Abstract: The method for classifying ambiguous objects, including: determining initial labels for an image set; determining N training sets from the initially-labelled image set; training M annotation models using the N training sets; determining secondary labels for each image of the image set using the M trained annotation models; and determining final labels for the image set based on the secondary labels. The method can optionally include training a runtime model using images from the image set labeled with the final labels; and optionally using the runtime model.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: The method for classifying ambiguous objects, including: determining initial labels for an image set; determining N training sets from the initially-labelled image set; training M annotation models using the N training sets; determining secondary labels for each image of the image set using the M trained annotation models; and determining final labels for the image set based on the secondary labels. The method can optionally include training a runtime model using images from the image set labeled with the final labels; and optionally using the runtime model.

Abstract: The method for classifying ambiguous objects, including: determining initial labels for an image set; determining N training sets from the initially-labelled image set; training M annotation models using the N training sets; determining secondary labels for each image of the image set using the M trained annotation models; and determining final labels for the image set based on the secondary labels. The method can optionally include training a runtime model using images from the image set labeled with the final labels; and optionally using the runtime model.

Abstract: A method for targeted heating element control can include: sampling measurements, determining one or more occupied regions based on the measurements, determining one or more food types, controlling appliance operation based on the occupied regions and food types, optionally providing real-time feedback to the user for food placement within the cavity, optionally sampling training measurements, optionally determining a cavity occupancy region classifier, and/or any other suitable elements.

Abstract: A method for foodstuff identification can include: detecting a trigger event; sampling a measurement set; optionally determining candidate measurements for subsequent analysis based on the measurement set; optionally determining a set of food parameter values from the measurements; optionally selecting a food parameter value for use; determining a cooking instruction based on the food parameter value; automatically operating the appliance based on the cooking instructions; optionally determining a foodstuff trajectory relative to the cook cavity; optionally training one or more modules; and/or any other suitable elements.

Abstract: The method for classifying ambiguous objects, including: determining initial labels for an image set; determining N training sets from the initially-labelled image set; training M annotation models using the N training sets; determining secondary labels for each image of the image set using the M trained annotation models; and determining final labels for the image set based on the secondary labels. The method can optionally include training a runtime model using images from the image set labeled with the final labels; and optionally using the runtime model.

Abstract: The method for dirty camera detection including: detecting a first predetermined state change event; sampling a set of cavity measurements; optionally determining a set of features of the set of cavity measurements; determining a class label based on the cavity measurements; optionally verifying the classification; and facilitating use of the appliance based on the classification.

Abstract: Disclosed wireless tunneling system includes two wireless tunneling apparatuses that communicate with each other through the wireless link. A local wireless tunneling apparatus is coupled to a local processing apparatus through a wired connection and a remote wireless tunneling apparatus is coupled to the remote processing apparatus through another wired connection. The two processing apparatuses bi-directionally communicate with each other through the wireless link using the two wireless tunneling apparatuses as if the two processing apparatuses were connected through a wired connection.

Abstract: Key/Value pairs, each comprising a keyword string and an associated value, are extracted automatically from a document image. Each document image has a plurality of pixels with each pixel having a plurality of bits. A first subset of the plurality of bits for each pixel represents information corresponding to the document image. The document image is processed to add information to a second subset of the plurality of bits for each pixel. The information added to the second subset alters the appearance of the document image in a manner that facilitates semantic recognition of textually encoded segments within the document image by a Deep Neural Network (DNN) trained to recognize images within image documents. The DNN detects groupings of text segments within detected spatial templates within the document image. The text segments are mapped to known string values to generate the keyword strings and associated values.

Abstract: A disclosed wireless tunneling system tunnels communications between two processing apparatuses through a wireless link, while maintaining compliance of the communications between the two processing apparatuses with a wired communication protocol. In one embodiment, the wireless tunneling system includes two wireless tunneling apparatuses that communicate with each other through the wireless link. A local wireless tunneling apparatus is coupled to a local processing apparatus through a wired connection and a remote wireless tunneling apparatus is coupled to the remote processing apparatus through another wired connection. In one aspect, the local wireless tunneling apparatus predicts a state of the remote processing apparatus, and mirrors the predicted state of the remote processing apparatus.

