Connected food preparation system and method of use
A connected oven, including a set of in-cavity sensors and a processor configured to automatically identify foodstuff within the cooking cavity, based on the sensor measurements; and automatically operate the heating element based on the foodstuff identity.
1. A method for controlling operation of a cooking appliance, comprising, at a remote system:
receiving a set of sensor measurements from a set of cooking appliance sensors, wherein the set of sensor measurements includes an image recorded at a camera arranged proximal to a cooking appliance internal cavity and defines a downward-facing field of view configured to image a superior surface of a foodstuff;
determining a set of control instructions for the cooking appliance associated with the set of sensor measurements using a neural network configured to determine a probability for each of a plurality of food classes, wherein the set of control instructions comprises a set of cooking temperatures and a set of cooking times; and
wherein the cooking appliance is configured to operate based on the set of control instructions.
2. The method of claim 1 , wherein the camera is mounted along an upper corner of the cooking appliance internal cavity.
3. The method of claim 2 , wherein the camera comprises a wide angle lens.
4. The method of claim 1 , wherein the set of cooking appliance sensors further comprises a temperature sensor configured to detect a temperature of the oven cavity and a temperature probe configured to detect a temperature of the foodstuff, and wherein the set of sensor measurements further comprises a set of temperatures.
5. The method of claim 1 , wherein the set of control instructions are transmitted to the cooking appliance and are capable of being stored in on-board storage of the cooking appliance before foodstuff insertion into the cooking appliance.
6. The method of claim 1 , further comprising determining a food class, associated with the set of control instructions, based on the set of sensor measurements.
7. The method of claim 6 , wherein the food class is determined using a machine learning model, trained to recognize food classes using the set of sensor measurements as a training set.
8. The method of claim 1 , further comprising training a neural network, configured to recognize food, using the set of sensor measurements as a training set.
9. The method of claim 1 , wherein the set of control instructions comprise at least one of carbon fiber heating element control instructions or convection fan control instructions.
10. The method of claim 1 , wherein the cooking apparatus receives a user instruction from a user device separate and distinct from the cooking apparatus, wherein the cooking apparatus operates based on the user instructions.
11. A system comprising a processing system configured to:
receive a set of sensor measurements from a set of cooking appliance sensors, wherein the set of sensor measurements includes an image recorded at a camera arranged proximal to a cooking appliance internal cavity, wherein the camera defines a downward-facing field of view configured to image a superior surface of a foodstuff;
determine a food class using a neural network configured to determine a probability for each of a plurality of food classes based on the set of sensor measurements;
determine a set of control instructions associated with the food class;
receive the set of control instructions from a remote system; and
operate a cooking appliance based on the set of control instructions.
12. The system of claim 11 , wherein the processing system is part of the cooking appliance.
13. The system of claim 12 , wherein the camera is mounted to a cooking appliance wall and arranged along an upper corner of the cooking appliance internal cavity with the field of view directed frontward, the system further comprising:
carbon fiber heating elements;
convection fans; and
a touchscreen mounted to a window of a cooking appliance door.
14. The system of claim 11 , wherein the processing system comprises a neural network, trained to determine the food class based on sensor measurements.
15. The system of claim 11 , wherein the processing system is further configured to operate the cooking appliance based on manually-entered instructions.
16. The system of claim 11 , wherein the set of control instructions are received at the cooking appliance before receiving the set of sensor measurements.
17. A method for controlling operation of a cooking appliance, comprising:
receiving a set of sensor measurements from a set of sensors mounted to the cooking appliance, wherein the set of measurements includes an image recorded at a camera mounted to a wall of an internal cavity of the cooking appliance;
determining a food class, based on the sensor measurements, using a neural network configured to determine a probability for each of a plurality of food classes;
determining a set of control instructions based on the food class;
receiving the set of control instructions from a remote system at the cooking appliance;
operating the cooking appliance based on the set of control instructions; and
receiving a second set of sensor measurements sampled during an operation of the cooking appliance.
18. The method of claim 17 , wherein the set of control instructions are received at the cooking appliance before receiving the set of sensor measurements.
19. The method of claim 11 , wherein the food class is determined using a machine learning model, trained to recognize food classes using the set of sensor measurements as a training set.
20. The method of claim 17 , further comprising receiving a series of inputs input by a user at a touchscreen of the cooking appliance, wherein the set of control instructions is further determined based on the series of inputs.
21. The method of claim 20 , wherein the series of inputs comprises a food class from the plurality of food classes.
22. The method of claim 17 , further comprising receiving an input from a user at a client application executing on a user device separate and distinct from the cooking appliance, wherein the set of control instructions is determined based on the input.
23. The method of claim 11 , wherein the set of control instructions comprises a set of cooking temperatures and a set of cooking times.
24. The method of claim 17 , wherein the set of control instructions comprises a set of cooking temperatures and a set of cooking times.
25. The method of claim 1 , wherein the set of control instructions are automatically generated based on the set of sensor measurements.