Patent Application
20230290149
This disclosure relates to an electronic apparatus and a controlling method thereof, and more particularly, to an electronic apparatus using a cooking history and a controlling method thereof.
Recently, various types of electronic apparatuses have been developed and distributed. In particular, various types of electronic apparatuses that help cook food in home kitchens are being actively developed and distributed.
In an electronic apparatus, such as an oven, when a user sets a cooking temperature and a cooking time, it may be difficult to check the state of a cooking object in the middle of an operation of the oven, and it may be difficult to change the cooking temperature and the cooking time in the middle of the operation once they are set.
Therefore, a user who is inexperienced in oven manipulation may frequently have the problem that the cooking object is overcooked and/or the cooking object is not properly cooked.
There has been a demand for a method for predicting and providing a cooking result to the user before operating the oven.
Provided are an electronic apparatus for cooking a cooking object in consideration of a cooking history, context information on the cooking object and user preference information and a controlling method thereof.
According to an aspect of the disclosure, an electronic apparatus includes a memory storing instructions, a plurality of cooking histories, and a plurality of cooking objects, and a processor configured to execute the instructions to, based on identification information regarding the plurality of cooking objects, identify a cooking object corresponding to the identification information from among the plurality of cooking objects; based on cooking setting information corresponding to a user input, identify a cooking history corresponding to the cooking setting information from among the plurality of cooking histories; obtain a cooking prediction result corresponding to the cooking history; and provide, to a user of the electronic apparatus, information regarding the cooking prediction result.
The processor may be further configured to execute the instructions to provide, to the user, guide information for adjusting the cooking setting information based on at least one of context information regarding the cooking object and user preference information. The context information regarding the cooking object may comprise at least one of a weight of the cooking object, temperature information of an interior of a cooking chamber, and state information of the cooking object.
The guide information may comprise a recommended adjustment range of at least one of a cooking time and a cooking temperature comprised by the cooking setting information.
The processor may be further configured to execute the instructions to receive a user command indicating a selection of a degree of cooking or roasting regarding the cooking object; and obtain the user command as the user preference information.
The electronic apparatus may further comprise a temperature sensor configured to sense a temperature of the cooking object, and the processor may be further configured to execute the instructions to identify, based on the temperature of the cooking object, at least one of temperature information of an interior of the cooking chamber and the state information of the cooking object.
The electronic apparatus may further comprise a communication interface, and the processor may be further configured to execute the instructions to receive, via the communication interface, information regarding whether a specific function has been executed in an external device; and obtain, based on the information regarding whether the specific function has been executed, the context information regarding the cooking object. The specific function may comprise at least one of a pre-cooking function, a freezing function, and a defrosting function regarding the cooking object.
The processor may be further configured to execute the instructions to identify, using a neural network model, the cooking prediction result by inputting the cooking object and the cooking setting information to the neural network model. The neural network model may be trained to output the cooking prediction result regarding the cooking object based on at least one of a cooking time and a cooking temperature comprised by the cooking setting information.
The electronic apparatus may further comprise a camera, and the processor may be further configured to execute the instructions to obtain an image capturing an interior of a cooking chamber through the camera; and obtain the identification information regarding the cooking object based on the image.
The processor may be further configured to execute the instructions to, based on a cooking command being received, cook the cooking object based on the cooking setting information; obtain a cooking result regarding the cooking object after the cooking of the cooking object is completed; and add the cooking result to the plurality of cooking histories.
The electronic apparatus may further comprise a camera, and the processor may be further configured to execute the instructions to obtain an image capturing an interior of a cooking chamber through the camera after the cooking of the cooking object is completed, and obtain the cooking result regarding the cooking object by analyzing the image.
The memory may further store recipe information corresponding to each of the plurality of cooking objects, and the processor may be further configured to execute the instructions to, based on the cooking history corresponding to the cooking setting information from among the plurality of cooking histories not being identified, obtain the recipe information corresponding to the cooking object; and provide, to the user, the recipe information.
According to an aspect of the disclosure, a controlling method of an electronic apparatus, includes: based on identification information regarding a plurality of cooking objects, identifying a cooking object corresponding to the identification information from among the plurality of cooking objects; based on cooking setting information corresponding to a user input, identifying a cooking history corresponding to the cooking setting information from among a plurality of cooking histories; obtaining a cooking prediction result corresponding to the cooking history; and providing, to a user of the electronic apparatus, information regarding the cooking prediction result.
