ABSTRACT
Abstract
An artificial intelligence cooking device includes a plate including a heater configured to heat ingredients in a cooking vessel placed on the plate; a vibration sensor disposed below the plate configured to detect a vibration signal of the ingredients in the cooking vessel transmitted through the plate; and a processor configured to determine, via an artificial intelligence model having learned properties of the vibration signal, whether or not the ingredients in the cooking vessel are boiling based on the detected vibration signal provided to the artificial intelligence model and the learned properties of the vibration signal; and output information indicating whether or not the ingredients are boiling based on the determination.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a National Phase of PCT International Application No. PCT/KR2019/008726 filed on Jul. 15, 2019, all of which is hereby expressly incorporated by reference into the present application.
BACKGROUND OF THE INVENTION
Field of the Invention
The present invention relates to an artificial intelligence cooking device that can determine whether ingredients in a cooking vessel are boiling by inputting a vibration signal of the cooking vessel into an artificial intelligence model.
Discussion of the Related Art
Artificial intelligence, which means that computers can imitate a human intelligence, is a field of computer engineering and information technology that studies a method for allowing the computers to think, learn, self-develop, and the like that can be performed by the human intelligence. Further, the artificial intelligence does not exist by itself, but is directly or indirectly related to other fields of computer science. Particularly in the modern age, attempts to introduce artificial intelligence elements in various fields of information technology and to utilize the artificial intelligence elements in solving problems in the field are being actively carried out.
In an example, a technology that uses the artificial intelligence to recognize and learn an ambient situation, provides information desired by a user in a desired form, or performs an operation or a function desired by the user is being actively researched. Further, an electronic device providing such various operations and functions may be referred to as an artificial intelligence device.
In another example, when ingredients in a cooking vessel are constantly boiling by an operation of a cooking device, problems such as overflow of the ingredients, fire, or the like may occur. Further, in order to prevent the above-mentioned problems, the user must constantly check whether the ingredients are boiling. In order to prevent such inconvenience, technologies for determining, by the cooking device, whether the ingredients are boiling are disclosed in Korean Patent KR1390397B1, U.S. Pat. Nos. 6,301,521, 9,395,078, and the like.
Korea Patent KR1390397B1 and U.S. Pat. No. 6,301,521 propose technologies that combine many sensors such as a vibration sensor, an infrared sensor, a weight sensor, a sound wave sensor, a photo sensor, a timer, an acoustic sensor, an optical sensor, a temperature sensor, and the like. However, these technologies require many sensors and complex logic for processing and combining data collected from the many sensors.
Further, U.S. Pat. No. 9,395,078 proposes a technology for extracting changes in a vibration signal based on heating of the ingredients in chronological order and analyzing the signal at each step to determine whether the ingredients are boiling. However, this technology may be likely to misjudge when the standardized chronological-order logic is broken and may only be used in limited conditions (chronological flow, absence of external noise).
SUMMARY OF THE INVENTION
The present invention is to solve the above-mentioned problems, and the purpose of the present invention is to provide an artificial intelligence cooking device that can determine whether ingredients in a cooking vessel are boiling by inputting a vibration signal of the cooking vessel into an artificial intelligence model.
An aspect of the present invention provides an artificial intelligence cooking device including a heating portion for heating ingredients in a cooking vessel, a vibration sensor for detecting a vibration signal of the ingredients in the cooking vessel, and a processor configured to provide data corresponding to the vibration signal to an artificial intelligence model to obtain information about whether the ingredients in the cooking vessel are boiling, and perform control based on the obtained information.
According to the present invention, since whether the ingredients are boiling is determined using the artificial intelligence model, which learned properties of vibration (intensity, frequency, and pattern) generated by boiling of the ingredients, an accuracy of the determination on whether the ingredients are boiling can be improved.
Further, according to the present invention, data of a predetermined time period (e.g., 1 second) is input to the artificial intelligence model. Then, the artificial intelligence model can determine whether the ingredients are boiling by considering only data of a current time period (that is, without considering data of a previous time period together). That is, the present invention can be much less likely to misjudge and show a higher accuracy in the determination on whether the ingredients are boiling, compared to U.S. Pat. No. 9,395,078, which detects boiling by extracting a property based on a change in vibration signals in a chronological order.
