Hub device, multi-device system including hub device and multiple devices, and operation method thereof

Abstract

A hub device, a multi-device system including the hub device, and an operation method thereof may include: the hub device converting received voice input into text; the hub device identifying a device capable of performing an operation corresponding to the text; identifying which device stores a function determination model corresponding to the device capable of performing the operation corresponding to the text from among the hub device and a plurality of other devices connected to the hub device; and sending at least a portion of the text to the identified device based on the identification that the device storing the function determination model is a device different from the hub device, wherein the hub device includes a hardware processor.

Description

Technical Field

[0001] The present disclosure relates to a hub device, a multi-device system including the hub device, and a method for operating the hub device. For example, according to an embodiment, the hub device can determine an operation execution device (e.g., an Internet of Things (IoT) device) for performing an operation in a multi-device environment according to a user's intention (e.g., including in a voice input received from the user) from among the hub device itself and a plurality of other electronic devices. According to an embodiment, the hub device can control the determined operation execution device. Background Art

[0002] With the development of multimedia technology and network technology, users can receive various services by using IoT devices such as air purifiers or televisions (TVs). In particular, with the development of voice recognition technology such as virtual personal assistant technology, users can input voice (e.g., speech) to a device (e.g., a listening device) and receive a response message to the voice input through a service providing agent (e.g., a virtual assistant).

[0003] However, in a multi-device system such as a home network environment including a plurality of IoT devices, when a user wants to receive a service by using an unregistered IoT device that has not been registered to interact through voice input, etc., the user has to inconveniently register the IoT device (e.g., including selecting an IoT device to provide the service). Specifically, because the types of services provided by a plurality of IoT devices are different, a technology capable of recognizing an intention included in a user's voice input and effectively providing a corresponding service is required.

[0004] In order to recognize the intention included in the user's voice input, artificial intelligence (AI) technology can be used, and rule-based natural language understanding (NLU) technology can also be used. When the user's voice input is received through the hub device, because the hub device may not directly select a device for providing a service according to the voice input, and has to control the device by using a separate voice assistant service providing server, the user may have to pay a network usage fee, and because the voice assistant service providing server is used, the response speed is reduced. Summary of the invention

[0005] Solution to the problem

[0006] The present disclosure relates to a hub device, a multi-device system including a hub device and multiple devices, and an operating method thereof, and more specifically, to a hub device, a multi-device system, and an operating method thereof, wherein the hub device receives a user's voice input, automatically determines a device for performing an operation according to the user's intention based on the received voice input, and provides multiple pieces of information required to perform a service according to the determined device.

[0007] Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the embodiments presented in the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The above and other aspects, features and advantages of specific embodiments of the present disclosure will become more apparent from the following description in conjunction with the accompanying drawings, and the same reference numerals represent the same structural elements, wherein:

[0009] Figure 1 is a block diagram illustrating some elements of a multi-device system including a hub device, a voice assistant server, an Internet of Things (IoT) server, and a plurality of devices according to an embodiment of the present disclosure;

[0010] Figure 2 is a block diagram illustrating elements of a hub device according to an embodiment of the present disclosure;

[0011] Figure 3 is a block diagram illustrating elements of a voice assistant server according to an embodiment of the present disclosure;

[0012] Figure 4 is a block diagram illustrating elements of an IoT server according to an embodiment of the present disclosure;

[0013] Figure 5 is a block diagram illustrating some elements of a plurality of devices according to an embodiment of the present disclosure;

[0014] Figure 6 is a flow chart of a method for controlling a device based on voice input performed by a hub device according to an embodiment of the present disclosure;

[0015] Figure 7 is a flowchart of a method performed by a hub device to provide at least a portion of a text according to a user's voice input to one of a hub device, a voice assistant server, and an operation execution device according to an embodiment of the present disclosure;

[0016] Figure 8 is a flowchart of a method of operating a hub device, a voice assistant server, an IoT server, and an operation execution device according to an embodiment of the present disclosure;

[0017] Fig. 9 is a flowchart of a method of operating a hub device and operating an execution device according to an embodiment of the present disclosure;

[0018] Fig.10 is a flowchart of a method of operating a hub device and operating an execution device according to an embodiment of the present disclosure;

[0019] Fig.11is a flow chart of a method of operating a hub device, a voice assistant server, an IoT server, a third-party IoT server, and a third-party device according to an embodiment of the present disclosure;

[0020] Fig. 12A is a conceptual diagram illustrating operations of a hub device and a plurality of devices according to an embodiment of the present disclosure;

[0021] Fig. 12B is a conceptual diagram illustrating operations of a hub device and a plurality of devices according to an embodiment of the present disclosure;

[0022] Fig.13 is a flowchart illustrating a method in which a hub device determines an operation execution device based on a voice signal received from a listening device and transmits a text to a device storing a function determination model corresponding to the operation execution device according to an embodiment of the present disclosure;

[0023] Fig.14 is a flowchart illustrating a method in which a hub device transmits a text to a device storing a function determination model corresponding to an operation execution device according to an embodiment of the present disclosure;

[0024] Fig.15 is a flow chart illustrating a method of operating a hub device, a voice assistant server, and a listening device according to an embodiment of the present disclosure;

[0025] Fig.16 is a flowchart illustrating a method of operating a hub device, a voice assistant server, a listening device, and an operation execution device according to an embodiment of the present disclosure;

[0026] Fig.17 is a diagram illustrating an example of an operation execution device update function determination model according to an embodiment of the present disclosure;

[0027] Fig.18 is a flowchart illustrating a method of operating a hub device, a voice assistant server, an IoT server, and an operation execution device according to an embodiment of the present disclosure;

[0028] Fig.19 is a flow chart illustrating a method of operating a hub device, a voice assistant server, an IoT server, and a new device according to an embodiment of the present disclosure;

[0029] Fig. 20 is a diagram illustrating a multi-device system environment including a hub device, a voice assistant server, and a plurality of devices; and

[0030] Fig.21A and Fig. 21B is a diagram illustrating a voice assistant model that can be executed by a hub device and a voice assistant server according to an embodiment of the present disclosure.

[0031] Best Mode for Carrying Out the Invention

[0032] According to an embodiment of the present disclosure, a method for controlling a device based on voice input performed by a hub device includes: receiving a user's voice input; converting the received voice input into text by performing automatic speech recognition (ASR); determining an operation execution device based on the text by using a device determination model; identifying a device that stores a function determination model corresponding to the determined operation execution device from among a plurality of devices connected to the hub device; and providing at least a portion of the text to the identified device.

[0033] The device determination model may include a first natural language understanding (NLU) model configured to analyze text and determine the operation execution device based on an analysis result of the text.

[0034] The function determination model may include a second NLU model configured to analyze at least a portion of the text and obtain operation information related to an operation to be performed by the determined operation execution device based on an analysis result of at least a portion of the text.

[0035] The method may further include obtaining, from at least one device storing a function determination model among the plurality of devices, information about a function determination model stored in the at least one device, wherein the function determination model is used to determine a function associated with each of the plurality of devices.

[0036] The identifying of the device may include identifying a device storing a function determination model corresponding to the determined operation performing device based on the obtained information about the function determination model.

[0037] According to another embodiment of the present disclosure, a hub device for controlling devices based on voice input includes: a communication interface configured to perform data communication with at least one of a plurality of devices, a voice assistant server, and an Internet of Things (IoT) server; a microphone configured to receive a user's voice input; a memory configured to store a program including one or more instructions; and a processor configured to execute one or more instructions of the program stored in the memory, wherein the processor is further configured to execute one or more instructions to convert the voice input received through the microphone into text by performing automatic speech recognition (ASR), determine an operation execution device from among a plurality of devices based on the text by using a device determination model, identify a device storing a function determination model corresponding to the determined operation execution device by using a function determination device determination module, and control the communication interface to provide at least a portion of the text to the identified device.

[0038] The device determination model may include a first natural language understanding (NLU) model configured to analyze text and determine the operation execution device based on an analysis result of the text.

[0039] The function determination model may include a second NLU model configured to analyze at least a portion of the text and obtain operation information related to an operation to be performed by the determined operation execution device based on an analysis result of at least a portion of the text.

[0040] The processor can also be configured to execute one or more instructions to control the communication interface to obtain information about a function determination model stored in at least one device from at least one device storing the function determination model among multiple devices, wherein the function determination model is used to determine functions associated with each of the multiple devices.

[0041] The processor may also be configured to execute one or more instructions to identify a device storing a function determination model corresponding to the determined operation performing device based on the obtained information about the function determination model.

[0042] According to another embodiment of the present disclosure, a method for operating a system, the system includes a hub device and a first device storing a function determination model, the method including: receiving a user's voice input by the hub device; performing automatic speech recognition (ASR) by using data about an ASR module stored in a memory of the hub device to convert the received voice input into text; determining the first device as an operation execution device based on the text by using data about a device determination model stored in the memory of the hub device; obtaining, by the hub device, information about the function determination model stored in the first device from the first device; and sending, by the hub device, at least a portion of the text to the first device based on the obtained information about the function determination model.

[0043] The device determination model may include a first natural language understanding (NLU) model configured to analyze text and determine a first device as the operation execution device from among a plurality of devices based on an analysis result of the text.

[0044] The function determination model may include a second NLU model configured to analyze at least a portion of the text received from the hub device and obtain operation information related to an operation to be performed by the first device based on a result of analyzing at least a portion of the text.

[0045] The method may further include analyzing, by the first device, at least a portion of the text by using a second NLU model of the function determination model, and obtaining operation information related to an operation to be performed by the first device based on a result of analyzing at least a portion of the text.

[0046] The method may further include: generating, by the first device, a control command for controlling an operation of the first device based on the operation information; and performing, by the first device, the operation based on the control command.

[0047] According to another embodiment of the present disclosure, a multi-device system includes a hub device and a first device storing a function determination model, wherein the hub device includes: a communication device configured to perform data communication with the first device; a microphone configured to receive a user's voice input; a memory configured to store a program including one or more instructions; and a processor configured to execute one or more instructions of the program stored in the memory, wherein the processor is further configured to execute one or more instructions to convert the voice input received through the microphone into text by performing automatic speech recognition (ASR), determine the first device as an operation execution device based on the text by using the device determination model, control the communication interface to obtain information about the function determination model stored in the first device from the first device, and based on the obtained information about the function determination model, control the communication interface to send at least a portion of the text to the first device.

[0048] The device determination model may include a first natural language understanding (NLU) model configured to analyze text and determine a first device as the operation execution device from among a plurality of devices based on an analysis result of the text.

[0049] The first device may include a communication interface configured to receive at least a portion of the text from the hub device, wherein the function determination model includes a second NLU model configured to analyze at least a portion of the received text and obtain operation information related to an operation to be performed by the first device based on the analysis result of at least a portion of the text.

[0050] The first device may further include a processor configured to analyze at least a portion of the text by using a second NLU model, and obtain operation information to be performed by the first device based on a result of analyzing the at least a portion of the text.

[0051] The processor of the first device may also be configured to control at least one element of the first device to generate a control command for controlling the operation of the first device based on the operation information, and perform an operation based on the control command.

[0052] According to another embodiment of the present disclosure, a method for controlling a device performed by a hub device includes: receiving a voice signal from a listening device; converting the received voice signal into text by performing automatic speech recognition (ASR); analyzing the text by using a first natural language understanding (NLU) model, and determining an operation execution device corresponding to the analyzed text by using a device determination model; identifying a device storing a function determination model corresponding to the determined operation execution device from the determined operation execution device and the listening device; and providing at least a portion of the text to the identified device.

[0053] The device determination model may include a first NLU model configured to analyze text and determine an operation execution device based on an analysis result of the text.

[0054] The function determination model may include a second NLU model configured to analyze at least a portion of the text and obtain operation information related to an operation to be performed by the determined operation execution device based on an analysis result of at least a portion of the text.

[0055] The method may further include determining whether the determined operation performing device is the same as the listening device.

[0056] Based on determining that the operation execution device is the same as the listening device, identifying the device that stores the function determination model may include: obtaining function determination model information about whether the listening device stores the function determination model in an internal memory; and based on the obtained function determination model information, determining the listening device as a device that stores the function determination model.

[0057] Based on determining that the operation execution device is a device different from the listening device, identifying the device that stores the function determination model may include: obtaining function determination model information about whether the determined operation execution device stores the function determination model in an internal memory; and determining whether the operation execution device is a device that stores the function determination model based on the obtained function determination model information.

[0058] The method may further include: receiving update data of the device determination model from the voice assistant server; and updating the device determination model by using the received update data.

[0059] The update data may include data for updating the device determination model based on update information of the function determination model included in at least one of the operation execution device or the listening device to determine the updated function from the text and determine the operation execution device corresponding to the updated function.

[0060] The method may also include: receiving device information of a new device from a voice assistant server, wherein the device information of the new device includes at least one of device identification information of the new device, storage information of a device determination model, and storage information of a function determination model; and updating the device determination model by adding the new device to device candidates that can be determined by the device determination model as an operation execution device using the received device information of the new device.

[0061] According to another embodiment of the present disclosure, a hub device for controlling a device includes: a communication interface configured to perform data communication with a voice assistant server and at least one of a plurality of devices including a listening device; a voice signal receiver configured to receive a voice signal from the listening device; a memory configured to store a program including one or more instructions; and a processor configured to execute one or more instructions of the program stored in the memory, wherein the processor is further configured to convert the received voice signal into text by performing automatic speech recognition (ASR), analyze the text by using a first natural language understanding (NLU) model, and determine an operation execution device corresponding to the analyzed text by using a device determination model, identify a device storing a function determination model corresponding to the determined operation execution device from the determined operation execution device and the listening device, and send at least a portion of the text to the identified device by using the communication interface.

[0062] The device determination model may include a first NLU model configured to analyze text and determine an operation execution device based on an analysis result of the text.

[0063] The function determination model may include a second NLU model configured to analyze at least a portion of the text and obtain operation information related to an operation to be performed by the determined operation execution device based on an analysis result of at least a portion of the text.

[0064] The processor may also be configured to determine whether the determined operation performing device is the same as the listening device.

[0065] Based on determining that the operation execution device is the same as the listening device, the processor can also be configured to obtain function determination model information about whether the listening device stores the function determination model in the internal memory, and determine the listening device as a device storing the function determination model based on the obtained function determination model information.

[0066] Based on determining that the operation execution device is a device different from the listening device, the processor can also be configured to obtain function determination model information about whether the determined operation execution device stores the function determination model in an internal memory, and determine whether the operation execution device is a device that stores the function determination model based on the obtained function determination model information.

[0067] The processor may also be configured to receive update data of the device determination model from the voice assistant server by using the communication interface, and update the device determination model by using the received update data.

[0068] The update data may include data used to update the device determination model based on update information of the function determination model included in at least one of the operation execution device and the listening device to determine an updated function from the text and determine the operation execution device corresponding to the updated function.

[0069] The processor may also be configured to receive device information of a new device including at least one of device identification information of the new device, storage information of a device determination model, and storage information of a function determination model from a voice assistant server using a communication interface, and update the device determination model by adding the new device to device candidates that can be determined by the device determination model as an operation execution device using the received device information of the new device.

[0070] According to an embodiment of the present disclosure, a method may include: based on receiving a user's voice input by a hub device: converting the received voice input into text by the hub device by performing automatic speech recognition (ASR); identifying, by the hub device, a device capable of performing an operation corresponding to the text; identifying which device stores a function determination model corresponding to the device capable of performing the operation corresponding to the text from the hub device and a plurality of other devices connected to the hub device; and based on identifying that the device storing the function determination model is a device different from the hub device, sending at least a portion of the text to the identified device, wherein the hub device includes a hardware processor.

[0071] The method may also include analyzing the text using a first natural language understanding (NLU) model, and determining a device capable of performing the operation based on an analysis result of the text, wherein the NLU is a device determination model.

[0072] The method may further include analyzing at least a portion of the text using a second natural language understanding (NLU) model included in the device determination model, and obtaining operation information related to an operation corresponding to the text based on a result of analyzing at least a portion of the text.

