Wayne Fueling Systems LLC

The Heil Co.

Vehicle Service Group, LLC

Sys-tech Solutions, Inc.

Delaware Capital Formation, Inc.

Atish Kamble

Prem Swaroop

Bodhayan Dev

Matthias Voigt

Philip Nicholas Hill

Michael L Soborski

Sreedhar Patnala

Richard Buteau

Krystian Kulig

Konstantin Streltsov

Girish Juneja

Michael Soborski

Vijay Karthick Baskar

In one aspect, data characterizing a fuel storage facility can be received from a sensor in operable communication with the fuel storage facility. An estimate of meter drift of a flow meter of a fuel dispenser in fluid communication with the fuel storage facility can be determined based on the received data. The estimate of meter drift can be determined based on at least one predictive model that predicts whether a calibration parameter characterizing a calibration of the flow meter has deviated from a predetermined flow meter calibration parameter. The estimate of meter drift can be provided.

Real-time determination of meter drift via loss qualification and quantification

Methods, systems, and apparatus, including medium-encoded computer program products, for glare mitigation techniques include: obtaining images containing a representation of a mark, the images comprising multiple poses of the mark and generating a single image from the images that contain a representation of the mark with reduced glare when compared to the images comprising multiple poses of the mark. The single image is provided for processing of the representation of the mark to identify information associated with the mark.

Glare mitigation techniques in symbologies

Systems and methods for detecting hail damage on a vehicle are described including, receiving an image of at least a section of a vehicle. Detecting a plurality of hail damage including, detecting a plurality of damaged areas distributed over the entire section of the vehicle, and differentiating the plurality of damaged areas from one or more areas of noise, processing the received image to classify one or more sections of the vehicle as one or more panels of the vehicle bodywork, and using the detected areas of damage, the classification of the seriousness of the damage and the classification of one or more panels to compute a panel damage density estimate.

Damage detection using machine learning

Data characterizing a fuel storage facility can be received from one or more of a plurality of sensors disposed in the fuel storage facility. A fuel leak prediction for the fuel storage facility can be determined by a server, based on the received data, and further based on at least one predictive model that predicts whether a fuel leak exists in the fuel storage facility. The fuel leak prediction can be provided by the server. Related apparatus, systems, methods, techniques, and articles are also described.

Fuel leak determination via predictive modeling

Methods, systems, and apparatus, including medium-encoded computer program products, for generating synthetic images, include: obtaining a captured image of a symbol that encodes data; producing a synthetic image from the captured image using a trained machine learning model, wherein the trained machine learning model has been trained using first images and second images of examples of symbols, wherein the second images have a second image quality that is different than a first image quality of first images, and wherein parameters of the machine learning model have been adjusted responsive to image features to tradeoff content loss versus style loss using measurements of information content correlation between third images produced during training and each of the first images and the second images, the measurements being from a comparison metric; and providing the synthetic image for use by a program configured to identify information in images of symbols.

Synthetic image generation using artificial intelligence

Among other things, the techniques described herein include a method for receiving a plurality of images of one or more containers while the one or more containers are being emptied, the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images; and optimizing the machine learning model by performing learning against the validation set, the optimized machine learning model being used to generate a prediction for a new image of a container, the prediction indicating whether the container in the new image was overfilled prior to the new container being emptied.

Identifying overfilled containers

In one aspect, fuel inventory data characterizing a level of fuel stored at a fuel storage facility can be received from a sensor in operable communication with the fuel storage facility. Flow meter data characterizing an amount of fuel dispensed from the fuel storage facility by a fuel dispenser nozzle can be received from a flow meter in fluid communication with the fuel dispenser nozzle and the fuel storage facility. Fuel transaction data characterizing the dispensing of fuel by the fuel dispenser nozzle and the dispensing of the fuel by one or more additional fuel dispenser nozzles in fluid communication with the fuel storage facility can be received. An estimate of meter drift characterizing a change in calibration of the flow meter can be determined based on the fuel inventory data, the flow meter data, and the fuel transaction data. The estimate of meter drift can be provided.

Preventive maintenance of fuel dispensers through inventory reconciliation and identification of meter drift

An automated wetstock management system can include a plurality of sensors disposed in a fuel storage facility, the plurality of sensors configured to sense fuel data characterizing one or more aspects of the fuel storage facility, and a wetstock management server communicatively coupled to the plurality of sensors. The wetstock management server can process the fuel data to detect whether the fuel data satisfies an exception indicative of an operational issue of the fuel storage facility based on one or more predefined rules or models stored in the wetstock management server. In some embodiments, the wetstock management server can generate a workflow for assisting a user of the fuel storage facility to resolve the operational issue. In some embodiments, the wetstock management server can assign a risk category to the exception and electronically transmit an alert characterizing the operational issue to the user.

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Arlington, MA, United States of America

Atish Kamble