阅读说明：本技术 电子装置及其控制方法 (Electronic device and control method thereof ) 是由 咸诚欥 权兑俊 V.伊耶 安大盛 于 2019-07-31 设计创作，主要内容包括：提供了电子装置和控制该电子装置的方法。该电子装置包括：通信器；存储器,存储关于其中物联网(IoT)设备所在地点的信息；以及处理器,被配置为基于通过通信器接收的用于控制位于特定地点的IoT设备的控制信号,基于存储在存储器中的关于该地点的信息来控制位于特定地点的IoT设备。处理器还被配置为从可穿戴设备接收基于可穿戴设备的运动而生成的运动信息,识别对应于运动信息的地点,并且将识别到的地点作为位于距可穿戴设备预定距离内的IoT设备的地点的信息存储在存储器中。(An electronic device and a method of controlling the same are provided. The electronic device includes: a communicator; a memory storing information about a location where an Internet of things (IoT) device is located; and a processor configured to control the IoT device located at the specific location based on the information about the location stored in the memory based on the control signal received through the communicator to control the IoT device located at the specific location. The processor is further configured to receive motion information from the wearable device generated based on motion of the wearable device, identify a location corresponding to the motion information, and store the identified location in the memory as information of a location of an IoT device located within a predetermined distance from the wearable device.)

1. An electronic device, comprising:

a communicator;

a memory; and

a processor configured to:

receive motion information from the wearable device, the motion information generated based on a motion of the wearable device,

a location corresponding to the motion information is identified,

storing the identified location as location information of an Internet of things (IoT) device located within a predetermined distance from the wearable device, an

Controlling the IoT device located at the particular location based on the location information based on a control signal received through the communicator for controlling the IoT device located at the particular location.

2. The electronic device as set forth in claim 1,

wherein the memory stores information about a place matched to each of the plurality of activities, and

wherein the processor is further configured to:

based on the motion information, identifying an activity of a user wearing the wearable device, the activity corresponding to the motion information, and

based on the identified activities and information about locations that match each of the plurality of activities, a location corresponding to the movement information is identified.

3. The electronic device of claim 2, wherein the processor is further configured to:

based on identifying that there are the plurality of activities corresponding to the athletic information, identifying an activity corresponding to the athletic information among the plurality of activities based on state information of a plurality of IoT devices located in locations matching the plurality of identified activities.

4. The electronic device of claim 3, wherein the processor is further configured to:

identifying power states of the plurality of IoT devices located in locations matching the plurality of identified activities,

identifying an activity related to an IoT device powered on among the plurality of identified activities, an

Location information of the location matching the identified activity is stored in a memory as information about the location of the IoT device.

5. The electronic device of claim 3, wherein the processor is further configured to:

identifying power states of the plurality of IoT devices located at locations matching the plurality of identified activities and weights set for the plurality of IoT devices,

identifying an activity among the plurality of identified activities based on the power state and the weight, an

Storing location information for the location that matches the identified activity in a memory as information about the location of the IoT device.

6. The electronic device of claim 5, wherein the processor is further configured to:

based on the weights, storing location information of locations matching activities related to an IoT device having a highest weight among the plurality of IoT devices in a memory as information about a location of the IoT device based on the power of the plurality of IoT devices being in an on state.

7. The electronic device of claim 5, wherein the processor is further configured to:

based on the power supplies of the plurality of IoT devices being in an off state, or the power supplies of some of the plurality of IoT devices being in an on state and the power supplies of the remaining IoT devices being in an off state, adjusting weights set for the IoT devices whose power supplies are in the off state based on the times at which the power supplies of the IoT devices whose power supplies are in the off state are off, and

based on the weights, storing location information of locations matching activities related to an IoT device having a highest weight among the plurality of IoT devices in a memory as information about a location of the IoT device.

8. The electronic device of claim 2, wherein the processor is further configured to:

based on identifying that the plurality of activities corresponding to the motion information exist, identifying an activity corresponding to the motion information among the plurality of activities based on sound information of an IoT device received from a wearable device.

9. The electronic device of claim 1, wherein the processor is further configured to:

based on received voice commands for controlling IoT devices located at a particular location, identifying voice characteristics of a user issuing the voice commands,

based on recognizing that a number of times a particular user issues voice commands for controlling an IoT device located at a particular location is equal to or greater than a predetermined number of times, storing the particular user by matching the user to the particular location based on voice characteristics, an

Identifying a specific place matching a specific user among the plurality of places having the same name based on a voice command received from the specific user for controlling the IoT device, and controlling the IoT device according to the voice command.

10. The electronic device of claim 1, wherein the processor is further configured to:

storing the wearable device by matching the wearable device with a particular location based on identifying that the wearable device is located at the particular location for a period of time equal to or greater than a predetermined period of time, an

Based on a voice command received from the wearable device for controlling the IoT device, a specific place matching the wearable device is identified among the plurality of places having the same name, and the IoT device is controlled according to the voice command.

11. A method of controlling an electronic device, comprising:

receiving motion information from a wearable device, the motion information generated based on a motion of the wearable device;

identifying a location corresponding to the motion information;

storing the identified location as location information of an Internet of things (IoT) device located within a predetermined distance from the wearable device;

receiving a control signal for controlling an IoT device located at a particular location; and

controlling an IoT device located at a particular location based on the location information.

12. The control method of claim 11, wherein the identifying comprises:

based on the motion information, identifying an activity of a user wearing a wearable device corresponding to the motion information, and

based on the identified activities and information about locations that match each of the plurality of activities, a location corresponding to the movement information is identified.

13. The control method of claim 12, wherein the identifying further comprises:

based on identifying that there are the plurality of activities corresponding to the athletic information, identifying an activity corresponding to the athletic information among the plurality of activities based on state information of the plurality of IoT devices located in locations matching the plurality of identified activities.

14. The control method according to claim 13, wherein the storing includes:

identifying power states of the plurality of IoT devices located in locations matching the plurality of identified activities,

identifying an activity related to an IoT device powered on among the plurality of identified activities, an

Storing location information of the location matching the identified activity as information about the location of the IoT device.

15. The control method according to claim 13, wherein the storing includes:

identifying power states of the plurality of IoT devices located at locations matching the plurality of identified activities and weights set for the plurality of IoT devices,

identifying an activity among the plurality of identified activities based on power status and weight, an

Storing location information of the location matching the identified activity as information about the location of the IoT device.

Technical Field

The present disclosure relates to an electronic device and a control method thereof, and more particularly, to an electronic device capable of controlling an internet of Things (IoT) device and a control method thereof.

Background

With the development of semiconductor technology and wireless communication technology, various technologies are being developed. In particular, recently, an IoT technology, which is a technology capable of transmitting and receiving data between things in real time, has been developed.

IoT technology is an evolved form of conventional Ubiquitous Sensor Network (USN) or machine to machine (M2M) communication, and is characterized by implementing interworking between things equipped with a communication function (hereinafter, referred to as "IoT devices") by connecting them to a network.

With IoT technology, users have been able to easily control IoT devices without time and place constraints. For example, users have been able to control power of a Television (TV) by inputting a command to turn on the TV to an electronic device such as a smart phone, or control power of an air conditioner by issuing a voice command such as "turn on the air conditioner".

Meanwhile, IoT devices may be located in different locations in the home. For example, the television may be located in a living room and the air conditioner may be located in a bedroom.

However, the same type of IoT device may be located in different places in the home, as the case may be. For example, a television may be located in each of the living room and the bedroom, respectively, and a light may be located in each of the living room, the kitchen, the bedroom, and the bathroom, respectively.

In this case, if the user issues a voice command (such as "turn on the light in the living room"), the power of the light located in the living room among the lights in the home should be controlled.

