Patent Description:
When food is heated for purposes such as cooking, if heating continues without continuous monitoring, the food may be overheated and thus evaporate, and in a serious case, a fire may be caused. Accordingly, technical attempts have been made to sense when the temperature rises excessively, and to automatically control or stop operation of a heater in such instance.

In <CIT>, entitled "Apparatus and method for controlling safety cooking appliance," a method for reducing the amount of heating when food boils over by installing a CCD camera on a main body of the heater to photograph an image of the food to be cooked, and analyzing the photographed image, is described. In order to implement the technology disclosed in the above-described document, installation of a separate CCD camera so that the heater may be viewed from the top is necessary, and there is a limitation in that considerable processing resources are required to analyze the photographed image.

In <CIT>, entitled "An apparatus and method to predict and detect a fire on cooking ranges" described is a method for warning a user when a warning time is reached by calculating a warning time based on a temperature variation pattern according to a temperature sensing value received through a remote temperature sensor for sensing the temperature of a cooking container. In order to implement the technology disclosed in the above-described document, the remote temperature sensor for sensing the temperature of the cooking container should be newly added, separately from the heater, and there is a limitation in that estimation may be inaccurate since the warning time is calculated based on the temperature variation pattern based on the temperature of the cooking container rather than the contents in the cooking container.

In <CIT>, entitled "Boil and boil dry detection apparatus," described is a method for determining that water is boiling by determining vibration of a cooking container using an ultrasonic wave transmitting apparatus for transmitting a transmission ultrasonic wave signal toward the cooking container placed on a cooking device, and an ultrasonic wave receiving apparatus for receiving a reflection ultrasonic wave signal reflected and returned from the cooking container. In order to implement the technology disclosed in the above-described document, an ultrasonic wave transmitting apparatus and an ultrasonic wave receiving apparatus should be additionally provided, separately from the heater, and there is a limitation in that the accuracy of estimation may be lowered since the frequency of vibration is different for every different container.

In order to overcome the above-described limitations, there is a need to provide a more advanced solution regarding a method for sensing the heating situation in the process of heating an object, such as when cooking, and automatically controlling the heating operation. <CIT> discloses a hob device and a method for operating a hob device. <CIT> relates to a heat treatment monitoring system. <CIT> relates to an apparatus for controlling a cooker. <CIT> discloses a cooking system for tracking a cooking device. <CIT> discloses a virtual sensor system.

Dependent claims describe preferred embodiments. One aspect of the present invention is to solve the above-noted and other problems in which, in addition to a heater, components such as a camera, an ultrasonic apparatus, and a remote temperature sensor apparatus should be additionally installed in order to monitor the process of heating an object so as to prevent excessive heating.

Another aspect is to address a shortcoming in which the accuracy of determination is lowered unless an additional apparatus is used in addition to a heater in determining whether the contents of a container heated by the heater are boiling.

Still another aspect is to address a shortcoming in which an additional apparatus for directly measuring the temperature of an object to be heated should be used in addition to the heating apparatus so that the cooking of the object to be heated is performed for a specific time within a certain temperature range.

Accordingly, one object of the present invention is to provide a heating apparatus for estimating a state of an object to be heated by sensing a sound which is generated when the object to be heated boils.

The heating apparatus for estimating a state of an object to be heated according to this embodiment of the present disclosure may be configured to estimate the state of the object to be heated based on the sound which is generated while the object to be heated is heated by disposing a sound sensor at a position adjacent to the object to be heated.

The heating apparatus for estimating a state of an object to be heated according to this embodiment of the present disclosure may be configured to estimate the state of the object to be heated based on the sound which is generated while the object to be heated is heated by disposing the sound sensor adjacent to a top plate that supports the object to be heated.

The heating apparatus for estimating a state of an object to be heated according to this embodiment of the present disclosure may be configured to estimate the state of the object to be heated during a heating operation by using a deep neural network that has been trained in advance to determine the state of the object to be heated according to the sound which is generated during heating.

The heating apparatus for estimating a state of an object to be heated according to this embodiment of the present disclosure includes a housing having a receiving space therein, a heating member disposed within the housing, a power supplier configured to supply power to the heating member, a top plate disposed on the top of the housing to support the object to be heated, a sound sensor disposed under the top plate, and a controller configured to control power supply to the heating member of the power supplier based on a sound signal received from the sound sensor.

Here, at least a portion of the bottom surface of the top plate has an embossed shape, the sound sensor is disposed to contact the bottom surface of the top plate, and at least a portion of the bottom surface of the top plate on which the sound sensor is disposed has a flat shape so that the sound sensor closely contacts the bottom surface of the top plate.

Further, the sound sensor may be configured to sense the sound generated when the object to be heated boils, and the controller may be configured to determine the state of the object to be heated based on the sound signal received from the sound sensor.

Further, the controller may be configured to determine the state of the object to be heated according to the sound signal received from the sound sensor by using a deep neural network model that has been trained in advance to estimate the state of the object to be heated based on the sound signal generated as the object to be heated is heated.

A heating apparatus for estimating a state of an object to be heated according to another embodiment of the present disclosure further includes a weight sensor disposed within the housing, and disposed to contact the bottom of the top plate so as to sense the weight of the object to be heated that is supported by the top plate.

Here, the controller is configured to determine the state of the object to be heated based on a weight signal received from the weight sensor and the sound signal received from the sound sensor.

Further, the heating member may be disposed to contact the bottom surface of the top plate, the heating member may be a coil having a hollow formed in the center thereof, and the sound sensor may be disposed to contact the bottom surface of the top plate at a position where the hollow has been formed.

A heating apparatus for estimating a state of an object to be heated according to still another embodiment of the present disclosure may further include a transceiver configured to communicate with a user terminal, and the controller may be configured to send an alarm signal to the user terminal through the transceiver when it is determined, based on the sound signal received from the sound sensor, that the object to be heated is boiling.

Here, the transceiver may be configured to receive, from the user terminal, information on a target time during which boiling is to be maintained, and the controller may be configured to control the power supplier such that the magnitude of the sound signal received from the sound sensor is maintained within a certain range during the target time from the time point at which it is determined, based on the sound signal received from the sound sensor, that the object to be heated is boiling, and to reduce the power supply of the power supplier after the target time has elapsed.