The method may further include providing, to the user, guide information for adjusting the cooking setting information based on at least one of context information regarding the cooking object and user preference information. The context information regarding the cooking object may comprise at least one of a weight of the cooking object, temperature information of an interior of a cooking chamber, and state information of the cooking object.
The guide information may comprise a recommended adjustment range of at least one of a cooking time and a cooking temperature comprises by the cooking setting information.
The method may further include receiving a user command indicating a selection of a degree of cooking or roasting regarding the cooking object; and obtaining the user command as the user preference information.
The method may further include identifying, based on a temperature of the cooking object, at least one of temperature information of an interior of the cooking chamber and the state information of the cooking object.
The method may further include identifying, using a neural network model, the cooking prediction result by inputting the cooking object and the cooking setting information to the neural network model. The neural network model may be trained to output the cooking prediction result regarding the cooking object based on at least one of a cooking time and a cooking temperature comprised by the cooking setting information.
The method may further include obtaining, using a camera of the electronic apparatus, an image capturing an interior of a cooking chamber; and obtaining the identification information regarding the cooking object based on the image.
The method may further include, based on a cooking command being received, cooking the cooking object based on the cooking setting information; obtaining a cooking result regarding the cooking object after the cooking of the cooking object is completed; and adding the cooking result to the plurality of cooking histories.
The method may further include, based on the cooking history corresponding to the cooking setting information from among the plurality of cooking histories not being identified, obtaining recipe information corresponding to the cooking object; and providing, to the user, the recipe information.
The above and other aspects, features, and advantages of certain embodiments of the disclosure will be more apparent from the following description taken in conjunction with the accompanying drawings, in which:
General terms that are currently widely used were selected as terms used in embodiments of the disclosure in consideration of functions in the disclosure, but may be changed depending on the intention of those skilled in the art or a judicial precedent, an emergence of a new technique, and the like. In addition, in a specific case, terms arbitrarily chosen by an applicant may exist. In this case, the meaning of such terms will be mentioned in detail in a corresponding description portion of the disclosure. Therefore, the terms used in the disclosure should be defined on the basis of the meaning of the terms and the contents throughout the disclosure rather than simple names of the terms.
In the disclosure, an expression “have”, “may have”, “include”, “may include”, or the like, indicates an existence of a corresponding feature (e.g., a numerical value, a function, an operation, a component such as a part, or the like), and does not exclude an existence of an additional feature.
The expression “at least one of A and/or B” should be understood to represent either “A” or “B” or any one of “A and B.”
Expressions such as “first,” or “second,” used in the disclosure may modify various components regardless of order and/or importance, and are used to distinguish one component from another component, and do not limit the corresponding components.
When it is mentioned that any component (e.g., a first component) is (operatively or communicatively) coupled with/to or is connected to another component (e.g., a second component), it is to be understood that any component is directly coupled to another component or may be coupled to another component through another component (e.g., a third component).
Singular expressions include plural expressions unless the context clearly indicates otherwise. It should be further understood that the term “include” or “constituted” used in the application specifies the presence of features, numerals, steps, operations, components, parts mentioned in the specification, or combinations thereof, but do not preclude the presence or addition of one or more other features, numerals, steps, operations, components, parts, or combinations thereof.
In the disclosure, a ‘module’ or a ‘unit’ may perform at least one function or operation, and be implemented by hardware or software or be implemented by a combination of hardware and software. In addition, a plurality of ‘modules’ or a plurality of ‘units’ may be integrated in at least one module and be implemented as at least one processor except for a ‘module’ or an ‘unit’ that needs to be implemented by specific hardware.
In the disclosure, a term “user” may be a person or a device (e.g., an artificial intelligence electronic device) that uses an electronic apparatus.
Hereinafter, various embodiments of the disclosure are described with reference to the accompanying drawings.
An electronic apparatus 100 illustrated in
The electronic apparatus 100 includes a main body 10 forming an exterior. Alternatively or additionally, the electronic apparatus 100 includes a cooking chamber 11 for accommodating a cooking object (or cooked food, food, foodstuff, etc.) and a door 12 for opening and closing the cooking chamber 11.