To this end, the electronic device controlling the IoT devices should remember information about each IoT device where each IoT device is located.

The above information is provided merely as background information to aid in understanding the present disclosure. No determination has been made as to whether any of the above can be applied as prior art to the present disclosure, and no assertion has been made.

Disclosure of Invention

[ problem ] to provide a method for producing a semiconductor device

The present disclosure is designed to solve the above-described needs, and an object of the present disclosure is to provide an electronic apparatus storing information on a place where each IoT device is located for each IoT device, and a control method thereof.

[ technical solution ] A method for producing a semiconductor device

According to an aspect of the present disclosure, an electronic device is provided. The electronic device includes a communicator; a memory storing information about where an IoT device is located; and a processor configured to control the IoT device located at the specific location based on the information about the location stored in the memory based on receiving a control signal for controlling the IoT device located at the specific location through the communicator. The processor may receive motion information generated based on motion of a user wearing the wearable device from the wearable device, identify a location corresponding to the motion information, and store the identified location in the memory as information of a location of an IoT device within a predetermined distance from the wearable device.

Further, the memory may store information about a location matching each of the plurality of activities, and the processor may identify an activity of a user wearing the wearable device corresponding to the motion information based on the motion information, and identify the location corresponding to the motion information based on the identified activity and the information about the location matching each of the plurality of activities.

Further, based on identifying that there are a plurality of activities corresponding to the athletic information, the processor may identify an activity corresponding to the athletic information from among the plurality of activities based on state information of a plurality of IoT devices located in locations matching the plurality of identified activities.

Further, the processor may identify power states of a plurality of IoT devices located at locations matching the plurality of identified activities, identify, among the plurality of identified activities, activities related to the IoT devices that are powered on, and store the locations matching the identified activities in the memory as information about the locations of the IoT devices.

Further, the processor may identify power states of a plurality of IoT devices located at locations matching the plurality of identified activities and weights set for the plurality of IoT devices, identify an activity among the plurality of identified activities based on the power states and weights, and store the location matching the identified activity in the memory as information about the location of the IoT device.

Further, the processor may store, in the memory, a location matching activity related to an IoT device having a highest weight among the plurality of IoT devices based on the weight as information about the location of the IoT device based on the weight based on the power of the plurality of IoT devices being in the on state.

Further, the processor may adjust the weights set for IoT devices that are powered off based on when the power of the IoT devices that are powered off are turned off based on when the power of the IoT devices that are powered off based on when the power of some of the plurality of IoT devices is powered off and the power of the remaining IoT devices is powered off, and store locations that match activities related to the IoT device with the highest weight among the plurality of IoT devices as information about the locations of the IoT devices in the memory based on the weights.

Further, based on identifying that there are a plurality of activities corresponding to the motion information, the processor may identify an activity corresponding to the motion information among the plurality of activities based on sound information of the IoT device received from the wearable device.

Further, the processor may identify voice characteristics of a user issuing a voice command based on the received voice command for controlling the IoT device located at the particular location. Then, the processor may store the specific user by matching the user with the specific location based on the voice feature based on recognizing that the number of times the specific user issues the voice command for controlling the IoT device located at the specific location is equal to or greater than a predetermined number of times, and recognize the specific location matching the specific user from among a plurality of locations having the same name based on the voice command for controlling the IoT device received from the specific user, and control the IoT device according to the voice command.

Further, the processor may store the wearable device by matching the wearable device to a particular location based on identifying that the wearable device is located at the particular location for a period of time equal to or greater than a predetermined period of time. Then, the processor may identify a specific place matching the wearable device from among a plurality of places having the same name based on a voice command received from the wearable device to control the IoT device, and control the IoT device according to the voice command.

According to another aspect of the present disclosure, a control method of an electronic device is provided. The control method comprises the following steps: the method includes receiving a control signal for controlling an IoT device located at a particular location, and controlling the IoT device located at the particular location based on information about the location in which the IoT device is located. Further, the control method may further include the steps of: the method includes receiving, from a wearable device, motion information generated based on motion of a user wearing the wearable device, identifying a location corresponding to the motion information, and storing the identified location as information about a location of an IoT device located within a predetermined distance from the wearable device.

Further, in the identifying, based on the motion information, an activity of a user wearing the wearable device corresponding to the motion information may be identified, and based on the identified activity and information on a place matching each of the plurality of activities, a place corresponding to the motion information may be identified.

Further, in the identifying, if it is identified that there are a plurality of activities corresponding to the movement information, an activity corresponding to the movement information may be identified among the plurality of activities based on state information of a plurality of IoT devices located at places matching the plurality of identified activities.

Meanwhile, in the storage, power states of a plurality of IoT devices located at places matching the plurality of identified activities may be identified, activities related to the IoT devices whose power is in an on state among the plurality of identified activities may be identified, and the places matching the identified activities may be stored as information on the places of the IoT devices.

Further, in the storage, power states of a plurality of IoT devices located at locations matching the plurality of identified activities and weights set for the plurality of IoT devices may be identified, activities among the plurality of identified activities may be identified based on the power states and weights, and the locations matching the identified activities may be stored as information about the locations of the IoT devices.

Further, in the storage, if the power of the plurality of IoT devices is in an on state, a place matching an activity related to an IoT device having a highest weight among the plurality of IoT devices may be stored as information on a place of the IoT device based on the weight.

Further, in the storage, if the power of the plurality of IoT devices is in an off state, or the power of some of the plurality of IoT devices is in an on state and the power of the remaining IoT devices is in an off state, the weight set for the IoT device whose power is in the off state may be adjusted based on the time the power of the IoT device whose power is in the off state is turned off, and a place matching the activity related to the IoT device having the highest weight among the plurality of IoT devices may be stored as information about the place of the IoT device based on the weight.

Meanwhile, in the identifying, if it is identified that there are a plurality of activities corresponding to the motion information, an activity corresponding to the motion information may be identified from among the plurality of activities based on sound information of the IoT device received from the wearable device.

Further, in the controlling, based on receiving a voice command for controlling the IoT device located at the specific location, a voice feature of the user who issued the voice command may be recognized. Then, if it is recognized that the number of times a specific user issues a voice command for controlling an IoT device located at a specific location is equal to or greater than a predetermined number of times, the specific user may be stored by matching the user with the specific location based on the voice feature. Further, based on a voice command received from a specific user for controlling the IoT device, a specific place matching the specific user among a plurality of places having the same name may be recognized, and the IoT device may be controlled according to the voice command.

Further, in the controlling, if it is recognized that the wearable device is located at the specific place for a period of time equal to or greater than a predetermined period of time, the wearable device may be stored by matching the wearable device with the specific place. Further, based on a voice command received from the wearable device for controlling the IoT device, a specific place matching the wearable device among a plurality of places having the same name may be recognized, and the IoT device may be controlled according to the voice command.

[ advantageous effects ]

According to various embodiments of the present disclosure as described above, an electronic apparatus may be provided that sets a location where each IoT device is located based on a user's motion without manually inputting the location where the IoT device is located.

Other aspects, advantages, and salient features of the disclosure will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses various embodiments of the disclosure.