A method for estimating a state of an object to be heated according to an embodiment of the present disclosure may include heating the object to be heated disposed on a top plate of a heating apparatus, sensing sound generated by the object to be heated through a sound sensor, and adjusting the amount of power supplied to a heating member of the heating apparatus based on a sound signal received from the sound sensor.

Here, the sound sensor may be disposed to closely contact the bottom surface of the top plate, and the sound sensor may sense the sound generated when the object to be heated boils.

Further, the adjusting may include determining the state of the object to be heated based on the sound signal received from the sound sensor.

Further, the determining may include estimating the state of the object to be heated according to the sound signal received from the sound sensor by using a deep neural network model that has been trained in advance to estimate the state of the object to be heated based on the sound signal generated as the object to be heated is heated.

Further, the adjusting includes determining the state of the object to be heated based on a weight signal received from a weight sensor configured to sense the weight of the object to be heated that is supported by the top plate and the sound signal received from the sound sensor.

A method for estimating a state of an object to be heated according to another embodiment of the present disclosure may further include generating an alarm signal and sending the alarm signal to a user terminal when it is determined, based on the sound signal received from the sound sensor, that the object to be heated is boiling.

A method for estimating a state of an object to be heated according to still another embodiment of the present disclosure may further include receiving, from a user terminal, information on a target time during which boiling is to be maintained.

Here, the adjusting may include controlling power supply to the heating member such that the magnitude of the sound signal received from the sound sensor is maintained within a certain range during the target time from a time point at which it is determined, based on the sound signal received from the sound sensor, that the object to be heated is boiling, and reducing the power supply to the heating member after the target time has elapsed.

Further, a computer readable recording medium for estimating a state of an object to be heated according to an embodiment of the present disclosure may be a computer readable recording medium in which a computer program for executing any one method of the above-described methods is stored.

Other aspects, features, and advantages in addition to those above-described will become apparent from the following drawings, claims, and detailed description of the disclosure.

Embodiments of the present disclosure provide a heating apparatus and a method capable of estimating the state of an object to be heated even without separately adding, in addition to the heater, components such as a camera, an ultrasonic apparatus, and a remote temperature sensor apparatus, unlike the related art. Further, the embodiments of the present disclosure provide a heating apparatus and a method capable of accurately determining whether the object to be heated is boiling even without using an additional apparatus in addition to the heating apparatus.

In addition, the embodiments of the present disclosure provide a method for disposing a sound sensor by which sound which is generated during heating of the object to be heated can be reliably collected. Further, the embodiments of the present disclosure provide a method for maintaining a boiling state only for the time desired by the user after the object to be heated has reached the boiling state, thereby implementing safe and effective cooking.

Also, the embodiments of the present disclosure provide a heating apparatus and method capable of preventing the object to be heated from boiling over by adjusting the magnitude of heat energy provided through the heating apparatus after the object to be heated has reached the boiling state, thereby providing convenience and stability to the user.

The effects of the present disclosure are not limited to the above-described effects, and other effects not described may be clearly understood by those skilled in the art from the following description.

The above and other aspects, features, and advantages of the present disclosure will become apparent from the detailed description of the following aspects in conjunction with the accompanying drawings, in which:.

The terminology used herein is used for the purpose of describing particular example embodiments only and is not intended to be limiting. The terms "comprises," "comprising," "includes," "including," "containing," "has," "having" or other variations thereof are inclusive and therefore specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Furthermore, the terms such as "first," "second," and other numerical terms may be used herein only to describe various elements, but these elements should not be limited by these terms. Furthermore, these terms such as "first," "second," and other numerical terms, are used only to distinguish one element from another element.

Like reference numerals designate like elements throughout the specification, and overlapping descriptions of the elements will not be provided.

<FIG> is a diagram illustrating the environment in which a heating apparatus for estimating a state of an object to be heated according to an embodiment of the present disclosure operates. Although a heating apparatus of the present disclosure includes various devices having heating mechanisms, the heating apparatus will be described below as an electric range as an example, for convenience of explanation.

As shown, an electric range <NUM> can operate in an Internet of Things (IoT) environment constructed by using a <NUM> communication network. The electric range <NUM> can communicate with an artificial intelligence speaker <NUM>, a user terminal <NUM>, and an external server <NUM>. Further, the user terminal <NUM> can receive a certain command from the user and transfer the command to the electric range <NUM>, and receive operation information of the electric range <NUM> and transfer the operation information to the user.

The user terminal may include a communication terminal capable of performing the function of a computing apparatus, and may be a desktop computer, a smart phone, a notebook, a tablet PC, a smart TV, a portable phone, a personal digital assistant (PDA), a laptop, a media player, a micro server, a global positioning system (GPS) apparatus, an electronic book terminal, a digital broadcast terminal, a navigation, a kiosk, a MP3 player, a digital camera, a consumer electronic, and other mobile or non-mobile computing apparatuses, which are operated by the user, but is not limited thereto. Further, the user terminal may be a wearable terminal such as a watch, eyeglasses, a hair band, and a ring, having a communication function and a data processing function. Such a user terminal is not limited to the above terminals, and the terminals capable of voice recognition may be borrowed without limitation.

The artificial intelligence speaker <NUM> can also receive a certain command from the user through voice and transfer the command to the electric range <NUM>, and also receive operation information of the electric range <NUM> and transfer the operation information to the user by voice. The external server <NUM> can also receive and store the operation information of the electric range <NUM>, and also provide a reference for the electric range <NUM> to perform determination through an accumulated database.

For example, the external server <NUM> may have an object-to-be-heated sound database in which, for each type of object to be heated, information about the sound generated while the object is heated is stored in association with the temperature of the object. The electric range <NUM> can collect the sound which is generated while heating the object, and estimate the state of the object by referring to the object-to-be-heated sound database through communication with the external server <NUM>.

As another example, the external server <NUM> may have an object-to-be-heated sound deep neural network model that has been trained in advance to estimate the state of the object to be heated based on the sound signal which is generated as the object is heated. In this instance, the electric range <NUM> can collect the sound which is generated while heating the object, and also estimate the state of the object to be heated by using the object-to-be-heated sound deep neural network model through communication with the external server <NUM>.

As still another example, the external server <NUM> can update the object-to-be-heated sound database and the object-to-be-heated sound deep neural network model by communicating with various electric ranges and collecting information. The external server <NUM> can also transmit the updated database and deep neural network model to the electric range <NUM> so that the electric range <NUM> itself estimates the state of the object to be heated based on the sound signal which is generated as the object is heated.

In addition, the electric range <NUM> can be connected to the above-described devices through a network, and the network can serve to connect the electric range <NUM> with the user terminal <NUM>, the artificial intelligence speaker <NUM>, and the external server <NUM>. Such a network may be a wired network such as a local area network (LAN), a wide area network (WANs), a metropolitan area network (MAN), and an integrated service digital networks (ISDN), or a wireless network such as a wireless LAN, CDMA, Bluetooth, and satellite communication, but the scope of the present disclosure is not limited thereto.

Further, the network can transmit and receive information by using short distance communication and/or long distance communication. Here, the short distance communication may include Bluetooth®, radio frequency identification (RFID), Infrared Data Association (IrDA), ultra-wideband (UWB), ZigBee, and Wi-Fi (wireless fidelity) technologies, and the long distance communication may include code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), and single carrier frequency division multiple access (SC-FDMA).

The network may include connections of network elements such as hubs, bridges, routers, switches, and gateways. The network may also include one or more connected networks, including a public network such as the Internet and a private network such as a secure corporate private network. For example, the network may include a multi-network environment. Access to the network may be provided through one or more wired or wireless access networks.

Next, <FIG> is a diagram illustrating sound generated while the object is heated by a heating apparatus according to an embodiment of the present disclosure. Although the object to be heated may include various types of contents contained in the container, the object to be heated will be described below as a liquid contained in a pot as an example.

As shown in <FIG>, as the liquid in the pot is heated, bubbles are generated in the liquid and the bubbles rise to the surface of the liquid, such that sound is generated from the pot or between the pot and the top plate of the electric range <NUM>. In the graph of <FIG>, the x-axis represents time and the y-axis represents the magnitude of the sound. At the initial stage of heating, only the temperature of the liquid in the pot rises, and no bubbles are generated. Accordingly, no sound is sensed.

However, as the heating time passes, the number of bubbles generated in the liquid contained in the pot increases, and the magnitude of the sound generated thereby increases. Accordingly, based on the magnitude of the sound, it is possible to determine whether the object to be heated is boiling, and also to estimate the temperature and degree of boiling of the object to be heated.

Based on this phenomenon, an apparatus capable of directly measuring the temperature of the contents of the pot and a microphone capable of collecting sound generated from the pot on the top plate of the electric range can be installed, and then the temperature of the contents and the sound generated at the corresponding temperature can be recorded while changing, for example, the type, weight, and size of the pot, and the type, weight, and size of the contents contained in the pot.

The recorded data is sound data for which a corresponding temperature is labeled. A matching table capable of estimating the temperature of the contents according to the sound generated from the pot on the top plate of the electric range can be made by using the labeled data. In addition, the deep neural network model capable of estimating the temperature of the contents according to the sound generated from the pot on the top plate of the electric range can learn and be trained with this labeled data.

Further, the database or the deep neural network model generated through this preliminary work can be embedded in a memory of the electric range <NUM>, or stored in the external server <NUM> with which the electric range <NUM> communicates, and be used to estimate the state of the object to be heated according to a sound signal received from a sound sensor (e.g., vibration sensor) of the electric range <NUM> during actual use.

Next, <FIG> shows an exploded view of the heating apparatus according to an embodiment of the present disclosure. The electric range <NUM> for estimating the state of the object to be heated according to an embodiment of the present disclosure includes a housing <NUM> having a receiving space therein, a heating member <NUM> disposed in the housing <NUM>, a power supplier <NUM> for supplying power to the heating member, a power management part <NUM> for managing the power supplier <NUM>, a top plate <NUM> disposed on the top of the housing <NUM> to support the object to be heated, a sound sensor <NUM> (e.g., vibration sensor) disposed under the top plate <NUM>, and an interface <NUM> for receiving an instruction from the user. The sound sensor <NUM> may or may not contact the top plate <NUM>.

Further, the electric range <NUM> may include additional sensors capable of sensing the operation situation of the electric range <NUM> such as a temperature sensor <NUM> disposed on the bottom of the top plate <NUM> to sense a temperature, and a weight sensor <NUM> disposed within the housing to measure the weight of the object to be heated disposed on the top plate <NUM>.

In addition, the electric range <NUM> includes a controller for controlling power supply of the power supplier <NUM> to the heating member <NUM> by controlling the power management part <NUM> based on the sound signal received from the sound sensor <NUM>. The controller can also stop the power supply from the power supplier <NUM> to the heating member <NUM> when it is determined, based on the sound signal received from the sound sensor <NUM>, that the object to be heated is boiling and may boil over due to an excessive degree of boiling.

Alternatively, upon receiving a signal according to which the object to be heated is to be continuously boiled for a certain duration, from the interface <NUM>, the artificial intelligence speaker <NUM>, or the user terminal <NUM>, the controller can also increase the power supply of the power supplier <NUM> when the sound signal is reduced to a threshold or less.

As shown in <FIG>, if a hollow is formed in the coil, the sound sensor <NUM> can be disposed to contact the bottom surface of the top plate <NUM> at the position where the hollow is formed.

The sound sensor <NUM> can also be disposed to closely contact the bottom surface of the top plate <NUM> in order to closely sense the sound from the object to be heated that is supported by the top plate <NUM>, but may also be disposed to have a slight distance from the bottom surface of the top plate <NUM> according to the sensitivity of the sound sensor <NUM>. Further, the sound sensor <NUM> may be an Electrets Condenser Microphone (ECM) capable of converting the sound to be collected into an electric signal, and further, various types of microphones may be used.

Next, <FIG> is a block diagram of the heating apparatus according to an embodiment of the present disclosure. The heating apparatus according to an embodiment of the present disclosure may be represented by the block diagram as in <FIG>, and as shown in <FIG>, a controller <NUM> can control the operation of various components in the electric range <NUM>.

When the sound sensor <NUM> collects the sound generated between the pot and the top plate while the pot vibrates as the object to be heated is heated, the controller <NUM> of the object to be heated controls the amount of power supplied by the power supplier <NUM> according to the collected sound signal.

When the sound signal indicates the object is boiling strongly and is likely to boil over, the controller <NUM> can cause the power supplier <NUM> to temporarily stop the power supply. In another example, upon receiving a signal according to which the object to be heated is to be continuously boiled during a certain duration, from the interface <NUM>, the artificial intelligence speaker <NUM>, or the user terminal <NUM>, the controller <NUM> can also increase the power supply of the power supplier <NUM> when the sound signal is reduced to a threshold or less.

In addition, the electric range <NUM> may include the weight sensor <NUM> in addition to the sound sensor <NUM>. In particular, the weight sensor <NUM> can acquire weight information of the object to be heated that is supported by the top plate <NUM> of the electric range <NUM>.

According to the weight of the object to be heated, a different sound or vibration may be generated even at the same degree of boiling of the object to be heated. Accordingly, the controller can also consider the weight signal received from the weight sensor <NUM> in addition to the sound signal received from the sound sensor <NUM> in determining the state of the object to be heated.

Further, the weight sensor <NUM> can be used to determine additional characteristics of the object to be heated. For example, the controller can use the sensed weight of ingredients added to the object to heated (e.g., water, a sauce, a pre-mixture, etc.) and control the power accordingly.

In one embodiment, if no additional weight change is sensed after a predetermined time period, the controller can reduce power supplied to the heating member. That is, no additional weight change after a predetermined time period can be determined as the user is finished adding ingredients, and the boiling time period has been completed. Thus, the controller can reduce the power and save wasted cooking resources.

In another embodiment, if an increased weight change is sensed during the predetermined time period, the controller can maintain the power supplied to the heating member. That is, an increased weight during the predetermined time period can be determined as the user is still adding ingredients (and thus not finished adding ingredients). Thus, the controller can efficiently maintain the power. In still another embodiment, if an increased weight change is sensed during the predetermined time period, the controller can increase the power supplied to the heating member. That is, the increased weight indicates additional ingredients have been added, which is likely to reduce the boiling of the ingredients for a short period of time until full boiling is resumed. Thus, in this example, the controller can increase the power supplied to the heating member, so quickly achieve the previous boiling state.

In another embodiment, if a reduced weight change is sensed after the predetermined time period, the controller can reduce the power supplied to the heater member including turning off the power and also stop detecting the vibration signal. That is, a reduction in weight after the predetermined time period can be determined as the user has removed ingredients (e.g., removing noodles from boiling water), and thus advantageously reduce/turn off the power because the user has completed cooking the ingredients. This advantageously save wasted cooking resources.

In yet another embodiment, if a reduced weight between a first weight and a second weight is sensed, the controller can reduce the power supplied to the heating member by a first amount, and if a reduced weight between the second weight and a third weight is then sensed, the controller can reduce the power by a second amount. That is, the reduction of weight from a first weight to a second weight can be determined as removing a first ingredient, and the reduction of weight from the second weight to a third weight can be determined as removing a second ingredient. Thus, the controller can advantageously reduce the power supplied to the heating member by a first amount when the first ingredient is removed, and by a second amount when the second ingredient is removed. This advantageously saves wasted cooking resources.

In still another embodiment, if an increased weight is sensed between a first weight and a second weight, the controller can increase the power to the heating member to maintain the boiling state. That is, this can be determined as adding an ingredient which reduces the boiling state, so the controller can advantageously increase the power to accommodate the addition ingredient.

Further, using the first and second weights, the controller can advantageously increase the power by predetermined increments. For example, for a small weight change between the first and second weights, the controller can increase the power by a small value corresponding the small weight change. For a large weight change between the first and second weights, the controller can increase the power by a large value corresponding the large weight change. A table can be stored in the memory indicating power increase values corresponding one-to-one to incremental weight changes. A similar approach can be used when decreasing the power to correspond with small or large reductions in weight.

Further, the electric range <NUM> may include the temperature sensor <NUM> in addition to the sound sensor <NUM>. In particular, the temperature sensor <NUM> can be disposed to contact the bottom surface of the top plate <NUM> to sense the temperature of the top plate <NUM>.

The temperature sensor <NUM> can be used to determine the authenticity of the sound signal received from the sound sensor <NUM>, and whether there is an error in the sound signal. For example, when a sound signal, which is generated when the object to be heated boils, is received from the sound sensor <NUM> but the temperature of the top plate <NUM> sensed from the temperature sensor <NUM> is lower than a certain temperature, the source of the signal sensed by the sound sensor <NUM> may be another sound rather than the sound generated by the boiling of the object to be heated. Accordingly, the controller <NUM> can be configured to ignore the sound signal received from the sound sensor <NUM> when the temperature of the top plate <NUM> sensed from the temperature sensor <NUM> is lower than a certain temperature (for example, <NUM>□).

Further, the electric range <NUM> may include a memory <NUM>. In particular, the memory <NUM> can store the database or the deep neural network model generated through the above-described preliminary work, and the controller <NUM> can estimate the state of the object to be heated according to the sound signal received from the sound sensor <NUM> during the use of the electric range <NUM> by using the database and the deep neural network model.

Further, the electric range <NUM> may include a transceiver <NUM>. Thus, the electric range <NUM> can communicate with the user terminal <NUM> or the external server <NUM> through the transceiver <NUM>.

The external server <NUM> can also receive and store the operation information of the electric range <NUM>, and provide a reference for the electric range <NUM> to perform the determination through the accumulated database. For example, the external server <NUM> can have the object-to-be-heated sound database in which, for each type of object to be heated, information about the sound generated while the object to be heated is heated is stored in association with the temperature of the object to be heated, and the electric range <NUM> can collect sound which is generated while heating the object to be heated, and estimate the state of the object to be heated by referring to the object-to-be-heated sound database through communication with the external server <NUM>.

As another example, the external server <NUM> can have the object-to-be-heated sound deep neural network model that has been trained in advance to estimate the state of the object to be heated based on the sound signal which is generated as the object to be heated is heated. In this instance, the electric range <NUM> can collect the sound which is generated while heating the object to be heated, and also estimate the state of the object to be heated by using the object-to-be-heated sound deep neural network model through communication with the external server <NUM>.

As still another example, the external server <NUM> can update the object-to-be-heated sound database and the object-to-be-heated sound deep neural network model by communicating with and collecting information from various electric ranges. The external server <NUM> can also transmit the updated database and deep neural network model to the electric range <NUM> so that the electric range <NUM> itself estimates the state of the object to be heated based on the sound signal which is generated as the object to be heated is heated.

In order to estimate the state of the object to be heated so that the object is not excessively heated, the electric range <NUM> can first perform an operation of heating the object to be heated disposed on the top plate <NUM> of the electric range <NUM>, sense the sound generated by the object to be heated through the sound sensor <NUM>, and adjust the amount of power supplied to the heating member <NUM> of the electric range <NUM> based on the sound signal received from the sound sensor <NUM>.

Further, the controller <NUM> of the electric range <NUM> can determine the state of the object to be heated based on the sound signal received from the sound sensor <NUM>. Upon this determination, the controller <NUM> of the electric range <NUM> can estimate the state of the object to be heated according to the sound signal received from the sound sensor <NUM> by using the deep neural network model that has been trained in advance to estimate the state of the object to be heated based on the sound signal which is generated as the object to be heated is heated.

Further, since a different sound signal may be generated at the same degree of boiling according to the weight of the object to be heated, the controller <NUM> of the electric range <NUM> can determine the state of the object to be heated based on the weight signal received from the weight sensor <NUM> for sensing the weight of the object to be heated supported by the top plate <NUM> and the sound signal received from the sound sensor <NUM>, when adjusting the amount of power supplied to the heating member <NUM>.

Next, <FIG> is a diagram illustrating the position where the sound sensor is disposed in the heating apparatus according to an embodiment of the present disclosure. As shown in <FIG>, the bottom surface of the top plate <NUM> of the electric range <NUM> is processed to have an embossed shape <NUM>. The embossed shape <NUM> is intended to disperse the pressure transferred downwards when the top plate <NUM> is pressed while supporting a heavy object to be heated.

However, in order for the sound sensor <NUM> to sensitively sense the sound transferred through the top plate <NUM>, it is preferable for the sound sensor <NUM> to contact the top plate <NUM> with as large an area as possible. Accordingly, a portion of the bottom surface of the top plate <NUM> that contacts the sound sensor <NUM> is formed to have a flat shape <NUM> through a grinding process, for example.

Accordingly, the sound sensor <NUM> can sensitively sense the sound generated by the object to be heated transferred through the top plate <NUM>, and accordingly, the controller <NUM> can more accurately confirm the state of the object to be heated. In addition, the sound sensor <NUM> may include a portion for sensing sound and a connector <NUM> for transferring the sensed sound as an electrical signal.

In addition, the connector of the sound sensor <NUM> can be connected to a PCB or the like disposed inside the housing <NUM>, and accordingly, a sound signal can be transferred to the controller <NUM>. Further, the controller <NUM> can send an alarm signal to the user terminal <NUM> or the artificial intelligence speaker <NUM> through the transceiver <NUM>, when it is determined, based on the sound signal received from the sound sensor <NUM>, that the object to be heated is boiling.

Since the user who receives the alarm signal through the user terminal <NUM> or the artificial intelligence speaker <NUM> recognizes that the food that he/she is cooking is boiling, it is possible to prevent a dangerous situation, which is caused by neglect, from occurring. In addition, the user can be informed of the situation occurring in the electric range <NUM> through the user terminal <NUM> even while at a remote location, and accordingly may remotely confirm and control the operation of the electric range <NUM>.

Next, <FIG> is a flowchart illustrating an operation of a heating apparatus according to another embodiment of the present disclosure. In particular, <FIG> illustrates when the user tries to boil ramen by using the electric range <NUM>. The user can first make a voice command such as "Connect the electric range" to the user terminal <NUM>, so that the electric range <NUM> is connected to the user terminal <NUM>. According to this voice command, the user terminal <NUM> sends a connection command signal to the electric range <NUM> (S110).

The electric range <NUM>, having received the connection command signal, performs communication initialization and connection with the user terminal <NUM> (S120). The user terminal <NUM>, having received a connection confirmation signal, can perform a voice report of "The electric range has been connected" to the user. The user who has confirmed that the user terminal <NUM> and the electric range <NUM> have been connected can say "Power level <NUM>, ramen," corresponding to the cooking that he or she is planning, thereby expressing the intention to boil water for cooking ramen at a power level <NUM>.

Accordingly, the user terminal <NUM> can send, to the electric range <NUM>, a signal that sets the power level to <NUM> and sets a timer for the ramen (a timer set to boil water for a further <NUM> minutes after the water initially boils, or a timer set to boil the water for a further <NUM> minutes after the ramen has been added by, after the water initially boils, sensing when the ramen is added via the weight sensor) (S130). While sending the signal, the user terminal <NUM> can notify the user of the details of the timer to be set by outputting, by voice, "When the water boils, a <NUM>-minute timer will be automatically set.

When receiving the signal, the electric range <NUM> can start an operation at power level <NUM> after confirming, through the weight sensor, whether the container containing the object to be heated has been placed on the top plate <NUM> (S140). While heating water by supplying power to the heating member <NUM>, the electric range <NUM> can sense that the water is boiling by receiving the sound signal in the above-described manner, and inform the user terminal <NUM> that the water is boiling (S150).

When the user terminal <NUM> receives a signal indicating that the water is boiling (a boiling signal), the user terminal <NUM> can inform the user that "The water is boiling. Please put in the ramen. " In response thereto, the user can inform the user terminal <NUM> that he or she has put in the ramen by voice, by saying "I have put in the ramen. " Accordingly, the user terminal <NUM> can send a timer start signal to the electric range <NUM> (S170).

As another example, even if the user puts in the ramen without any signal, the weight sensor of the electric range <NUM> can automatically sense that the ramen has been put in (S160). In response to this sensing, the electric range <NUM> can ask the user for confirmation, or start the timer without asking for confirmation.

Since the electric range <NUM> has already received information about the cooking of the ramen from the user, the electric range <NUM> can set the timer to operate for <NUM> minutes from the time point when the ramen was put in. After boiling the water for a further <NUM> minutes, the electric range <NUM> can stop the heating operation (S170). When a signal indicating that the heating operation has been stopped is transferred to the user terminal <NUM>, the user terminal <NUM> can inform the user that the cooking has been completed by saying "The ramen is ready.

Next, <FIG> is a flowchart illustrating an operation of a heating apparatus according to still another embodiment of the present disclosure. In particular, <FIG> illustrates when the user desires to boil a specific food by using the electric range <NUM>, and then maintain the temperature for <NUM> minutes while preventing boiling over. As shown, the user can first make a voice command such as "Connect the electric range" to the user terminal <NUM>, so that the electric range <NUM> is connected to the user terminal <NUM>. According to this voice command, the user terminal <NUM> sends a connection command signal to the electric range <NUM> (S210).

The electric range <NUM>, having received the connection command signal, performs communication initialization and connection with the user terminal <NUM> (S220). The user terminal <NUM>, having received the connection confirmation signal, can perform a voice report of "Connected" to the user. The user who has confirmed that the user terminal <NUM> and the electric range <NUM> have been connected can say "Prevent boiling over for <NUM> minutes" regarding the cooking that he or she is planning, thereby expressing the intention to boil the water and have the water boil for a further <NUM> minutes after the water initially starts to boil while preventing boiling over.

Accordingly, the user terminal <NUM> can send, to the electric range <NUM>, a signal that sets power for boiling the object to be heated and sets a <NUM>-minute timer (a timer set to boil the object to be heated for a further <NUM> minutes after initially starting to boil) (S230). While sending the signal, the user terminal <NUM> can notify the user of the details of the timer to be set by outputting, by voice, "When the water boils, automatic power control will automatically operate for <NUM> minutes.

When receiving the signal, the electric range <NUM> can confirm, through the weight sensor, whether the container containing the object to be heated has been placed on the top plate <NUM>, and then start an operation at the maximum power level (S240). While heating the object to be heated by supplying power to the heating member <NUM>, the electric range <NUM> can sense that the object to be heated is boiling by receiving the sound signal in the above-described manner, and start the <NUM>-minute timer (S250).

The electric range <NUM> can maintain the temperature of the object to be heated for <NUM> minutes while the object to be heated is prevented from boiling over by the automatic power level control (S260). The electric range <NUM> can perform the automatic power level control for <NUM> minutes from the time point at which water initially started to boil, and then stop the heating (S270).

Further, the user terminal <NUM> can receive notification of the fact that the heating has been stopped, and inform the user that the cooking has been completed by voice, such as "The cooking has been completed.

Next, <FIG> is a diagram illustrating an automatic power level control, which is an operation for automatically controlling power after sensing a boiling state of the object to be heated in the heating apparatus according to an embodiment of the present disclosure. The controller <NUM> of the electric range <NUM> can start the automatic power control from the time point when it is sensed that the object to be heated is boiling, and can control the power level supplied to the heating member <NUM> in the manner shown in <FIG>.

The time point when the power level is changed corresponds to a time point when there is a change in the sound signal, and the controller <NUM> can increase or reduce the power level such that the magnitude of the sound signal received from the sound sensor is maintained within a certain range during a target time set by the user. After the target time set by the user has elapsed, the controller <NUM> can reduce the power level supplied to the heating member <NUM>, or bring the power level to zero.

<FIG> is a diagram illustrating a deep neural network model for predicting a state of the object to be heated used in the heating apparatus according to an embodiment of the present disclosure. The electric range <NUM> uses a deep neural network model that has been trained in advance using machine learning, which is an area of artificial intelligence, to estimate the state of the object to be heated through the sound signal.

Here, artificial intelligence (AI) is an area of computer engineering science and information technology that studies methods to make computers mimic intelligent human behaviors such as reasoning, learning, self-improving, and the like. Further, artificial intelligence does not exist on its own, but is rather directly or indirectly related to a number of other fields in computer science. Particularly, in recent years, there have been numerous attempts to introduce an element of AI into various fields of information technology to solve problems in the respective fields.

Machine learning is an area of artificial intelligence that includes the field of study that gives computers the capability to learn without being explicitly programmed. More specifically, machine learning is a technology that investigates and builds systems, and algorithms for such systems, which are capable of learning, making predictions, and enhancing their own performance based on experiential data. Machine learning algorithms, rather than only executing rigidly set static program commands, may be used to take an approach that builds models for deriving predictions and decisions from input data.

Numerous machine learning algorithms have been developed for data classification in machine learning. Representative examples of such machine learning algorithms for data classification include a decision tree, a Bayesian network, a support vector machine (SVM), an artificial neural network (ANN), and so forth.

Decision tree refers to an analysis method that uses a tree-like graph or model of decision rules to perform classification and prediction. Bayesian network may include a model that represents the probabilistic relationship (conditional independence) among a set of variables. Bayesian network may be appropriate for data mining through unsupervised learning.

SVM may include a supervised learning model for pattern detection and data analysis, heavily used in classification and regression analysis. ANN is a data processing system modeled after the mechanism of biological neurons and interneuron connections, in which a number of neurons, referred to as nodes or processing elements, are interconnected in layers.

ANNs are models used in machine learning and may include statistical learning algorithms conceived from biological neural networks (particularly of the brain in the central nervous system of an animal) in machine learning and cognitive science. ANNs may refer generally to models that have artificial neurons (nodes) forming a network through synaptic interconnections, and acquires problem-solving capability as the strengths of synaptic interconnections are adjusted throughout training. The terms 'artificial neural network' and 'neural network' may be used interchangeably herein.

An ANN may include a number of layers, each including a number of neurons. Furthermore, the ANN may include synapses that connect the neurons to one another. An ANN may be defined by the following three factors: (<NUM>) a connection pattern between neurons on different layers; (<NUM>) a learning process that updates synaptic weights; and (<NUM>) an activation function generating an output value from a weighted sum of inputs received from a previous layer.

ANNs include, but are not limited to, network models such as a deep neural network (DNN), a recurrent neural network (RNN), a bidirectional recurrent deep neural network (BRDNN), a multilayer perception (MLP), and a convolutional neural network (CNN). An ANN may be classified as a single-layer neural network or a multi-layer neural network, based on the number of layers therein.

An ANN may be classified as a single-layer neural network or a multi-layer neural network, based on the number of layers therein. In general, a single-layer neural network may include an input layer and an output layer. In general, a multi-layer neural network may include an input layer, one or more hidden layers, and an output layer.

The input layer receives data from an external source, and the number of neurons in the input layer is identical to the number of input variables. The hidden layer is located between the input layer and the output layer, and receives signals from the input layer, extracts features, and feeds the extracted features to the output layer. The output layer receives a signal from the hidden layer and outputs an output value based on the received signal. Input signals between the neurons are summed together after being multiplied by corresponding connection strengths (synaptic weights), and if this sum exceeds a threshold value of a corresponding neuron, the neuron may be activated and output an output value obtained through an activation function.

A deep neural network with a plurality of hidden layers between the input layer and the output layer may be the most representative type of artificial neural network which enables deep learning, which is one machine learning technique. An ANN may be trained using training data. Here, the training may refer to the process of determining parameters of the artificial neural network by using the training data, to perform tasks such as classification, regression analysis, and clustering of input data. Such parameters of the artificial neural network may include synaptic weights and biases applied to neurons. An artificial neural network trained using training data may classify or cluster input data according to a pattern within the input data.

Throughout the present specification, an artificial neural network trained using training data may be referred to as a trained model. Hereinbelow, learning paradigms of an artificial neural network will be described in detail.

Learning paradigms, in which an artificial neural network operates, may be classified into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning is a machine learning method that derives a single function from the training data.

Among the functions that may be thus derived, a function that outputs a continuous range of values may be referred to as a regressor, and a function that predicts and outputs the class of an input vector may be referred to as a classifier. In supervised learning, an artificial neural network may be trained with training data that has been given a label. Here, the label may refer to a target answer (or a result value) to be guessed by the artificial neural network when the training data is input to the artificial neural network.

Throughout the present specification, the target answer (or a result value) to be guessed by the artificial neural network when the training data is input may be referred to as a label or labeling data. Throughout the present specification, assigning one or more labels to training data in order to train an artificial neural network may be referred to as labeling the training data with labeling data.

Training data and labels corresponding to the training data together may form a single training set, and as such, they may be input to an artificial neural network as a training set. The training data may exhibit a number of features, and the training data being labeled with the labels may be interpreted as the features exhibited by the training data being labeled with the labels. In this instance, the training data may represent a feature of an input object as a vector.

Using training data and labeling data together, the artificial neural network may derive a correlation function between the training data and the labeling data. Then, through evaluation of the function derived from the artificial neural network, a parameter of the artificial neural network may be determined (optimized).

Unsupervised learning is a machine learning method that learns from training data that has not been given a label. More specifically, unsupervised learning may be a training scheme that trains an artificial neural network to discover a pattern within given training data and perform classification by using the discovered pattern, rather than by using a correlation between given training data and labels corresponding to the given training data.

Examples of unsupervised learning include, but are not limited to, clustering and independent component analysis. Examples of artificial neural networks using unsupervised learning include, but are not limited to, a generative adversarial network (GAN) and an autoencoder (AE).

GAN is a machine learning method in which two different artificial intelligences, a generator and a discriminator, improve performance through competing with each other. The generator may be a model generating new data that generates new data based on true data.

The discriminator may be a model recognizing patterns in data that determines whether input data is from the true data or from the new data generated by the generator. Furthermore, the generator may receive and learn from data that has failed to fool the discriminator, while the discriminator may receive and learn from data that has succeeded in fooling the discriminator. Accordingly, the generator may evolve so as to fool the discriminator as effectively as possible, while the discriminator evolves so as to distinguish, as effectively as possible, between the true data and the data generated by the generator.

An auto-encoder (AE) is a neural network which aims to reconstruct its input as output. More specifically, AE may include an input layer, at least one hidden layer, and an output layer. Since the number of nodes in the hidden layer is smaller than the number of nodes in the input layer, the dimensionality of data is reduced, thus leading to data compression or encoding.

Furthermore, the data output from the hidden layer may be input to the output layer. Given that the number of nodes in the output layer is greater than the number of nodes in the hidden layer, the dimensionality of the data increases, thus leading to data decompression or decoding.

Furthermore, in the AE, the input data is represented as hidden layer data as interneuron connection strengths are adjusted through training. The fact that when representing information, the hidden layer can reconstruct the input data as output by using fewer neurons than the input layer may indicate that the hidden layer has discovered a hidden pattern in the input data and is using the discovered hidden pattern to represent the information.

Semi-supervised learning is machine learning method that makes use of both labeled training data and unlabeled training data. This technique may be used advantageously when the cost associated with the labeling process is high.

Reinforcement learning may be based on a theory that given the condition under which a reinforcement learning agent may determine what action to choose at each time instance, the agent may find an optimal path to a solution solely based on experience without reference to data. Reinforcement learning may be performed mainly through a Markov decision process.

Markov decision process consists of four stages: first, an agent is given a condition containing information required for performing a next action; second, how the agent behaves in the condition is defined; third, which actions the agent should choose to get rewards and which actions to choose to get penalties are defined; and fourth, the agent iterates until future reward is maximized, thereby deriving an optimal policy.

Also, the hyperparameters are set before learning, and model parameters may be set through learning to specify the architecture of the artificial neural network. For instance, the structure of an artificial neural network may be determined by a number of factors, including the number of hidden layers, the number of hidden nodes included in each hidden layer, input feature vectors, target feature vectors, and so forth.

Hyperparameters may include various parameters which need to be initially set for learning, much like the initial values of model parameters. Also, the model parameters may include various parameters sought to be determined through learning. For instance, the hyperparameters may include initial values of weights and biases between nodes, mini-batch size, iteration number, learning rate, and so forth. Furthermore, the model parameters may include a weight between nodes, a bias between nodes, and so forth.

Loss function may be used as an index (reference) in determining an optimal model parameter during the learning process of an artificial neural network. Learning in the artificial neural network involves a process of adjusting model parameters so as to reduce the loss function, and the purpose of learning may be to determine the model parameters that minimize the loss function. Loss functions typically use means squared error (MSE) or cross entropy error (CEE), but the present disclosure is not limited thereto.

Cross-entropy error may be used when a true label is one-hot encoded. One-hot encoding may include an encoding method in which among given neurons, only those corresponding to a target answer are given <NUM> as a true label value, while those neurons that do not correspond to the target answer are given <NUM> as a true label value. In machine learning or deep learning, learning optimization algorithms may be deployed to minimize a cost function, and examples of such learning optimization algorithms include gradient descent (GD), stochastic gradient descent (SGD), momentum, Nesterov accelerate gradient (NAG), Adagrad, AdaDelta, RMSProp, Adam, and Nadam.

GD includes a method that adjusts model parameters in a direction that decreases the output of a cost function by using a current slope of the cost function. The direction in which the model parameters are to be adjusted may be referred to as a step direction, and a size by which the model parameters are to be adjusted may be referred to as a step size. Here, the step size may mean a learning rate.

GD obtains a slope of the cost function through use of partial differential equations, using each of model parameters, and updates the model parameters by adjusting the model parameters by a learning rate in the direction of the slope. SGD may include a method that separates the training dataset into mini batches, and by performing gradient descent for each of these mini batches, increases the frequency of gradient descent.

Adagrad, AdaDelta and RMSProp may include methods that increase optimization accuracy in SGD by adjusting the step size, and may also include methods that increase optimization accuracy in SGD by adjusting the momentum and step direction. Adam may include a method that combines momentum and RMSProp and increases optimization accuracy in SGD by adjusting the step size and step direction. Nadam may include a method that combines NAG and RMSProp and increases optimization accuracy by adjusting the step size and step direction.

Learning rate and accuracy of an artificial neural network rely not only on the structure and learning optimization algorithms of the artificial neural network but also on the hyperparameters thereof. Accordingly, in order to obtain a good learning model, it is important to choose a proper structure and learning algorithms for the artificial neural network, but also to choose proper hyperparameters.

In general, the artificial neural network is first trained by experimentally setting hyperparameters to various values, and based on the results of training, the hyperparameters may be set to optimal values that provide a stable learning rate and accuracy. It is possible to further refine the estimation of the state of the object to be heated by using the above-described methods.

Although there may be various methods for generating the deep neural network model to be used in an embodiment of the present disclosure, in the case of supervised learning, the following training process may be performed as preliminary work. After installing an apparatus capable of directly measuring the temperature of the contents of a pot and a microphone capable of collecting the sound generated from the pot on the top plate of the electric range, the temperature of the contents and the sound generated at the corresponding temperature may be recorded while changing, for example, the type, weight, and size of the pot, and the type, weight, and size of contents contained in the pot.

The recorded data is sound data for which the corresponding temperature is labeled, and the deep neural network model may learn using the labeled data. Specifically, a deep neural network model capable of estimating the temperature of the contents according to the sound generated from the pot on the top plate of the electric range may be trained with the labeled data.

The deep neural network model generated through this preliminary work may be embedded in the memory of the electric range <NUM>, or stored in the external server <NUM> with which the electric range <NUM> communicates, and may be used to estimate the state of the object to be heated according to the sound signal received from the sound sensor <NUM> of the electric range <NUM> during actual use.

Information such as sound information collected by the electric range <NUM> from the object to be heated which is being heated, information of the type of cooking input by the user, and weight information sensed by the weight sensor <NUM> may be input to the deep neural network model that is trained as described above, and accordingly, the current state of the object to be heated or an estimation result relating to the degree of boiling of the object to be heated may be output.

Meanwhile, the input information may include various information such as the material of the container, the shape of the container, and the type of contents, in addition to the information described in <FIG>. In this instance, it is natural that a deep neural network model suitable for such input information may be trained and used.

The example embodiments described above may be implemented through computer programs executable through various components on a computer, and such computer programs may be recorded in computer-readable media. Examples of the computer-readable media include, but are not limited to: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks and DVD-ROM disks; magneto-optical media such as floptical disks; and hardware devices that are specially configured to store and execute program codes, such as ROM, RAM, and flash memory devices.

The computer programs may be those specially designed and constructed for the purposes of the present disclosure or they may be of the kind well known and available to those skilled in the computer software arts. Examples of program code include both machine code, such as produced by a compiler, and higher level code that may be executed by the computer using an interpreter.

Claim 1:
A heating apparatus, comprising:
a housing (<NUM>) having a receiving space therein;
a heating member (<NUM>)
a power supplier (<NUM>) configured to supply power to the heating member (<NUM>)
a top plate (<NUM>) disposed on a top of the housing (<NUM>) to support an object to be heated;
a vibration sensor (<NUM>) disposed under the top plate (<NUM>) and configured to detect a vibration signal generated when the objected is heated by the heating member (<NUM>) wherein the vibration sensor (<NUM>) is disposed in a center area of the heating member (<NUM>) and
a controller (<NUM>) configured to:
determine, via an artificial intelligence model having learned properties of the vibration signal, whether the object to be heated is boiling, and
control the power supplier (<NUM>) to adjust the power supplied to the heating member (<NUM>) based on the determination of whether the object to be heated is boiling,
wherein:
a bottom surface of the top plate (<NUM>) has an embossed shape, (<NUM>)
the vibration sensor (<NUM>) is disposed to contact the bottom surface of the top plate (<NUM>), and
a portion of the bottom surface of the top plate (<NUM>) on which the vibration sensor (<NUM>) is disposed has a flat shape (<NUM>) so that the vibration sensor (<NUM>) closely contacts the bottom surface of the top (<NUM>),
wherein the heating apparatus further comprises a weight sensor (<NUM>) disposed to contact a bottom surface of the top plate (<NUM>) and configured to sense a weight of the object to be heated that is supported by the top plate,
wherein the controller is configured to determine whether the object to be heated is boiling based on the weight sensed by the weight sensor (<NUM>) and the vibration signal detected by the vibration sensor (<NUM>).