[0073] The method may further include obtaining, from at least one device storing the functionality determination model, information about the functionality determination model stored in the at least one device.

[0074] Identifying which device stores the function determination model may include identifying a device storing the function determination model corresponding to the identified device capable of performing the operation based on the obtained information about the function determination model.

[0075] According to an embodiment, a hub device for controlling devices based on voice input may include: a communication interface configured to perform data communication with at least one of a plurality of devices, a voice assistant server, and an Internet of Things (IoT) server; a microphone configured to receive a user's voice input; a memory configured to store a program including one or more instructions; and a processor configured to execute one or more instructions of the program stored in the memory to: convert the voice input received through the microphone into text by performing automatic speech recognition (ASR), and identify a device capable of performing an operation corresponding to the text; identify which device from the hub device and a plurality of other devices connected to the hub device stores a function determination model corresponding to a device capable of performing an operation corresponding to the text; and based on the identification that the device storing the function determination model is a device different from the hub device, control the communication interface to send at least a portion of the text to the identified device storing the function determination model, wherein the hub device includes a hardware processor.

[0076] The processor may also be configured to identify devices capable of performing operations corresponding to the text using a device determination model including a first natural language understanding (NLU) model configured to analyze text, and determine devices capable of performing operations corresponding to the text based on analysis results of the text.

[0077] The function determination model may include a second NLU model configured to analyze at least a portion of the text and obtain operation information related to an operation to be performed by a device capable of performing an operation corresponding to the text based on an analysis result of at least a portion of the text.

[0078] The processor can also be configured to execute one or more instructions to control the communication interface to obtain information about a function determination model stored in at least one device from at least one device storing the function determination model among multiple devices, wherein the function determination model is used to determine functions associated with each of the multiple devices.

[0079] The processor may also be configured to execute one or more instructions to identify which device stores a function determination model corresponding to a device capable of performing an operation corresponding to the text based on the obtained information about the function determination model.

[0080] According to an embodiment, a method of operating a system, the system includes a hub device and a first device storing a function determination model, the method including: based on a voice input of a user received by the hub device: the hub device converts the received voice input into text by performing automatic speech recognition (ASR) by using data about an ASR module stored in a memory of the hub device; the hub device identifies the first device as a device capable of performing an operation corresponding to the text by using data stored in the memory of the hub device; the hub device obtains information about the function determination model stored in the first device from the first device; and the hub device sends at least a portion of the text to the first device based on the obtained information about the function determination model.

[0081] The method may further include analyzing the text using a first natural language understanding (NLU) model, and determining a first device from among a plurality of devices as a device capable of performing an operation corresponding to the text based on an analysis result of the text.

[0082] The method may also include performing analysis using a function determination model, including: using a second natural language understanding (NLU) model included in the device determination model to analyze at least a portion of the text received from the hub device, and obtaining operation information related to the operation to be performed by the first device based on the analysis result of at least a portion of the text.

[0083] The method may further include analyzing, by the first device, at least a portion of the text by using a second NLU model of the function determination model, and obtaining operation information related to the operation to be performed by the first device based on a result of analyzing at least a portion of the text.

[0084] The method may further include: generating, by the first device, a control command for controlling an operation of the first device based on the operation information; and performing, by the first device, an operation based on the control command.

[0085] According to an embodiment, a multi-device system may include a hub device and a first device storing a function determination model, wherein the hub device includes: a communication interface configured to perform data communication with the first device storing the function determination model; a microphone configured to receive a user's voice input; a memory configured to store a program including one or more instructions; and a processor configured to execute one or more instructions of the program stored in the memory to: convert the voice input received through the microphone into text by performing automatic speech recognition (ASR), identify the first device as a device capable of performing an operation corresponding to the text, control the communication interface to obtain information about the function determination model stored in the first device from the first device, and based on the obtained information about the function determination model, control the communication interface to send at least a portion of the text to the first device, wherein the hub device includes a hardware processor.

[0086] The processor may also be configured to analyze text by using a device determination model including a first natural language understanding (NLU) model, identify a first device as a device capable of performing operations corresponding to the text, and based on the analysis results of the text, determine a first device among multiple devices as a device capable of performing operations corresponding to the text.

[0087] The first device may include a communication interface configured to receive at least a portion of the text from the hub device, wherein the function determination model includes a second NLU model configured to analyze at least a portion of the received text and obtain operation information related to an operation to be performed by the first device based on the analysis result of at least a portion of the text.

[0088] The first device may further include a processor configured to analyze at least a portion of the text by using a second NLU model, and obtain operation information to be performed by the first device based on a result of analyzing the at least a portion of the text.

[0089] The processor of the first device may also be configured to control at least one element of the first device to generate a control command for controlling the operation of the first device based on the operation information, and perform an operation based on the control command.

[0090] According to an embodiment, a method may include: based on receiving a voice signal from a listening device by a hub device: converting the received voice signal into text by the hub device by performing automatic speech recognition (ASR); analyzing the text by using a first natural language understanding (NLU) model, and identifying a device capable of performing an operation corresponding to the analyzed text by using a device determination model; identifying which device stores a function determination model corresponding to the device capable of performing the operation from the device capable of performing the operation and the listening device; and sending at least a portion of the text to the identified device.

[0091] The method may further include analyzing the text using a first NLU model included in the device determination model, and determining a device capable of performing the operation based on an analysis result of the text.

[0092] The method may also include analyzing at least a portion of the text using a second natural language understanding (NLU) model included in the device determination model, and obtaining operation information related to an operation to be performed by a device capable of performing the operation based on the analysis result of at least a portion of the text.

[0093] The method may also include determining whether the device capable of performing the operation is the same as the listening device.

[0094] The method may also include identifying a device storing the function determination model based on determining that the device capable of performing the operation is the same as the listening device, including: obtaining function determination model information about whether the listening device stores the function determination model in an internal memory; and determining the listening device as a device storing the function determination model based on the obtained function determination model information.

[0095] The method may also include identifying a device storing a function determination model based on determining that the device capable of performing the operation is a device different from the listening device, including: obtaining function determination model information about whether the device capable of performing the operation stores the function determination model in an internal memory; and determining whether the device capable of performing the operation is a device storing the function determination model based on the obtained function determination model information.

[0096] The method may further include: receiving update data of the device determination model from the voice assistant server; and updating the device determination model by using the received update data.

[0097] The update data may include data for updating a device determination model to determine an updated function from text and determining a device capable of performing an operation corresponding to the updated function based on update information of the function determination model included in at least one of a device capable of performing the operation and a listening device.

[0098] The method may also include: receiving device information of a new device from a voice assistant server, the device information of the new device including at least one of device identification information of the new device, storage information of a device determination model, and storage information of a function determination model; and updating the device determination model by adding the new device to device candidates that can be determined by the device determination model as a device capable of performing an operation using the received device information of the new device.

[0099] According to an embodiment, the hub device may include: a communication interface configured to perform data communication with a voice assistant server and at least one of a plurality of devices including a listening device; a voice signal receiver configured to receive a voice signal from the listening device; a memory configured to store a program including one or more instructions; and a processor configured to execute one or more instructions of the program stored in the memory to: convert the received voice signal into text by performing automatic speech recognition (ASR), analyze the text by using a first natural language understanding (NLU) model, and identify a device capable of performing an operation corresponding to the analyzed text by using a device determination model, identify which device stores a function determination model corresponding to the device capable of performing the operation from the device capable of performing the operation and the listening device, and send at least a portion of the text to the identified device storing the function determination model by using the communication interface.

[0100] The device determination model may include a first NLU model configured to analyze text and determine a device capable of performing an operation based on an analysis result of the text.

[0101] The function determination model may include a second NLU model configured to analyze at least a portion of the text and obtain operation information related to an operation to be performed by a device capable of performing the operation based on an analysis result of at least a portion of the text.

[0102] The processor may also be configured to determine whether the device capable of performing the operation is the same as the listening device.

[0103] The processor can also be configured to: based on determining that the device capable of performing the operation is the same as the listening device, the processor is also configured to obtain function determination model information about whether the listening device stores the function determination model in an internal memory, and determine the listening device as a device that stores the function determination model based on the obtained function determination model information.

[0104] The processor can also be configured to: based on determining that the device capable of performing the operation is a device different from the listening device, the processor is also configured to obtain function determination model information about whether the device capable of performing the operation stores the function determination model in an internal memory, and determine whether the device capable of performing the operation is a device that stores the function determination model based on the obtained function determination model information.

[0105] The processor may also be configured to: receive update data of the device determination model from the voice assistant server by using the communication interface, and update the device determination model by using the received update data.

[0106] The update data may include data for updating a device determination model to determine an updated function from text and determining a device capable of performing an operation corresponding to the updated function based on update information of the function determination model included in at least one of a device capable of performing the operation and a listening device.

[0107] The processor can also be configured to: receive device information of a new device from a voice assistant server using a communication interface, wherein the device information of the new device includes at least one of device identification information of the new device, storage information of a device determination model, and storage information of a function determination model, and update the device determination model by adding the new device to device candidates that can be determined by the device determination model as a device capable of performing an operation using the received device information of the new device.

[0108] According to an embodiment, a method may include: based on detecting a user's voice by a hub device: converting the received voice input into text by the hub device by performing automatic speech recognition (ASR); recognizing the user's intention by the hub device; identifying an Internet of Things (IoT) device capable of performing operations corresponding to the text by the hub device; identifying which device stores a function determination model corresponding to the IoT device capable of performing operations corresponding to the text from among the hub device and a plurality of other devices connected to the hub device; and based on identifying that the device storing the function determination model is a device different from the hub device, sending at least a portion of the text to the identified device, wherein the hub device includes a hardware processor.

[0109] The method may also include: storing, by the hub device in the form of a lookup table (LUT), information about whether a function determination model of each of a plurality of IoT devices previously registered using a user account is associated with information about a storage location of the function determination model of each of the plurality of IoT devices.

[0110] The method may further include obtaining operation information related to an operation corresponding to the text to be performed by the IoT device by using the function determination model. DETAILED DESCRIPTION

[0111] This application is based on and claims the benefit of U.S. Provisional Patent Application Nos. 62 / 862,201 and 62 / 905,707, filed in the United States Patent and Trademark Office on June 17, 2019 and September 25, 2019, respectively, and claims priority to Korean Patent Application Nos. 10-2019-0051824, 10-2019-0123310 and 10-2020-0027217, filed in the Korean Intellectual Property Office on May 2, 2019, October 4, 2019, and March 4, 2020, respectively, the disclosures of which are incorporated herein by reference in their entireties.

[0112] Although the terms used herein are selected from common terms currently in wide use in consideration of their functions in the present disclosure, these terms may vary according to the intention of a person of ordinary skill in the art, precedents, or the emergence of new technologies. In addition, in certain cases, the terms are arbitrarily selected by the applicant of the present disclosure, and the meanings of these terms will be described in detail in the corresponding parts of the specific embodiments. Therefore, the terms used in the present disclosure are not merely designations of the terms, but are defined based on the meanings of the terms and content throughout the present disclosure.

[0113] Throughout the disclosure, the expression "at least one of a, b, and c" means only a, only b, only c, both a and b, both a and c, both b and c, all of a, b, and c, or variations thereof.

[0114] As used herein, the singular form is intended to include the plural form as well unless the context clearly indicates otherwise.The terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs.

[0115] Throughout this application, when a component "includes" an element, it should be understood that, unless otherwise specifically stated, the component additionally includes other elements rather than excluding other elements. In addition, terms such as "unit", "module", etc. used in this disclosure indicate a unit that processes at least one function or movement, and the unit can be implemented as hardware or software, or a combination of hardware and software.

[0116] The expression "configured to (or set to)" used in this document may be replaced with, for example, "suitable for", "having the ability to...", "designed to", "suitable for", "manufactured to", or "capable of", depending on the circumstances. The expression "configured to (or set to)" may not necessarily mean "specially designed to" in hardware. On the contrary, in some cases, the expression "a system configured to..." may mean that the system, together with other devices or components, "is capable of...". For example, "a processor configured to (or set to) perform A, B, and C" may refer to a dedicated processor (e.g., an embedded processor) for performing the corresponding operations, or a general-purpose processor (e.g., a central processing unit (CPU) or an application processor (AP)) that is capable of performing the corresponding operations by executing one or more software programs stored in a memory.

[0117] According to an embodiment of the present disclosure, the term "first natural language understanding (NLU) model" used herein may refer to a model trained to analyze text converted from voice input and determine an operation execution device based on the analysis result. The first NLU model may be used to determine intent by interpreting text and determine an operation execution device based on the intent.

[0118] According to an embodiment of the present disclosure, the term "second NLU model" used herein may refer to a model trained to analyze text related to a specific device. The second NLU model may be a model trained to obtain operation information related to an operation to be performed by a specific device by interpreting at least a portion of the text. The storage capacity of the second NLU model may be greater than the storage capacity of the first NLU model.

[0119] According to an embodiment of the present disclosure, the term "intent" used herein may refer to information indicating the user's intent determined by interpreting text. The intent, which is information indicating the user's utterance intent, may be information indicating the operation of the operation execution device requested by the user. The intent may be determined by interpreting the text using an NLU model. For example, based on the text converted from the user's voice input being "play the movie Avengers on TV", it may be determined that the intent is "content playback". Alternatively, based on the text converted from the user's voice input being "lower the air conditioning temperature to 18°C", it may be determined that the intent is "temperature control".

[0120] According to an embodiment of the present disclosure, an intent may include not only information indicating the user's utterance intent (hereinafter referred to as intent information), but also a numerical value corresponding to the information indicating the user's intent. The numerical value may indicate the probability that the text is associated with the information indicating a specific intent. After interpreting the text by using an NLU model, when multiple pieces of intent information indicating the user's intent are obtained, the intent information having the maximum numerical value among multiple numerical values ​​corresponding to the multiple pieces of intent information may be determined as the intent.

[0121] According to an embodiment of the present disclosure, the term "operation" of a device as used herein may refer to at least one action performed by the device when the device performs a specific function. According to an embodiment of the present disclosure, an operation may indicate at least one action performed by the device when the device executes an application. For example, when the device executes an application, the operation may indicate, for example, one of the following: video playback, music playback, email creation, weather information reception, news information display, playing games, and photography performed. However, the operation is not limited to the above examples.

[0122] According to an embodiment of the present disclosure, the operation of the device can be performed based on the information about the detailed operation output from the action plan management module. According to an embodiment of the present disclosure, the device can perform at least one action by executing a function corresponding to the detailed operation output from the action plan management module. According to an embodiment of the present disclosure, the device can store instructions for executing functions corresponding to the detailed operation, and when the detailed operation is determined, the device can determine the instructions corresponding to the detailed operation, and can perform a specific function by executing the instructions.

[0123] In addition, according to an embodiment, the device may store instructions for executing an application corresponding to a detailed operation. According to an embodiment of the present disclosure, the instructions for executing an application may include instructions for executing the application itself and instructions for executing detailed functions constituting the application. Based on determining the detailed operation, the device may execute the application by executing instructions for executing the application corresponding to the detailed operation, and may execute the detailed function by executing instructions for executing the detailed function of the application corresponding to the detailed operation.

[0124] According to an embodiment of the present disclosure, the term "operation information" used herein may refer to information related to a detailed operation to be determined by a device, the relationship between each detailed operation and another detailed operation, and the execution order of the detailed operations. According to an embodiment of the present disclosure, when a first operation is to be performed, the relationship between each detailed operation and another detailed operation may include information about a second operation that has to be performed before performing the first operation. For example, when the operation to be performed is "music playback", "power on" may be another detailed operation that has to be performed before performing "music playback". According to an embodiment of the present disclosure, the operation information may include, but is not limited to, one or more of the following items: functions to be performed by an operation execution device to perform a specific operation, the execution order of functions, input values ​​required to execute functions, and output values ​​output as a result of the execution of the functions.