Drawings

The above and other aspects, features and advantages of particular embodiments of the present disclosure will become more apparent from the following description when taken in conjunction with the accompanying drawings, in which:

fig. 1 is a schematic diagram illustrating an electronic system according to an embodiment of the present disclosure;

FIG. 2 is a block diagram illustrating an electronic device according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram illustrating motion information according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram illustrating motion information according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram illustrating information about locations matching each of a plurality of activities in accordance with an embodiment of the present disclosure;

fig. 6 is a schematic diagram illustrating information about a location where an internet of things (IoT) device is located, according to an embodiment of the present disclosure;

fig. 7 is a schematic diagram illustrating matching information about a place with an IoT device, in accordance with an embodiment of the present disclosure;

fig. 8 is a schematic diagram illustrating matching information about a place with an IoT device, in accordance with an embodiment of the present disclosure;

fig. 9 is a schematic diagram illustrating an embodiment of controlling an IoT device located at a particular location, in accordance with an embodiment of the present disclosure;

fig. 10 is a schematic diagram illustrating a case where there are a plurality of activities corresponding to motion information according to an embodiment of the present disclosure;

FIG. 11 is a schematic diagram illustrating how much motion information is identified as matching a particular activity according to an embodiment of the present disclosure;

fig. 12 is a schematic diagram illustrating identifying activity corresponding to motion information based on status information of IoT devices in accordance with an embodiment of the present disclosure;

fig. 13 is a schematic diagram illustrating identifying activities corresponding to motion information based on power states of IoT devices and weights set for the IoT devices in accordance with an embodiment of the present disclosure;

fig. 14 is a schematic diagram illustrating identifying activities corresponding to motion information based on a time at which a power supply of an IoT device is turned off, in accordance with an embodiment of the present disclosure;

fig. 15 is a schematic diagram illustrating identifying activities corresponding to motion information based on a time at which a power supply of an IoT device is turned off, in accordance with an embodiment of the present disclosure;

fig. 16 is a schematic diagram illustrating identifying activity corresponding to motion information based on sound information of IoT devices in accordance with an embodiment of the present disclosure;

fig. 17 is a schematic diagram illustrating controlling an IoT device located at a particular location based on a particular user voice or a particular wearable device, in accordance with an embodiment of the present disclosure;

FIG. 18 is a flow chart illustrating operation of an electronic device according to an embodiment of the present disclosure; and is

Fig. 19 is a block diagram of an electronic device according to an embodiment of the disclosure.

Throughout the drawings, it should be noted that the same reference numerals are used to describe the same or similar elements, features and structures.

Detailed Description

The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the disclosure as defined by the claims and their equivalents. It includes various specific details that are helpful for understanding, but these are to be considered merely illustrative. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Moreover, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are not limited to bibliographic meanings, but are used only by the inventors to enable a clear and consistent understanding of the disclosure. Therefore, it will be apparent to those skilled in the art that the following descriptions of the various embodiments of the present disclosure are provided for illustration only and not for the purpose of limiting the disclosure as defined by the appended claims and their equivalents.

It should be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more of such surfaces.

Moreover, some terms are specified by the applicant himself. The meaning of the terms may be construed according to the definitions in the present specification or may be construed based on the entire contents of the present specification and general technical knowledge in the related technical field.

Further, in explaining the present disclosure, in the case where it is determined that a detailed explanation of a related known function or feature may unnecessarily obscure the gist of the present disclosure, the detailed explanation will be omitted or omitted.

Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings.

Fig. 1 is a schematic diagram illustrating an electronic system according to an embodiment of the present disclosure.

Referring to fig. 1, an electronic system 1000 according to an embodiment of the present disclosure may include at least one electronic device 100, an electronic apparatus 200, and a wearable device 300.

Here, the electronic device 100 may be an IoT device to which an IoT technology is applied. The IoT technology refers to a technology of connecting an IoT device equipped with a communication function to a network, and thereby transmitting and receiving information between people and things or between things and things.

Meanwhile, hereinafter, description will be made based on a case in which the electronic device 100 is implemented as an IoT device, but this does not mean that the present disclosure must be applied only to the IoT device. The technical idea of the present disclosure can be applied to various electronic devices equipped with a communication function.

As shown in fig. 1, IoT devices that can be connected to a network based on IoT may be various electronic devices 100 such as an air conditioner, a washing machine, a refrigerator, and a robot cleaner. However, this is only one example, and the type of IoT device is not limited to the above. As an example, an IoT device may be everything around, such as a smartphone, computer, laptop, air purifier, automobile, door lock, gaming machine, and security device.

The IoT device may form an IoT network with the electronic apparatus 200.

To this end, the IoT device may be communicatively connected to the electronic apparatus 200. In particular, the IoT device may be connected to the electronic apparatus 200 through wireless communication.

Further, the IoT devices may be indirectly communicatively connected to the electronic apparatus 200 through the IoT hub. In this case, the IoT devices may connect to the IoT hub through a communication link, such as ZigBee, Wi-Fi, and bluetooth.

Wearable device 300 may generate motion information. Here, the motion information may be generated based on a motion of the user wearing the wearable device 300.

For example, in the case where a user wearing the wearable device 300 on his wrist moves his wrist from left to right, the wearable device 300 may generate motion information including information that the wearable device 300 moves from left to right.

To this end, the wearable device 300 may include various sensors capable of detecting user motion, such as an acceleration sensor and a gyro sensor.

Thereafter, the wearable device 300 may transmit the motion information to the electronic apparatus 200. To this end, the wearable device 300 may include various communication chips such as a Wi-Fi chip, a bluetooth chip, a wireless communication chip, and a Near Field Communication (NFC) chip.

Meanwhile, in fig. 1, the wearable device 300 is illustrated in the form of a smart watch. However, this is merely one example, and the wearable device 300 may be implemented as various types of devices, such as in the form of patches, that may be worn around the wrist, arm, waist, or ankle of the user and detect the user's motion.

The electronic apparatus 200 may be communicatively connected to the IoT device and constitute an IoT network.

Here, the electronic apparatus 200 may be not only a server but also a specific IoT device. For example, the electronic device 200 may be a smartphone, a smart television, a computer, a laptop computer, or the like.

The electronic apparatus 200 may control the IoT device through the IoT network.

For example, if a control signal for turning on the power of the air conditioner is received from the smartphone, the electronic device 200 may turn on the power of the air conditioner in the off state by transmitting the received control signal to the air conditioner.

Further, if a voice command (such as "turn on the air conditioner") is received, the electronic device 200 may turn on the power of the air conditioner in a turned-off state by transmitting a control signal corresponding to the received voice command to the air conditioner.

Specifically, the electronic apparatus 200 may control an IoT device located at a specific location through an IoT network.

For example, in the case where a television is located in each of the living room and the bedroom, respectively, if a control signal for turning on the power of the television in the living room is received from the smartphone, the electronic apparatus 200 may turn on the power of the television in the living room by transmitting the received control signal to the television in the living room. Here, the control signal is not transmitted to the television in the bedroom.

To this end, the electronic apparatus 200 may use information about where each IoT device is located.

Meanwhile, in the conventional art, a place where each IoT device is located is manually set for each IoT device. For example, after running the IoT management application on the smartphone, the location of each IoT device is manually entered for each IoT device, and thus the location where each IoT device is located is set.

However, in the case where the location of the IoT device is manually set in the conventional art, there are some problems. In the case where the IoT devices are numerous, the locations of all the IoT devices should be set one by one, and thus the user may feel inconvenience. Further, in a case where the locations of all IoT devices are set, if the arrangement of furniture is changed or a new house is moved in, the user should set the locations of the IoT devices again, and thus inconvenience may be increased.

To overcome such problems, the present disclosure aims to set the location where each IoT device is located without manual input by the user.

In particular, the present disclosure is directed to setting the location where each IoT device is located by using motion information received from the wearable device 300. This will be described in more detail hereinafter with reference to the accompanying drawings.

Fig. 2 is a block diagram illustrating an electronic device according to an embodiment of the present disclosure.

Referring to fig. 2, an electronic device 200 according to an embodiment of the present disclosure may include a communicator 210, a memory 220, and a processor 230.

The communicator 210 may perform communication with the IoT device and transmit and receive various data.

In particular, the communicator 210 may receive control signals for controlling other IoT devices from the IoT devices and transmit the received control signals to the aforementioned other IoT devices. For example, the communicator 210 may receive a control signal for turning on a power of an air conditioner from a smart phone and transmit the received control signal to the air conditioner.

