Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: Various embodiments described herein relate to device abuse detection using machine learning. In this regard, a system compares accelerometer data of an electronic device with a plurality of defined accelerometer threshold values to identify a primary abuse event category associated with the electronic device. In response to the primary abuse event category being identified, the system generates a first prediction for a secondary abuse event category associated with the electronic device based on a machine learning technique associated with inertial data of the electronic device, image data generated by the electronic device, and audio data captured by the electronic device. Furthermore, the system transmits the inertial data, the image data and the audio data to a network server device associated with a machine learning service to facilitate generation of a second prediction for the secondary abuse event category based on the inertial data, the image data, and the audio data.

Abstract: A mobile device comprising: a data collection device; a trigger to activate the data collection device; a communication system for wireless communications; a display for displaying information; a processor for controlling software and firmware operation; a keypad for entering data for the processor; a power supply for providing power to the mobile device, the power supply comprising a fuel cell or an ultracapacitor; and a housing for supporting the data collection device, trigger, communication system, display, processor, keypad and power supply.

Abstract: A mobile electronic device includes a microprocessor, a wireless transceiver, a power supply manager, and a controller coupled to a system bus. The power supply manager is configured to operate the mobile device for a predetermined discharge time, and is coupled to a battery for supplying primary power to the mobile electronic device. The power supply manager is also coupled to a fuel cell for supplying auxiliary power. The power supply manager monitors a dynamic capacity discharge rate, which deviates from a baseline capacity discharge rate due to one or more factors. The controller operates the fuel cell responsive to the deviation of the dynamic capacity discharge rate from the baseline capacity discharge rate to restore the predetermined discharge time. In one aspect, the deviation is due to the temperature of the battery.

Abstract: A mobile device comprising: a data collection device; a trigger to activate the data collection device; a communication system for wireless communications; a display for displaying information; a processor for controlling software and firmware operation; a keypad for entering data for the processor; a power supply for providing power to the mobile device, the power supply comprising a fuel cell or an ultracapacitor; and a housing for supporting the data collection device, trigger, communication system, display, processor, keypad and power supply.