METHOD AND SYSTEM FOR CAVITY STATE DETERMINATION

1 . A method for cooking cavity control, comprising:

receiving a pre-heat instruction associated with an expected cavity state;

capturing a sensor measurement for a the cooking cavity of an appliance;

determining a belief cavity state using the sensor measurement;

determining whether the belief cavity state matches the expected cavity state;

when the belief cavity state matches the expected cavity state, operating the appliance according to the pre-heat instruction; and

when the belief cavity state does not match the expected cavity state, not operating the appliance according to the pre-heat instruction.

2 . The method of claim 1 :

wherein operating the appliance according to the pre-heat instruction, comprises operating the appliance in a high-power mode;

wherein the method further comprises operating the appliance in a second mode with a lower power output than the high-power mode when the belief cavity state does not match the expected cavity state.

3 . The method of claim 2 , wherein a minimum power output in the high-power mode is 1800 watts.

4 . The method of claim 1 , wherein the belief cavity state matches the expected cavity state when a confidence score for an empty class label determined for the belief cavity state is higher than other confidence scores for food class labels.

5 . The method of claim 1 , further comprising:

while operating the appliance according to the pre-heat instruction, detecting a foodstuff insertion event; and

in response to the foodstuff insertion event, automatically operating the appliance in a second mode with a lower power output than a power output associated with the preheat instruction.

6 . The method of claim 1 , wherein the sensor measurement is captured using an image sensor.

7 . The method of claim 1 , wherein the pre-heat instruction is received from a remote device.

8 . The method of claim 1 , wherein the sensor measurement is part of a sensor measurement set, wherein each sensor measurement of the sensor measurement set is captured by a different sensor, and wherein each sensor measurement of the sensor measurement set is associated with a different belief state vote, and wherein the belief cavity state is determined by a majority vote of the belief state votes.

9 . The method of claim 1 , wherein the belief cavity state is determined using a trained neural network.

10 . The method of claim 9 , wherein the trained neural network is trained to determine a plurality of classes, wherein the plurality of classes comprises food classes and a cavity state class.

11 . The method of claim 10 , wherein the plurality of classes further comprises camera state classes.

12 . The method of claim 9 , wherein the trained neural network is trained on images depicting an empty cavity, wherein the images are captured by a dirty camera.

13 . The method of claim 1 , wherein the method is performed onboard the appliance.

14 . A system for cavity state determination, comprising:

an appliance, comprising a sensor set and a cook cavity;

a processor, configured to:

a) receive a pre-heat instruction and secondary operation instructions, wherein the pre-heat instruction is associated with an empty cavity state;

b) determine a cavity state of the cook cavity using a sensor measurement of the cook cavity, captured by the sensor set;

c) operate the appliance according to the pre-heat instruction when the cavity state matches the empty cavity state; and

d) operate the appliance according to the secondary operation instructions when the cavity state does not match the empty cavity state.

15 . The system of claim 14 , further comprising selectively sending a user notification based on a prior cavity state in combination with the sensor measurement.

16 . The system of claim 14 , wherein the appliance comprises variable control heating elements that are operable in at least an off mode, a medium mode, and a maximum mode, wherein the maximum mode operates the heating elements at a minimum power output of 1800 watts.

17 . The system of claim 14 , wherein determining the cavity state comprises classifying the sensor measurement as one of a set of predetermined classes using a trained neural network, wherein the set of predetermined classes comprises an empty cavity state.

18 . The system of claim 17 , wherein the set of predetermined classes further comprises food classes.

19 . The system of claim 18 , wherein the secondary operation instructions are specific to a food class, wherein the processor operates the appliance according to the secondary operation instructions when the sensor measurement is classified with a food class associated with the secondary operation instructions.

20 . The system of claim 14 , wherein the processor is further configured to:

detect a state change event; and

repeat b)-d) when the state change event is detected.