In addition, the communicator 210 may receive a voice command for controlling other IoT devices from the IoT device and transmit a control signal corresponding to the received voice command to the aforementioned other IoT devices. For example, the communicator 210 may receive a voice command (such as "turn on an air conditioner") from a smartphone, and transmit a control signal corresponding to the received voice command to the air conditioner.

Further, the communicator 210 may receive status information of the IoT device from the IoT device and transmit the received status information to other IoT devices. Here, the state information may be information on the surrounding environment detected by the IoT device or the power state of the IoT device itself. For example, the communicator 210 may receive information about current humidity in the surrounding environment from an air conditioner and transmit the received information about current humidity in the surrounding environment to a smartphone.

Meanwhile, the foregoing embodiments are only examples, and the communicator 210 may transmit and receive various data related to the IoT service to and from the IoT device.

To this end, the communicator 210 may be connected with the IoT device through wireless communication. For example, the communicator 210 may connect with the IoT device by using various Wireless communication technologies such as 5th generation Mobile communication (5G), long-term evolution (LTE), LTE-advanced (LTE-a), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Universal Mobile Telecommunications System (UMTS), Wireless Broadband (WiBro), and Global System for Mobile Communications (GSM).

Meanwhile, the wireless communication may include near field communication. For example, the communicator 210 may connect with the IoT devices by using near field communication technologies such as Wi-Fi direct, bluetooth, Near Field Communication (NFC), and ZigBee.

To this end, the communicator 210 may include a Wi-Fi module, a bluetooth module, a wireless communication chip, and the like.

Meanwhile, the communicator 210 may also be connected with the IoT device through wired communication. For example, the communicator 210 may connect with the IoT device by using wired communication technologies such as Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), recommended standard 232 (RS-232), and Plain Old Telephone Service (POTS).

The memory 220 may store various programs and data required for the operation of the electronic device 200.

In particular, the memory 220 may store information about where the IoT device is located. In particular, the memory 220 may match a particular location for each IoT device and store the location.

For example, the memory 220 may store information about where IoT devices are located, such as televisions matching a living room, microwave ovens matching a kitchen, and lights matching a bathroom.

Further, the memory 220 may store information about locations matching each of the plurality of activities.

Here, the place matched to each of the plurality of activities may vary according to the feature of each activity. In particular, the place matched with each of the plurality of activities may be a representative place in which the corresponding activity mainly occurs.

For example, the memory 220 may store information of locations matching events, such as TV viewing events matching living rooms, dining events matching kitchens, and shower events matching bathrooms.

Meanwhile, the memory 220 may be implemented as various storage media such as a hard disk, a nonvolatile storage, and a volatile storage.

The processor 230 controls the overall operation of the electronic device 200. To this end, the processor 230 may include one or more of a Central Processing Unit (CPU), an Application Processor (AP), or a Communication Processor (CP).

Further, the processor 230 may control the IoT device.

In particular, in case of receiving a control signal for controlling the IoT device through the communicator 210, the processor 230 may control the IoT device by transmitting the received control signal to the IoT device.

Here, control signals may be received from various IoT devices.

For example, in the case where a user command for controlling a specific IoT device is input through an IoT management application installed on a smartphone, the processor 230 may receive a control signal for controlling the specific IoT device from the smartphone. The processor 230 may then control the particular IoT device by sending the received control signal to the IoT device.

Further, in the case where a voice command for controlling a specific IoT device is input to the microphone-equipped IoT device, the processor 230 may receive the voice command from the microphone-equipped IoT device. The processor 230 may then control the particular IoT device by sending a control signal corresponding to the received voice command to the IoT device.

Meanwhile, the processor 230 may receive status information of the IoT device from the IoT device and transmit the received status information to other IoT devices. Here, the state information may be information on the surrounding environment detected by the IoT device or the power state of the IoT device itself.

For example, the processor 230 may receive information about the current humidity in the ambient environment from the air conditioner and transmit the received information about the current humidity in the ambient environment to the smartphone.

Processor 230 may control IoT devices located at a particular location. In particular, in case of receiving a control signal for controlling an IoT device located at a specific location through the communicator 210, the processor 230 may control the IoT device located at the specific location by transmitting the received control signal to the aforementioned IoT device located at the specific location.

Here, the processor 230 may use information about the place stored in the memory 220. Here, the information on the place is information on a place where each IoT device is located, and means information in which a specific place is matched with each IoT device and stored.

For example, the information about the location stored in the memory 220 may include a TV matched to a living room, a microwave oven matched to a kitchen, a lamp matched to a bathroom, and the like.

Accordingly, in case of receiving a voice command (such as "turn on the lamp in the bathroom"), the processor 230 may recognize the lamp in the bathroom among the lamps in the home based on the stored information on the place and transmit a signal for controlling the power of the lamp to the lamp in the bathroom, thereby controlling the power of the lamp in the bathroom.

Meanwhile, information about the place as described above may be stored based on motion information received from the wearable device 300.

To this end, the processor 230 may receive motion information from the wearable device 300. Here, the motion information may be generated based on a motion of the user wearing the wearable device 300.

Fig. 3 is a schematic diagram illustrating motion information according to an embodiment of the present disclosure.

Referring to fig. 3, in a case where a user wearing the wearable device 300 around the wrist picks up the remote controller and drops it down, the processor 230 may receive motion information including information that the wearable device 300 is moved from top to bottom from the wearable device 300.

Fig. 4 is a schematic diagram illustrating motion information according to an embodiment of the present disclosure.

Referring to fig. 4, in the case where a user wearing the wearable device 300 around the wrist opens the refrigerator door, the processor 230 may receive motion information including information that the wearable device 300 is moved from front to back from the wearable device 300.

The processor 230 may then identify an activity corresponding to the received movement information.

In particular, processor 230 may identify activities corresponding to the movement information through machine learning. Here, machine learning is a field of artificial intelligence technology, and is a technology of performing self-learning by data supplied from the outside and predicting an output value with respect to an input value. For this reason, the electronic device 200 according to an embodiment of the present disclosure may treat activities related to various types of motion information as big data and store the data.

For example, in the case of receiving motion information from the wearable device 300 that the wearable device 300 is moved from top to bottom, the processor 230 may recognize through machine learning that the user picks up the remote controller and puts it down, and recognize that the activity of the user wearing the wearable device 300 is a TV viewing activity.

Further, in the event that motion information is received from the wearable device 300 that the wearable device 300 is repeatedly moved around, the processor 230 may identify that the user is washing hands by machine learning, and identify that the activity of the user wearing the wearable device 300 is a washing activity.

Meanwhile, the foregoing embodiments are merely examples, and the electronic apparatus 200 according to an embodiment of the present disclosure may recognize various activities such as dining activities and sound sleep activities through machine learning. However, machine learning is merely an example, and the electronic device 200 according to an embodiment of the present disclosure may recognize activities corresponding to motion information by using various artificial intelligence techniques (such as deep learning). That is, the technical idea of the present disclosure is not necessarily limited to machine learning.

Processor 230 may then identify a location corresponding to the movement information based on the user's activity.

To this end, the processor 230 may use information stored in the memory 220 about locations that match each of the plurality of activities. Hereinafter, a description in this respect will be made with reference to fig. 5.

Fig. 5 is a schematic diagram illustrating information on a place matched with each of a plurality of activities according to an embodiment of the present disclosure.

The electronic device 200 may store information about a location matching each of the plurality of activities. In particular, the electronic device 200 may match a specific location for each activity and store the location.

For example, referring to fig. 5, the electronic device 200 may store information about places matching activities, such as TV viewing activities matching a living room, dining activities matching a kitchen, and washing activities matching a bathroom.

