Abstract: Embodiments described include apparatuses, computer program products, and methods for automatic proof of delivery, for example by capturing and decoding a visual indicia. Some embodiments minimize possibilities of errors, false proof, and/or the like in item delivery. At least one embodiment enables automatic proof of delivery in a multi-sensor environment. Example embodiments are configured for capturing an image data object set utilizing at least one of an image sensor set, where the image data object set includes a captured-indicia image data object including a visual indicia; decoding the visual indicia to identify delivery information; identifying a delivery proof image data object from the image data object set; and processing the delivery proof image data object for use as a proof of delivery. Some embodiments include a near-field image sensor for capturing the delivery proof image data object and a far-field sensor for capturing the captured-indicia image data object.

Abstract: A method for alerting a user on an electronic device is described. The method includes identifying a signal at an ultrasound transmission frequency received at a first electronic device from a second electronic device. Further, the method includes receiving timing information corresponding to an exchange of an ultrasound transmission between the first electronic device and the second electronic device. Furthermore, a first distance value between the first electronic device and the second electronic device is computed by performing a time of flight estimation using the timing information. Furthermore, the first distance value is refined by using an output of a machine learning model to determine a second distance value. Furthermore, the method includes generating an alert based on the comparison of the second distance value with a predefined threshold distance value. In some examples, the alert can be indicative of a violation of a physical distancing norm in an environment.

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 disclose a method for tracking assets. Method includes determining one or more locations of an asset within an indoor environment based on metadata associated an RF signal received from an RF tag associated with the asset. The method further includes identifying a first set of locations of the one or more locations. The method further includes identifying a second set of locations of the one or more locations, wherein the second set of locations corresponds to uncalibrated locations of the asset within indoor environment. Additionally, the method includes receiving a third set of locations of the asset. Furthermore, the method includes training a machine learning (ML) model based on the first set of locations, the second set of locations, and the third set of locations, and the metadata associated with the RF signal. The ML model predicts a fourth set of locations of another asset.

Abstract: A method for alerting a user on an electronic device is described. The method includes identifying a signal at an ultrasound transmission frequency received at a first electronic device from a second electronic device. Further, the method includes receiving timing information corresponding to an exchange of an ultrasound transmission between the first electronic device and the second electronic device. Furthermore, a first distance value between the first electronic device and the second electronic device is computed by performing a time of flight estimation using the timing information. Furthermore, the first distance value is refined by using an output of a machine learning model to determine a second distance value. Furthermore, the method includes generating an alert based on the comparison of the second distance value with a predefined threshold distance value. In some examples, the alert can be indicative of a violation of a physical distancing norm in an environment.

Abstract: Embodiments of the present disclosure provide for automatic item searching and verification. In example contexts, embodiments provide such functionality to improve the accuracy and efficiency of item searching during item loading and/or unloading, such as when delivering packages. Some embodiments provide for capturing an image, or set of images, identifying relevant items represented in the captured images, generating augmented reality elements associated with such relevant items, and rendering the augmented reality elements in an augmented reality environment, which can be maintained as the user continues to perform user action. Some embodiments additionally or alternatively detect improper actions based on captured image(s), and generate and provide notifications to a user for viewing.

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 described include apparatuses, computer program products, and methods for automatic proof of delivery, for example by capturing and decoding a visual indicia. Some embodiments minimize possibilities of errors, false proof, and/or the like in item delivery. At least one embodiment enables automatic proof of delivery in a multi-sensor environment. Example embodiments are configured for capturing an image data object set utilizing at least one of an image sensor set, where the image data object set includes a captured-indicia image data object including a visual indicia; decoding the visual indicia to identify delivery information; identifying a delivery proof image data object from the image data object set; and processing the delivery proof image data object for use as a proof of delivery. Some embodiments include a near-field image sensor for capturing the delivery proof image data object and a far-field sensor for capturing the captured-indicia image data object.

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: The system and process described herein will utilize an application configured in an image capturing system such as a handheld terminal to detect and mark a plurality of barcodes in the field of view of the terminal and show the details to the operator. The application will guide the operator through text and/or voice to configure the terminal to read the intended barcode labels. For reading multiple barcodes on an object, the application will provide various barcode sequencing configurations support. The application may suggest sequencing criteria based on past configuration options.

Abstract: The system and process described herein will utilize an application configured in an image capturing system such as a handheld terminal to detect and mark a plurality of barcodes in the field of view of the terminal and show the details to the operator. The application will guide the operator through text and/or voice to configure the terminal to read the intended barcode labels. For reading multiple barcodes on an object, the application will provide various barcode sequencing configurations support. The application may suggest sequencing criteria based on past configuration options.

Abstract: A GUI is described, which is operable on mobile device touchscreens. The GUI has a programmable scan zone, placed over a first portion of the touchscreen, operable on the touchscreen for receiving a first input at an instance in time, and invoking one or more functions of the mobile device, based on a user programmed context, programmably in response to that received input. The GUI also comprises a configurable virtual trigger icon placed over a second portion of the touchscreen with an area smaller than the first. The icon is operable for receiving a second input and, based on a user configured selection or context, triggering responsively a corresponding action related to the one or more functions of the mobile device. The mobile device functions include applications, tools, macros or menus related to collecting or accessing visible graphic and other data (e.g., barcodes, images, RFID/NFC tags).

Abstract: There is set forth herein an indicia reading apparatus having a plurality of configurations that can be activated with use of a manually actuated multiple state trigger. According to a first configuration the indicia reading apparatus can project a light pattern while maintaining in an inactive state decoding operations for attempting to decode a decodable indicia by processing of image data. According to a second configuration the indicia reading apparatus can activate decoding operations.