Electronic oven with splatter prevention

1. An electronic oven, comprising:

a chamber;

a microwave energy source coupled to an injection port in the chamber;

a visible light sensor;

an infrared light sensor;

a controller communicatively coupled to the visible light sensor, the infrared light sensor, and the microwave energy source; and

a non-transitory computer-readable medium on the controller that stores instructions to:

generate a preprocessed sensor dataset, wherein the generation of the preprocessed sensor dataset comprises: combining sensor data from the visible light sensor and sensor data from the infrared light sensor;

evaluate, using the preprocessed sensor dataset, the combined sensor data from the visible light sensor and sensor data from the infrared light sensor;

generate a splatter prediction in response to the evaluation of the combined sensor data from the visible light sensor and sensor data from the infrared light sensor; and

decrease a power level of the microwave energy source in response to the splatter prediction.

2. The electronic oven of claim 1 , wherein the instructions to evaluate the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, include instructions to:

identify an item in the chamber using the sensor data from the visible light sensor from the preprocessed sensor dataset;

initialize a classifier based on an identity of the item as obtained during the identifying of the item; and

apply the sensor data from the infrared light sensor from the preprocessed sensor dataset to the classifier.

3. The electronic oven of claim 2 , wherein:

an output of the classifier is a probability of a splatter event occurring in a proximate time period; and

the splatter prediction is generated when the output of the classifier exceeds a threshold.

4. The electronic oven of claim 2 , wherein:

the classifier is a neural network; and

the initializing of the classifier includes initializing a set of weights for the neural network based on the identity of the item.

5. The electronic oven of claim 1 , wherein the instructions to evaluate the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, include instructions to:

identify an item in the chamber using the combined sensor data from the visible light sensor and sensor data from the infrared light sensor from the preprocessed sensor dataset;

initialize a classifier based on an identity of the item as obtained during the identifying of the item; and

apply the sensor data from the infrared light sensor from the preprocessed sensor dataset to the classifier.

6. The electronic oven of claim 1 , wherein the instructions to evaluate the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, include instructions to:

apply the combined sensor data from the infrared light sensor and sensor data from the visible light sensor from the preprocessed sensor dataset to a classifier.

7. The electronic oven of claim 1 , wherein the instructions to evaluate the combined sensor data from the visible light sensor and the sensor data from the infrared light sensor, using the preprocessed sensor dataset, include instructions to:

segment an item in the chamber from a container for the item using at least the sensor data from the infrared light sensor from the preprocessed sensor dataset;

blank a set of pixels in the sensor data from the infrared light sensor from the preprocessed sensor dataset based on the segmenting of the item in the chamber; and

apply the sensor data from the infrared light sensor from the preprocessed sensor dataset, after blanking the set of pixels in the sensor data, to a classifier.

8. The electronic oven of claim 1 , further comprising:

an inverter to modulate an amount of energy emitted by the microwave energy source from a first level to a second level;

wherein the instructions to evaluate the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, include instructions to:

generate an array of the combined sensor data from the visible light sensor and sensor data from the infrared light sensor from the preprocessed sensor dataset;

provide the array to a classifier; and

obtain an output of the classifier, wherein the output of the classifier is a probability of a splatter event occurring in a proximate time period; and

wherein the instructions to decrease the power level of the microwave energy source in response to the splatter prediction include instructions to:

select the second level based on the output of the classifier.

9. The electronic oven of claim 1 , wherein the instructions to decrease the power level of the microwave energy source in response to the splatter prediction include instructions to turn off the microwave energy source.

10. The electronic oven of claim 1 , wherein the instructions to decrease the power level of the microwave energy source in response to the splatter prediction include instructions to gate an AC input to the microwave energy source to thereby reduce a number of AC cycles delivered to a rectifier.

11. A method for heating an item in a chamber of an electronic oven using a microwave energy source, wherein each step is executed by a controller in the electronic oven comprising:

generating a preprocessed sensor dataset, wherein the generation of the preprocessed sensor dataset comprises: combining sensor data from the visible light sensor and sensor data from the infrared light sensor;

evaluating, using the preprocessed sensor dataset, the combined sensor data from a visible light sensor and sensor data from an infrared light sensor, wherein the controller is communicatively coupled to the visible light sensor and the infrared light sensor;

generating a splatter prediction in response to the evaluation of the combined sensor data from the visible light sensor and sensor data from the infrared light sensor; and

decreasing a power level of the microwave energy source in response to the splatter prediction, wherein the controller is also communicatively coupled to the microwave energy source.

12. The method of claim 11 , wherein evaluating the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, includes:

identifying an item in the chamber using the sensor data from the visible light sensor from the preprocessed sensor dataset;

initializing a classifier based on an identity of the item as obtained during the identifying of the item; and

applying the sensor data from the infrared light sensor from the preprocessed sensor dataset to the classifier.

13. The method of claim 12 , wherein:

an output of the classifier is a probability of a splatter event occurring in a proximate time period; and

the splatter prediction is generated when the output of the classifier exceeds a threshold.

14. The method of claim 12 , wherein:

the classifier is a neural network; and

the initializing of the classifier includes initializing a set of weights for the neural network based on the identity of the item.

15. The method of claim 11 , further comprising:

identifying an item in the chamber using the combined sensor data from the visible light sensor and sensor data from the infrared light sensor from the preprocessed sensor dataset;

initializing a classifier based on an identity of the item as obtained during the identifying of the item; and

applying the sensor data from the infrared light sensor from the preprocessed sensor dataset to the classifier.

16. The method of claim 11 , wherein the evaluating of the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, includes:

applying the combined sensor data from the infrared light sensor and sensor data from the visible light sensor from the preprocessed sensor dataset to a classifier.

17. The method of claim 11 , wherein the evaluating of the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, includes:

segmenting an item in the chamber from a container for the item using at least the sensor data from the infrared light sensor from the preprocessed sensor dataset;

blanking a set of pixels in the sensor data from the infrared light sensor from the preprocessed sensor dataset based on the segmenting of the item in the chamber; and

applying the sensor data from the infrared light sensor from the preprocessed sensor dataset, after blanking the set of pixels in the sensor data, to a classifier.

18. The method of claim 11 , wherein:

evaluating the combined sensor data from the visible light sensor and sensor data from the infrared light sensor, using the preprocessed sensor dataset, includes:

generating an array of the combined sensor data from the visible light sensor and sensor data from the infrared light sensor from the preprocessed sensor dataset;

providing the array to a classifier; and

obtaining an output of the classifier, wherein the output of the classifier is a probability of a splatter event occurring in a proximate time period; and

decreasing the power level of the microwave energy source in response to the splatter prediction includes:

selecting a level for an amount of energy emitted by the microwave energy source, as modulated by an inverter, based on the output of the classifier.

19. The method of claim 11 , wherein decreasing the power level of the microwave energy source in response to the splatter prediction include instructions to turn off the microwave energy source.

20. The method of claim 11 , wherein decreasing the power level of the microwave energy source in response to the splatter prediction include instructions to gate an AC input to the microwave energy source to reduce a number of AC cycles delivered to a rectifier.