Then, the processor 230 may identify a location corresponding to the motion information of the user based on the information on the location matched with each of the plurality of activities.

As in the foregoing embodiment, in the case where it is recognized that the event corresponding to the sports information is a TV viewing event, the processor 230 may recognize a living room as a place matching the TV viewing event as a place corresponding to the sports information.

Also, if it is recognized that the activity corresponding to the motion information is a dining activity, the processor 230 may recognize a kitchen as a place matching the dining activity as a place corresponding to the motion information.

The processor 230 may then store the identified location as information about the location of the IoT device.

In particular, the processor 230 may receive location information from each of the wearable device 300 and the IoT device, and if it is identified that the IoT device is within a predetermined distance from the wearable device 300, the processor 230 may store information about the location identified from the motion information as information about the location of the IoT device.

For example, if it is identified that the location corresponding to the motion information is a living room, and it is identified that a television is located within a radius from the wearable device 3001 m, the processor 230 may match the living room with the TV and store the location.

By the method described above, the processor 230 may match a location for each IoT device and store the location.

Fig. 6 is a schematic diagram illustrating information about a location where an IoT device is located, according to an embodiment of the present disclosure.

Referring to fig. 6, the processor 230 may match a living room with a TV, match a bedroom with a lamp, match a bathroom with a faucet, and match a kitchen with a microwave.

Accordingly, in the case of receiving a voice command from a user (such as "turn on the television in the living room"), the processor 230 may recognize the television in the living room from among the plurality of televisions located in the home and control the power of the television in the living room.

Meanwhile, for the method of recognizing whether the IoT device is within a predetermined distance from the wearable device 300, various techniques may be applied.

For example, each of the wearable device 300 and the IoT device may receive a GPS signal including information about a location from a satellite by using a Global Positioning System (GPS) chip and transmit the received GPS signal to the electronic apparatus 200. Accordingly, the processor 230 may identify an IoT device within a predetermined distance from the wearable device 300 by using the information on the location received from each of the wearable device 300 and the IoT device, and match the information on the location identified from the motion information with the IoT device and store the information.

Alternatively, the processor 230 may identify whether the IoT device is within a predetermined distance from the wearable device 300 by using a beacon that is a near field wireless communication device based on the bluetooth protocol.

Further, processor 230 may identify whether the IoT device is within a predetermined distance from wearable device 300 based on an NFC tag installed on the IoT device tagged as wearable device 300.

Further, the processor 230 may identify whether the IoT device is within a predetermined distance from the wearable device 300 based on the signal strength between the wearable device 300 and the IoT device.

In particular, the processor 230 may receive information about signal strength between the wearable device 300 and the IoT device from at least one of the wearable device 300 or the IoT device, and if the received signal strength is equal to or greater than a predetermined threshold, the processor 230 may identify that the IoT device is within a predetermined distance from the wearable device 300.

Meanwhile, in identifying whether the IoT device is within a predetermined distance from the wearable device 300, the processor 230 may identify based on the area in which the wearable device 300 is located.

Hereinafter, description will be made with reference to fig. 7 and 8.

Fig. 7 and 8 are schematic diagrams illustrating matching information about a place with an IoT device, according to various embodiments of the present disclosure.

Hereinafter, for convenience of explanation, description will be made based on an example in which a place corresponding to motion information is identified as a living room.

Referring to fig. 7, a plurality of IoT devices in a home may be respectively located in various places such as a bedroom, a living room, and a bathroom.

In this case, the processor 230 may identify the area in which each IoT device is located based on the signal strength between the AP and the IoT device and the signal strength between the IoT devices.

In particular, referring to fig. 7, processor 230 may receive signal strength between the AP and the IoT device from at least one of AP 750 or the IoT device. Processor 230 may then identify the extent of distance of each IoT device from AP 750 based on the received signal strength.

For example, if the signal strength between the AP and the IoT device is strong, the processor 230 may identify that the IoT device is located relatively close to the AP. Conversely, if the signal strength between the AP and the IoT device is weak, the processor 230 may identify that the IoT device is located relatively far away from the AP.

Further, the processor 230 may receive signal strength between IoT devices from at least one IoT device. The processor 230 may then identify a degree of distance between the IoT devices based on the received signal strength.

For example, if the signal strength between IoT devices is strong, the processor 230 may identify that IoT devices are located in proximity to each other. Conversely, if the signal strength between IoT devices is weak, the processor 230 may identify that the IoT devices are relatively far from each other.

Meanwhile, for a technology of recognizing a degree of a relative distance between IoT devices based on signal strength, various technical ideas may be applied. For example, the processor 230 may identify the degree of relative distance between IoT devices by using a valley coefficient algorithm.

Thereafter, the processor 230 may identify an area where each IoT device is located based on a degree of distance between the AP and the IoT device and a degree of distance between the IoT devices.

Referring to fig. 8, the processor 230 may recognize that the lamps 710, 730, 740 are located in the first, third, and fourth regions, respectively, the microwave oven 720 and the refrigerator 721 are located in the second region, the AP 750, the first TV751, and the air conditioner 752 are located in the fifth region, and the second TV760 is located in the sixth region.

Then, the processor 230 may identify the area where the wearable device 300 is located based on the location information of the wearable device 300 received from the wearable device 300. Here, as described above, the location information of the wearable device 300 may be included in the GPS signal and the beacon signal, and also (andalso), the location information may be at least one of information on signal strength between the AP and the wearable device 300 and information on signal strength between the wearable device 300 and the IoT device.

For example, if the signal strength between the AP and the wearable device 300 is strong, the processor 230 may identify that the wearable device 300 is located relatively close to the AP. Conversely, if the signal strength between the wearable device 300 and the AP is weak, the processor 230 may identify that the wearable device 300 is located relatively far away from the AP.

Further, if the signal strength between the wearable device 300 and the IoT device is strong, the processor 230 may identify that the wearable device 300 is located relatively close to the IoT device. Conversely, if the signal strength between the wearable device 300 and the IoT device is weak, the processor 230 may identify that the wearable device 300 is located relatively far away from the IoT device.

Further, processor 230 may identify an IoT device located in the same area as the area in which wearable device 300 is located as an IoT device within a predetermined distance from wearable device 300.

For example, if it is identified that the wearable device 300 is located in the fifth area, the processor 230 may identify an IoT device located in the fifth area as an IoT device within a predetermined distance from the wearable device 300.

Further, the processor 230 may store the location identified as corresponding to the motion information as information about the location of IoT devices located in the same area as the wearable device 300.

In the foregoing embodiment, the processor 230 may recognize the fifth area as the living room, and match the living room with the AP 750, the first tv751 and the air conditioner 752 located in the fifth area, and store the location.

As described above, by dividing the location into a plurality of areas and matching information about the location with each IoT device based on the area where the IoT device is located, the location of each IoT device can be set more accurately.

Further, in the event that a new IoT device is added to a particular area, the processor 230 may match information about the location with the new IoT device and store the location of the new IoT device by merely identifying the area in which the IoT device is located.

That is, in the aforementioned embodiment, in the case where a new IoT device is added to the fifth area identified as the living room, when the device is located in the fifth area, the processor 230 may match the living room with the new IoT device and store the location.

Accordingly, in case of receiving a control signal for controlling an IoT device located at a specific location, the processor 230 may control the IoT device located at the specific location.

Fig. 9 is a schematic diagram illustrating controlling an IoT device located at a particular location according to an embodiment of the present disclosure.

Referring to fig. 9, in the case where a user located in a bedroom issues a voice command (such as "turn on a TV in a living room"), the processor 230 may recognize a TV751 matching the living room and transmit a signal for controlling power to the TV751 matching the living room.

Fig. 10 is a schematic diagram illustrating a case where there are a plurality of activities corresponding to motion information according to an embodiment of the present disclosure.