The cooking chamber 11 may refer to a space for accommodating a cooking object (e.g., an accommodation space). The front of the cooking chamber 11 may be opened and closed by the door 12 connected to the main body 10.
Alternatively or additionally, a heater for heating a cooking object may be provided in the cooking chamber 11. In this case, the heater may be an electric heater comprising an electric resistor. However, the heater is not limited to the electric heater, and it may be a gas heater that generates heat by burning gas.
In some embodiments, a control panel may be disposed on the upper portion of the main body 10. The control panel may display various operation information of the electronic apparatus 100 and include a touch-type display that receives a user command and a user input for controlling the operation of the electronic apparatus 100. However, the control panel is not limited thereto, and it may include a plurality of buttons that receive a user command for controlling the operation of the electronic apparatus 100.
The electronic apparatus 100, according to an embodiment, may cook a cooking object located in the cooking chamber 11 based on cooking setting information corresponding to a user input. The cooking setting information may include at least one of a cooking time and a cooking temperature.
In some embodiments, the electronic apparatus 100 may identify a cooking object located in the cooking chamber 11, and predict a cooking result when cooking the identified cooking object according to cooking setting information. For example, if the cooking object is cooked according to cooking setting information corresponding to a user input, the cooking result showing to which degree the cooking object is boiled or roasted may be predicted, and the predicted cooking result may be provided to a user. Hereinafter, a method of predicting a cooking result by the electronic apparatus 100 is described according to various embodiments of the disclosure.
Referring to
The memory 110, according to an embodiment, may store data necessary for various embodiments of the disclosure. The memory 110 may be implemented in a form of a memory embedded in the electronic apparatus 100 or in a form of a memory attachable to and/or detachable from the electronic apparatus 100, depending on a data storing purpose.
For example, data for driving the electronic apparatus 100 may be stored in the memory embedded in the electronic apparatus 100, and data for an extension function of the electronic apparatus 100 may be stored in the memory attachable to and detachable from the electronic apparatus 100. Meanwhile, the memory embedded in the electronic apparatus 100 may be implemented by at least one of a volatile memory (e.g., a dynamic RAM (DRAM), a static RAM (SRAM), or a synchronous dynamic RAM (SDRAM)), a non-volatile memory (e.g., a one-time programmable ROM (OTPROM), a programmable ROM (PROM), an erasable and programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a mask ROM, a flash ROM, a flash memory (e.g., a NAND flash or a NOR flash), a hard drive, or a solid state drive (SSD). Alternatively or additionally, the memory attachable to and detachable from the electronic apparatus 100 may be implemented by a memory card (e.g., a compact flash (CF), a secure digital (SD), a micro secure digital (Micro-SD), a mini secure digital (Mini-SD), an extreme digital (xD), or a multi-media card (MMC)), an external memory (e.g., a USB memory) connectable to a USB port, or the like.
The memory 110, according to an embodiment, may store a computer program including at least one instruction or instructions for controlling the electronic apparatus 100.
In some embodiments, the memory 110 stores a cooking history corresponding to each of a plurality of cooking objects. The cooking object may be referred to as cooked food, food, foodstuff, etc., but it will be collectively referred to as a cooking object for convenience of explanation.
The cooking history, according to an embodiment, may include a cooking result regarding a cooking object after cooking the cooking object. For example, the cooking history may include a cooking result of the cooking object (e.g., a result of a degree of cooking or roasting) according to a cooking time or a cooking temperature. The cooking history stored in the memory 110 may include a cooking result obtained after cooking by the electronic apparatus 100, as well as, a cooking result obtained after cooking by another electronic apparatus 100′ (not shown), which is received through an external server, etc. (not shown). The detailed description thereof is provided with reference to
The memory 110, according to an embodiment, may store recipe information corresponding to each of the plurality of cooking objects. The recipe information may refer to a standard recipe regarding a cooking object, rather than a cooking result of the electronic apparatus 100. For example, a recipe corresponding to a cooking object stored in the memory 110 may refer to any one of recipes included in a search result of the cooking object.
The processor 120, according to an embodiment, performs the overall control operations of the electronic apparatus 100. Specifically, the processor 120 performs the function of controlling the overall operations of the electronic apparatus 100.