Abstract: A disclosed wireless tunneling system tunnels communications between two processing apparatuses through a wireless link, while maintaining compliance of the communications between the two processing apparatuses with at least two different wired communication protocols. In one embodiment, the wireless tunneling system includes two wireless tunneling apparatuses that communicate with each other through the wireless link. A local wireless tunneling apparatus is coupled to a local processing apparatus through a wired connection and a remote wireless tunneling apparatus is coupled to the remote processing apparatus through another wired connection. The two processing apparatuses may communicate with each other through the wireless link using the two wireless tunneling apparatuses as if the two processing apparatuses were connected through wired connections.

Abstract: An optical character recognition system employs a deep learning system that is trained to process a plurality of images within a particular domain to identify images representing text within each image and to convert the images representing text to textually encoded data. The deep learning system is trained with training data generated from a corpus of real-life text segments that are generated by a plurality of OCR modules. Each of the OCR modules produces a real-life image/text tuple, and at least some of the OCR modules produce a confidence value corresponding to each real-life image/text tuple. Each OCR module is characterized by a conversion accuracy substantially below a desired accuracy for an identified domain. Synthetically generated text segments are produced by programmatically converting text strings to a corresponding image where each text string and corresponding image form a synthetic image/text tuple.

Abstract: A disclosed wireless tunneling system determines a suitable configuration of a wireless tunneling apparatus for tunneling communications between two processing apparatuses through a wireless link. Responsive to determining the configuration of the wireless tunneling apparatus, the wireless tunneling system establishes a communication with another wireless tunneling apparatus through the wireless link, while maintaining compliance of the communications between the two processing apparatuses with a wired communication protocol. Moreover, the wireless tunneling apparatus can supply power to or source power from a processing apparatus coupled to the wireless tunneling apparatus through a wired cable.

Abstract: Disclosed wireless tunneling system includes two wireless tunneling apparatuses that communicate with each other through the wireless link. A local wireless tunneling apparatus is coupled to a local processing apparatus through a wired connection and a remote wireless tunneling apparatus is coupled to the remote processing apparatus through another wired connection. The two processing apparatuses bi-directionally communicate with each other through the wireless link using the two wireless tunneling apparatuses as if the two processing apparatuses were connected through a wired connection.

Abstract: A disclosed wireless tunneling system determines a suitable configuration of a wireless tunneling apparatus for tunneling communications between two processing apparatuses through a wireless link. Responsive to determining the configuration of the wireless tunneling apparatus, the wireless tunneling system establishes a communication with another wireless tunneling apparatus through the wireless link, while maintaining compliance of the communications between the two processing apparatuses with a wired communication protocol. Moreover, the wireless tunneling apparatus can supply power to or source power from a processing apparatus coupled to the wireless tunneling apparatus through a wired cable.

Abstract: A disclosed wireless tunneling system tunnels communications between two processing apparatuses through a wireless link, while maintaining compliance of the communications between the two processing apparatuses with a wired communication protocol. In one embodiment, the wireless tunneling system includes two wireless tunneling apparatuses that communicate with each other through the wireless link. A local wireless tunneling apparatus is coupled to a local processing apparatus through a wired connection and a remote wireless tunneling apparatus is coupled to the remote processing apparatus through another wired connection. In one aspect, the local wireless tunneling apparatus predicts a state of the remote processing apparatus, and mirrors the predicted state of the remote processing apparatus.

Abstract: A disclosed wireless tunneling system tunnels communications between two processing apparatuses through a wireless link, while maintaining compliance of the communications between the two processing apparatuses with a wired communication protocol. In one embodiment, the wireless tunneling system includes two wireless tunneling apparatuses that communicate with each other through the wireless link. A local wireless tunneling apparatus is coupled to a local processing apparatus through a wired connection and a remote wireless tunneling apparatus is coupled to the remote processing apparatus through another wired connection. In one aspect, the local wireless tunneling apparatus predicts a state of the remote processing apparatus, and mirrors the predicted state of the remote processing apparatus.

Abstract: A disclosed wireless tunneling system determines a suitable configuration of a wireless tunneling apparatus for tunneling communications between two processing apparatuses through a wireless link. Responsive to determining the configuration of the wireless tunneling apparatus, the wireless tunneling system establishes a communication with another wireless tunneling apparatus through the wireless link, while maintaining compliance of the communications between the two processing apparatuses with a wired communication protocol. Moreover, the wireless tunneling apparatus can supply power to or source power from a processing apparatus coupled to the wireless tunneling apparatus through a wired cable.