[0125] According to an embodiment of the present disclosure, the term "operation execution device" used herein may refer to a device determined to perform an operation based on an intent obtained from a text among a plurality of devices. The text may be analyzed by using a first NLU model, and the operation execution device may be determined based on the analysis result. According to an embodiment of the present disclosure, the operation execution device may perform at least one action by executing a function corresponding to a detailed operation output from an action plan management module. According to an embodiment of the present disclosure, the operation execution device may perform an operation based on operation information.

[0126] According to an embodiment of the present disclosure, the term "action plan management module" used herein may refer to a module for managing detailed operations to be performed by an operation execution device and operation information related to the detailed operations of the device to generate an execution order of the detailed operations. According to an embodiment of the present disclosure, the action plan management module may manage operation information about detailed operations of the device according to the device type and the relationship between the detailed operations.

[0127] According to an embodiment of the present disclosure, the term "Internet of Things (IoT) server" used herein may refer to a server that obtains, stores, and manages IoT device information about each of a plurality of devices (e.g., including IoT devices, mobile phones, etc.). The IoT server may obtain, determine, or generate a control command for controlling a device (e.g., IoT device) by using the stored device information. According to an embodiment of the present disclosure, the IoT server may send a control command to a device that determines to perform an operation based on operation information. According to an embodiment of the present disclosure, the IoT server may be implemented as, but not limited to, a hardware device independent of the "server" of the present disclosure. According to an embodiment, the IoT server may be an element of the "voice assistant server" of the present disclosure, or may be a server designed to be classified as software. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that a person of ordinary skill in the art can easily implement and practice the present disclosure. However, according to embodiments of the present disclosure, the present disclosure may be implemented in many different forms and should not be construed as being limited to the embodiments of the present disclosure set forth herein.

[0128] Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings.

[0129] Figure 1 is a block diagram illustrating some elements of a multi-device system including a hub device 1000, a voice assistant server 2000, an IoT server 3000, and a plurality of devices 4000 according to an embodiment of the present disclosure.

[0130] exist Figure 1 In the embodiment of the present invention, elements for describing the operation of the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the plurality of devices 4000 are illustrated. The elements included in the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the plurality of devices 4000 are not limited to Figure 1 Those components shown in the figure.

[0131] Figure 1 The reference numerals S1 to S16 marked by arrows in the figure represent data movement operations (sending or receiving) between multiple entities through a network. The numbers attached to the English letter S in S1 to S16 are for convenience of explanation and have nothing to do with the order of the data movement operations (sending or receiving).

[0132] refer to Figure 1According to an embodiment of the present disclosure, the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the plurality of devices 4000 may be connected to each other by using a wired communication or wireless communication method, and may perform communication. In an embodiment of the present disclosure, the hub device 1000 and the plurality of devices 4000 may be directly connected to each other or connected via a communication network, but the present disclosure is not limited thereto. According to an embodiment, the hub device 1000 and the plurality of devices 4000 may be connected to the voice assistant server 2000, and the hub device 1000 may be connected to the plurality of devices 4000 through the voice assistant server 2000. In addition, according to an embodiment, the hub device 1000 and the plurality of devices 4000 may be connected to the IoT server 3000. In another embodiment of the present disclosure, each of the hub device 1000 and the plurality of devices 4000 may be connected to the voice assistant server 2000 through a communication network, and may be connected to the IoT server 3000 through the voice assistant server 2000.

[0133] According to an embodiment, the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the plurality of devices 4000 may be connected via one or more of the following: a local area network (LAN), a wide area network (WAN), a value-added network (VAN), a mobile radio communication network, a satellite communication network, or a combination thereof. Examples of wireless communication methods may include, but are not limited to, Wi-Fi (Wireless Fidelity), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, Wi-Fi Direct (WFD), Ultra Wideband (UWB), Infrared Data Association (IrDA), or Near Field Communication (NFC).

[0134] According to an embodiment, the hub device 1000 may be a device that receives a user's voice input and controls at least one of the plurality of devices 4000 based on the received voice input. According to an embodiment of the present disclosure, the hub device 1000 may be a listening device that receives a voice input from a user.

[0135] According to an embodiment, at least one of the plurality of devices 4000 may be an operation execution device that performs a specific operation by receiving a control command of the hub device 1000 or the IoT server 3000. According to an embodiment of the present disclosure, the plurality of devices 4000 may be IoT devices that are logged in using the same user account as that of the hub device 1000 and previously registered with the IoT server 3000 by using the user account of the hub device 1000.

[0136] According to an embodiment, at least one of the multiple devices 4000 may be a listening device that receives voice data (e.g., voice input from a user). The listening device may be, but is not limited to, a device designed to process user voice (e.g., a device that only receives and processes voice input from a human user or from a specific registered user). In an embodiment of the present disclosure, the listening device may be an operation execution device that receives a control command from the hub device 1000 and performs an operation for a specific function.

[0137] According to an embodiment of the present disclosure, at least one of the plurality of devices 4000 may receive a control command (S12, S14, and S16) from the IoT server 3000, or may receive at least a portion of a text converted from an input voice (S3 and S5) from the hub device 1000. According to an embodiment of the present disclosure, at least one of the plurality of devices 4000 may receive a control command (S12, S14, and S16) from the IoT server 3000 without receiving at least a portion of a text from the hub device 1000.

[0138] According to an embodiment, the hub device 1000 may include a device determination model 1330, which determines a device for performing an operation based on a user's voice input. According to an embodiment, the device determination model 1330 may determine an operation execution device from among a plurality of devices 4000 registered according to a user account. In an embodiment of the present disclosure, the hub device 1000 may receive device information (S2) including identification information (e.g., device id information) of each of a plurality of devices 4000, a device type of each of a plurality of devices 4000, a function execution capability of each of a plurality of devices 4000, location information, and at least one of status information from a voice assistant server 2000. According to an embodiment, each of the plurality of devices 4000 is an IoT device. According to an embodiment, the hub device 1000 may determine a device (e.g., IoT device) for performing an operation according to a user's voice input from among a plurality of devices 4000 based on the received device information by using data about the device determination model 1330.

[0139] According to an embodiment of the present disclosure, a function determination model corresponding to an operation execution device determined by the hub device 1000 may be stored in the memory 1300 (see Figure 2 ), can be stored in the operation execution device itself, or can be stored in the memory 2300 of the voice assistant server 2000 (see Figure 3 According to an embodiment of the present disclosure, the term “function determination model” corresponding to each device refers to a model for obtaining operation information about detailed operations of performing operations according to a determined function of the device and relationships between the detailed operations.

[0140] According to an embodiment of the present disclosure, the function determination device determination module 1340 of the hub device 1000 can identify a device in which a function determination model corresponding to the operation execution device is stored from among the hub device 1000, the voice assistant server 2000, and the operation execution device by using a database 1360 including information about the function determination model of the device stored in the memory. According to an embodiment of the present disclosure, the database 1360 may include information about a plurality of devices 4000 registered with a user account associated with the hub device 1000. Specifically, identification information (e.g., device identifier (ID) information) of each of the plurality of devices 4000, information about whether a function determination model of each of the plurality of devices 4000 exists, and information about a storage location of a function determination model of each of the plurality of devices 4000 (e.g., identification information of the stored device / server, an Internet Protocol (IP) address of the stored device / server, or a media access control (MAC) address of the stored device / server) can be stored. In an embodiment of the present disclosure, the function determination device determination module 1340 can search the database 1360 according to the device identification information of the operation execution device output by the device determination model 1330, and can obtain information about the storage location of the function determination model corresponding to the operation execution device based on the search results of the database 1360.

[0141] According to an embodiment of the present disclosure, the hub device 1000 may transmit at least a portion of a text converted from a user's voice input to a device identified as storing a function determination model corresponding to an operation execution device by using the function determination device determination module 1340 .

[0142] For example, based on the hub device 1000 receiving the voice input of the user saying "increase the temperature by 1°C", the hub device 1000 can determine through the device determination model 1330 that the device for increasing the temperature is an air conditioner. Next, according to an embodiment, the function determination device determination module 1340 can check whether the function determination model corresponding to the air conditioner is stored in the air conditioner (IoT device), and based on determining that it is stored in the air conditioner, a text corresponding to "increase the temperature by 1°C" can be sent to the air conditioner. According to an embodiment, an IoT device (e.g., an air conditioner) capable of performing an operation of increasing the air temperature can analyze the received text received through the stored function determination model corresponding to the air conditioner, and perform a temperature control operation by using the text analysis result. That is, based on the device determination model 1330 determining that the operation execution device capable of executing the user's voice input is the first device 4100 as "air conditioner", because the function determination model corresponding to the air conditioner is stored in the first device 4100 itself, the function determination device determination module 1340 can send at least a part of the text to the first device 4100 (S3) (e.g., air conditioner).

[0143] For example, based on the hub device 1000 receiving a voice input of a user saying "change channel", the hub device 1000 can determine that the device for changing the channel is a TV through the device determination model 1330. Next, according to an embodiment, the function determination device determination module 1340 can check whether the function determination model corresponding to the TV is stored in the hub device 1000, and can analyze the text corresponding to "change channel" through the stored function determination model corresponding to the TV. According to an embodiment, the hub device 1000 can determine the channel change operation as an operation to be performed by the TV by using the text analysis result, and can send operation information about the channel change operation to the TV. That is, based on the device determination model 1330 determining that the operation execution device is the second device 4200 as "TV", because the function determination model corresponding to the TV is stored in the hub device 1000, the function determination device determination module 1340 can provide (e.g., send) at least a portion of the text to the TV function determination model 1354, so that the hub device 1000 itself can process at least a portion of the text.

[0144] For example, based on the hub device 1000 receiving a voice input of the user saying "execute deodorization mode", the hub device 1000 can determine that the device for executing the deodorization mode is an air purifier through the device determination model 1330. Next, according to an embodiment, the function determination device determination module 1340 can check whether the function determination model corresponding to the air purifier is stored in the voice assistant server 2000, and can send the text corresponding to "execute deodorization mode" to the voice assistant server 2000 based on determining that the function determination model corresponding to the air purifier is stored in the voice assistant server 2000. According to an embodiment of the present disclosure, the voice assistant server 2000 can analyze the received text through the function determination model corresponding to the air purifier, and can determine the deodorization mode execution operation as the operation to be performed by the air purifier by using the text analysis result. According to an embodiment of the present disclosure, the voice assistant server 2000 can send the operation information about the deodorization mode execution operation to the air purifier, in which case the operation information related to the deodorization mode execution operation can be sent through the IoT server 3000. That is, according to an embodiment, based on the determination by the device determination model 1330 that the operation execution device is the third device 4300 as an "air purifier", since the function determination model corresponding to the air purifier is stored in the voice assistant server 2000, the function determination device determination module 1340 can send at least a part of the text to the voice assistant server 2000 (S1).

[0145] In an embodiment of the present disclosure, the hub device 1000 itself may store a function determination model corresponding to at least one of the plurality of devices 4000. For example, when the hub device 1000 is a voice assistant speaker, the hub device 1000 may store a speaker function determination model 1352 for obtaining operation information about detailed operations for performing the functions of the voice assistant speaker and the relationship between the detailed operations.

[0146] According to an embodiment, the hub device 1000 may also store a function determination model corresponding to another device. For example, the hub device 1000 may store a TV function determination model 1354 for obtaining operation information about detailed operations corresponding to a TV and relationships between the detailed operations. According to an embodiment of the present disclosure, the TV may be a device previously registered to the IoT server 3000 by using the same user account as the user account of the hub device 1000.

[0147] According to an embodiment, the speaker function determination model 1352 and the TV function determination model 1354 may include second NLU models 1352a and 1354a and action plan management modules 1352b and 1354b, respectively. Figure 2The second NLU models 1352a and 1354a and the action plan management modules 1352b and 1354b are described.

[0148] According to an embodiment, the voice assistant server 2000 may determine an operation execution device for executing the operation intended by the user based on the text received from the hub device 1000. According to an embodiment, the voice assistant server 2000 may receive user account information from the hub device 1000 (S1). According to an embodiment of the present disclosure, based on the voice assistant server 2000 receiving the user account information from the hub device 1000, the voice assistant server 2000 may send a query to the IoT server 3000 for requesting device information about a plurality of devices 4000 previously registered according to the received user account information (S9), and may receive device information about a plurality of devices 4000 from the IoT server 3000 (S10). According to an embodiment, the voice device information may include at least one of identification information (e.g., device id information) of each of the plurality of devices 4000, a device type of each of the plurality of devices 4000, a function execution capability of each of the plurality of devices 4000, location information, and status information. According to an embodiment, the voice assistant server 2000 may send the device information received from the IoT server 3000 to the hub device 1000 (S2).

[0149] Will refer to Figure 2 Elements of the hub device 1000 according to the embodiment are described.

[0150] According to an embodiment, the voice assistant server 2000 may include a device determination model 2330 and a plurality of function determination models 2342, 2344, 2346, and 2348. According to an embodiment, the voice assistant server 2000 may select a function determination model corresponding to at least a portion of the text received from the hub device 1000 from among the plurality of function determination models 2342, 2344, 2346, and 2348 by using the device determination model 2330, and may obtain operation information required for the operation execution device to perform the operation by using the selected function determination model. According to an embodiment, the voice assistant server 2000 may send the operation information to the IoT server 3000 (S9).

[0151] refer to Figure 3 The elements of the voice assistant server 2000 according to an embodiment of the present disclosure are described.

[0152] According to an embodiment, the IoT server 3000 may be connected through a network and may store information about a plurality of devices 4000 previously registered by using a user account of the hub device 1000. In an embodiment of the present disclosure, the IoT server 3000 may receive at least one of user account information with which each of the plurality of devices 4000 logs in, identification information (e.g., device id information) of each of the plurality of devices 4000, a device type of each of the plurality of devices 4000, and function execution capability information of each of the plurality of devices 4000 (S11, S13, and S15). In an embodiment of the present disclosure, the IoT server 3000 may receive status information about power on / off or an operation being performed by each of the plurality of devices 4000 from the plurality of devices 4000 (S11, S13, and S15). The IoT server 3000 may store the device information and status information received from the plurality of devices 4000.

[0153] According to an embodiment, the IoT server 3000 may generate a control command readable and executable by the operation execution device based on the operation information received from the voice assistant server 2000. According to an embodiment of the present disclosure, the IoT server 3000 may send a control command to a device determined as an operation execution device among the plurality of devices 4000 (S12, S14, and S16).

[0154] refer to Figure 4 The elements of the IoT server 3000 according to the embodiment are described.

[0155] exist Figure 1 In the embodiment of , the plurality of devices 4000 may include a first device 4100, a second device 4200, and a third device 4300. Figure 1 The first device 4100 may be an air conditioner, the second device 4200 may be a TV, and the third device 4300 may be an air purifier, but the present disclosure is not limited thereto. The plurality of devices 4000 may include not only air conditioners, TVs, and air purifiers, but may also include other IoT devices, such as household appliances such as robot cleaners, washing machines, ovens, microwave ovens, scales (e.g., weight scales), refrigerators, or electronic photo frames, and mobile devices such as smart phones, tablet personal computers (PCs), mobile phones, video phones, e-book readers, desktop PCs, laptop PCs, netbook computers, workstations, servers, personal digital assistants (PDAs), portable multimedia players (PMPs), MP3 players, mobile medical devices, cameras, or wearable devices.

[0156] According to an embodiment, at least one of the plurality of devices 4000 may store a function determination model itself. For example, a function determination model 4132 for obtaining operation information about detailed operations required for the first device 4100 to perform an operation determined from a user's voice input and the relationship between the detailed operations, and generating a control command based on the operation information may be stored in the memory 4130 of the first device 4100.

[0157] According to an embodiment, each of the second device 4200 and the third device 4300 among the plurality of devices 4000 may not store a function determination model.

[0158] According to an embodiment, at least one of the plurality of devices 4000 may transmit information on whether the device itself stores a function determination model to the hub device 1000 (S4, S6, and S8).