Further, according to the present invention, despite a change in a type of the ingredients, a type of the cooking vessel, an amount of the ingredients, or the like, the accurate prediction on whether the ingredients are boiling may be achieved. Further, according to the present invention, since the vibration signal only needs to be processed in a usual signal processing scheme and then input into the artificial intelligence model, a processing algorithm may be simplified.
According to the present invention, when all of a plurality of information obtained corresponding to data of a plurality of consecutive time periods indicate that the ingredients in the cooking vessel are boiling, it is determined that the ingredients in the cooking vessel are boiling. Therefore, the accuracy of the prediction may be further improved.
According to the present invention, since whether the ingredients are boiling is detected and control is performed accordingly, overflow of the ingredients, fire, or the like can be prevented and inconvenience of the user of constantly checking whether the ingredients are boiling can be prevented. According to the present invention, cooking in accordance with various cooking schemes may be performed by only setting a temperature by the user.
According to the present invention, a vibration sensor module 106 is accommodated in an internal space of a main body 11 . Accordingly, sensing, by the vibration sensor 610 , of a vibration signal (e.g., ambient noise due to use of a cutting board, a mixer, or the like) generated from outside of an induction heating cooking device 1000 may be minimized. Further, according to the present invention, an outer holder 630 for receiving the vibration sensor module 106 therein is connected to and fixed to a plate 12 . Accordingly, sensing, by the vibration sensor 610 , of a vibration signal (e.g., vibration transmitted by the main body 11 ) transmitted through a structure other than the plate 12 may be minimized.
In an example, according to the present invention, the outer holder 630 is disposed in close contact with a lower face 15 of the plate 12 and surrounds side and the lower portions of the vibration sensor 610 . Accordingly, the sensing, by the vibration sensor 610 , of the vibration signal (e.g., ambient noise or the like due to use of a cutting board, a mixer, or the like) generated from the outside may be minimized.
According to the present invention, transmission of the vibration signal to a vibration sensor 710 through routes other than the plate 12 may be minimized by contacting a housing with the plate 12 by pressing of a pressing mechanism.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an AI device 100 according to an embodiment of the present invention.
FIG. 2 illustrates an AI server 200 according to an embodiment of the present invention.
FIG. 3 illustrates an AI system 1 according to an embodiment of the present invention.
FIG. 4 is a perspective view of an induction heating cooking device according to an embodiment of the present invention.
FIG. 5 is a cross-sectional view of II-IIâ² in FIG. 4 .
FIG. 6 is an exploded perspective view for illustrating a vibration sensor module 106 according to a first embodiment of the present invention.
FIG. 7 is an exploded perspective view for illustrating a vibration sensor module and a pressing mechanism according to another embodiment of the present invention.
FIG. 8 is a simplified diagram illustrating a circuit configuration of an induction heating cooking device.
FIG. 9 is a diagram illustrating a method for operating an artificial intelligence cooking device according to an embodiment of the present invention.
FIG. 10 illustrates 2D images of vibration signals when ingredients are boiling and of vibration signals when the ingredients are not boiling.
FIG. 11 illustrates diagrams for describing a method for generating an artificial intelligence model according to an embodiment of the present invention.
FIG. 12 is a view for illustrating a method for determining whether ingredients in a cooking vessel are boiling, according to an embodiment of the present invention.
FIG. 13 is a view for illustrating an operation of an artificial intelligence cooking device when ingredients are boiling, according to an embodiment of the present invention.
FIG. 14 is a diagram illustrating a method for operating an artificial intelligence cooking device according to another embodiment of the present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present invention are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present invention is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present invention are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components. In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a l
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a National Phase of PCT International Application No. PCT/KR2019/008726 filed on Jul. 15, 2019, all of which is hereby expressly incorporated by reference into the present application.
BACKGROUND OF THE INVENTION
Field of the Invention
The present invention relates to an artificial intelligence cooking device that can determine whether ingredients in a cooking vessel are boiling by inputting a vibration signal of the cooking vessel into an artificial intelligence model.
Discussion of the Related Art
Artificial intelligence, which means that computers can imitate a human intelligence, is a field of computer engineering and information technology that studies a method for allowing the computers to think, learn, self-develop, and the like that can be performed by the human intelligence. Further, the artificial intelligence does not exist by itself, but is directly or indirectly related to other fields of computer science. Particularly in the modern age, attempts to introduce artificial intelligence elements in various fields of information technology and to utilize the artificial intelligence elements in solving problems in the field are being actively carried out.