The processor 120 may be implemented as a digital signal processor (DSP) for processing digital signals, a microprocessor, or a time controller (TCON), but is not limited thereto. The processor 120 may include at least one of a central processing unit (CPU), a micro controller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), a graphics-processing unit (GPU), a communication processor (CP) or an ARM processor, or may be defined as the corresponding term. Further, the processor 120 may be implemented as a System on Chip (SoC) integrated with a processing algorithm, a large scale integration (LSI), or in the form of a field programmable gate array (FPGA). Alternatively or additionally, the processor 120 may perform various functions by executing computer executable instructions stored in the memory 130.
In particular, when identification information regarding cooking objects is obtained, the processor 120 may identify a cooking object corresponding to the identification information from among a plurality of cooking objects.
Subsequently, when cooking setting information corresponding to a user input is obtained, the processor 120 may identify a cooking history corresponding to the cooking setting information from among a plurality of cooking histories stored in the memory 110. Then, the processor 120 may obtain a cooking prediction result corresponding to the identified cooking history and provide information regarding the obtained cooking prediction result.
The detailed description thereof is provided with reference to
Referring to
The food recognition module 121 may be configured to identify a cooking object based on identification information regarding the cooking object. The identification information regarding the cooking object may refer to an image photographing the inside of the cooking chamber 11, which is received from a camera provided in the electronic apparatus 100 or an image photographing the inside of the cooking chamber 11, which is received from an external device (e.g., a camera provided in the external device). However, this is only an example, and the disclosure is not limited thereto. For example, the identification information regarding the cooking object may include information regarding the cooking object selected through a control panel, etc. (e.g., a menu or a food name).
The cooking prediction module 122 may be configured to obtain a cooking history corresponding to the cooking object identified through the food recognition module 121 from among cooking histories corresponding to a plurality of cooking objects, respectively, stored in the memory 110.
The cooking prediction module 122 may be configured to identify a cooking history corresponding to cooking setting information from among the plurality of cooking histories corresponding to the cooking objects. The cooking setting information may include at least one of a cooking time or a cooking temperature.
The cooking prediction module 122 may identify a cooking history corresponding to cooking setting information and then provide the identified cooking history. The detailed description thereof is provided with reference to a sequence view of
Referring to
The food recognition module 121, according to an embodiment, may obtain identification information regarding a cooking object by analyzing the image capturing the inside of the cooking chamber 11 and identify a cooking object corresponding to the identification information from among a plurality of cooking objects (S420).
Subsequently, the cooking prediction module 122 may obtain a cooking history corresponding to a cooking object and cooking setting information from among a plurality of cooking histories 1 based on the cooking object and the cooking setting information received from the food recognition module 121 (S430, S440).
For example, if a cooking object received from the food recognition module 121 is ‘a potato’, the cooking prediction module 122 may obtain cooking histories corresponding to ‘the potato’ from among a plurality of cooking histories. Subsequently, if cooking setting information is a cooking time of 30 minutes and a cooking temperature of 140° C., the cooking prediction module 122 may obtain a cooking history corresponding to the cooking setting information from among cooking histories corresponding to ‘the potato.’
Subsequently, the cooking prediction module 122 may obtain a cooking result that is predicted when cooking the cooking object according to the cooking setting information based on a cooking result (e.g., the degree of boiling, roasting, etc.) included in the cooking history (S450).
Then, the cooking prediction module 122 may provide the cooking prediction result (S460). For example, the cooking prediction module 122 may provide the cooking prediction result (e.g., ‘potatoes may burn’, ‘potatoes are likely to be boiled properly’, etc.) through a display or may provide the cooking prediction result through a speaker.
The specific description regarding cooking histories stored in the memory 110, according to an embodiment, is provided with reference to
Referring to
When the cooking object identified through the food recognition module 121 is ‘chicken’, the cooking prediction module 122, according to an embodiment, may obtain cooking histories corresponding to ‘chicken.’
Subsequently, the cooking prediction module 122 may obtain a cooking result corresponding to cooking setting information (e.g., 60 minutes, 210° C.) from among the cooking histories. For example, when the cooking result obtained by the cooking prediction module 122 is ‘5’ (e.g., the cooking object (or food) is burnt), the cooking prediction module 122 may obtain and provide a predicted cooking result (e.g., ‘chicken’ is burnt) when the cooking object (e.g., ‘chicken’) is cooked according to the cooking setting information (e.g., 60 minutes, 210° C.).