[0159] According to an embodiment, the plurality of devices 4000 may also include a third-party device that is not manufactured by the same manufacturer as the manufacturer of the hub device 1000 (for example, the third-party device is manufactured by a different manufacturer than the manufacturer of the hub device 1000), a voice assistant server 2000, and an IoT server 3000, and is not directly controlled by the hub device 1000, the voice assistant server 2000, and the IoT server 3000. Fig.11 Describe the third-party device in detail.

[0160] Figure 2 is a block diagram illustrating elements of a hub device 1000 according to an embodiment of the present disclosure.

[0161] According to an embodiment, the hub device 1000 may be a device that receives a user's voice input and controls at least one of the plurality of devices 4000 based on the received voice input. According to an embodiment, the hub device 1000 may be a listening device that receives a voice input from a user.

[0162] refer to Figure 2 According to an embodiment, the hub device 1000 may include a microphone 1100, a processor 1200, a memory 1300, and a communication interface 1400. According to an embodiment, the hub device 1000 may receive a voice input (e.g., a user's speech) from a user through the microphone 1100, and may obtain a voice signal from the received voice input. In an embodiment of the present disclosure, the processor 1200 of the hub device 1000 may convert the sound received through the microphone 1100 into an acoustic signal, and may obtain a voice signal by removing noise (e.g., a non-voice component) from the acoustic signal.

[0163] However, the present disclosure is not limited thereto, and the hub device 1000 may receive a voice signal from a listening device.

[0164] According to an embodiment, the hub device 1000 may include a voice recognition module having a function of detecting a specified voice input (e.g., a wake-up input such as “Hi, Bixby” or “OK, Google”) or a function of preprocessing a voice signal obtained from a portion of the voice input.

[0165] According to an embodiment of the present disclosure, the processor 1200 may execute one or more instructions of a program stored in the memory 1300. According to an embodiment of the present disclosure, the processor 1200 may include hardware components that perform arithmetic, logic, and input / output operations and signal processing. According to an embodiment, the processor 1200 may include, but is not limited to, at least one of a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), and a field programmable gate array (FPGA).

[0166] According to an embodiment, a program may include one or more instructions for controlling the plurality of devices 4000 based on a user's voice input received through the microphone 1100, and the program may be stored in the memory 1300. According to an embodiment, instructions and / or program codes readable by the processor 1200 may be stored in the memory 1300. In an exemplary embodiment of the present disclosure, the processor 1200 may be implemented by executing instructions or codes stored in the memory.

[0167] According to an embodiment, one or more or all of data about an automatic speech recognition (ASR) module 1310, data about a natural language generator (NLG) module 1320, data about a device determination model 1330, data about a function determination device determination module 1340, data corresponding to each of a plurality of function determination models 1350, and data corresponding to a database 1360 may be stored in the memory 1300.

[0168] According to an embodiment, the memory 1300 may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro, a card type memory (for example, an SD (Secure Digital) memory or an XD (eXtreme Digital) memory), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, and an optical disk.

[0169] According to an embodiment, the processor 1200 may perform ASR by using data about the ASR module 1310 stored in the memory 1300, and may convert a voice signal received through the microphone 1100 into text.

[0170] However, the present disclosure is not limited thereto. In an embodiment of the present disclosure, the processor 1200 may receive a voice signal from a listening device through the communication interface 1400 and may convert the voice signal into text by performing ASR using the ASR module 1310 .

[0171] According to an embodiment, the processor 1200 may analyze the text by using the data about the device determination model 1330 stored in the memory 1300, and may determine the operation execution device (e.g., IoT device) from among the plurality of devices 4000 (e.g., a plurality of IoT devices) based on the analysis result of the text. According to an embodiment of the present disclosure, the device determination model 1330 may include a first NLU model 1332. In an embodiment of the present disclosure, the processor 1200 may analyze the text by using the data about the first NLU model 1332 included in the device determination model 1330, and may determine the operation execution device for performing the operation according to the user's intention from among the plurality of devices 4000 based on the analysis result of the text.

[0172] According to an embodiment, the first NLU model 1332 may be a model trained to analyze text converted from voice input and determine an operation execution device based on the analysis result. According to an embodiment of the present disclosure, the first NLU model 1332 may be used to determine intent by interpreting text and determine an operation execution device based on the intent.

[0173] In an embodiment of the present disclosure, the processor 1200 may parse the text in units of morphemes, words, or phrases by using the data about the first NLU model 1332 stored in the memory 1300, and may infer the meaning of the word extracted from the parsed text by using the language features (e.g., grammatical components) of the morphemes, words, or phrases. According to an embodiment, the processor 1200 may compare the inferred meaning of the word with the predefined intent provided by the first NLU model 1332, and may determine the intent corresponding to the inferred meaning of the word.

[0174] According to an embodiment, the processor 1200 may determine a device related to the intent recognized from the text as an operation execution device based on a matching model for determining a relationship between the intent and the device. In an embodiment of the present disclosure, the matching model may be included in the data about the device determination model 1330 stored in the memory 1300 and may be obtained by learning through a rule-based system, but the present disclosure is not limited thereto.

[0175] In an embodiment of the present disclosure, the processor 1200 may obtain multiple numerical values ​​indicating the degree of relationship between the intent and the multiple devices 4000 by applying the matching model to the intent, and may determine the device having the largest numerical value among the obtained multiple numerical values ​​as the final operation execution device (e.g., an IoT device capable of executing the operation intended by the user's voice (voice input)). For example, based on the intent and the first device 4100 (see Figure 1 ) and the second device 4200 (see Figure 1 ), the processor 1200 can obtain a first numerical value indicating the degree of relationship between the indication diagram and the first device 4100 and a second numerical value indicating the degree of relationship between the indication diagram and the second device 4200, and can determine the first device 4100 having the larger value between the first numerical value and the second numerical value as the final operation execution device.

[0176] For example, based on the hub device 1000 receiving a voice input from the user saying "lower the set temperature by 2°C because it's hot", the processor 1200 may perform ASR to convert the voice input into text, and may analyze the text by using data related to the first NLU model 1332 to obtain (e.g., by inference) an intent corresponding to "set temperature adjustment". According to an embodiment, the processor 1200 may obtain a first numerical value indicating the degree of relationship between the intent of "set temperature adjustment" and the first device 4100 being an air conditioner, a second numerical value indicating the degree of relationship between the intent of "set temperature adjustment" and the second device 4200 being a TV, and a third numerical value indicating the degree of relationship between the intent of "set temperature adjustment" and the third device 4300 being an air purifier (see Figure 1 ). According to an embodiment, the processor 1200 may determine the first device 4100 as an operation execution device related to “setting temperature adjustment” by using the first value which is the maximum value among the obtained values.

[0177] According to an embodiment, based on the hub device 1000 receiving a voice input from the user saying "play movie Avengers", the processor 1200 may analyze the text converted from the voice input, and may obtain an intent corresponding to "content playback". According to an embodiment, the processor 1200 may determine the second device 4200 as an operation execution device related to "content playback" based on the second numerical information being the maximum value among a first numerical value indicating the degree of relationship between the intent of "content playback" and the first device 4100 as an air conditioner calculated by using a matching model, a second numerical value indicating the degree of relationship between the intent of "content playback" and the second device 4200 as a TV, and a third numerical value indicating the degree of relationship between the intent of "content playback" and the third device 4300 as an air purifier.

[0178] However, the present disclosure is not limited to the above examples, and the processor 1200 may arrange the numerical values ​​indicating the degree of relationship between the intent and the plurality of devices in ascending order, and may determine a predetermined number of devices as operation execution devices. In an embodiment of the present disclosure, the processor 1200 may determine a device whose numerical value indicating the degree of relationship is equal to or greater than a predetermined critical value as an operation execution device associated with the intent. In this case, a plurality of devices may be determined as operation execution devices.

[0179] According to an embodiment, the processor 1200 can train the matching model between the intention and the operation execution device by using, for example, a rule-based system, but the present disclosure is not limited thereto. The AI ​​model used by the processor 1200 can be, for example, a neural network-based system (e.g., a convolutional neural network (CNN) or a recursive neural network (RNN)), a support vector machine (SVM), linear regression, logistic regression, naive Bayes, random forest, decision tree, or k-nearest neighbor algorithm. Alternatively, the AI ​​model can be a combination of the above examples or any other AI model. According to an embodiment, the AI ​​model used by the processor 1200 can be stored in the device determination model 1330.

[0180] According to an embodiment, the device determination model 1330 stored in the memory 1300 of the hub device 1000 may determine an operation execution device from among the plurality of devices 4000 registered according to the user account of the hub device 1000. According to an embodiment, the hub device 1000 may receive device information about each of the plurality of devices 4000 from the voice assistant server 2000 by using the communication interface 1400. According to an embodiment, the device information may include, for example, at least one of identification information (e.g., device id information) of each of the plurality of devices 4000, a device type of each of the plurality of devices 4000, a function execution capability of each of the plurality of devices 4000, location information, and status information. According to an embodiment, the processor 1200 may determine a device for executing an operation according to an intention from among the plurality of devices 4000 based on the device information by using data about the device determination model 1330 stored in the memory 1300.

[0181] In an embodiment of the present disclosure, the processor 1200 can analyze the numerical value indicating the degree of relationship between the intention and multiple devices 4000 by using the device determination model 1330, and the multiple devices 4000 are previously registered by using the same user account as the user account of the hub device 1000, and can determine the device with the maximum value among the numerical values ​​indicating the degree of relationship between the intention and the multiple devices 4000 as the operation execution device.

[0182] Since the device determination model 1330 is configured to determine the operation execution device by using only the plurality of devices 4000 logged in and registered by using the same user account as the user account of the hub device 1000 as device candidates, there may be the following technical effect: the amount of calculation performed by the processor 1200 in order to determine the degree of relationship with the intention can be reduced to be smaller than the amount of calculation of the processor 2200 of the voice assistant server 2000. In addition, due to the reduction in the amount of calculation, the processing time required to determine the operation execution device can be reduced, and thus the response speed can be improved.

[0183] In an embodiment of the present disclosure, the processor 1200 may obtain the name of the device from the text by using the first NLU model 1332, and may determine the operation execution device based on the name of the device by using the data about the device determination model 1330 stored in the memory 1300. In an embodiment of the present disclosure, the processor 1200 may extract a common name related to the device and a word or phrase about the installation location of the device from the text by using the first NLU model 1332, and may determine the operation execution device based on the extracted common name and the installation location of the device. For example, when the text converted from the voice input is "playing the movie Avengers on TV", the processor 1200 may parse the text in units of words or phrases by using the first NLU model 1332, and may identify the name of the device corresponding to "TV" by comparing the word or phrase with the pre-stored word or phrase. According to an embodiment, the processor 1200 may determine the second device 4200, which is a TV among the plurality of devices 4000 connected to the hub device 1000 and logged in by using the same account as the user account of the hub device 1000, as the operation execution device.

[0184] In an embodiment of the present disclosure, any one of the plurality of devices 4000 may be a listening device that receives a voice input from a user, and the processor 1200 may determine the listening device as an operation execution device by using the device determination model 1330. However, the present disclosure is not limited thereto, and the processor 1200 may determine a device other than the listening device as an operation execution device from among the plurality of devices 4000 by using the device determination model 1330.

[0185] According to an embodiment, the processor 1200 may receive update data of the device determination model 1330 from the voice assistant server 2000 by using the communication interface 1400. In an embodiment of the present disclosure, the updated latest function may be included in at least one of the multiple devices 4000, or a new device with a new function may be added to the user account. In this case, the device determination model 1330 of the hub device 1000 may not be able to determine the new functions of the multiple devices 4000, or may not be able to determine the added new device as an operation execution device. According to an embodiment, the processor 1200 may update the device determination model 1330 to the latest version by using the received update data. According to an embodiment, the latest version may be compatible with the device determination model 2330 (see Figure 3 ) versions.

[0186] According to an embodiment, the update data of the device determination model 1330 may include updated data, so that the device determination model 1330 can determine detailed operation information about the updated latest function of each of the multiple devices 4000 connected through the user account, and can determine the operation execution device that executes the updated latest function from the text. In an embodiment of the present disclosure, according to an embodiment, the update data of the device determination model 1330 may include information about the function of a new device newly added to the user account.

[0187] Will refer to Fig.17 and 18 An embodiment of updating the device determination model 1330 is described.

[0188] According to an embodiment, the processor 1200 may receive device information of a new device from the voice assistant server 2000 by using the communication interface 1400. According to an embodiment, the device information obtained by the processor 1200 of the hub device 1000 from the voice assistant server 2000 may include, for example, at least one of device identification information (e.g., device ID information) of the new device, storage information of a device determination model of the new device, and storage information of a function determination model of the new device. According to an embodiment, the new device may be a target device expected to be registered in a user account of the voice assistant server 2000.

[0189] According to an embodiment, the processor 1200 may add a new device to the device candidates that may be determined as the operation execution device by the device determination model 1330 by using the device information of the new device obtained from the voice assistant server 2000. In an embodiment of the present disclosure, because the device determination model 1330 causes the new device to be included in the device candidates, the processor 1200 may enhance the device determination capability to determine the operation execution device from the text.

[0190] refer to Fig.19An embodiment of registering a new device is described.

[0191] According to an embodiment, the NLG model 1320 may be used to provide a response message during interaction between the hub device 1000 and the user. For example, the processor 1200 may generate a response message such as "I will play a movie on TV" or "I will lower the set temperature of the air conditioner by 2°C" by using the NLG model 1320.

[0192] According to an embodiment, when there are multiple operation execution devices determined by the processor 1200 or there are multiple devices having a similar degree of relationship with the intention, the NLG model 1320 may store data for generating a query message for determining a specific operation execution device. In an embodiment of the present disclosure, the processor 1200 may generate a query message for selecting an operation execution device from among multiple device candidates by using the NLG model 1320. According to an embodiment, the query message may be a message for requesting a response from a user as to which of the multiple device candidates will be identified (determined) as the operation execution device.

[0193] According to an embodiment, the function determination device determination module 1340 may be a module for identifying a device in which a function determination model corresponding to an operation execution device is stored and determining a target device to which at least a portion of a text or all of the entire text is to be sent. According to an embodiment, the function determination device determination module 1340 may identify a device in which a function determination model corresponding to an operation execution device is stored from among the hub device 1000, the voice assistant server 2000, and the operation execution device by using a database 1360 including information about the function determination model of the device. In an embodiment of the present disclosure, when the hub device 1000 receives a voice signal from a listening device, the function determination device determination module 1340 may identify a device in which a function determination model corresponding to an operation execution device is stored from among the hub device 1000, the internal memory of the operation execution device, the internal memory of the listening device, and the voice assistant server 2000 by using the database 1035.

[0194] According to an embodiment, information about whether a function determination model of each of the plurality of devices 4100 previously registered by using a user account can be stored, and information about a storage location of the function determination model of each of the plurality of devices 4000 (e.g., device identification information, IP address, or MAC address) may be stored in the form of a lookup table (LUT) in the database 1360. In an embodiment of the present disclosure, the function determination device determination module 1340 may search the lookup table in the database 1360 according to the device identification information of the operation execution device output by the device determination model 1330, and may obtain information about the storage location of the function determination model corresponding to the operation execution device based on the search result of the lookup table.

[0195] In an embodiment of the present disclosure, the function determination device determination module 1340 itself may store data for determining a target device to which the entire text or at least a portion of the entire text is to be sent. According to an embodiment, the processor 1200 may identify a device storing a function determination model corresponding to an operation execution device by using data about the function determination device determination module 1340.

[0196] According to an embodiment, the function determination model corresponding to the operation execution device may be stored in the memory 1300 of the hub device 1000, may be stored in the memory of the operation execution device itself, or may be stored in the memory 2300 of the voice assistant server 2000.

[0197] According to an embodiment, the term "function determination model corresponding to an operation execution device" refers to a model for obtaining operation information about detailed operations of a function execution operation determined according to the operation execution device and the relationship between the detailed operations. In an embodiment of the present disclosure, the speaker function determination model 1352 and the TV function determination model 1354 stored in the memory 1300 of the hub device 1000 may respectively correspond to a plurality of devices logged in using the same account as the user account and connected to the hub device 1000 through a network.