In an example, a technology that uses the artificial intelligence to recognize and learn an ambient situation, provides information desired by a user in a desired form, or performs an operation or a function desired by the user is being actively researched. Further, an electronic device providing such various operations and functions may be referred to as an artificial intelligence device.
In another example, when ingredients in a cooking vessel are constantly boiling by an operation of a cooking device, problems such as overflow of the ingredients, fire, or the like may occur. Further, in order to prevent the above-mentioned problems, the user must constantly check whether the ingredients are boiling. In order to prevent such inconvenience, technologies for determining, by the cooking device, whether the ingredients are boiling are disclosed in Korean Patent KR1390397B1, U.S. Pat. Nos. 6,301,521, 9,395,078, and the like.
Korea Patent KR1390397B1 and U.S. Pat. No. 6,301,521 propose technologies that combine many sensors such as a vibration sensor, an infrared sensor, a weight sensor, a sound wave sensor, a photo sensor, a timer, an acoustic sensor, an optical sensor, a temperature sensor, and the like. However, these technologies require many sensors and complex logic for processing and combining data collected from the many sensors.
Further, U.S. Pat. No. 9,395,078 proposes a technology for extracting changes in a vibration signal based on heating of the ingredients in chronological order and analyzing the signal at each step to determine whether the ingredients are boiling. However, this technology may be likely to misjudge when the standardized chronological-order logic is broken and may only be used in limited conditions (chronological flow, absence of external noise).
SUMMARY OF THE INVENTION
The present invention is to solve the above-mentioned problems, and the purpose of the present invention is to provide an artificial intelligence cooking device that can determine whether ingredients in a cooking vessel are boiling by inputting a vibration signal of the cooking vessel into an artificial intelligence model.
An aspect of the present invention provides an artificial intelligence cooking device including a heating portion for heating ingredients in a cooking vessel, a vibration sensor for detecting a vibration signal of the ingredients in the cooking vessel, and a processor configured to provide data corresponding to the vibration signal to an artificial intelligence model to obtain information about whether the ingredients in the cooking vessel are boiling, and perform control based on the obtained information.
According to the present invention, since whether the ingredients are boiling is determined using the artificial intelligence model, which learned properties of vibration (intensity, frequency, and pattern) generated by boiling of the ingredients, an accuracy of the determination on whether the ingredients are boiling can be improved.
Further, according to the present invention, data of a predetermined time period (e.g., 1 second) is input to the artificial intelligence model. Then, the artificial intelligence model can determine whether the ingredients are boiling by considering only data of a current time period (that is, without considering data of a previous time period together). That is, the present invention can be much less likely to misjudge and show a higher accuracy in the determination on whether the ingredients are boiling, compared to U.S. Pat. No. 9,395,078, which detects boiling by extracting a property based on a change in vibration signals in a chronological order.
Further, according to the present invention, despite a change in a type of the ingredients, a type of the cooking vessel, an amount of the ingredients, or the like, the accurate prediction on whether the ingredients are boiling may be achieved. Further, according to the present invention, since the vibration signal only needs to be processed in a usual signal processing scheme and then input into the artificial intelligence model, a processing algorithm may be simplified.
According to the present invention, when all of a plurality of information obtained corresponding to data of a plurality of consecutive time periods indicate that the ingredients in the cooking vessel are boiling, it is determined that the ingredients in the cooking vessel are boiling. Therefore, the accuracy of the prediction may be further improved.
According to the present invention, since whether the ingredients are boiling is detected and control is performed accordingly, overflow of the ingredients, fire, or the like can be prevented and inconvenience of the user of constantly checking whether the ingredients are boiling can be prevented. According to the present invention, cooking in accordance with various cooking schemes may be performed by only setting a temperature by the user.
According to the present invention, a vibration sensor module 106 is accommodated in an internal space of a main body 11 . Accordingly, sensing, by the vibration sensor 610 , of a vibration signal (e.g., ambient noise due to use of a cutting board, a mixer, or the like) generated from outside of an induction heating cooking device 1000 may be minimized. Further, according to the present invention, an outer holder 630 for receiving the vibration sensor module 106 therein is connected to and fixed to a plate 12 . Accordingly, sensing, by the vibration sensor 610 , of a vibration signal (e.g., vibration transmitted by the main body 11 ) transmitted through a structure other than the plate 12 may be minimized.