The specific numbers in
In the above-described example, it is assumed that the cooking prediction module 122 obtains and provides a cooking history corresponding to a cooking object and cooking setting information identified in a cooking history corresponding to each of a plurality of cooking objects. However, this is only an example, and the disclosure is not limited thereto. For example, the cooking prediction module 122 may obtain and provide a cooking prediction result corresponding to a cooking object and cooking setting information identified using a neural network model.
Among the steps illustrated in
The cooking prediction module 122 of the processor 120, according to an embodiment, may be implemented as a cooking prediction neural network model 122′.
The cooking prediction neural network model 122′ may be a model trained to, when a cooking object identified through the food recognition module 121 and cooking setting information corresponding to a user input are input, output a cooking prediction result regarding the cooking object.
For example, the cooking prediction neural network model 122′ may be a model trained to output a cooking prediction result regarding the cooking object based on at least one of a cooking time or a cooking temperature included in the cooking setting information.
The cooking prediction module 122 described in
Meanwhile, the cooking prediction neural network model 122′ may be a model trained to output a cooking prediction result regarding a cooking object when at least one of the cooking object, a cooking time or a cooking temperature is input with a cooking history corresponding to the cooking object and recipe information corresponding to the cooking object as learning data.
In the disclosure, training a neural network model may refer to creating a predefined operation rule or an artificial intelligence model that is set to perform a desired characteristic (or purpose) as a basic artificial intelligence model (e.g., an artificial intelligence model including an arbitrary random parameter) is trained by a learning algorithm using a plurality of learning data. Such learning may be conducted in a separate server and/or system but is not limited thereto. For example, the training may be conducted in the electronic apparatus 100. Examples of the learning algorithm may include supervised learning, unsupervised learning, semi-supervised learning, transfer learning or reinforcement learning, but is not limited thereto.
Each neural network model may be implemented as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), Deep Q-Networks, etc., but is not limited thereto.
The processor 120 for executing a neural network model, according to an embodiment, may be implemented as a general-purpose processor such as a CPU, an AP, a Digital Signal Processor (DSP), etc., a graphics-only processor such as a GPU and a Vision Processing Unit (VPU), or a combination of an AI-only processor such as an NPU and software. The processor 120 may be controlled to process input data according to a predefined operation rule or a neural network model stored in the memory 110. Alternatively or additionally, when the processor 120 is a dedicated processor (and/or an AI-only processor), it may be designed with a hardware structure specialized for processing a specific neural network model. For example, hardware specialized for processing a specific neural network model may be designed as a hardware chip such as an ASIC, FPGA, etc. When the processor 120 is implemented as a dedicated processor, it may be configured to include a memory for implementing an embodiment of the disclosure or may be configured to include a memory processing function for using an external memory.
When a cooking object and cooking setting information identified through the food recognition module 121 are input, the cooking prediction neural network model 122′, according to an embodiment, may output a cooking prediction result regarding the cooking object (S450′).
Referring to
Subsequently, the processor 120 may obtain a cooking result regarding the cooking object after cooking is completed. For example, when cooking is completed, the processor 120 may control the camera 200 to photograph the cooking object located in the cooking chamber 11 (S710).
The cooking analysis module 123 of the processor 120 may obtain an image capturing the inside of the cooking chamber 11 through the camera 200 and obtain a cooking result regarding the cooking object by analyzing the obtained image (S720). The obtained image may refer to cooking result information.
The cooking analysis module 123, according to an embodiment, may identify a degree of boiling, roasting, etc. of the cooking object based on the cooking setting information. For example, the cooking analysis module 123 may obtain at least one of a plurality of degree of cooking values (e.g., -5 to 5) as the degree of cooking or roasting as illustrated in
Subsequently, the cooking analysis module 123 may add the obtained degree of cooking or roasting to a plurality of cooking histories 1 stored in the memory 110 (S740). For example, the cooking analysis module 123 may map a cooking object (e.g., ‘chicken’), cooking setting information (e.g., 10 minutes, 200° C.), a degree of cooking or roasting (e.g., undercooked: ‘-2’) and add the same to the plurality of cooking histories 1.