[0198] For example, the speaker function determination model 1352 as the first function determination model may be used to obtain information about the first device 4100 (see Figure 1 ) function execution operation and the model of operation information of the relationship between the detailed operations. In the embodiment of the present disclosure, the speaker function determination model 1352 may be, but is not limited to, a model for obtaining operation information according to the function of the hub device 1000. Similarly, the TV function determination model 1354 as a second function determination model may be a model for obtaining information about the operation according to the second device 4200 (see Figure 1 )'s functional execution operations and a model of operational information about the relationships between the detailed operations.

[0199] According to an embodiment, the speaker function determination model 1352 and the TV function determination model 1354 may include second NLU models 1352a and 1354a, respectively, configured to analyze at least a portion of the text and obtain operation information about the operation to be performed by the operation execution device determined based on the analysis result of at least a portion of the text. According to an embodiment, the speaker function determination model 1352 and the TV function determination model 1354 may include action plan management modules 1352b and 1354b, respectively, configured to manage the operation information related to the detailed operation of the device, so as to generate the detailed operation to be performed by the device and the execution order of the detailed operation. According to an embodiment, the action plan management modules 1352b and 1354b can manage the operation information about the detailed operation of the device according to the relationship between the device and the detailed operation. According to an embodiment, the action plan management modules 1352b and 1354b can plan the detailed operation to be performed by the device and the execution order of the detailed operation based on the analysis result of at least a portion of the text.

[0200] According to an embodiment, a plurality of function determination models of a plurality of devices, for example, a speaker function determination model 1352 and a TV function determination model 1354 , may be stored in the memory 1300 of the hub device 1000 .

[0201] As described above, according to an embodiment, the processor 1200 may check whether a function determination model corresponding to the operation execution device is stored in the memory 1300 by searching a lookup table stored in the database 1360 using data about the function determination device determination module 1340. For example, based on determining that the operation execution device is the first device 4100, the function determination model corresponding to the first device 4100 may not be stored in the memory 1300 of the hub device 1000. In this case, according to an embodiment, the processor 1200 may check that the function determination model corresponding to the operation execution device is not stored in the hub device 1000. As another example, based on determining that the operation execution device is the second device 4200, the TV function determination model 1354 corresponding to the second device 4200 may be stored in the memory 1300 of the hub device 1000. In this case, according to an embodiment, the processor 1200 may check that the function determination model corresponding to the operation execution device is stored in the hub device 1000.

[0202] According to an embodiment, when the processor 1200 checks that a function determination model corresponding to an operation execution device is stored in the hub device 1000, the processor 1200 may provide at least a portion of the text to the function determination model corresponding to the operation execution device stored in the hub device 1000 by using data about the function determination device determination module 1340. For example, when the operation execution device is the second device 4200, a TV function determination model 1354 corresponding to a TV as the second device 4200 may be stored in the memory 1300 of the hub device 1000, and thus the processor 1200 may provide at least a portion of the text to the TV function determination model 1354 by using the function determination device determination module 1340.

[0203] In an embodiment of the present disclosure, the processor 1200 may send only a portion of the entire text (rather than the entire text) to the TV function determination model 1354. For example, when the text converted from the voice input is "Play the movie Avengers on TV", "On TV" specifies the name of the operation execution device, and thus may be unnecessary information for the TV function determination model 1354. According to an embodiment, the processor 1200 may parse the text in units of words or phrases by using the first NLU model 1332, may recognize a word or phrase that specifies a name, a common name, or an installation location of a device, and may provide the rest of the text except for the word or phrase recognized in the entire text to the TV function determination model 1354.

[0204] According to an embodiment, the processor 1200 may obtain operation information related to the operation to be performed by the operation execution device by using a function determination model corresponding to the operation execution device stored in the memory 1300, for example, the second NLU model 1354a of the TV function determination model 1354. According to an embodiment, the second NLU model 1354a, which is a model dedicated to a specific device (e.g., TV), may be an AI model trained to obtain an intent related to the device corresponding to the operation execution device determined by the first NLU model 1332 and corresponding to the text. In addition, the second NLU model 1354a may be a model trained to determine the operation of the device related to the user's intent by interpreting the text. According to an embodiment, an operation may refer to at least one action performed by the device when the device performs a specific function. An operation may refer to at least one action performed by the device when the device executes an application.

[0205] In an embodiment of the present disclosure, the processor 1200 may analyze the text by using the second NLU model 1354a of the TV function determination model 1354 corresponding to the determined operation execution device (e.g., TV). According to an embodiment, the processor 1200 may parse the text in units of morphemes, words, or phrases by using the second NLU model 1354a, may identify the meaning of the morphemes, words, or phrases parsed by grammatical or semantic analysis, and may determine the intent and parameter by matching the recognized meaning with the predefined words. According to an embodiment, the term "parameter" used herein refers to variable information used to determine the detailed operation of the operation execution device related to the intent. For example, when the text sent to the TV function determination model 1354 is "Play the movie Avengers on TV", the intent may be "Content playback", and the parameter may be "Movie Avengers" as information about the content to be played.

[0206] According to an embodiment, the processor 1200 can obtain operation information about at least one detailed operation related to intention and parameters by using the action plan management module 1354b of the TV function determination model 1354. According to an embodiment, the action plan management module 1354b can manage information about the detailed operation of the device according to the relationship between the device and the detailed operation. According to an embodiment, the processor 1200 can plan the detailed operation to be performed by the operation execution device (e.g., TV) and the execution order of the detailed operation based on the intention and parameters by using the action plan management module 1354b, and can obtain operation information. According to an embodiment, the operation information can be information related to the detailed operation to be performed by the device and the execution order of the detailed operation. According to an embodiment, the operation information may include information related to the detailed operation to be performed by the device, the relationship between each detailed operation and another detailed operation, and the execution order of the detailed operation. The operation information may include, but is not limited to, the function executed by the operation execution device to perform a specific operation, the execution order of the function, the input value required for executing the function, and the output value output as the execution result of the function.

[0207] According to an embodiment, the processor 1200 may generate a control command for controlling the operation execution device based on the operation information. According to an embodiment, the control command may refer to an instruction readable and executable by the operation execution device so that the operation execution device performs the detailed operation included in the operation information. According to an embodiment, the processor 1200 may control the communication interface 1400 to send the generated control command to the operation execution device.

[0208] In an embodiment of the present disclosure, the processor 1200 may obtain information about a function determination model from a plurality of devices 4000 that are logged in using the same account as a user account of the hub device 1000 and connected to the hub device 1000 through a network through the communication interface 1400. According to an embodiment, the information about the function determination model may include information about whether each of the plurality of devices 4000 stores a function determination model. Figure 1 , despite Figure 1 In an embodiment of the present invention, the first device 4100 among the plurality of devices 4000 may store the function determination model 4132 in the memory 4130, but the second device 4200 and the third device 4300 may not have the function determination model (or may not have the function determination model 4132). According to an embodiment, when information about whether at least one of the first device 4100, the second device 4200, or the third device 4300 stores the function determination model is sent to the hub device 1000 (S4, S6, and S8), the hub device 1000 may provide the information about the function determination model obtained through the communication interface 1400 to the processor 1200.

[0209] In an embodiment of the present disclosure, the processor 1200 may receive the voice assistant server 2000 (see Figure 1 ) obtain information about multiple devices 4000 (see Figure 1 ) of the function determination model. According to an embodiment, each of the plurality of devices 4000 may be registered according to a user account logged in when a user inputs a user id and a password, and the user account information and device information of each of the plurality of devices 4000 may be sent to the IoT server 3000. In this case, information on whether each of the plurality of devices 4000 itself stores a device determination model and stores a function determination model may also be sent to the IoT server 3000. According to an embodiment, the IoT server 3000 may send device information about the plurality of devices 4000 registered in the user account information, information about the device determination model, and information about the function determination model to the voice assistant server 2000. According to an embodiment, the voice assistant server 2000 may send device information about the plurality of devices 4000, information about the device determination model, and information about the function determination model to the hub device 1000 having the same user account information. According to an embodiment, the hub device 1000 may store information about the function determination model of each of the plurality of devices 4000 in the form of a lookup table in the database 1360.

[0210] However, the present disclosure is not limited thereto, and the hub device 1000 may receive information about the function determination model from at least one of the plurality of devices 4000. In an embodiment of the present disclosure, the processor 1200 may control the communication interface 1400 to receive information about the function determination model from at least one device storing a function determination model for determining a function of each of the plurality of devices 4000. According to an embodiment, based on receiving the information about the function determination model of the plurality of devices 4000, the processor 1200 may control the communication interface 1400 to further receive information about a storage location of the function determination model of each of the plurality of devices 4000 (e.g., device identification information, IP address, or MAC address).

[0211] According to an embodiment, the processor 1200 may store the received information about the function determination model and the received information about the storage location of the function determination model of each device in the database 1360. In an embodiment of the present disclosure, the processor 1200 may store the information about the storage location of the function determination model and the information about whether to store the function determination model in the form of a lookup table according to the name or identification information of the device.

[0212] According to an embodiment, the processor 1200 may obtain information about the function determination model of each device by searching the lookup table stored in the database 1360, and may identify the device storing the function determination model corresponding to the operation execution device based on the obtained information about the function determination model. For example, according to an embodiment, when the operation execution device determined based on the text is the first device 4100, the processor 1200 may determine the first device 4100 itself as storing the function determination model 4132 (see FIG. 4 ) based on the information about the function determination model obtained from the first device 4100. Figure 1 ) device. In this case, the processor 1200 may determine the first device 4100 as a target device to which at least a portion of the text is to be sent by using data about the function determination device determination module 1340. According to an embodiment, the processor 1200 may control the communication interface 1400 to send at least a portion of the text to the function determination model 4132 of the first device 4100.

[0213] In an embodiment of the present disclosure, the processor 1200 may control the communication interface 1400 to separate the part about the name of the operation execution device from the text, and send only the remaining part of the text to the first device 4100. For example, when the first device 4100 is an air conditioner and the text is "lower the set temperature by 2°C in the air conditioner", when the text is sent to the air conditioner, it is not necessary to send "in the air conditioner". In this case, the processor 1200 may parse the text in units of words or phrases, may recognize words or phrases that specify the name, common name, installation location, etc. of the first device 4100, and may provide the first device 4100 with the remaining part of the text except for the words or phrases recognized in the entire text.

[0214] According to an embodiment, when the processor 1200 is to identify a device storing a function determination model corresponding to an operation execution device based on the information obtained about the function determination model, the processor 1200 may check whether the function determination model is not stored in the operation execution device. For example, based on determining that the operation execution device is the third device 4300, according to an embodiment, the function determination model is not stored in the third device 4300. According to an embodiment, the function determination model corresponding to the third device 4300 is also not stored in the hub device 1000. In this case, the processor 1200 can check that the function determination model corresponding to the operation execution device is not stored in any of the hub device 1000 and the plurality of devices 4000 by using data about the function determination device determination module 1340. According to an embodiment, the processor 1200 can determine that the target device to which at least a portion of the text is to be sent is the voice assistant server 2000 by using data about the function determination device determination module 1340. According to an embodiment, the processor 1200 can control the communication interface 1400 to send at least a portion of the text to the voice assistant server 2000.

[0215] In an embodiment of the present disclosure, the function determination device determination module 1340 may be configured to determine whether the operation execution device determined by the device determination model 1330 is the same as the listening device. In an embodiment of the present disclosure, when the processor 1200 receives a voice signal from the listening device through the communication interface 1400, the processor 1200 may also receive device identification information of the listening device (e.g., device ID information of the listening device). According to an embodiment, the function determination device determination module 1340 may check the function information of the listening device from the device identification information of the listening device, and may determine whether the listening device and the operation execution device are the same by comparing the function information of the listening device with the function information of the operation execution device.

[0216] According to an embodiment, based on determining that the operation execution device is the same as the listening device, the processor 1200 may determine whether the listening device stores the function determination model in the internal memory by using the data about the function determination device determination module 1340. According to an embodiment, when the function determination model stored in the internal memory of the listening device is the same as the function determination model corresponding to the operation execution device, the processor 1200 may send at least a portion of the text to the function determination model previously stored in the internal memory of the listening device by using the communication interface 1400. In an embodiment of the present disclosure, the processor 1200 may control the communication interface 1400 to separate the part about the name of the operation execution device from the text and send only the remaining part of the text to the listening device. For example, when the listening device is an air conditioner and the text is "lower the set temperature to 20°C in the air conditioner", when the text is sent to the air conditioner, it is not necessary to send "in the air conditioner". In this case, the processor 1200 may parse the text in units of words or phrases, may recognize words or phrases specifying the name, common name, installation location, etc. of the listening device, and may provide the remaining part of the text except for the words or phrases recognized in the entire text to the listening device.

[0217] According to an embodiment, when the function determination model stored in the internal memory of the listening device is different from the function determination model corresponding to the operation execution device, the processor 1200 may send at least a portion of the text to the voice assistant server 2000 by using the communication interface 1400.

[0218] According to an embodiment, based on determining that the operation execution device is a separate device different from the listening device, the processor 1200 may determine whether the operation execution device stores the function determination model in the internal memory by using data about the function determination device determination module 1340. According to an embodiment, when the function determination model is stored in the internal memory of the operation execution device, the processor 1200 may send at least a portion of the text to the function determination model previously stored in the internal memory of the operation execution device by using the communication interface 1400. In an embodiment of the present disclosure, the processor 1200 may control the communication interface 1400 to separate the portion about the name of the operation execution device from the text, and send only the remaining portion of the text to the operation execution device.

[0219] According to an embodiment, when the function determination model is not stored in the internal memory of the operation execution device, the processor 1200 may transmit at least a portion of the text to the voice assistant server 2000 by using the communication interface 1400 .

[0220] According to an embodiment, it may be determined whether the operation execution device and the listening device are the same, and at least a portion of the text may be sent to the reference device. Fig.14The described operations may be performed by any one of a device, a listening device, and a voice assistant server.

[0221] According to an embodiment, the communication interface 1400 may perform data communication with the voice assistant server 2000, the IoT server 3000, and the plurality of devices 4000. According to an embodiment, the communication interface 1400 may perform data communication with the voice assistant server 2000, the IoT server 3000, and one or more of the plurality of devices 4000 by using at least one of data communication methods including wireless LAN, Wi-Fi, Bluetooth, Zigbee, WFD, IrDA, BLE, NFC, Wireless Broadband Internet (Wibro), World Interoperability for Microwave Access (WiMAX), Shared Wireless Access Protocol (SWAP), Wireless Gigabit Alliance (WiGig), and Radio Frequency (RF) communication.

[0222] According to an embodiment, the database 1360 may store identification information of each of the multiple devices 4000, information about whether a function determination model exists, and information about the storage location of the function determination model of each of the multiple devices 4000 (for example, the identification information of the stored device, the IP address of the stored device, or the MAC address of the stored device).

[0223] Although database 1360 may be stored in Figure 2 The database 1360 may be stored in the memory 1300 in the embodiment of the present invention, but the database 1360 may also be stored in a memory separate from the memory 1300.

[0224] In (for example, Figure 1 and Figure 2In an embodiment of the present invention, the hub device 1000 may include a device determination model 1330 configured to determine an operation execution device by using only a plurality of devices 4000 as device candidates, the plurality of devices 4000 being logged in by using the same user account information as the user account information of the hub device 1000 and registered according to the user account information, and the function determination device determination module 1340 may be configured to identify a device storing a function determination model corresponding to the operation execution device determined by the device determination model 1330, and may determine at least a portion of a text to be sent to the identified device. Since the hub device 1000 includes some models included in the voice assistant server 2000, determines the operation execution device by using the models, and controls the operation of the operation execution device, it is not necessary to operate the voice assistant server 2000 by using a network in all processes, so that the network usage fee can be reduced and the server operation efficiency can be improved. In addition, since the operation execution device is determined by using only a plurality of devices registered by using the user account of the hub device 1000 as device candidates, the amount of calculation and processing time can be reduced, and the response speed can be improved.

