Abstract: A job box includes a base defining a storage area, a lid coupled to the base and moveable between an open position, in which the storage area is accessible, and a closed position, in which the storage area in not accessible, and a charging station supported by the base. The station includes a first charging port configured to charge a battery pack of a first type, a second charging port configured to charge a battery pack of a second type, a light, and a first power outlet. The charging station is moveable relative to the job box to a plurality of different positions.

Abstract: Historical device positioning data captured from one or more devices over a period of time is received. The historical device positioning data includes historical latitude, longitude, and elevation data of the one or more devices. Building boundaries for a give building are identified based upon the historical latitude and longitude data. The historical device positioning data corresponding to locations within the building boundaries of the building is clustered using a machine learning-based clustering algorithm, resulting in clusters with corresponding cluster centroids. The cluster centroids are associated with respective floors within the building. A current floor of the building on which a specific device is located is determined by mapping current device positioning data of the specific device to the closest cluster centroid.

Abstract: A job box includes a base defining a storage area, a lid coupled to the base and moveable between an open position, in which the storage area is accessible, and a closed position, in which the storage area in not accessible, and a charging station supported by the base. The station includes a first charging port configured to charge a battery pack of a first type, a second charging port configured to charge a battery pack of a second type, a light, and a first power outlet. The charging station is moveable relative to the job box to a plurality of different positions.

Abstract: A job box comprises a base defining a storage area, a lid coupled to the base and moveable between an open position, in which the storage area is accessible, and a closed position, in which the storage area in not accessible, and a light supported by the base or the lid and configured to illuminate the storage area.

Abstract: Known geospatial device coordinates are clustered using a clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including geographic location and elevation, are received. An approximate current floor upon which the first device is located is determined using the elevation of the first device and the annotated regional floor-height value of a closest cluster centroid, the closest cluster centroid determined based, at least in part, on the geographic location of the first device. An individual is directed to the device's geographic location and approximate current floor. The device clusters are re-computed based upon feedback from the individual regarding the device's actual floor.

Abstract: Using a location sensor, a location including a latitude and a longitude of a device is determined. Using an air pressure sensor, a first elevation of a device is determined. Using the location, a corresponding street address is determined. Using the street address and the first elevation, a floor number is determined. The street address and the floor number are reported to an emergency response system.

Abstract: At an emergency response system, a first location including a latitude and a longitude at which a first device is located is received. At an emergency response system, an air pressure reading measured by the first device is received. Using the air pressure reading and a mean sea-level barometric measurement, a first elevation of a device is computed. Using the first location, a corresponding street address is computed. Using the street address and the first elevation, a floor number is computed. The street address and the floor number are transmitted to a second device.

Abstract: Known geospatial device coordinates are clustered using a machine learning-based clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including latitude, longitude, and elevation, are received, and a geographic location of the device is determined using the latitude and longitude. An approximate current floor upon which the first device is located is determined by mapping the current device data of the first device to a closest cluster centroid, and calculating the approximate current floor using the elevation of the first device and the annotated regional floor-height value of the closest cluster centroid. An individual is directed to the device's geographic location and approximate current floor.

Abstract: At an emergency response system, a first location including a latitude and a longitude at which a first device is located is received. At an emergency response system, an air pressure reading measured by the first device is received. Using the air pressure reading and a mean sea-level barometric measurement, a first elevation of a device is computed. Using the first location, a corresponding street address is computed. Using the street address and the first elevation, a floor number is computed. The street address and the floor number are transmitted to a second device.

Abstract: Historical device positioning data captured from one or more devices over a period of time is received. The historical device positioning data includes historical latitude, longitude, and elevation data of the one or more devices. Building boundaries for a give building are identified based upon the historical latitude and longitude data. The historical device positioning data corresponding to locations within the building boundaries of the building is clustered using a machine learning-based clustering algorithm, resulting in clusters with corresponding cluster centroids. The cluster centroids are associated with respective floors within the building. A current floor of the building on which a specific device is located is determined by mapping current device positioning data of the specific device to the closest cluster centroid.

