splitting the contact information to generate split contact records based on the determining the overlap. And then the method comprises storing said consolidated contact record or split contact records in a memory location of said mobile electronic device.

Abstract: A method for contact management comprises retrieving, using a mobile electronic device of a user, contact information from multiple external sources. The method next comprises storing said contact information in a memory location of said mobile electronic device. Next, the method comprises determining with a computer processor whether contact information from a portion of said multiple sources at least partially overlaps with contact information from a remainder of said multiple sources. The method next comprises either merging the contact information to generate a consolidated contact record or splitting the contact information to generate split contact records based on the determining the overlap. And then the method comprises storing said consolidated contact record or split contact records in a memory location of said mobile electronic device.

Abstract: A multimode personal communication system including at least one personal communication device (PCD) that communicates via wireless transmitter and receiver with at least one multiplexing communication device (MCD) for transmitting and receiving voice communication with local and remote persons via conventional telephone mode, wireless telephone mode, wireless data mode, and/or dataline data mode, based upon the PCD user's proximity to the at least one MCD and the least cost provider or other predetermined protocol for the particular voice communication. A method of using the system is also included in the present invention.

Abstract: A facility is provided for locating a file in a backup memory, in which each of the components forming a hierarchical pathname identifying the file is converted into a unique identity using a minimum of bytes, and in which the location of the file in backup memory is determined as a function of each such identity rather than the component names forming the hierarchical pathname.