[0225] Figure 3 is a block diagram illustrating elements of a voice assistant server 2000 according to an embodiment of the present disclosure.

[0226] According to an embodiment, the voice assistant server 2000 may be a server that receives text converted from a user's voice input from the hub device 1000, determines an operation execution device based on the received text, and obtains operation information by using a function determination model corresponding to the operation execution device.

[0227] refer to Figure 3 According to an embodiment, the voice assistant server 2000 may include at least a communication interface 2100, a processor 2200, and a memory 2300.

[0228] According to an embodiment, the communication interface 2100 of the voice assistant server 2000 can communicate with the IoT server 3000 (see Figure 1 ) performs data communication to receive device information including at least one of the following items from the IoT server 3000: a plurality of devices 4000 (see Figure 1), identification information (e.g., device id information) of each of the multiple devices 4000, a device type of each of the multiple devices 4000, function execution capabilities of each of the multiple devices 4000, location information, and status information. In an embodiment of the present disclosure, the voice assistant server 2000 may receive information about a function determination model of each of the multiple devices 4000 from the IoT server 3000 through the communication interface 2100. According to an embodiment, the voice assistant server 2000 may receive user account information from the hub device 1000 through the communication interface 2100, and may send device information about the multiple devices 4000 registered according to the received user account information and information about the function determination model to the hub device 1000.

[0229] According to an embodiment, the processor 2200 and the memory 2300 of the voice assistant server 2000 may execute the communication with the hub device 1000 (see Figure 2 ) of the processor 1200 (see Figure 2 ) and memory 1300 (see Figure 2 ) or functions that are the same as or similar to the functions of the hub device 1000. Therefore, the same description of the processor 2200 and the memory 2300 of the voice assistant server 2000 as the description of the processor 1200 and the memory 1300 of the hub device 1000 is not provided here.

[0230] According to an embodiment, the memory 2300 of the voice assistant server 2000 may store data about one or more or all of the following items: the ASR module 2310, the data about the NLG module 2320, the data about the device determination model 2330, and the data corresponding to each of the plurality of function determination models 2340. According to an embodiment, the memory 2300 of the voice assistant server 2000 may store a plurality of function determination models 2340 corresponding to a plurality of devices associated with a plurality of different user accounts, instead of the plurality of function determination models 1350 stored in the memory 1300 of the hub device 1000 (see Figure 2 ). In addition, according to an embodiment, a plurality of function determination models 2340 for more types of devices than the plurality of function determination models 1350 stored in the memory 1300 of the hub device 1000 may be stored in the memory 2300 of the voice assistant server 2000. The total capacity of the plurality of function determination models 2340 stored in the memory 2300 of the voice assistant server 2000 may be greater than the capacity of the plurality of function determination models 1350 stored in the memory 1300 of the hub device 1000.

[0231] According to an embodiment, when at least a portion of the text is received from the hub device 1000, the communication interface 2100 of the voice assistant server 2000 may send at least a portion of the received text to the processor 2200, and the processor 2200 may analyze at least a portion of the text by using the first NLU model 2332 stored in the memory 2300. According to an embodiment, the processor 2200 may determine an operation execution device related to at least a portion of the text based on the analysis result by using data about the device determination model 2330 stored in the memory 2300. According to an embodiment, the processor 2200 may select a function determination model corresponding to the operation execution device from a plurality of function determination models stored in the memory 2300, and may obtain operation information about detailed operations for executing functions of the operation execution device and relationships between the detailed operations by using the selected function determination model.

[0232] For example, based on determining that the operation execution device is the third device 4300 as an air purifier, the processor 2200 can analyze at least a portion of the text by using the second NLU model 2346a of the function determination model 2346 corresponding to the air purifier, and can obtain operation information by planning the detailed operations to be performed by the device and the execution order of the detailed operations using the action plan management module 2346b. The detailed description thereof is the same as that of the hub device 1000, so repeated description will be omitted.

[0233] According to an embodiment, based on at least a portion of the entire text of the user voice sent by the hub device 1000, the voice assistant server 2000 can determine the operation execution device related to at least a portion of the entire text, and can obtain operation information for performing the operation of the operation execution device. According to an embodiment, the portion of the entire text may be smaller than the entire text of the user voice. According to an embodiment, the voice assistant server 2000 may send the obtained operation information to the IoT server 3000 through the communication interface 2100.

[0234] According to an embodiment, the voice assistant server 2000 may receive update request information and device identification information of a function determination model from an operation execution device among the plurality of devices 4000. According to an embodiment, the update request information of the function determination model may be information for requesting that the version of the function determination model stored in the memory of the operation execution device be synchronized with the version of the function determination model corresponding to the operation execution device among the plurality of function determination models 2342, 2344, 2346, and 2348 stored in the voice assistant server 2000. According to an embodiment, the processor 2200 of the voice assistant server 2000 may identify the function determination model corresponding to the operation execution device based on the device identification information received from the operation execution device, and may check the version information of the identified function determination model. According to an embodiment, the processor 2200 may send update data for updating the function determination model of the operation execution device to the operation execution device by using the communication interface 2100.

[0235] In an embodiment of the present disclosure, based on the operation execution device receiving a control command from the IoT server 3000 , the processor 2200 may control the communication interface 2100 to send update data for updating the function determination model to the operation execution device.

[0236] In an embodiment of the present disclosure, the processor 2200 may control the communication interface 2100 to periodically send update data of the function determination model to a device storing the function determination model among the plurality of devices 4000 connected through the user account of the hub device 1000. In an embodiment of the present disclosure, when the processor 2200 sends update data for updating an application or firmware to a device storing the function determination model among the plurality of devices 4000 connected through the user account of the hub device 1000, the processor 2200 may control the communication interface 2100 to also send the update data of the function determination model.

[0237] In an embodiment of the present disclosure, a new device may be registered in the user account information registered to the IoT server 300. In this case, the processor 2200 may receive the user account information, the device list updated to include the new device, and the storage information of the device determination model and the function determination model of each device candidate included in the device list from the IoT server 3000 by using the communication interface 2100. According to an embodiment, the processor 2200 may identify a device in which the device determination model is stored from among a plurality of devices registered according to the user account based on the storage information of the device determination model, and may determine the identified device as a hub device. According to an embodiment, the processor 2200 may send the device identification information of the new device and the storage information of the device determination model and the function determination model to the hub device by using the communication interface 2100. Reference Fig.19An embodiment of registering a new device is described.

[0238] Figure 4 is a block diagram illustrating elements of the IoT server 3000 according to an embodiment of the present disclosure.

[0239] According to an embodiment, the IoT server 3000 may be a device for obtaining, storing, and managing information about a plurality of devices 4000 (see Figure 1 ) of each device information. According to an embodiment, the IoT server 3000 may obtain, determine, or generate a control command for controlling the device by using the stored device information. Although the IoT server 3000 is implemented as Figure 1 The IoT server 3000 may be an independent hardware device separated from the voice assistant server 2000 in the embodiment of the present disclosure, but the present disclosure is not limited thereto. Figure 1 ) element, or may be a server designed to be classified as software.

[0240] According to the embodiment, reference Figure 4 , the IoT server 3000 may include at least a communication interface 3100 , a processor 3200 , and a memory 3300 .

[0241] According to an embodiment, the IoT server 3000 may be connected to the operation execution device or the voice assistant server 2000 via a network through the communication interface 3100, and may receive or send data. According to an embodiment, the IoT server 3000 may send data stored in the memory 3300 to the voice assistant server 2000 or the operation execution device through the communication interface 3100 under the control of the processor 3200. In addition, the IoT server 3000 may receive data from the voice assistant server 2000 or the operation execution device through the communication interface 3100 under the control of the processor 3200.

[0242] In the embodiment of the present disclosure, the communication interface 3100 can be connected to multiple devices 4000 (see Figure 1 ) each receives device information including at least one of device identification information (e.g., device ID information), function execution capability information, location information, and status information. In an embodiment of the present disclosure, the communication interface 3100 may receive user account information from each of the plurality of devices 4000. In addition, the communication interface 3100 may receive information about power on / off or operations being performed from the plurality of devices 4000. According to an embodiment, the communication interface 3100 may provide the received device information to the memory 3300.

[0243] According to an embodiment, the memory 3300 may store device information received through the communication interface 3100. In an embodiment of the present disclosure, the memory 3300 may classify the device information according to user account information received from the plurality of devices 4000 and may store the classified device information in the form of a lookup table.

[0244] In an embodiment of the present disclosure, the communication interface 3100 may receive a query for requesting user account information and device information about a plurality of devices 4000 previously registered by using the user account information from the voice assistant server 2000. According to an embodiment, in response to the received query, the processor 3200 may obtain device information about a plurality of devices 4000 previously registered by using the user account from the memory 3300, and may control the communication interface 3100 to send the obtained device information to the voice assistant server 2000.

[0245] According to an embodiment, the processor 3200 may control the communication interface 3100 to send a control command to an operation execution device determined to perform an operation based on the operation information received from the voice assistant server 2000. According to an embodiment, the IoT server 3000 may receive an operation execution result according to the control command from the operation execution device through the communication interface 3100.

[0246] Figure 5 is a block diagram illustrating some elements of a plurality of devices 4000 according to an embodiment of the present disclosure.

[0247] According to an embodiment, the plurality of devices 4000 may be a hub device 1000 (see Figure 1 ) or IoT Server 3000 (see Figure 1 ) controlled by the hub device 1000 or the IoT server 3000. In an embodiment of the present disclosure, the plurality of devices 4000 may be actuator devices that perform operations based on control commands received from the hub device 1000 or the IoT server 3000. According to an embodiment, the plurality of devices 4000 may be IoT devices.

[0248] refer to Figure 5 , the plurality of devices 4000 may include a first device 4100, a second device 4200, and a third device 4300. Figure 5 In the embodiment of the present invention, the first device 4100 is an air conditioner, the second device 4200 is a TV, and the third device 4300 is an air purifier, but this is only an example, and the plurality of devices 4000 of the present disclosure are not limited to Figure 5 Those devices shown.

[0249] According to an embodiment, each of the first device 4100, the second device 4200, and the third device 4300 may be as follows Figure 5The processor, memory and communication interface shown. According to an embodiment, multiple elements required for the device 4000 to perform operations based on the control may also be included. According to an embodiment, the required element may be, for example, a fan for an air conditioner.

[0250] According to an embodiment, one or more of the plurality of devices 4000 may store a function determination model. Figure 5 In an embodiment of the present invention, the first device 4100 may include a communication interface 4110, a processor 4120, and a memory 4130, and a function determination model 4132 may be stored in the memory 4130. According to an embodiment, the function determination model 4132 stored in the first device 4100 may be a model for obtaining operation information about detailed operations for performing operations of the first device 4100 and relationships between the detailed operations. According to an embodiment, the function determination model 4132 may include a first NLU model 4134 configured to analyze at least a portion of a text received from the hub device 1000 or the IoT server 3000, and obtain operation information about an operation to be performed by the first device 4100 based on an analysis result of at least a portion of the text. According to an embodiment, the function determination model 4132 may include an action plan management module 4136 configured to manage operation information related to detailed operations of the device so as to generate detailed operations to be performed by the first device 4100 and an execution order of the detailed operations. The action plan management module 4136 may plan detailed operations to be performed by the first device 4100 and the execution order of the detailed operations based on the analysis result of at least a portion of the text.

[0251] According to an embodiment, the second device 4200 may include a communication interface 4210, a processor 4220, and a memory 4230. According to an embodiment, the third device 4300 may include a communication interface 4310, a processor 4320, and a memory 4330. According to an embodiment, unlike the first device 4100, the second device 4200 and the third device 4300 may not store a function determination model. According to an embodiment, the second device 4200 and the third device 4300 may not receive the function determination model from the hub device 1000 (see Figure 1 ) or IoT Server 3000 (see Figure 1 ) receives at least a portion of the text. According to an embodiment, the second device 4200 and the third device 4300 may receive a control command from the hub device 1000 or the IoT server 3000, and may perform an operation based on the received control command.

[0252] According to an embodiment, the plurality of devices 4000 may send information about user account information, device information, and information about a function determination model to the IoT server 3000 by using the communication interfaces 4110, 4210, and 4310. In an embodiment of the present disclosure, when a user logs in to the plurality of devices 4000, the plurality of devices 4000 may send the user account information, information about whether each of the plurality of devices 4000 itself stores a device determination model, and information about whether each of the plurality of devices 4000 itself stores a function determination model to the IoT server 3000. According to an embodiment, the device information may include at least one of identification information (e.g., device id information) of the plurality of devices 4000, a device type of each of the plurality of devices 4000, a function execution capability of each of the plurality of devices 4000, location information, and status information.

[0253] In the embodiment of the present disclosure, the plurality of devices 4000 may communicate with the hub device 1000 (see Figure 1 ) sends user account information, information about whether each of the multiple devices 4000 itself stores a device determination model, and information about whether each of the multiple devices 4000 itself stores a function determination model.

[0254] According to an embodiment, one or more of the plurality of devices 4000 may be a third-party device manufactured by a manufacturer different from the manufacturer of the hub device 1000. For example, the third device 4300 may be a third-party device. When the third device 4300 is a third-party device, a third-party IoT server may be logged in using a third-party user account through the third device 4300, and the IoT server 3000 may access the third-party IoT server by using a temporary account, and may obtain device information and information about a function determination model of the third device 4300. According to an embodiment, based on determining that the third device 4300 is an operation execution device, the IoT server 3000 may convert a control command into a control command readable and executable by the third device 4300, and may send the control command to the third-party IoT server. According to an embodiment, the third-party IoT server may send the converted control command to the third device 4300, and the third device 4300 may perform an operation based on the received control command.

[0255] Figure 6 is a flowchart of a method of controlling a device based on a voice input, performed by the hub device 1000 according to an embodiment of the present disclosure.

[0256] According to an embodiment, in operation S610, the hub device 1000 may receive voice data (eg, voice input of a user). In an embodiment of the present disclosure, the hub device 1000 may receive voice data (eg, voice input of a user) through the microphone 1100 (see Figure 2 ) receives a voice input (eg, a user's speech) from a user, and may obtain a voice signal or voice data from the received voice input. In an embodiment of the present disclosure, the processor 1200 (see Figure 2 ) can convert voice data (e.g., sound received through the microphone 1100) into an acoustic signal, and can obtain the voice signal by removing noise (e.g., non-voice components) from the acoustic signal.

[0257] According to an embodiment, in operation S620, the hub device 1000 may convert the received voice input into text by performing ASR. In an embodiment of the present disclosure, the hub device 1000 may perform ASR, which converts a voice signal into a computer-readable text by using a predefined model such as an acoustic model (AM) or a language model (LM). When an acoustic signal from which noise is not removed is received, the hub device 1000 may obtain a voice signal by removing noise from the received acoustic signal, and may perform ASR on the voice signal.

[0258] According to an embodiment, in operation S630, the hub device 1000 may analyze the text by using a first NLU model, and may determine an operation execution device corresponding to the analyzed text by using a device determination model. In an embodiment of the present disclosure, the hub device 1000 may analyze the text by using a first NLU model included in the device determination model, and may determine an operation execution device for performing an operation according to the user's intention from a plurality of devices based on the analysis result of the text. According to an embodiment, the plurality of devices may refer to devices such as IoT devices that are logged in by using the same user account as the user account of the hub device 1000 and connected to the hub device 1000 through a network. The plurality of IoT devices may be devices registered to an IoT server by using the same user account as the user account of the hub device 1000.

[0259] According to an embodiment, the first NLU model may be a model trained to analyze text converted from voice input and determine an operation execution device based on the analysis result. According to an embodiment, the first NLU model may be used to determine the intent by interpreting the text, and determine the operation execution device based on the intent. According to an embodiment, the hub device 1000 may parse the text in units of morphemes, words, or phrases by using the first NLU model, and may infer the meaning of the words extracted from the parsed text by using the language features (e.g., grammatical components) of the morphemes, words, or phrases. According to an embodiment, the hub device 1000 may compare the meaning of the inferred word with the predefined intent provided by the first NLU model, and may determine the intent corresponding to the inferred meaning of the word.

