Abstract: In variants, the method can include: sampling cavity measurements of the cook cavity; determining an image representation using the cavity measurements; determining a food class based on the image representation; optionally comparing the image representation to prior image representations; optionally determining a new food class based on the image representation; optionally updating a food identification module with the new food class; and optionally determining a cook program associated with the new food class.

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: A method for cavity state determination including: receiving a cook instruction; sampling a set of cavity measurements; determining a cavity state; and selectively operating the cook cavity based on the match.

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: In variants, the method for in-appliance sous vide cooking control can include: determining a thermal model, determining an equilibrium temperature based on the thermal model, facilitating control of a cooking appliance based on the equilibrium temperature, and/or other processes.

Abstract: A method for timer-based cooking can include: heating a cooking cavity to a cooking temperature, detecting an transition event, determining a food classification, determining a cooking time, tracking a food residence time within the cooking cavity, optionally notifying a user when the residence time satisfies the cooking time, and/or any other suitable element.

Abstract: In variants, the method can include: sampling cavity measurements of the cook cavity; determining an image representation using the cavity measurements; determining a food class based on the image representation; optionally comparing the image representation to prior image representations; optionally determining a new food class based on the image representation; optionally updating a food identification module with the new food class; and optionally determining a cook program associated with the new food class.

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: A method for cavity state determination including: receiving a cook instruction; sampling a set of cavity measurements; determining a cavity state; and selectively operating the cook cavity based on the match.

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: Techniques are provided for calculating an average time physical objects spend at a particular location, such as a store. A plurality of entrance times is identified, where each entrance time corresponds to when one or more objects entered the particular location. A plurality of exit times is also identified, where each exit time corresponds to when one or more objects exited the particular location. An average entrance time is determined based on the plurality of entrance times. An average exit time is determined based on the plurality of exit times. An average time people spend at the particular location is determined based on a difference between the average exit time and the average entrance time.

Abstract: Techniques are provided for calculating an average time people spend at a particular location, such as a store. A plurality of entrance times is identified, where each entrance time corresponds to when one or more objects entered the particular location. A plurality of exit times is also identified, where each exit time corresponds to when one or more objects exited the particular location. An average entrance time is determined based on the plurality of entrance times. An average exit time is determined based on the plurality of exit times. An average time people spend at the particular location is determined based on a difference between the average exit time and the average entrance time.

Abstract: Techniques are provided for calculating an average time people spend at a particular location, such as a store. A plurality of entrance times is identified, where each entrance time corresponds to when one or more objects entered the particular location. A plurality of exit times is also identified, where each exit time corresponds to when one or more objects exited the particular location. An average entrance time is determined based on the plurality of entrance times. An average exit time is determined based on the plurality of exit times. An average time people spend at the particular location is determined based on a difference between the average exit time and the average entrance time.