Referring to
Among the steps illustrated in
The cooking prediction module 122 may obtain a cooking history corresponding to a cooking object and cooking setting information from among the plurality of cooking histories 1 based on the cooking object and the cooking setting information received from the food recognition module 121 (S430, S440).
Alternatively or additionally, if a cooking history corresponding to the cooking setting information is not identified from among the plurality of cooking histories 1, the cooking prediction module 122 may obtain recipe information corresponding to the cooking object from among recipe information 2 corresponding to a plurality of cooking objects based on the cooking object and the cooking setting information (S445-1, S445-2).
For example, when the cooking object received from the food recognition module 121 is ‘steak’, the cooking prediction module 122 may obtain cooking histories corresponding to ‘steak’ from among a plurality of cooking objects. Subsequently, when the cooking setting information is 20 minutes of cooking time and 170° C. of cooking temperature, the cooking prediction module 122 may obtain a cooking history corresponding to the cooking setting information from among the cooking histories corresponding to ‘steak.’
Subsequently, when cooking histories corresponding to ‘steak’ are not identified or cooking histories corresponding to the cooking setting information (e.g., 20 minutes of cooking time and 170° C. of cooking temperature) are not identified from among the cooking histories corresponding to ‘steak’, the cooking prediction module 122 may obtain recipe information corresponding to ‘steak.’ For example, the cooking prediction module 122 may obtain and provide recipe information close to the cooking setting information (e.g., 20 minutes of cooking time and 170° C. of cooking temperature) from among recipe information corresponding to ‘steak.’
Referring to
If some embodiments, if the cooking object identified through the food recognition module 121 is ‘chicken’, for example, the cooking prediction module 122, according to an embodiment, may obtain recipe information corresponding to ‘chicken.’
Subsequently, the cooking prediction module 122 may obtain recipe information corresponding to cooking setting information (e.g., 50 minutes, 180° C.) from among the recipe information. For example, when the recipe information obtained by the cooking prediction module 122 is ‘0’ (e.g., the cooking object (or food) is properly cooked), the cooking prediction module 122 may obtain and provide a cooking result (e.g., ‘chicken’ is properly cooked) that is predicted when the cooking object (e.g., ‘chicken’) is cooked according to the cooking setting information (e.g., 50 minutes, 180° C.) based on the recipe information.
The specific numbers in
In some embodiments, the cooking prediction module 122 may obtain and provide a cooking time and a cooking temperature for cooking a cooking object of a certain weight (e.g., 1.6 kg) according to a degree of cooking (or roasting) selected by a user input. The degree of cooking (or roasting) selected according to a user input may be collectively referred to as user preference information.
For example, the cooking prediction module 122 may receive a weight of a cooking object through a weight measurement sensor provided in the electronic apparatus 100, and identify a cooking time and a cooking temperature for a cooking result of the cooking object to correspond to user preference information. For another example, in response to a user input of ‘chicken’ of 1.6 kg, the cooking prediction module 122 may obtain and provide a cooking time (e.g., 50 minutes) and a cooking temperature (e.g., 190° C.) for cooking the cooking object according to a degree of cooking (e.g., slightly overcooked ‘1’) based on recipe information.
Referring to
The cooking prediction module 122 of the processor 120 may be configured to obtain a cooking prediction result (S1010)(same as S460). Subsequently, the cooking prediction module 122 may receive context information or user preference information regarding a cooking object through a user setting or from the external device 300 (S1020).
The context information regarding a cooking object may include at least one of a weight of the cooking object, temperature information of an interior of the cooking chamber 11 or state information of the cooking object.
Subsequently, the cooking prediction module 122 may provide guide information for adjusting cooking setting information based on at least one of the context information of the cooking object or the user preference information (S1030).
The guide information may include a recommended adjustment range regarding a cooking time or a recommended adjustment range regarding a cooking temperature included in the cooking setting information.