According to circumstances, the processor 230 may identify that there are a plurality of activities corresponding to the motion information.

For example, in case of receiving motion information from the wearable device 300 that the wearable device 300 moves from top to bottom, the processor 230 may recognize through machine learning that the user picks up the remote controller and drops it, or recognize that the user picks up a spoon and drops it.

In this case, processor 230 may first identify a degree of matching of the motion information to a particular activity through machine learning.

Referring to fig. 10, the processor 230 may recognize through machine learning that the received motion information matches 70% with TV viewing activity, 3% with sleeping activity, 10% with washing activity, and 40% with dining activity.

Then, the processor 230 may identify an activity having a matching probability equal to or greater than a predetermined matching probability as an activity corresponding to the received motion information.

Here, the predetermined matching probability may be set in various ways according to a user command. For example, the predetermined matching probability may be set to 60%.

In the case where the predetermined matching probability is set to 60%, as described above, in the foregoing embodiment, the processor 230 may recognize that the activity corresponding to the motion information is a TV viewing activity.

Fig. 10 is a diagram showing a case where there are a plurality of activities whose matching probability is equal to or greater than a predetermined matching probability identified.

As described above, in the case where it is recognized that there are a plurality of activities corresponding to the motion information, the processor 230 may recognize to what extent the motion information matches a specific activity.

Then, the processor 230 may identify an activity having a matching probability equal to or greater than a predetermined matching probability as an activity corresponding to the received motion information.

Meanwhile, according to circumstances, the processor 230 may identify that there are a plurality of activities having a matching probability equal to or greater than a predetermined matching probability.

Fig. 11 is a schematic diagram illustrating how much motion information is identified to match a specific activity according to an embodiment of the present disclosure.

Referring to fig. 11, in case of receiving motion information from the wearable device 300 that the wearable device 300 moves from top to bottom, the processor 230 may recognize that the received motion information matches 70% with TV viewing activity, 3% with sleep activity, 10% with washing activity, and 65% with dining activity.

Here, in the case where the predetermined matching probability is 60%, the processor 230 may recognize a TV viewing activity and a dining activity having a matching probability equal to or greater than the predetermined matching probability as activities corresponding to the sports information.

In this case, the processor 230 may identify an activity corresponding to the motion information from among a plurality of activities based on the state information of the IoT device.

In particular, processor 230 may identify an activity corresponding to the motion information from among the plurality of activities based on state information of IoT devices located at locations matching each of the plurality of activities.

Hereinafter, description will be made with reference to fig. 12 to 15.

Meanwhile, in the following, it will be described that, in the case where there are identified a plurality of activities having matching probabilities equal to or greater than a predetermined matching probability, the activity corresponding to the motion information is identified in consideration of the state information of the IoT device. However, this does not mean that in the case where there are multiple activities identified for which the match probability is equal to or greater than the predetermined match probability, the state information of the IoT device must be considered. That is, in the present disclosure, in the case where it is recognized that there are a plurality of activities corresponding to the motion information, the activities corresponding to the motion information may be recognized in consideration of the state information of the IoT device regardless of the predetermined matching probability.

Fig. 12 is a schematic diagram illustrating identifying activities corresponding to motion information based on status information of IoT devices in accordance with an embodiment of the present disclosure.

If a plurality of activities having matching probabilities equal to or greater than the predetermined matching probability are identified, the processor 230 may identify an activity corresponding to the movement information from the plurality of activities based on the state information of the IoT devices located at the location matching each of the plurality of activities.

Here, the IoT devices located at locations matching each of the plurality of activities may have been previously set. For example, referring again to fig. 5, the location matching the TV viewing activity is the living room, and the IoT device located at the location matching the TV viewing activity may have been previously set as the TV. Further, the place matching the dining activity is a kitchen, and the IoT device located at the place matching the dining activity may have been set as a microwave oven in advance.

Meanwhile, the state information of the IoT device may be a power state of the IoT device.

That is, if there are multiple activities having matching probabilities equal to or greater than the predetermined matching probability, the processor 230 may identify the power states of the IoT devices located at the locations matching each of the multiple activities.

For example, in the case where a TV viewing activity and a dining activity are identified as activities having a matching probability equal to or greater than a predetermined matching probability, the processor 230 may identify the power state of each of the television and the microwave oven.

To this end, the processor 230 may receive information on the power state from each of the television and the microwave oven.

The processor 230 may then identify an activity related to the IoT device that is powered on from among the plurality of activities.

Referring to fig. 12, in case that the power of the TV is in an on state and the power of the microwave oven is in an off state, the processor 230 may recognize an activity related to the TV whose power is in the on state (i.e., recognize a TV viewing activity).

The processor 230 may then store the location matching the identified activity in the memory 220 as information about the location of the IoT device.

That is, in the foregoing embodiment, the processor 230 may identify the living room as a location matching the TV viewing activity, and match the living room with an IoT device within a predetermined distance from the wearable device 300, and store the location.

This reflects that the activity of the user is likely to be activity related to an IoT device that is powered on, and thus, the activity corresponding to the movement information may be more accurately identified.

Fig. 13 is a schematic diagram illustrating identifying activities corresponding to motion information based on power states of IoT devices and weights set for the IoT devices in accordance with an embodiment of the present disclosure.

The processor 230 may further identify an activity corresponding to the movement information from among the plurality of activities in consideration of the power state of the IoT device and the weight set for the IoT device.

Referring to fig. 13, an electronic apparatus 200 according to an embodiment of the present disclosure may store a table in which different weights are set for each IoT device. For example, the electronic apparatus 200 may store a table in which a weight of 30% is set for the TV and a weight of 50% (range) is set for the microwave oven.

Meanwhile, the processor 230 may identify an activity corresponding to the motion information from among a plurality of activities based on the power state of the IoT device and the weight set for the IoT device.

In particular, if multiple activities are identified for which the match probability is equal to or greater than the predetermined match probability, the processor 230 may identify the power state of the IoT devices located at the locations that match each of the multiple activities. Then, if it is recognized that the power of all IoT devices is in the on state, the processor 230 may recognize, from among the plurality of activities, an activity related to an IoT device having a high weight as an activity corresponding to the motion information.

For example, in the case where the activities having the matching probability equal to or greater than the predetermined matching probability are TV viewing activities and dining activities, the processor 230 may identify the power state of each of the TV and the microwave oven.

Then, as shown in fig. 13, in case that the power sources of both the TV and the microwave oven are in the on state, the processor 230 may recognize an activity (e.g., a dining activity) related to the microwave oven having a high weight as an activity corresponding to the sports information.

Further, the processor 230 may store the location matching the identified activity as information about the location of the IoT device in the memory 220.

That is, in the foregoing embodiment, the processor 230 may identify the kitchen as a place matching the dining activity, and match the kitchen with an IoT device within a predetermined distance from the wearable device 300, and store the place.

Meanwhile, the aforementioned weight may be set in advance according to a user command.

Further, the processor 230 may set a weight for each IoT device. In particular, the processor 230 may set a higher weight for IoT devices used in a relatively short period of time than for IoT devices used in a longer period of time.

To this end, the processor 230 may receive log data (logging data) from the IoT device. Here, the log data may include a login time (i.e., a time when the power is turned on) and a logout time (i.e., a time when the power is turned off).

In particular, the processor 230 may identify a usage time of each IoT device based on a login time and a logout time included in the log data, and set a higher weight for IoT devices used in a relatively short period of time than IoT devices used in a long period of time.

For example, based on the log data, the processor 230 may set a higher weight for a microwave oven used for a short period of time than for a television used for a relatively long period of time during cooking.

This reflects that, in a case where both the IoT device for the short period of time and the IoT device for the long period of time are in the on state, the user is likely to have made a certain motion in the vicinity of the IoT device for the short period of time.