Abstract: Known geospatial device coordinates are clustered using a clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including geographic location and elevation, are received. An approximate current floor upon which the first device is located is determined using the elevation of the first device and the annotated regional floor-height value of a closest cluster centroid, the closest cluster centroid determined based, at least in part, on the geographic location of the first device. An individual is directed to the device's geographic location and approximate current floor. The device clusters are re-computed based upon feedback from the individual regarding the device's actual floor.

Abstract: Known geospatial device coordinates are clustered using a machine learning-based clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including latitude, longitude, and elevation, are received, and a geographic location of the device is determined using the latitude and longitude. An approximate current floor upon which the first device is located is determined by mapping the current device data of the first device to a closest cluster centroid, and calculating the approximate current floor using the elevation of the first device and the annotated regional floor-height value of the closest cluster centroid. An individual is directed to the device's geographic location and approximate current floor.

Abstract: A processor-implemented method, system, and/or computer program product detects illicit activities in micro-commerce transactions. An historic transaction pattern of known legitimate past micro-commerce transactions by a cohort is identified. A current transaction pattern for a current set of multiple micro-commerce transactions is also identified. The current transaction pattern and the historic transaction pattern are compared. In response to determining that the current transaction pattern deviates from the historic transaction pattern beyond a predetermined level, a determination is made that the current set of multiple micro-commerce transactions are being used for money laundering.

Abstract: Using a location sensor, a location including a latitude and a longitude of a device is determined. Using an air pressure sensor, a first elevation of a device is determined. Using the location, a corresponding street address is determined. Using the street address and the first elevation, a floor number is determined. The street address and the floor number are reported to an emergency response system.

Abstract: At an emergency response system, a first location including a latitude and a longitude at which a first device is located is received. At an emergency response system, an air pressure reading measured by the first device is received. Using the air pressure reading and a mean sea-level barometric measurement, a first elevation of a device is computed. Using the first location, a corresponding street address is computed. Using the street address and the first elevation, a floor number is computed. The street address and the floor number are transmitted to a second device.

Abstract: A job box comprises a base defining a storage area, a lid coupled to the base and moveable between an open position, in which the storage area is accessible, and a closed position, in which the storage area in not accessible, and a light supported by the base or the lid and configured to illuminate the storage area.

Abstract: A visual inspection device includes a housing with a support portion and a grip portion extending from the support portion, a flexible cable having a first end coupled to the housing and a second end, a camera assembly coupled to the second end of the flexible cable and operable to transmit image data through the flexible cable, and a display movably coupled to the support portion of the housing and operable to present an image derived from the image data.

Abstract: A reciprocating power tool has a shoe that can be adjusted while the reciprocating power tool is in use. The reciprocating power tool has a main body, a first grip, a second grip moveable with respect to the main body, and a shoe for engaging a workpiece. The shoe is coupled to the second grip such that the shoe moves with the second grip for adjustment of a position of the shoe when the second grip is moved with respect to the main body.

Abstract: The present invention applies gaming theory and well-understood sales processes and techniques to allow the operator of an interactive sales medium to control what is displayed to a user of the medium in a manner that signals their intentions (e.g., looking for a lower price, looking for a particular incentive, etc.) so that the “strategies” being used by the consumer can be identified and exploited to lead the consumer to a desired end choice. In particular, upon identification of the strategies being used by the consumer, incentives (e.g., gradually increasing rewards and/or decreasing “punishments”) are presented to the consumer in such a way that the margins achieved by an eventual sale are slowly decreased with each presentation of the incentives to the consumer. Since the presentation of each incentive increases the likelihood the consumer will make a purchase, margins are maximized for the seller.

Abstract: Traditional methods of prediction of the likelihood of churning in a given market such as wireless telecommunications are improved by inclusion of social network analysis to predict churning by members of a population in response to churning by any given individual member of that population. A sphere of influence of individual members of the population on other members of the population are modeled and analyzed through a plurality of degrees of separation and quantitatively evaluated by a number of relational algebraic joins in which at least one such join includes weights to assert a diminishing influence with number of degrees of separation.