For example, upon receiving that a cooking object is properly cooked (e.g., the degree of cooking is ‘0’) as user preference information after the cooking prediction module 122 provides a cooking prediction result that the cooking object is slightly overcooked (e.g., the degree of cooking ‘2’) in the step of S1010 (S460 of
In another example, when the cooking object is in a frozen state or the temperature inside the cooking chamber 11 is less than a threshold temperature according to context information after the cooking prediction module 122 provides a cooking prediction result that the cooking object is slightly overcooked (e.g., the degree of cooking ‘2’) in the step of S1010 (S460 of
In another example, when the cooking object is in a high temperature state (e.g., in a state after being primed or in a pre-cooked state) or the temperature inside the cooking chamber 11 is equal to or greater than a threshold temperature after the cooking prediction module 122 provides a cooking prediction result that the cooking object is moderately cooked (e.g., the degree of cooking ‘0’) in the step of S1010 (S460 of
The electronic apparatus 100 may further include a temperature sensor, and the processor 120 may identify at least one of temperature information of an interior of the cooking chamber 11 or state information of a cooking object based on the temperature sensed through the temperature sensor.
When receiving cooking setting information adjusted by a user after providing guide information (S1040), the cooking prediction module 122 may obtain and provide a cooking prediction result through S1050 to S1080 as illustrated in
Referring to
The electronic apparatus 100, according to an embodiment, may further include a communication interface.
The communication interface, according to an embodiment, performs communication with an external device to receive various types of data and information. For example, the communication interface may receive various types of data and information from home appliances (e.g., display apparatus, air conditioner, air purifier, etc.), external storage media (e.g., USB memory), external server (e.g., web hard drive) and the like through a communication method such as AP-based Wi-Fi (e.g., Wireless LAN Network), Bluetooth, Zigbee, wired/wireless Local Area (LAN), Wide Area Network (WAN), Ethernet, IEEE 1394, High-Definition Multimedia Interface (HDMI), Universal Serial Bus (USB), Mobile High-Definition Link (MHL), Audio Engineering Society/European Broadcasting Union (AES/EBU), Optical, Coaxial, etc.
In particular, the communication interface, according to an embodiment, may perform communication with at least one external device (e.g., refrigerator, induction microwave oven, etc.) and may receive information regarding a function performed in the external device, context information of a cooking object, etc.
Referring to
In another example, when a cooking object is identified inside the cooking chamber 11 within a threshold time after information indicating that the door of an external device (e.g., a refrigerator or a freezer) has been opened or closed is received through a communication interface (S1020A), the processor 120 may provide a UI (e.g., 1110 or 1120) for confirming the state information of the cooking object (e.g., whether the cooking object is refrigerated or frozen).
In another example, when a cooking object is identified inside the cooking chamber 11 within a threshold time after information indicating that an external device (e.g., an induction or a microwave oven) has been operated through a communication interface (S1020B), the processor 120 may provide a UI (e.g., 1110 or 1120) for confirming the state information of the cooking object (e.g., whether it is pre-cooked).
In another example, when a cooking object is identified inside the cooking chamber 11 within a threshold time after information indicating that an external device (e.g., a microwave oven) has executed a defrosting function through a communication interface (not shown), the processor 120 may provide a UI (e.g., 1110 or 1120) for confirming the state information of the cooking object (e.g., whether it is defrosted).
However, the above embodiments are only examples, and the processor 120 may provide a UI (e.g., 1110 or 1120) for confirming the state information of the cooking object (e.g., whether it is frozen, refrigerated, defrosted, or in a room-temperature state) even if information regarding whether a specific function has been executed in an external device is not received through a communication interface (S1020C).
Alternatively or additionally, referring to
Referring to
For example, in order to cook a cooking object to a degree of cooking corresponding to user preference information, upper and lower limit ranges of a cooking temperature setting may be set, and upper and lower limit ranges of a cooking time setting may be set.
However, when a user command to view all possible cooking temperature setting values or cooking time setting values is received, all possible cooking temperatures and cooking times can be provided without providing guide information.
Referring to
For example, a UI including ‘when cooked at a temperature of 220° C. and a time of 300 minutes (degree of cooking ‘5’), it may be completely burnt. Would you like to continue cooking?’, and selectable start and back buttons may be displayed.
As the user is provided with a cooking prediction result regarding a cooking object, there is an effect that the electronic apparatus 100 can be controlled to cook the cooking object to a desired degree of roasting without a failure process.
Referring back to
The camera may include a lens and an image sensor. The type of lens may include a general-purpose lens, a wide-angle lens, a zoom lens, etc., and it may be determined according to the type, characteristics, use environment, etc. of the electronic apparatus 100. As for the image sensor, a Complementary Metal Oxide Semiconductor (CMOS), a Charge Coupled Device (CCD), etc. may be implemented.