Accordingly, the activity corresponding to the motion information can be more accurately identified.

Meanwhile, in the foregoing embodiments, it is described that the activity related to the IoT device having the high weight is identified as the activity corresponding to the motion information. However, the processor 230 may identify an activity corresponding to the motion information based on a value that adds the match probability and the weight.

Referring to fig. 13, the processor 230 may compare a total value of 100% in the case where the motion information received from the wearable device 300 matches 70% with the TV viewing activity and the weight set for the TV is 30% with a total value of 115% in the case where the motion information received from the wearable device 300 matches 65% with the dining activity and the weight set for the microwave oven is 50%, and recognize the activity related to the IoT device having a relatively greater value as the activity corresponding to the motion information.

That is, in the foregoing embodiment, since the value of adding the matching probability for the dining activity and the weight set for the microwave oven is greater than the value of adding the matching probability for the TV viewing activity and the weight set for the TV, the processor 230 may recognize the dining activity as an activity related to the microwave oven as an activity corresponding to the motion information.

Fig. 14 and 15 are schematic diagrams illustrating identifying activities corresponding to motion information based on a time at which a power supply of an IoT device is turned off, according to embodiments of the present disclosure.

As described above, in the event that there are multiple activities for which the match probability is equal to or greater than the predetermined match probability, the processor 230 may identify the power state of the IoT devices located in the location that matches each of the multiple activities.

For example, in the case where the activities having the matching probability equal to or greater than the predetermined matching probability are TV viewing activities and dining activities, the processor 230 may identify the power state of each of the TV and the microwave oven.

Further, in the presence of an IoT device in an off state, the processor 230 may identify a time when the IoT device is off.

In particular, the processor 230 may identify a time when the IoT device is turned off based on log data received from the IoT device.

Fig. 14 is a schematic diagram illustrating identifying activities corresponding to motion information based on a time at which a power supply of an IoT device is turned off, according to an embodiment of the present disclosure.

Referring to fig. 14, in case that the power state of the microwave oven is in the off state, the processor 230 may recognize that the microwave oven is turned off at 18:35(07121835) on 12 th 7 th based on log data received from the microwave oven.

In this case, the processor 230 may adjust the weights based on the time the IoT device is turned off.

In particular, if it is recognized that the power of the IoT device is turned off within a predetermined time period from the current time, the processor 230 may apply the weight as it is, and if it is recognized that the power of the IoT device is turned off before the predetermined time period from the current time, the processor 230 may adjust the weight to be lower.

Here, the predetermined period of time may be set according to a user command. Hereinafter, for convenience of explanation, description will be made on the assumption that the predetermined time is set to 10 minutes.

For example, as shown in fig. 14, if the current time and date is 7 months, 12 days, 18:40(07121840), and it is recognized that the microwave oven is closed in 7 months, 12 days, 18:35(07121835), the processor 230 may apply the set weight of 50% because the microwave oven is closed within a predetermined time period from the current time.

Accordingly, the processor 230 may identify an activity related to the microwave oven having a high weight (i.e., a dining activity) as an activity corresponding to the exercise information.

This reflects that even if the power of a particular IoT device is in an off state, the user is likely to have been in motion near the particular IoT device if the power is off for a predetermined period of time.

Accordingly, the activity corresponding to the motion information can be more accurately identified.

Fig. 15 is a schematic diagram illustrating identifying activities corresponding to motion information based on a time at which a power supply of an IoT device is turned off, according to an embodiment of the present disclosure.

Referring to fig. 15, if it is recognized that the current time and date is 7 months, 12 days, 21:30(07122130), and the microwave oven is turned off at 7 months, 11 days, 18:35(07111835), because the microwave oven is turned off before a predetermined time period from the current time, the processor 230 may set the set weight to be lower than 50%. For example, if it is recognized that the microwave oven is turned off for 24 hours or more, the processor 230 may adjust the weight of the microwave oven to 0%.

Accordingly, the processor 230 may recognize an activity related to a TV having a high weight between the TV and the microwave oven (i.e., a TV viewing activity) as an activity corresponding to the motion information.

The processor 230 may then store the location matching the identified activity as information about the location of the IoT device in the memory 220.

That is, in the foregoing embodiment, the processor 230 may identify the living room as a location matching the TV viewing activity, and match the living room with an IoT device within a predetermined distance from the wearable device 300, and store the location.

As described above, by adjusting the weights based on the time the IoT device is turned off, the activity corresponding to the motion information may be more accurately identified.

Meanwhile, here, the description is made based on an embodiment in which power supplies of some IoT devices among a plurality of IoT devices are in an on state and power supplies of the remaining IoT devices are in an off state. However, the foregoing technical idea may be applied even when the power of all the plurality of IoT devices is in the off state.

For example, in the case where the TV is turned off for a predetermined period of time and the microwave oven is turned off before the predetermined period of time, the processor 230 may adjust the weight of the microwave oven to be low and recognize the TV viewing activity as an activity corresponding to the sports information. Meanwhile, in case that the TV is turned off before the predetermined period of time and the microwave oven is turned off within the predetermined period of time, the processor 230 may adjust the weight of the TV to be low and recognize the dining activity as an activity corresponding to the sports information. Further, in the case where both the TV and the microwave oven are turned off within a predetermined period of time, the processor 230 may not adjust the weight set as it is, and recognize the dining activity as an activity corresponding to the exercise information.

Fig. 16 is a schematic diagram illustrating identifying activity corresponding to motion information based on sound information of IoT devices, according to an embodiment of the present disclosure.

As described above, the processor 230 may identify a plurality of activities corresponding to the movement information.

In this case, the processor 230 may identify an activity corresponding to the motion information among a plurality of activities based on the sound information of the IoT device.

In particular, if it is identified that there are multiple activities corresponding to the motion information, the processor 230 may send a signal requesting sound information to the wearable device 300. Then, when sound information is received from the wearable device 300, the processor 230 may identify an IoT device corresponding to the received sound information.

To this end, the electronic apparatus 200 according to an embodiment of the present disclosure may store the sound data output of each IoT device. Here, the sound data may be inherent sound data generated from a product.

For example, where the sound information received from the wearable device 300 corresponds to sound data generated from a microwave oven, the processor 230 may identify the microwave oven as an IoT device corresponding to the received sound information.

The processor 230 may then identify an activity related to the microwave oven (e.g., a dining activity) as an activity corresponding to the athletic information.

Fig. 17 is a schematic diagram illustrating controlling an IoT device located at a particular location based on a particular user voice or a particular wearable device, in accordance with an embodiment of the present disclosure.

If a voice command to control a particular IoT device is received, the processor 230 may identify the voice characteristics of the user that issued the voice command. In particular, processor 230 may identify speech characteristics of the user based on energy, frequency bandwidth, speech-to-noise ratio, etc. of the user's speech.

Then, the processor 230 may recognize whether a specific user issued voice commands for controlling IoT devices located at a specific location a number of times equal to or greater than a predetermined number of times based on the voice characteristics of the user.

Here, the predetermined number of times may be set in various ways according to a user command. For example, the predetermined number of times may be set to 50 times.

Further, in the case where it is recognized that the number of times a specific user issues a voice command for controlling an IoT device located at a specific location is equal to or greater than a predetermined number of times, the processor 230 may match the specific location with the specific user and store the location.

For example, in the case where the user a controls the lamp located in the room 1 a number of times equal to or more than a predetermined number of times, the processor 230 may match the user a with the room 1 and store the location.

As described above, the electronic apparatus 200 according to an embodiment of the present disclosure may match a specific place for each user and store the place.