[0260] According to an embodiment, the hub device 1000 can determine the device related to the intent recognized from the text as the operation execution device based on the matching model for determining the relationship between the intent and the device. In an embodiment of the present disclosure, the matching model can be obtained by learning through a rule-based system, but the present disclosure is not limited thereto.

[0261] In an embodiment of the present disclosure, the hub device 1000 can obtain multiple numerical values ​​indicating the degree of relationship between the intent and multiple devices by applying the matching model to the intent, and can determine the device having the maximum value among the obtained multiple numerical values ​​as the final operation execution device. For example, when the intent is related to each of the first device and the second device, the hub device 1000 can obtain a first numerical value indicating the degree of relationship between the intent and the first device and a second numerical value indicating the degree of relationship between the intent and the second device, and can determine the first device having the larger numerical value of the first numerical value and the second numerical value as the operation execution device.

[0262] Although the hub device 1000 can train the matching model between the intention and the operation execution device by using, for example, a rule-based system, the present disclosure is not limited thereto. The AI ​​model used by the hub device 1000 may include, for example, a neural network-based system (e.g., CNN or RNN), SVM, linear regression, logistic regression, naive Bayes, random forest, decision tree, or k-nearest neighbor algorithm. Alternatively, the AI ​​model may be a combination of the above examples or any other AI model.

[0263] According to an embodiment, the memory 1300 (see Figure 2 ) in the device determination model 1330 (see Figure 2) may determine the operation execution device from among a plurality of devices registered according to the user account of the hub device 1000. In an embodiment of the present disclosure, the device determination model may analyze the numerical value of the degree of relationship between the indication diagram and a plurality of devices logged in and previously registered by using the same user account as the user account of the hub device 1000, and may determine a device having the maximum value among the numerical values ​​of the degree of relationship between the indication diagram and the plurality of devices as the operation execution device.

[0264] In an embodiment of the present disclosure, the hub device 1000 can receive the voice assistant server 2000 (see Figure 1 ) receives device information about each of the multiple devices previously registered according to the user account. The device information may include, for example, identification information of each of the multiple devices (e.g., device ID information), device type of each of the multiple devices, function execution capability of each of the multiple devices, location information, and at least one of status information. The hub device 1000 may determine a device for performing an operation according to the intention from among the multiple devices based on the received device information.

[0265] According to an embodiment, in operation S640, the hub device 1000 may identify a device storing a function determination model corresponding to an operation execution device determined from among a plurality of devices.

[0266] According to an embodiment, the function determination model corresponding to the operation execution device may be stored in the memory of the hub device 1000, may be stored in the memory of the operation execution device itself, or may be stored in the memory of the voice assistant server 2000 (see Figure 1 According to an embodiment, the term “function determination model corresponding to the operation execution device” may refer to a model for obtaining operation information related to detailed operations for executing operations according to the determination of the operation execution device and the relationship between the detailed operations.

[0267] In the embodiment of the present disclosure, the hub device 1000 can use the information stored in the database 1360 (see Figure 2) and information about the storage location of the function determination model corresponding to each of the multiple devices, and identify the device storing the function determination model from the memory of the hub device 1000, the memory of the voice assistant server 2000, or the memory of the operation execution device itself. According to an embodiment, the hub device 1000 may obtain information about the function determination model from at least one device storing a function determination model for determining a function related to each of the multiple devices among the multiple devices, and may store the obtained information about the function determination model in the database 1360. According to an embodiment, based on receiving the information about the function determination model of the multiple devices, the hub device 1000 may also receive information about the storage location of the function determination model of each of the multiple devices (for example, device identification information, IP address, or MAC address). According to an embodiment, information about whether to store the function determination model of each of the multiple devices previously registered according to the user account and information about the storage location of the function determination model of each of the multiple devices may be stored in the database 1360 in the form of a lookup table.

[0268] In an embodiment of the present disclosure, the hub device 1000 can obtain the device identification information of the operation execution device determined in operation S630, can search the lookup table in the database 1360 according to the device identification information, and can obtain information about the storage location of the function determination model corresponding to the operation execution device based on the search results of the lookup table. By using the above method, the hub device 1000 can identify the device storing the function determination model corresponding to the operation execution device from the memory of the hub device 1000, the memory of the voice assistant server 2000, and the memory of the operation execution device itself.

[0269] According to an embodiment, in operation S650, the hub device 1000 may provide at least a portion of the text to the recognized device. For example, when it is checked in operation S640 that the function determination model corresponding to the operation execution device is stored in the hub device 1000, the hub device 1000 may provide at least a portion of the text to the function determination model corresponding to the operation execution device.

[0270] For example, when it is checked in operation S640 that a function determination model corresponding to the operation execution device is stored in the voice assistant server 2000 , the hub device 1000 may transmit at least a portion of the text to the voice assistant server 2000 .

[0271] For example, when it is checked in operation S640 that the function determination model corresponding to the operation execution device is stored in the memory of the operation execution device itself, the hub device 1000 may transmit at least a part of the text to the operation execution device.

[0272] In an embodiment of the present disclosure, the hub device 1000 may provide only a portion of the text, rather than providing the entire text to the recognized device. For example, when the text converted from the voice input is "play the movie Avengers on TV", "on TV" specifies the name of the operation execution device, so it may be unnecessary information for the TV function determination model 1354. According to an embodiment, the hub device 1000 may provide only a portion of the text, rather than providing the entire text to the recognized device. For example, when the text converted from the voice input is "play the movie Avengers on TV", "on TV" specifies the name of the operation execution device, so it may be unnecessary information for the TV function determination model 1354. Figure 2 ) parses text in units of words or phrases, can identify words or phrases that specify a name, a common name, or an installation location of a device, and can provide the remainder of the text except for the words or phrases identified in the entire text to a recognition device.

[0273] Figure 7 is a flowchart illustrating a method of providing at least a portion of a text to one of the hub device 1000, the voice assistant server 2000, and the operation execution device 4000a according to a user's voice input, which is performed by the hub device 1000 according to an embodiment of the present disclosure. Figure 7 Picture shows Figure 6 Detailed embodiments of operations S640 and S650. Figure 7 Operations S710 to S730 are detailed embodiments of operation S640, and operations S740 to S760 are Figure 6 Detailed embodiment of operation S650.

[0274] According to an embodiment, it is possible to Figure 6 Operation S710 is performed after operation S630. In operation S710, according to an embodiment, the hub device 1000 may obtain information about the function determination model from at least one device storing the function determination model among the plurality of devices. In an embodiment of the present disclosure, the hub device 1000 may obtain information about the function determination model by using the communication interface 1400 (see Figure 2 ) receives information about a function determination model from at least one device storing a function determination model for determining a function associated with each of a plurality of devices. According to an embodiment, the term "information about a function determination model" may refer to information about whether each of a plurality of devices itself stores a function determination model for obtaining operation information about detailed operations for performing operations according to functions and relationships between the detailed operations. In an embodiment of the present disclosure, when receiving information about a function determination model from a plurality of devices, the hub device 1000 may also obtain information about a storage location of a function determination model for each of the plurality of devices (e.g., device identification information, an IP address, or a MAC address).

[0275] According to an embodiment, the hub device 1000 may store the received information about the function determination model and the information about the storage location of the function determination model of each device in the database 1360 (see Figure 2 In an embodiment of the present disclosure, the hub device 1000 may store information about whether to store the function determination model and information about the storage location of the function determination model in the database 1360 in the form of a lookup table according to the name or identification information of the device.

[0276] According to an embodiment, in operation S720, the hub device 1000 may check whether a function determination model corresponding to the determined operation execution device is stored in a memory of the hub device 1000. According to an embodiment, the term "function determination model corresponding to the operation execution device" may be a model for obtaining operation information about detailed operations of performing operations according to the determination of the operation execution device and the relationship between the detailed operations.

[0277] According to an embodiment, a function determination model corresponding to an operation execution device may be stored in the memory 1300 of the hub device 1000 (see Figure 2 ), can be stored in the memory of the operation execution device itself, or can be stored in the voice assistant server 2000.

[0278] In an embodiment of the present disclosure, the hub device 1000 may obtain information about the function determination model of each device by searching a lookup table stored in the database 1360. In an embodiment of the present disclosure, the hub device 1000 may check whether the function determination model corresponding to the operation execution device is stored in the memory 1300 of the hub device 1000 based on the obtained information about the function determination model. In another embodiment of the present disclosure, the hub device 1000 may determine whether the function determination model corresponding to the operation execution device is stored in the memory 1300 by accessing the memory 1300 and scanning data stored in the memory 1300.

[0279] Multiple function determination models of multiple devices may be stored in the memory 1300 of the hub device 1000. In an embodiment of the present disclosure, multiple function determination models corresponding to multiple devices logged in using the same account as the user account and connected to the hub device 1000 through a network may be stored in the memory 1300 of the hub device 1000.

[0280] According to an embodiment, in operation S730, based on the determination in operation S720 that there is no function determination model corresponding to the operation execution device in the memory 1300 of the hub device 1000, the hub device 1000 may check whether the function determination model corresponding to the operation execution device is stored in the memory of the operation execution device based on the information about the function determination model. In an embodiment of the present disclosure, the hub device 1000 may check whether the function determination model corresponding to the operation execution device is stored in the memory of the operation execution device itself by searching a lookup table stored in the database 1360. For example, when the operation execution device determined based on the text is the first device, the hub device 1000 may check whether the information about whether the function determination model is stored in the first device itself by searching the lookup table.

[0281] In an embodiment of the present disclosure, the function determination model may not be stored in the memory of the first device determined as the operation execution device. According to an embodiment, in operation S740, when the hub device 1000 determines in operation S730 that the function determination model is not stored in the operation execution device based on the information about the function determination model, the hub device 1000 sends at least a portion of the text to the voice assistant server. In an embodiment of the present disclosure, the hub device 1000 may send at least a portion of the text to the function determination model corresponding to the operation execution device stored in the voice assistant server. In this case, the hub device 1000 may also send the device identification information of the operation execution device together with at least a portion of the text.

[0282] According to an embodiment, in operation S750, when the hub device 1000 checks in operation S720 that the function determination model corresponding to the operation execution device is stored in the memory of the hub device 1000, the hub device 1000 may provide at least a portion of the text to the previously stored function determination model. In an embodiment of the present disclosure, the hub device 1000 may select the function determination model previously stored in the memory 1300 (see Figure 2 ), and can provide at least a portion of the text to the selected function determination model. For example, when the operation execution device is the second device, the processor 1200 of the hub device 1000 can select a second function determination model corresponding to the second device from the first function determination model and the second function determination model previously stored in the memory 1300, and can provide at least a portion of the text to the second function determination model.

[0283] According to an embodiment, in operation S760, when the hub device 1000 checks in operation S730 that a function determination model corresponding to the operation execution device is stored in the memory of the operation execution device itself, the hub device 1000 may send at least a portion of the text to the function determination model stored in the operation execution device.

[0284] According to an embodiment, in operations S740, S750, and S760, the hub device 1000 may send only at least a portion of the text, rather than the entire text. In an embodiment of the present disclosure, the processor 1200 of the hub device 1000 may express the text in units of words or phrases by using a first NLU model, may recognize a word or phrase specifying a name, a common name, or an installation location of a device, and may send the remainder of the text except for the words or phrases recognized in the entire text to a function determination model of an operation execution device.

[0285] Figure 8 is a flowchart illustrating a method of operating the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the operation execution device 4000a according to an embodiment of the present disclosure.

[0286] Figure 8 It is shown in Figure 7 The steps described in What follows is a flowchart of operations of entities in a multi-device system environment including the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the operation execution device 4000a. Figure 7 The steps described in Indicates a state in which it is checked that the function determination model corresponding to the operation execution device 4000a is not stored in both the memory of the hub device 1000 and the memory of the operation execution device 4000a itself.

[0287] refer to Figure 8 , the hub device 1000 may include an ASR module 1310, an NLG module 1320, a device determination model 1330, and a function determination device determination module 1340. Figure 8 In an embodiment of the present invention, the hub device 1000 may not store a function determination model, or may not store a function determination model corresponding to an operating device.

[0288] According to an embodiment, the voice assistant server 2000 may store a plurality of function determination models 2342, 2344, and 2346. For example, the function determination model 2342 as the first function determination model and stored in the voice assistant server 2000 may be a model for determining the function of the air conditioner and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations. For example, the function determination model 2344 as the second function determination model may be a model for determining the function of the TV and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations, and the function determination model 2346 may be a model for determining the function of the washing machine and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations.

[0289] exist Figure 8 In the embodiment, it can be determined that the operation execution device 4000a is “TV”, and the function determination model 2344 corresponding to TV can be stored in the voice assistant server 2000.

[0290] In operation S810, the hub device 1000 sends at least a portion of the text and identification information of the operation execution device 4000a to the voice assistant server 2000. The hub device 1000 may send at least a portion of the text converted from the voice input to the voice assistant server 2000 without sending the entire text. In an embodiment of the present disclosure, based on determining that the operation execution device 4000a is "TV", in the text corresponding to "Play the movie Avengers on TV", "On TV" specifies the name or generic name of the operation execution device 4000a, and thus may be unnecessary information. In addition, because the hub device 1000 sends the identification information (e.g., device ID) of the operation execution device 4000a to the voice assistant server 2000, "On TV" is unnecessary information. The processor 1200 (see Figure 2 ) can parse the text in units of words or phrases by using the first NLU model 1332, can identify words or phrases that specify a name, a common name, or an installation location of a device, and can send the remainder of the text except the words or phrases recognized in the entire text to the voice assistant server 2000.

[0291] In operation S810 , upon receiving at least a portion of the text and identification information of the operation execution device 4000 a , the hub device 1000 may transmit user account information of the hub device 1000 and the operation execution device 4000 a to the voice assistant server 2000 .

[0292] According to an embodiment, in operation S820, the voice assistant server 2000 selects a function determination model corresponding to the operation execution device 4000a. In an embodiment of the present disclosure, the voice assistant server 2000 can identify the operation execution device 4000a based on the identification information of the operation execution device 4000a received from the hub device 1000, and can select a function determination model corresponding to the operation execution device 4000a from a plurality of function determination models 2342, 2344, and 2346. The term "function determination model corresponding to the operation execution device" refers to a model for obtaining operational information about detailed operations of executing a determined function of the operation execution device 4000a and the relationship between the detailed operations. Figure 8 In an embodiment, when the operation execution device 4000a is a TV, the voice assistant server 2000 can select a function determination model 2344 from a plurality of function determination models 2342, 2344, and 2346 stored in the memory for obtaining operation information about detailed operations of performing operations according to a function of the TV, for example, a function of playing a movie, and the relationship between the detailed operations.

[0293] In operation S830, the voice assistant server 2000 interprets the text by using the NLU model of the selected function determination model, and determines the intent based on the analysis result. The voice assistant server 2000 can analyze at least a portion of the text received from the hub device 1000 by using the NLU model 2344a of the function determination model 2344. The NLU model 2344a, which is an AI model trained to interpret text related to a specific device, can be a model trained to determine the intent and parameters related to the operation expected by the user. The NLU model 2344a can be a model trained to determine the function related to the type of a specific device when the text is input.

[0294] In an embodiment of the present disclosure, the voice assistant server 2000 can parse at least a portion of the text in units of words or phrases by using the NLU model 2344a, can infer the meaning of the words extracted from the parsed text by using the language features (e.g., grammatical elements) of the parsed morphemes, words or phrases, and can obtain the intention and parameters from the text by matching the inferred meaning with the predefined intention and parameters. The intention, which is information indicating the user's utterance intention included in the text, can be used to determine the operation to be performed by the operation execution device 4000a. Parameters refer to variable information for determining the detailed operation of the operation execution device 4000a related to the intention. Parameters can be information corresponding to the intention, and multiple types of parameters can correspond to one intention. For example, when the text is "play the movie Avengers on TV", the intention can be "video content playback", and the parameter can be "movie" or "movie Avengers".

