Abstract: A facility for automatically establishing measurement location controls for Doppler ultrasound studies is described. The facility receives a first ultrasound image, and user input selecting an anatomical structure appearing in it. The facility performs localization to determine the location of the selected anatomical structure in the initial image, and determines a first placement of measurement location controls relative to the structure. On the ultrasound machine, the facility invokes one or more first Doppler ultrasound modes using the first placement. The facility receives a second ultrasound image produced by the ultrasound machine using the one or more first modes; determines a flow location and direction based on the second ultrasound image; and determines a second placement relative to the flow location. The facility invokes one or more second Doppler ultrasound modes using the second placement, and receives results from the invocation of the one or more second Doppler ultrasound modes.

Abstract: A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

Abstract: A facility for assessing an ultrasound image captured from a patient with a particular depth setting is described. The facility subjects the received ultrasound image to at least one neural network to produce, for each neural network, an inference. On the basis of the produced inferences, the facility determines whether the depth setting at which the ultrasound image was captured was optimal.

Abstract: There is provided a robot fruit picking system comprising an autonomous robot. The system includes a picking arm or other end effector positioning device; and an end effector mounted on the picking arm configured to either cut a fruit or stalk or stem for a specific fruit or bunch of fruits or pluck that fruit or bunch, and to transfer the fruit or bunch to a storage buffer subsystem. The storage buffer subsystem is configured to receive the fruit or bunch, and to transfer the fruit or bunch to a transportation or inspection or storage subsystem.

Abstract: A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

Abstract: A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

Abstract: Systems and methods for automated recording of an ultrasound clip are based on quality scores of ultrasound images in a sequence of ultrasound image frames. An ultrasound imaging system includes a probe for capturing ultrasound images, an image buffer to store a sequence of image frames, a quality buffer to store a sequence of quality scores, and a computing subsystem that automatically records an ultrasound clip when the quality scores in the quality buffer corresponding to a set of contiguous image frames in the image buffer equal or exceed a first quality threshold and the set of contiguous image frames is at least a first predetermined size. Additionally, a smart capture feature automatically records an ultrasound clip including an alternate set of contiguous image frames having quality scores equaling or exceeding a second quality threshold that is less than the first quality threshold and meets a second predetermined size.

Abstract: A machine learning model is described that usable to improve the quality of an ultrasound image captured from a person using a set of ultrasound machine setting values that is collectively sub-optimal. Different versions of the model predict from such a starting ultrasound image either (a) a new set of setting values that can be used to reimage the person to produce a higher-quality ultrasound image, or (b) this higher-quality ultrasound image directly.

Abstract: A facility for automatically establishing measurement location controls for Doppler ultrasound studies is described. The facility receives a first ultrasound image, and user input selecting an anatomical structure appearing in it. The facility performs localization to determine the location of the selected anatomical structure in the initial image, and determines a first placement of measurement location controls relative to the structure. On the ultrasound machine, the facility invokes one or more first Doppler ultrasound modes using the first placement. The facility receives a second ultrasound image produced by the ultrasound machine using the one or more first modes; determines a flow location and direction based on the second ultrasound image; and determines a second placement relative to the flow location. The facility invokes one or more second Doppler ultrasound modes using the second placement, and receives results from the invocation of the one or more second Doppler ultrasound modes.

Abstract: A set of user accounts are maintained, each associated with a corresponding mobile device. Each user account comprises a set of user-identifying features, including at least one of: an image of a user-identifying asset, and information that is derivable from an image of a user-identifying asset. Each user account also comprises the location of the associated mobile device and transactional data, such as payment card data, for performing a transaction. An image of a user-identifying asset is captured at a known service location, and a match is determined with one of the user accounts dependent on image and location similarity. The use of transactional data from the matched user account is therefore authorized, and so service provision at the service location is permitted.

Abstract: The following relates generally to facilitating delivery of prescription medication. In some embodiments, a pharmacy computer system: (i) receives a prescription, (ii) reviews the prescription, and (iii) sends the prescription to an intermediate cloud layer. The intermediate cloud layer may then determine a warehouse to send the prescription to based on at least one criteria. The at least one criteria may comprise at least one of: (i) a health insurance plan; (ii) whether the medication comprises a controlled substance; (iii) whether the medication must be refrigerated during shipping; (iv) a warehouse shipping address; or (v) a fulfillment cap of a warehouse.

Abstract: A facility for assessing an ultrasound image captured from a patient with a particular depth setting is described. The facility subjects the received ultrasound image to at least one neural network to produce, for each neural network, an inference. On the basis of the produced inferences, the facility determines whether the depth setting at which the ultrasound image was captured was optimal.

Abstract: A control system is provided for a diesel particulate filter (DPF) system of a diesel engine configured for operation in an off-highway vehicle. The control system includes a controller configured to receive a signal corresponding to a fill state of the DPF being at or above a first threshold. The controller is configured to selectively induce a parasitic load on the diesel engine to increase an operating temperature of the engine in response to receiving the signal.

Abstract: A facility for processing a medical imaging image is described. The facility applies to the image a first machine learning model trained to recognize a view to which an image corresponds, and a second machine learning model trained to identify any of a set of anatomical features visualized in an image. The facility accesses a list of permitted anatomical features for images corresponding to the recognized view, and filters the identified anatomical features to exclude any not on the accessed list. The facility causes the accessed image to be displayed, overlaid with a visual indication of each of the filtered identified anatomical features.

Abstract: A control system is provided for a diesel particulate filter (DPF) system of a diesel engine configured for operation in an off-highway vehicle. The control system includes a controller configured to receive a signal corresponding to a fill state of the DPF being at or above a first threshold. The controller is configured to selectively induce a parasitic load on the diesel engine to increase an operating temperature of the engine in response to receiving the signal.

Abstract: A duct attachment molded within a cellular foam pad is disposed between an air duct and an internal air flow channel of an automotive seat assembly for increasing the air flow between the air duct and the seat assembly. The duct attachment is insert molded within the foam pad which provides a mechanical connection and a seal between the duct attachment and the foam pad and reduces the potential for air leaks between the duct attachment and the foam pad.

Abstract: A facility for performing patient diagnosis is described. The facility accesses a set of diagnostic signs involved in a diagnostic protocol. Until the presence or absence of each of the diagnostic signs of the set has been identified in ultrasound images of a patient, the facility causes an ultrasound image to be captured from the patient, and applies to the captured ultrasound image a trained machine learning model to identify the presence or absence of one or more diagnostic signs of the set. The facility evaluates the diagnostic protocol with respect to the identified presence or absence of each of the set of diagnostic signs to obtain a preliminary diagnosis, and stores the preliminary diagnosis.

Abstract: A facility for processing a medical imaging image is described. The facility applies to the image a first machine learning model trained to recognize a view to which an image corresponds, and a second machine learning model trained to identify any of a set of anatomical features visualized in an image. The facility accesses a list of permitted anatomical features for images corresponding to the recognized view, and filters the identified anatomical features to exclude any not on the accessed list. The facility causes the accessed image to be displayed, overlaid with a visual indication of each of the filtered identified anatomical features.

Abstract: Automated ultrasound image labeling and quality grading systems and methods are provided. An ultrasound system includes an ultrasound imaging device configured to acquire ultrasound images of a patient. An anatomical structure recognition and labeling module receives the acquired ultrasound images from the ultrasound imaging device, and automatically recognizes anatomical structures in the received ultrasound images. The anatomical structure recognition and labeling module automatically labels the anatomical structures in the images with information that identifies the anatomical structures. The acquired ultrasound images and the labeled anatomical structures are displayed on a display of the ultrasound imaging device.

Abstract: A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.