Accordingly, in a case where there are a plurality of places having the same name in a home, if a voice command for controlling an IoT device located at the place having the name is received, the electronic apparatus 200 according to an embodiment of the present disclosure may recognize the user a who issued the voice command and control an IoT device located at a place matching the recognized user.

Referring to fig. 17, in the case where there are three rooms in the home and user a issues a voice command (such as "turn on the lights of my room") at a particular location 1510, the processor 230 may recognize that the user who issued the voice command is user a based on the voice characteristics of the user. Then, the processor 230 may identify a room 11520 matching the user a among the plurality of rooms, and control power of the lamps located in the room 11520 from among the plurality of lamps located in the home.

Meanwhile, if it is recognized that the wearable device 300 is located at a specific place for a period of time equal to or greater than a predetermined period of time, the processor 230 may match the wearable device 300 with the specific place and store the place.

For example, if it is recognized that wearable device 300 is located in room 1 for 8 hours or more continuously, processor 230 may match wearable device 300 with room 1 and store the location.

Accordingly, in a case where there are a plurality of places having the same name in a home, if a voice command for controlling an IoT device located at the place having the name is received, the electronic apparatus 200 according to an embodiment of the present disclosure may recognize the wearable device 300 transmitting the voice command and control an IoT device located at a place matching the recognized wearable device 300.

For example, as shown in fig. 17, where there are three rooms in a home and a user a wearing the wearable device 300 issues a voice command such as "turn on my room light" at a particular location 1510, the processor 230 may receive the voice command from the wearable device 300, identify a room 11520 matching the wearable device 300 from among the multiple rooms, and control the power of the lights located in the room 11520 from among the multiple lights located in the home.

Fig. 18 is a flowchart illustrating operations of an electronic device according to an embodiment of the present disclosure.

In operation S1810, the electronic apparatus 200 may receive motion information generated based on a motion of a user wearing the wearable device 300 from the wearable device 300.

Here, the motion information may be generated based on a motion of the user wearing the wearable device 300.

For example, in the case where a user wearing the wearable device 300 around the wrist picks up the remote controller and puts it down, the electronic apparatus 200 may receive motion information including information that the wearable device 300 is moved from top to bottom from the wearable device 300.

The electronic device 200 may then identify an activity corresponding to the received athletic information.

In particular, the electronic device 200 may recognize an activity corresponding to the motion information through machine learning.

For example, in the case of receiving motion information from the wearable device 300 that the wearable device 300 is moved from top to bottom, the electronic apparatus 200 may recognize through machine learning that the user picks up the remote controller and puts it down, and the activity of the user wearing the wearable device 300 is a TV viewing activity.

Then, the electronic apparatus 200 may identify a location corresponding to the motion information based on the user' S activity in operation S1820.

For this, the electronic apparatus 200 may use information about a place matched with each of the plurality of activities stored in the electronic apparatus 200.

Here, the information on the place matched with each of the plurality of activities may be information on a place matched with an activity, such as a TV viewing activity matched with a living room, a dining activity matched with a kitchen, and a washing activity matched with a bathroom.

Further, the electronic apparatus 200 may identify a location corresponding to the motion information of the user based on the information on the location matched with each of the plurality of activities.

As in the foregoing embodiment, in the case where it is recognized that the event corresponding to the sports information is a TV viewing event, the electronic apparatus 200 may recognize the living room as a place matching the TV viewing event as a place corresponding to the sports information.

Then, the electronic apparatus 200 may store the identified location as information on locations of IoT devices within a predetermined distance from the wearable device 300 in operation S1830.

In particular, the electronic apparatus 200 may receive location information from each of the wearable device 300 and the IoT device, and if it is recognized that the IoT device is within a predetermined distance from the wearable device 300, the electronic apparatus 200 may store the location information recognized according to the motion information as information on the location of the IoT device.

For example, if it is recognized that a place corresponding to the motion information is a living room and a TV is located within a radius from the wearable device 3001 m, the electronic apparatus 200 may match the living room with the TV and store the place.

By the method described above, the electronic apparatus 200 may match a location for each IoT device and store the location.

Accordingly, in the case of receiving a voice command (such as "turn on the television in the living room") from the user, the electronic apparatus 200 may recognize the television in the living room from among the plurality of televisions located in the home and control the power of the television in the living room.

Fig. 19 is a block diagram of an electronic device according to an embodiment of the disclosure.

Referring to fig. 19, an electronic device 200' according to an embodiment of the present disclosure may include a communicator 210, a memory 220, a voice processor 240, and a processor 230. Hereinafter, with respect to the portion overlapping with the foregoing portion, description will be omitted or omitted.

The memory 220 may store an Operating System (OS) for controlling the overall operation of the components of the electronic device 200 'and commands or data related to the components of the electronic device 200'.

Accordingly, the processor 230 may control a plurality of hardware or software components of the electronic device 200' by using various commands or data stored in the memory 220, load commands or data received from at least one other component onto the volatile storage and process the commands or data, and store various data in the non-volatile storage.

The processor 230 is a component that controls the overall operation of the electronic device 200'.

In particular, the processor 230 includes a Random Access Memory (RAM) (not shown), a read-only memory (ROM) (not shown), a CPU (not shown), first to nth interfaces (not shown), and a bus (not shown). Here, the RAM, the ROM, the CPU, the first to nth interfaces, and the like may be connected to each other through a bus.

Processor 230 may identify an activity corresponding to the motion information received from wearable device 300. The processor 230 may then identify a location that matches the activity and store the identified location as information about the location of the IoT devices within a predetermined distance from the wearable device 300.

Meanwhile, the processor 230 may be applied to various fields, such as the field of geriatric care.

In particular, the processor 230 may identify whether the situation is a situation in which the user is at risk based on the activities of the user and the state information of the IoT device identified through machine learning.

Here, the state information may be information detected by a sensor of the IoT device.

For example, in a case where state information in which the user lies in the bed is received from the bed 7 hours before the current time, and state information in which the user does not lie in the bed is received from the bed at the current time, if it is recognized that the current activity of the user is a sleep activity, the processor 230 may recognize that the user falls off the bed.

The processor 230 may then protect the user in a dangerous situation by sending a warning message to a secured user terminal device or the like that the user has fallen.

The voice processor 240 may convert the voice signal received from the IoT device into text. According to an embodiment of the present disclosure, the speech processor 240 may convert a speech signal into text by using a Speech To Text (STT) algorithm.

Meanwhile, the methods according to the foregoing various embodiments of the present disclosure may be implemented only by software/hardware upgrade of a conventional electronic device.

Furthermore, the foregoing various embodiments of the present disclosure may be implemented by an embedded server or an external server provided on the electronic device 200.

Meanwhile, in the case where the electronic apparatus 200 'according to an embodiment of the present disclosure is implemented as a display device like a smart TV, the electronic apparatus 200' may further include a display (not shown), a microphone (not shown), an image receiver (not shown), an audio outputter (not shown), and various sensors (not shown).

Meanwhile, a non-transitory computer readable medium storing a program sequentially executing the control method of the electronic device 200 according to the present disclosure may be provided.

In particular, the non-transitory computer readable medium may comprise the steps of: the method includes receiving motion information generated based on a motion of a user wearing the wearable device 300 from the wearable device 300, identifying a location corresponding to the motion information, and storing the identified location as information about locations of IoT devices within a predetermined distance from the wearable device 300.

Meanwhile, a non-transitory computer-readable medium refers to a medium that stores data semi-permanently and is readable by a machine, and not a medium that stores data for a short time, such as registers, caches, and memories. In particular, the aforementioned various applications or programs may be provided while stored in a non-transitory computer readable medium such as a Compact Disc (CD), a Digital Versatile Disc (DVD), a hard disk, a blu-ray disc, a USB, a memory card, a ROM, and the like.

While the disclosure has been shown and described with reference to various embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents.