Abstract: A driver assist analysis system includes a processing system and a database that stores vehicle data, vehicle operational data, vehicle accident data, and environmental data related to the configuration and operation of a plurality of vehicles with driver assist systems or features. The driver assist analysis system also includes one or more analysis engines that execute on the processing system to determine one or more driving anomalies (e.g., accidents or poor driving operation) based on the vehicle operational data, and that correlate or determine a relationship between the driving anomalies and the operation of the driver assist systems or features. The driver assist analysis system then determines an effectiveness of operation of one or more of the driver assist systems or features as a statistical measure based on the determined relationship and the driver assist analysis system notifies a user or receiver, such as a manufacturer or an insurance company of the statistical measure.

Abstract: Intelligent vehicle repair estimating techniques include an image processing component that extracts image attributes from one or more images of a damaged vehicle, and utilizes the attributes to predict an initial set of parts that are globally-identified. Based on a jurisdiction associated with the damaged vehicle, the initial set of parts is transformed into a set of jurisdictionally-based repairs (e.g., parts, labor operations, time intervals, costs, etc.), which may be included in a draft vehicle repair estimate. An estimate refinement component iteratively modifies/refines the draft estimate using a machine-only loop nested within a larger human-machine loop, where system-generated modifications are incrementally incorporated into the draft within the smaller loop, and user-generated modifications are incrementally incorporated into the draft within the larger loop.

Abstract: Techniques for automatic image tagging and selection at a mobile device include generating a smart image tagging model by first training an initial model based on different angles of image capture of subject vehicles, and then re-training the trained model using weights discovered from the first training and images that have been labeled with additional tags indicative of different vehicle portions and/or vehicle parameters. Nodes that are training-specific are removed from the re-trained model, and the lightweight model is serialized to generate the smart image tagging model. The generated model may autonomously execute at an imaging device to predict respective tags associated with a stream of frames; select, capture and store respective suitable frames as representative images corresponding to the predicted tags; and provide the set of representative images and associated tags for use in determining vehicle damage, insurance claims, and the like.

Abstract: A driver assist design analysis system includes a processing system and a database that stores vehicle data, vehicle operational data, vehicle accident data, and environmental data related to the configuration and operation of a plurality of vehicles with driver assist systems or features. The driver assist design analysis system also includes one or more analysis engines that execute on the processing system to determine one or more driving anomalies (e.g., accidents or poor driving operation) based on the vehicle operational data, and that correlate or determine a statistical relationship between the driving anomalies and the operation of the driver assist systems or features.

Abstract: A method and system for analyzing images of a target vehicle according to a base image model corresponding to the target vehicle. According to certain aspects, the method and system receive a set of images from an electronic device and analyze the set of images to identify the target vehicle and a set of information associated with the target vehicle. The method and system may determine that the set of images does not meet a threshold criteria for further image analysis using a base image model corresponding to the target vehicle. In response, the method and system may generate and transmit a notification to the electronic device indicating that the set of images does not meet the threshold criteria.

Abstract: Techniques for automatic image tagging and selection at a mobile device include generating a smart image tagging model by first training an initial model based on different angles of image capture of subject vehicles, and then re-training the trained model using weights discovered from the first training and images that have been labeled with additional tags indicative of different vehicle portions and/or vehicle parameters. Nodes that are training-specific are removed from the re-trained model, and the lightweight model is serialized to generate the smart image tagging model. The generated model may autonomously execute at an imaging device to predict respective tags associated with a stream of frames; select, capture and store respective suitable frames as representative images corresponding to the predicted tags; and provide the set of representative images and associated tags for use in determining vehicle damage, insurance claims, and the like.

Abstract: An image processing system and method obtains one or more source images in which a damaged vehicle is represented, and performs one or more image processing techniques on the obtained images to determine, predict, estimate, and/or detect damage that has occurred at various locations on the vehicle. Specifically, the system/method generates damage parameter values based on the image-processed images, where each value corresponds to damage at a particular location on the vehicle. The image processing techniques may include generating a composite image of the damaged vehicle, aligning and/or isolating the image, applying convolutional neural network techniques to the image to generate the damage parameter values, etc. Based on the generated values, the image processing system/method determines, predicts, estimates, and/or selects a set of parts that are needed to repair the vehicle and, in some embodiments, orders the parts needed to repair the vehicle.

Abstract: A driver assist analysis system includes a processing system and a database that stores vehicle data, vehicle operational data, vehicle accident data, and environmental data related to the configuration and operation of a plurality of vehicles with driver assist systems or features. The driver assist analysis system also includes one or more analysis engines that execute on the processing system to determine one or more driving anomalies (e.g., accidents or poor driving operation) based on the vehicle operational data, and that correlate or determine a relationship between the driving anomalies and the operation of the driver assist systems or features. The driver assist analysis system then determines an effectiveness of operation of one or more of the driver assist systems or features as a statistical measure based on the determined relationship and the driver assist analysis system notifies a user or receiver, such as a manufacturer or an insurance company of the statistical measure.

Abstract: In-line memory module (IMM) computing nodes with an embedded processor(s) to support local processing of memory-based operations for lower latency and reduced power consumption, and related methods are disclosed. The IMM computing node that includes one or more memory chips mounted on a circuit board. The IMM computing node also includes one or more embedded processor(s) on the circuit board that are each interfaced to at least one memory chip among the one or more memory chips. The processor(s) can be configured to access its interfaced memory chip(s) through an internal memory bus on the circuit board to perform processing onboard the IMM computing node in an offload computing access mode. The embedded processors(s) can also be configured to forward memory access requests received from an external processor to the memory chip(s) for data storage and retrieval in a transparent access mode without further local processing of the memory access requests.

Abstract: A memory system supporting programmable selective access to subsets of parallel-arranged memory chips for efficient memory accesses is disclosed. A memory controller is programmable to selectively control a number of parallel-arranged memory chips in the memory system activated in a grouping for a memory access based on a memory access policy. The memory access policy is based on the number of memory chips to be activated to achieve the desired data line size for a given memory access. This programmability of the memory controller is made possible by separate dedicated chip select lines being coupled to each memory chip. Being able to only activate a subset of the memory chips for a memory access allows conservation of data bus bandwidth and power that would otherwise by consumed by asserting unused data on the data buses.

Abstract: For each well of the plurality of wells, a plurality of model outputs are generated for the well. A range of productivity is determined for the well by plotting the plurality of model outputs in the form of a probability distribution function, projecting an actual production indicator result for the well onto the probability distribution function, comparing the actual production indicator result and the probability distribution function to identify a well production potential for the well, and determining a quality of the well production potential based on a plurality of categorization rules including a lowest quality category and a highest quality category. All of the wells in the plurality of wells are analyzed to identify wells in the highest quality category based on the determined quality of well production potentials for the plurality of wells for hydraulic refracturing.

Abstract: A computer system, comprising: one or more processors; and a memory coupled to the one or more processors, wherein the computer system is programmed with a set of computer-readable instructions configured to implement a method for determining a drill site in a shale, tight gas sands, coal bed methane or other unconventional well field for an oil or gas well and one or more parameter settings therefore, the computer-readable instructions comprising: one or more data mining instructions for data mining of a first part of a set of measurement or objective data parameters from existing oil or gas wells in the well field that, when executed, produce a ranking of each parameter and its respective effect on oil or gas well production from the well field; one or more training instructions that, when executed, produce a model comprising an artificial neural network (ANN) trained with the first part of the set of measurement or objective data parameters and/or the ranking that is useful for producing a group of optimiz

Abstract: A computer system, comprising: one or more processors; and a memory coupled to the one or more processors, wherein the computer system is programmed with a set of computer-readable instructions configured to implement a method for determining a drill site in a shale, tight gas sands, coal bed methane or other unconventional well field for an oil or gas well and one or more parameter settings therefore, the computer-readable instructions comprising: one or more data mining instructions for data mining of a first part of a set of measurement or objective data parameters from existing oil or gas wells in the well field that, when executed, produce a ranking of each parameter and its respective effect on oil or gas well production from the well field; one or more training instructions that, when executed, produce a model comprising an artificial neural network (ANN) trained with the first part of the set of measurement or objective data parameters and/or the ranking that is useful for producing a group of optimiz