A controlling method 1300 of an electronic apparatus including a cooking history and recipe information corresponding to each of a plurality of cooking objects, according to an embodiment, includes, when identification information regarding a cooking object is obtained, a cooking object corresponding to the obtained identification information is identified from among a plurality of cooking objects (S1310).
When cooking setting information corresponding to a user input is obtained, a cooking history corresponding to the cooking setting information is identified from among a plurality of cooking histories stored in a memory (S1320).
A cooking prediction result corresponding to the identified cooking history is obtained and information regarding the obtained cooking prediction result is provided (S1030).
The controlling method 1300, according to an embodiment, may further include providing guide information for adjusting cooking setting information based on at least one of context information or user preference information regarding a cooking object, and the content information regarding a cooking object may include at least one of a weight of the cooking object, temperature information of an interior of a cooking chamber, or state information of the cooking object.
The guide information may be information including a recommended adjustment range of at least one of a cooking time or a cooking temperature included in the cooking setting information.
The controlling method 1300, according to an embodiment, may further include receiving a user command for selecting a degree of cooking or roasting regarding a cooking object and obtaining the received user command as user preference information.
The controlling method 1300, according to an embodiment, may further include identifying at least one of temperature information of an interior of a cooking chamber or state information of a cooking object based on a temperature sensed by a temperature sensor provided in an electronic apparatus.
The controlling method 1300, according to an embodiment, may further include, when information regarding whether a specific function is executed in an external device is received, obtaining context information regarding a cooking object, and the specific function may include at least one of a pre-cooking function, a freezing function or a defrosting information regarding the cooking object.
The step of S1330 in which information regarding a cooking prediction result, according to an embodiment, is provided may include identifying a cooking prediction result by inputting a cooking object and cooking setting information to a neural network model, and the neural network model may be a model trained to output a cooking prediction result regarding the cooking object based on at least one of a cooking time or a cooking temperature included in the cooking setting information.
The step of S1310 in which a cooking object, according to an embodiment, is identified may include obtaining an image capturing an interior of a cooking chamber and obtaining identification information regarding a cooking object based on the image.
The controlling method 1300, according to an embodiment, may include, when a cooking command is received, cooking a cooking object based on cooking setting information, obtaining a cooking result regarding the cooking object after cooking is completed and adding the obtained cooking result to a cooking history.
The various embodiments of the disclosure may be applied not only to electronic apparatuses but also to various types of electronic apparatuses.
The above-described embodiments may be implemented in a recording medium that is readable by a computer or a similar device using software, hardware or a combination thereof. In some cases, the embodiments described in the disclosure may be implemented by a processor itself. According to software implementation, the embodiments such as procedures and functions described in the disclosure may be implemented by separate software modules. Each of the software modules may perform one or more functions and operations described in the disclosure.
The computer instructions for performing the processing operation of the electronic apparatus according to the above-described embodiments of the disclosure may be stored in a non-transitory computer-readable medium. The computer instructions stored in such a non-transitory computer-readable medium may cause a specific device to perform the processing operation of the electronic apparatus 100 according to the above-described various embodiments when they are executed by the processor of the specific device.
The non-transitory computer readable medium is not a medium that stores data for a while, such as a register, a cache, a memory, or the like, but means a medium that semi-permanently stores data and is readable by an apparatus. Specifically, the non-transitory readable medium may include a compact disc (CD), a digital versatile disc (DVD), a hard disc, a Blu-ray disc, a universal serial bus (USB), a memory card, a read only memory (ROM), or the like.
Although the embodiments of the disclosure have been illustrated and described hereinabove, the disclosure is not limited to the specific embodiments described above, but may be variously modified by those skilled in the art to which the disclosure pertains without departing from the gist of the disclosure as disclosed in the accompanying claims. These modifications should also be understood to fall within the scope and spirit of the disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2022-0005318 | Jan 2022 | KR | national |
This application is a continuation application of International Application No. PCT/KR2023/000531, filed on Jan. 11, 2023, which claims priority to Korean Patent Application No. 10-2022-0005318, filed on Jan. 13, 2022, in the Korean Intellectual Property Office, the disclosures of which are incorporated by reference herein in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/KR2023/000531 | Jan 2023 | WO |
| Child | 18127449 | US |