In an example, according to the present invention, the outer holder 630 is disposed in close contact with a lower face 15 of the plate 12 and surrounds side and the lower portions of the vibration sensor 610 . Accordingly, the sensing, by the vibration sensor 610 , of the vibration signal (e.g., ambient noise or the like due to use of a cutting board, a mixer, or the like) generated from the outside may be minimized.
According to the present invention, transmission of the vibration signal to a vibration sensor 710 through routes other than the plate 12 may be minimized by contacting a housing with the plate 12 by pressing of a pressing mechanism.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an AI device 100 according to an embodiment of the present invention.
FIG. 2 illustrates an AI server 200 according to an embodiment of the present invention.
FIG. 3 illustrates an AI system 1 according to an embodiment of the present invention.
FIG. 4 is a perspective view of an induction heating cooking device according to an embodiment of the present invention.
FIG. 5 is a cross-sectional view of II-IIâ² in FIG. 4 .
FIG. 6 is an exploded perspective view for illustrating a vibration sensor module 106 according to a first embodiment of the present invention.
FIG. 7 is an exploded perspective view for illustrating a vibration sensor module and a pressing mechanism according to another embodiment of the present invention.
FIG. 8 is a simplified diagram illustrating a circuit configuration of an induction heating cooking device.
FIG. 9 is a diagram illustrating a method for operating an artificial intelligence cooking device according to an embodiment of the present invention.
FIG. 10 illustrates 2D images of vibration signals when ingredients are boiling and of vibration signals when the ingredients are not boiling.
FIG. 11 illustrates diagrams for describing a method for generating an artificial intelligence model according to an embodiment of the present invention.
FIG. 12 is a view for illustrating a method for determining whether ingredients in a cooking vessel are boiling, according to an embodiment of the present invention.
FIG. 13 is a view for illustrating an operation of an artificial intelligence cooking device when ingredients are boiling, according to an embodiment of the present invention.
FIG. 14 is a diagram illustrating a method for operating an artificial intelligence cooking device according to another embodiment of the present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present invention are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present invention is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present invention are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components. In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.
The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.
Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function.
The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network. Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.
The supervised learning refers to a method of learning an artificial neural network when a label for learning data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the learning data is input to the artificial neural network. The unsupervised learning refers to a method of learning an artificial neural network when a label for learning data is not given. The reinforcement learning refers to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state.
Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep running is part of machine running. In the following, machine learning is used to mean deep running.
<Robot>
A robot refers to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot. Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.
The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air.
<Self-Driving>
Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user. For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set.
The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like. At this time, the self-driving vehicle may be regarded as a robot having a self-driving function.
<eXtended Reality (XR)>
Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world.
The MR technology is similar to the AR technology in that the real object and the virtual object are shown together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner.
The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device.
FIG. 1 illustrates an AI device 100 according to an embodiment of the present invention. The AI device 100 may be implemented by a stationary device or a mobile device, such as a TV, a projector, a mobile phone, a smartphone, a desktop computer, a notebook, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, a tablet PC, a wearable device, a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like.
Referring to FIG. 1 , the AI device 100 may include a communication unit 110 , an input unit 120 , a learning processor 130 , a sensing unit 140 , an output unit 150 , a memory 170 , and a processor 180 . The communication unit 110 can transmit and receive data to and from external devices such as other AI devices 100 a to 100 e and the AI server 200 by using wire/wireless communication technology. For example, the communication unit 110 can transmit and receive sensor information, a user input, a learning model, and a control signal to and from external devices.
The communication technology used by the communication unit 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetoothâ¢, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.
The input unit 120 can acquire various kinds of data. Further, the input unit 120 may include a camera for inputting a video signal, a microphone for receiving an audio signal, and a user input unit for receiving information from a user. The camera or the microphone may be treated as a sensor, and the signal acquired from the camera or the microphone can be referred to as sensing data or sensor information.
The input unit 120 can acquire a learning data for model learning and an input data to be used when an output is acquired by using learning model. The input unit 120 can acquire raw input data. In this case, the processor 180 or the learning processor 130 can extract an input feature by preprocessing the input data.
In addition, the learning processor 130 can learn a model composed of an artificial neural network by using learning data. The learned artificial neural network can be referred to as a learning model. The learning model can be used to an infer result value for new input data rather than learning data, and the inferred value can be used as a basis for determination to perform a certain operation.
Further, the learning processor 130 can perform AI processing together with the learning processor 240 of the AI server 200 ( FIG. 2 ). Further, the learning processor 130 may include a memory integrated or implemented in the AI device 100 . Alternatively, the learning processor 130 may be implemented by using the memory 170 , an external memory directly connected to the AI device 100 , or a memory held in an external device.
The sensing unit 140 can acquire at least one of internal information about the AI device 100 , ambient environment information about the AI device 100 , and user information by using various sensors. Examples of the sensors included in the sensing unit 140 include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, and a radar.
In addition, the output unit 150 can generate an output related to a visual sense, an auditory sense, or a haptic sense. Further, the output unit 150 may include a display unit for outputting time information, a speaker for outputting auditory information, and a haptic module for outputting haptic information.
The memory 170 can store data that supports various functions of the AI device 100 . For example, the memory 170 can store input data acquired by the input unit 120 , learning data, a learning model, a learning history, and the like. Also, the processor 180 can determine at least one executable operation of the AI device 100 based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor 180 can also control the components of the AI device 100 to execute the determined operation.
To this end, the processor 180 can request, search, receive, or utilize data of the learning processor 130 or the memory 170 . The processor 180 can also control the components of the AI device 100 to execute the predicted operation or the operation determined to be desirable among the at least one executable operation. When the connection of an external device is required to perform the determined operation, the processor 180 can generate a control signal for controlling the external device and may transmit the generated control signal to the external device.
In addition, the processor 180 can acquire intention information for the user input and determine the user's requirements based on the acquired intention information. The processor 180 can also acquire the intention information corresponding to the user input by using at least one of a speech to text (STT) engine for converting speech input into a text string or a natural language processing (NLP) engine for acquiring intention information of a natural language.
At least one of the STT engine or the NLP engine can be configured as an artificial neural network, at least part of which is learned according to the machine learning algorithm. At least one of the STT engine or the NLP engine can be learned by the learning processor 130 , be learned by the learning processor 240 of the AI server 200 , or be learned by their distributed processing.
In addition, the processor 180 can collect history information including the operation contents of the AI apparatus 100 or the user's feedback on the operation and store the collected history information in the memory 170 or the learning processor 130 or transmit the collected history information to the external device such as the AI server 200 . The collected history information can be used to update the learning model.
Further, the processor 180 can control at least part of the components of AI device 100 so as to drive an application program stored in memory 170 . Furthermore, the processor 180 may operate two or more of the components included in the AI device 100 in combination so as to drive the application program.
Next, FIG. 2 illustrates an AI server 200 according to an embodiment of the present invention. Referring to FIG. 2 , the AI server 200 refers to a device that learns an artificial neural network by using a machine learning algorithm or uses a learned artificial neural network. The AI server 200 may include a plurality of servers to perform distributed processing, or may be defined as a 5G network. Further, the AI server 200 may be included as a partial configuration of the AI device 100 , and may perform at least part of the AI processing together.
The AI server 200 may include a communication unit 210 , a memory 230 , a learning processor 240 , a processor 260 , and the like. The communication unit 210 can transmit and receive data to and from an external device such as the AI device 100 . The memory 230 may include a model storage unit 231 . The model storage unit 231 can store a learning or learned model (or an artificial neural network 231 a ) through the learning processor 240 .
Further, the learning processor 240 can learn the artificial neural network 231 a by using the learning data. The learning model may be used in a state of being mounted on the AI server 200 of the artificial neural network, or may be used in a state of being mounted on an external device such as the AI device 100 .
In addition, the learning model may be implemented in hardware, software, or a combination of hardware and software. If all or part of the learning models are implemented in software, one or more instructions that constitute the learning model may be stored in memory 230 . The processor 260 can also infer the result value for new input data by using the learning model and may generate a response or a control command based on the inferred result value.
Next, FIG. 3 illustrates an AI system 1 according to an embodiment of the present invention. Referring to FIG. 3 , in the AI system 1 , at least one of an AI server 200 , a robot 100 a , a self-driving vehicle 100 b , an XR device 100 c , a smartphone 100 d , or a home appliance 100 e is connected to a cloud network 10 . The robot 100 a , the self-driving vehicle 100 b , the XR device 100 c , the smartphone 100 d , or the home appliance 100 e , to which the AI technology is applied, can be referred to as AI devices 100 a to 100 e.
In addition, the cloud network 10 refers to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure. The cloud network 10 may be configured by using a 3G network, a 4G or LTE network, or a 5G network. That is, the devices 100 a to 100 e and 200 configuring the AI system 1 can be connected to each other through the cloud network 10 . In particular, each of the devices 100 a to 100 e and 200 may communicate with each other through a base station, but may directly communicate with each other without using a base station.
Further, the AI server 200 may include a server that performs AI processing and a server that performs operations on big data. The AI server 200 can be connected to at least one of the AI devices constituting the AI system 1 , that is, the robot 100 a , the self-driving vehicle 100 b , the XR device 100 c , the smartphone 100 d , or the home appliance 100 e through the cloud network 10 , and assist at least part of AI processing of the connected AI devices 100 a to 100 e.
Further, the AI server 200 can learn the artificial neural network according to the machine learning algorithm instead of the AI devices 100 a to 100 e , and directly store the learning model or transmit the learning model to the AI devices 100 a to 100 e . Also, the AI server 200 can receive input data from the AI devices 100 a to 100 e , infer the result value for the received input data by using the learning model, generate a response or a control command based on the inferred result value, and transmit the response or the control command to the AI devices 100 a to 100 e.
Alternatively, the AI devices 100 a to 100 e can infer the result value for the input data by directly using the learning model, and generate the response or the control command based on the inference result.
Hereinafter, various embodiments of the AI devices 100 a to 100 e to which the above-described technology is applied will be described. The AI devices 100 a to 100 e illustrated in FIG. 3 may be regarded as a specific embodiment of the AI device 100 illustrated in FIG. 1 .
<AI+Robot>
The robot 100 a , to which the AI technology is applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like. The robot 100 a may include a robot control module for controlling the operation, and the robot control module refers to a software module or a chip implementing the software module by hardware.
Further, the robot 100 a can acquire state information about the robot 100 a by using sensor information acquired from various kinds of sensors, detect (recognize) surrounding environment and objects, generate map data, determine the route and the travel plan, determine the response to user interaction, or determine the operation. The robot 100 a can also use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan.
In addition, the robot 100 a can perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the robot 100 a can recognize the surrounding environment and the objects by using the learning model, and determine the operation by using the recognized surrounding information or object information. The learning model may also be learned directly from the robot 100 a or be learned from an external device such as the AI server 200 .
Further, the robot 100 a can perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server 200 and the generated result may be received to perform the operation. The robot 100 a may also use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external apparatus to determine the travel route and the travel plan, and control the driving unit such that the robot 100 a travels along the determined travel route and travel plan.
Further, the map data may include object identifica
CLAIMS
Claims ( 19 )
The invention claimed is:
1. An artificial intelligence cooking device comprising:
a plate including a heater configured to heat ingredients in a cooking vessel placed on the plate;
a main body forming a lower outer surface of the artificial intelligence cooking device;
a vibration sensor module including a vibration sensor disposed below the plate and configured to detect a vibration signal of the ingredients in the cooking vessel transmitted through the plate, and a housing accommodating the vibration sensor therein;
a pressing mechanism including a spring and a spring supporter connected to a lower plate of the main body to support the spring and configured to press the vibration sensor module in a direction of the plate; and
a processor configured to:
determine, via an artificial intelligence model having learned properties of the vibration signal, whether or not the ingredients in the cooking vessel are boiling based on the detected vibration signal provided to the artificial intelligence model and the learned properties of the vibration signal; and
output information indicating whether or not the ingredients are boiling based on the determination.
2. The artificial intelligence cooking device of claim 1 , wherein the artificial intelligence model is a neural network trained by labeling information about whether ingredients are boiling on training data corresponding to a respective vibration signal.
3. The artificial intelligence cooking device of claim 1 , wherein the vibration sensor directly contacts the plate.
4. The artificial intelligence cooking device of claim 1 , wherein the artificial intelligence model is a neural network trained by labeling temperature information on training data corresponding to a respective vibration signal.
5. The artificial intelligence cooking device of claim 4 , wherein the processor is configured to:
provide data corresponding to the vibration signal to the artificial intelligence model; and
determine whether or not the ingredients in the cooking vessel boil are boiling, based on temperature information output from the artificial intelligence model using the provided data.
6. The artificial intelligence cooking device of claim 1 , wherein data corresponding to the vibration signal includes a feature vector representing at least one of a vibration intensity, a frequency, or a pattern of the vibration signal.
7. The artificial intelligence cooking device of claim 1 , wherein the processor is configured to:
provide data of a first time period corresponding to the vibration signal to the artificial intelligence model to obtain first information about whether or not the ingredients in the cooking vessel are boiling;
provide data of a second time period that is a next time period after the first time period, to the artificial intelligence model to obtain second information on whether or not the ingredients of the cooking vessel are boiling; and
determine that the ingredients in the cooking vessel are boiling when the first information and the second information indicate that the ingredient are boiling.
8. The artificial intelligence cooking device of claim 1 , further comprising:
a power converter configured to supply a voltage to the heater,
wherein the processor is configured to control the power converter such that a heating intensity of the heater is reduced or a heating operation of the heater is stopped when the processor determines the ingredients in the cooking vessel are boiling.
9. The artificial intelligence cooking device of claim 8 , wherein the processor is further configured to:
provide data corresponding to the vibration signal to the artificial intelligence model; and
control the power converter such that a heating intensity of the heater is reduced or a heating operation of the heater is stopped when a temperature of the ingredients is above a preset value based on temperature information output from the artificial intelligence model using the provided data.
10. The artificial intelligence cooking device of claim 1 , further comprising:
wherein the vibration sensor module is accommodated in an internal space defined in the main body.
11. The artificial intelligence cooking device of claim 10 , wherein the vibration sensor module further includes an outer holder receiving the vibration sensor therein,
wherein the outer holder is disposed below the plate, and
wherein the outer holder has an open upper face thereof such that an upper face of the vibration sensor faces with a lower face of the plate.
12. The artificial intelligence cooking device of claim 11 , wherein the outer holder is in contact with the lower face of the plate.
13. The artificial intelligence cooking device of claim 11 , wherein the outer holder surrounds side and lower portions of the vibration sensor.
14. The artificial intelligence cooking device of claim 11 , wherein the vibration sensor module further includes:
an inner holder receiving the vibration sensor therein; and
a connector connected to the outer holder and the inner holder to support the inner holder.
15. A method of controlling an artificial intelligence cooking device, the method comprising:
detecting, via a vibration sensor module disposed below a heating plate, a vibration signal of ingredients in the cooking device transmitted through the heating plate;
determining, via an artificial intelligence model having learned properties of the vibration signal, whether or not the ingredients in the cooking device are boiling based on the detected vibration signal provided to the artificial intelligence model and the learned properties of the vibration signal; and
outputting information indicating whether or not the ingredients are boiling based on the determination,
wherein the vibration sensor module comprises:
a vibration sensor below the heating plate,
a housing accommodating the vibration sensor therein,
a main body forming a lower outer surface of the artificial intelligence cooking device, and
a pressing mechanism including a spring and a spring supporter connected to a lower plate of the main body to support the spring and configured to press the vibration sensor module in a direction of the heating plate.
16. The method of claim 15 , wherein the artificial intelligence model is a neural network trained by labeling information about whether ingredients are boiling on training data corresponding to a respective vibration signal.
17. The method of claim 15 , wherein the vibration sensor directly contacts the plate.
18. The method of claim 15 , wherein the artificial intelligence model is a neural network trained by labeling temperature information on training data corresponding to a respective vibration signal.
19. A non-transitory computer readable medium storing instructions that when executed by a processor, perform the following:
receiving, via a vibration sensor module disposed below a heating plate, a vibration signal of ingredients in the cooking device transmitted through the heating plate;
determining, using an artificial intelligence model having learned properties of the vibration signal, whether or not the ingredients in the cooking device are boiling based on the detected vibration signal provided to the artificial intelligence model and the learned properties of the vibration signal; and
outputting information indicating whether or not the ingredients are boiling based on the determination,
wherein the vibration sensor module comprises:
a vibration sensor below the heating plate,
a housing accommodating the vibration sensor therein,
a main body forming a lower outer surface of the artificial intelligence cooking device, and
a pressing mechanism including a spring and a spring supporter connected to a lower plate of the main body to support the spring and configured to press the vibration sensor module in a direction of the heating plate.
US16/617,434
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