[0295] In an embodiment of the present disclosure, the voice assistant server 2000 may determine the intent from only at least a portion of the text.

[0296] In operation S840, the voice assistant server 2000 obtains operation information about the operation to be performed by the operation execution device 4000a based on the intention. In an embodiment of the present disclosure, the voice assistant server 2000 plans the operation information to be performed by the operation execution device 4000a based on the intention and the parameter by using the action plan management module 2344b of the function determination model 2344. The action plan management module 2344b can interpret the operation to be performed by the operation execution device 4000a based on the intention and the parameter. The action plan management module 2344b can select the detailed operation related to the explained operation from the operation of the previously stored device, and can plan the execution order of the selected detailed operation. The action plan management module 2344b can obtain the operation information about the detailed operation to be performed by the operation execution device 4000a by using the plan result. The term "operation information" can be information related to the detailed operation to be performed by the device, the relationship between the detailed operations, and information related to the execution order of the detailed operations. The operation information may include, but is not limited to, functions executed by the operation execution device 4000a to perform detailed operations, execution order of the functions, input values ​​required to execute the functions, and output values ​​output as execution results of the functions.

[0297] In operation S850 , the voice assistant server 2000 transmits the obtained operation information and identification information of the operation execution device 4000 a to the IoT server 3000 .

[0298] In operation S860, the IoT server 3000 obtains a control command based on the identification information of the operation execution device 4000a and the received operation information. The IoT server 3000 may include a database in which control commands and operation information for multiple devices are stored. In an embodiment of the present disclosure, the IoT server 3000 may select a control command for controlling a detailed operation of the operation execution device 4000a from control commands related to multiple devices previously stored in the database based on the identification information of the operation execution device 4000a.

[0299] According to an embodiment, the control command, which is information readable and executable by the device, may include instructions for sequentially performing detailed operations in an execution order according to the operation information when the device performs a function.

[0300] According to an embodiment, in operation S870, the IoT server 3000 may transmit a control command to the operation execution device 4000a by using identification information of the operation execution device 4000a.

[0301] In operation S880, the operation execution device 4000a may execute an operation corresponding to the control command according to the received control command. For example, the operation execution device 4000a may play a movie based on the control command.

[0302] In an embodiment of the present disclosure, after performing an operation, the operation execution device 4000 a may transmit information on the operation execution result to the IoT server 3000 .

[0303] Fig. 9 is a flowchart illustrating a method of operating the hub device 1000 and the operation execution device 4000 a according to an embodiment of the present disclosure.

[0304] Fig. 9 It is shown in Figure 7 Steps What follows is a flowchart of operations of entities in a multi-device system including the hub device 1000 and the operation execution device 4000a. Figure 7 The steps depicted Indicates a state in which it is checked that the function determination model corresponding to the operation execution device 4000a is not stored in the memory of the hub device 1000 but is stored in the memory of the operation execution device 4000a itself. Fig. 9 The voice assistant server 2000 and the IoT server 3000 are not shown in the figure, but this does not mean that the multi-device system does not include the voice assistant server 2000 and the IoT server 3000.

[0305] refer to Fig. 9 , the hub device 1000 may include an ASR module 1310, an NLG module 1320, a device determination model 1330, and a function determination device determination module 1340. Figure 8 In an embodiment of the present invention, the hub device 1000 may not store a function determination model, or may not store a function determination model corresponding to an operating device.

[0306] The operation execution device 4000a may store a function determination model 4032 of the operation execution device 4000a itself. The operation execution device 4000a itself may analyze the text, and the function determination model 4032 for performing the operation according to the user's intention based on the analysis result of the text may be stored in the memory of the operation execution device 4000a. For example, when the operation execution device 4000a is "air conditioner", the function determination model 4032 stored in the memory of the operation execution device 4000a may be a model for determining the function of the air conditioner and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations.

[0307] In operation S910, the hub device 1000 sends at least a portion of the text and a text sending notification signal to the function determination model 4032 of the operation execution device 4000a. The hub device 1000 may send at least a portion of the text converted from the voice input to the operation execution device 4000a without sending the entire text. For example, in a text saying "lower the temperature by 2°C in the air conditioner", "in the air conditioner" specifies the name or generic name of the operation execution device 4000a, and thus may be unnecessary information. Furthermore, because the operation execution device 4000a itself is an air conditioner, "in the air conditioner" is unnecessary information. The processor 1200 (see Figure 2 ) can parse the text in units of words or phrases by using the first NLU model 1332, can identify words or phrases that specify a name, a common name, or an installation location of a device, and can send the remaining part of the text except the words or phrases recognized in the entire text to the operation execution device 4000a.

[0308] In an embodiment of the present disclosure, the hub device 1000 may send a text sending notification signal to the operation execution device 4000a. The text sending notification signal is a signal notifying that the text is sent to the operation execution device 4000a. When the operation execution device 4000a receives the text sending notification signal, the ASR operation, the operation of determining the operation execution device, and the operation of selecting the function determination model may be omitted, and the operation execution device 4000a may directly provide at least a portion of the text to the function determination model 4032, and may determine the intent from the text by using the function determination model 4032.

[0309] However, the present disclosure is not limited thereto, and in operation S910, the hub device 1000 may not send a text transmission notification signal to the operation execution device 4000a. In this case, when the operation execution device 4000a receives at least a portion of the text from the hub device 1000, the operation execution device 4000a may be preset to provide at least a portion of the received text to the function determination model 4032. In an embodiment of the present disclosure, data about a policy set to recognize the received text and provide the text to the function determination model 4032 may be stored in a memory of the operation execution device 4000a.

[0310] According to an embodiment, in operation S920, the operation execution device 4000a may determine the intent by interpreting the text using the NLU model 4034. In an embodiment of the present disclosure, the operation execution device 4000a may analyze at least a portion of the text received from the hub device 1000 by using the NLU model 4034 of the function determination model 4032. The NLU model 4034, which is an AI model trained to interpret text related to the operation execution device 4000a, may be a model trained to determine the intent and parameters related to the operation of the user's intent. The NLU model 4034 may be a model trained to determine a function related to the type of a specific device when a text is input.

[0311] In an embodiment of the present disclosure, the operation execution device 4000a can parse at least a part of the text in units of words or phrases by using the NLU model 4034, can infer the meaning of the words extracted from the parsed text by using the language features (e.g., grammatical elements) of the parsed morphemes, words or phrases, and can obtain the intention and parameters from the text by matching the inferred meaning with the predefined intention and parameters. The intention, which is information indicating the utterance intention of the user included in the text, can be used to determine the operation to be performed by the operation execution device 4000a. Parameters refer to variable information used to determine the detailed operation of the operation execution device 4000a related to the intention. Parameters can be information corresponding to the intention, and multiple types of parameters can correspond to one intention. For example, when the text is "lower the temperature by 2°C in the air conditioner", the intention can be "set temperature control" or "set temperature drop", and the parameter can be "2°C".

[0312] In an embodiment of the present disclosure, the operation performing device 4000a may determine the intention only from at least a portion of the text.

[0313] According to an embodiment, in operation S930, the operation execution device 4000a obtains operation information about the operation to be performed by the operation execution device 4000a based on the intention. In an embodiment of the present disclosure, the operation execution device 4000a plans the operation information to be performed by the operation execution device 4000a based on the intention and parameters by using the action plan management module 4036 of the function determination model 4032. The action plan management module 4036 can interpret the operation to be performed by the operation execution device 4000a based on the intention and the parameters. The action plan management module 4036 can select the detailed operation related to the explained operation from the operation of the previously stored device, and can plan the execution order of the selected detailed operation. The action plan management module 4036 can obtain the operation information about the detailed operation to be performed by the operation execution device 4000a by using the plan result. The term "operation information" can be information related to the detailed operation to be performed by the device, the relationship between the detailed operations, and the execution order of the detailed operations. The operation information may include, but is not limited to, functions executed by the operation execution device 4000a to perform detailed operations, execution order of the functions, input values ​​required to execute the functions, and output values ​​output as execution results of the functions.

[0314] According to an embodiment, in operation S940, the operation execution device 4000a obtains a control command based on the operation information. In an embodiment of the present disclosure, a plurality of control commands corresponding to a plurality of different operation information may be stored in a memory of the operation execution device 4000a. In an embodiment of the present disclosure, the operation execution device 4000a may select a control command for controlling a detailed operation according to the operation information from a plurality of control commands previously stored in the memory. The control command as information readable and executable by the operation execution device 4000a may include instructions for sequentially executing detailed operations according to the operation information in an execution order when the operation execution device 4000a performs a function.

[0315] According to an embodiment, in operation S950, the operation execution device 4000a may execute an operation corresponding to the control command according to the control command. For example, the operation execution device 4000a may reduce the set temperature by 2°C based on the control command.

[0316] Fig.10 is a flowchart illustrating a method of operating the hub device 1000 and the operation execution device 4000 a according to an embodiment of the present disclosure.

[0317] Fig.10 It is shown in Figure 7 Steps What follows is a flowchart of operations of entities in a multi-device system environment including the hub device 1000 and the operation execution device 4000a. Figure 7 Steps shown This indicates a state in which it is checked that the function determination model corresponding to the operation execution device 4000a is stored in the memory of the hub device 1000. Fig.10 The voice assistant server 2000 and the IoT server 3000 are not shown in the figure, but this does not mean that the multi-device system does not include the voice assistant server 2000 and the IoT server 3000.

[0318] refer to Fig.10 According to an embodiment, the hub device 1000 may include an ASR module 1310, an NLG module 1320, a device determination model 1330, a function determination device determination module 1340, a speaker function determination model 1352, and a TV function determination model 1354. Fig.10 In an embodiment of the present invention, the hub device 1000 may store a function determination model corresponding to the operation execution device 4000a. For example, when the operation execution device 4000a is a TV, the hub device 1000 may store a TV function determination model 1354 corresponding to the TV in a memory. The TV function determination model 1354 may be a model for determining a function of the TV and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations.

[0319] According to an embodiment, in operation S1010, the hub device 1000 may select a function determination model corresponding to the operation execution device 4000a from the speaker function determination model 1352 and the TV function determination model 1354 previously stored in the hub device 1000. In an embodiment of the present disclosure, based on determining that the operation execution device 4000a is a TV, the hub device 1000 may select a TV function determination model 1354 corresponding to the TV from the speaker function determination model 1352 and the TV function determination model 1354.

[0320] In operation S1020, the hub device 1000 provides at least a portion of the text to the selected function determination model. In an embodiment of the present disclosure, the processor 1200 (see Figure 2) may provide at least a portion of the text to a TV function determination model 1354 stored in a memory. In this case, the processor 1200 may provide at least a portion of the text to the TV function determination model 1354 without providing the entire text. For example, based on determining that the operation execution device 4000a is "TV", in the text saying "play the movie Avengers on TV", "on TV" specifies the name or common name of the operation execution device 4000a, and thus may be unnecessary information. In addition, because the TV function determination model 1354 is a function determination model corresponding to TV, "on TV" is unnecessary information for the TV function determination model 1354. The processor 1200 may parse the text in units of words or phrases by using the first NLU model 1332, may identify words or phrases that specify a name, common name, or installation location of a device, and may provide the rest of the text except for the words or phrases recognized in the entire text to the TV function determination model 1354.

[0321] In operation S1030, the hub device 1000 determines the intent by interpreting at least a portion of the text using the second NLU model of the selected function determination model. Figure 2 ) can analyze at least a portion of the text by using the second NLU model 1354a of the TV function determination model 1354. The second NLU model 1354a, which is a model dedicated to a specific device, can be an AI model trained to obtain an intent associated with a device corresponding to the operation execution device 4000a determined by the first NLU model 1332 and corresponding to at least a portion of the text. In addition, the second NLU model 1354a can be a model trained to determine the operation of the device related to the user's intent by interpreting the text.

[0322] According to an embodiment, in an embodiment of the present disclosure, the processor 1200 can parse the text in units of morphemes, words, or phrases by using the second NLU model 1354a, can recognize the meaning of the morphemes, words, or phrases parsed by grammatical and semantic analysis, and can determine the intent and parameters by matching the recognized meaning with predefined words. The term "parameter" used herein refers to variable information used to determine the detailed operation of the target device related to the intent. Reference is not provided here. Figure 7 Same intent and description of parameters.

[0323] In an embodiment of the present disclosure, the processor 1200 may determine the intent from only at least a portion of the text by using the second NLU model 1354a.

[0324] In operation S1040, the hub device 1000 obtains operation information about an operation to be performed by the operation execution device 4000a based on the intention. Figure 2 ) can obtain operation information about at least one detailed operation related to the intention and the parameter by using the action plan management module 1354b of the TV function determination model 1354. The action plan management module 1354b can manage information about the detailed operations of the operation execution device 4000a and the relationship between the detailed operations. The processor 1200 of the hub device 1000 can plan the detailed operations to be performed by the operation execution device 4000a and the execution order of the detailed operations based on the intention and the parameter by using the action plan management module 1354b, and can obtain the operation information.

[0325] The operation information may be information related to the detailed operations to be performed by the operation execution device 4000a and the execution order of the detailed operations. The operation information may include information related to the detailed operations to be performed by the operation execution device 4000a, the relationship between each detailed operation and another detailed operation, and the execution order of the detailed operations. The operation information may include, but is not limited to, the functions performed by the operation execution device 4000a to perform a specific operation, the execution order of the functions, the input values ​​required to perform the functions, and the output values ​​output as the execution results of the functions.

[0326] According to an embodiment, in operation S1050, the hub device 1000 generates a control command based on the operation information. The control command refers to an instruction readable and executable by the operation execution device 4000a so that the operation execution device 4000a executes a detailed operation included in the operation information.

[0327] Fig.10 and Fig. 9 The difference is that the operations of determining the intention (S1030), obtaining the operation information (S1040), and generating the control command (S1050) are performed by the hub device 1000. Fig. 9 and Fig.10 , the entity that performs the operations of determining intention, obtaining operation information, and generating control commands varies depending on whether the function determination model corresponding to the operation execution device 4000a is stored in the hub device 1000 or in the operation execution device 4000a.

[0328] According to an embodiment, in operation S1060, the hub device 1000 sends a control command to the operation execution device 4000a by using the identification information of the operation execution device 4000a. In an embodiment of the present disclosure, the hub device 1000 can identify the operation execution device 4000a by using the identification information of the operation execution device 4000a among a plurality of devices connected through a network and pre-registered by using the same user account, and can send the control command to the identified operation execution device 4000a.

[0329] According to an embodiment, in operation S1070, the operation execution device 4000a executes an operation according to the received control command.

[0330] Fig.11 is a flowchart illustrating a method of operating the hub device 1000, the voice assistant server 2000, the IoT server 3000, the third-party IoT server 5000, and the third-party device 5100 according to an embodiment of the present disclosure.

[0331] Fig.11 It is shown in Figure 7 Steps What follows is a flowchart of the operations of entities in a multi-device system environment including the hub device 1000, the voice assistant server 2000, the IoT server 3000, the third-party IoT server 5000, and the third-party device 5100. Figure 7 Steps shown This indicates a state in which it is checked that the function determination model corresponding to the operation execution device 4000a is not stored in both the memory of the hub device 1000 and the memory of the operation execution device 4000a itself.

[0332] refer to Fig.11 , the hub device 1000 may include an ASR module 1310, an NLG module 1320, a device determination model 1330, and a function determination device determination module 1340. Fig.11 In an embodiment of the present invention, the hub device 1000 may not store a function determination model, or may not store a function determination model corresponding to an operating device.

[0333] The voice assistant server 2000 may store a plurality of function determination models 2342, 2344, and 2348. The voice assistant server 2000 may store a function determination model 2348 (referred to as a third-party function determination model).

[0334] The third-party device 5100 refers to a device manufactured by a manufacturer, an affiliated company, or a company with a technology bundle other than the manufacturer of the hub device 1000. In an embodiment of the present disclosure, when the user of the third-party device 5100 is the same as the user of the hub device 1000, when the user registers the third-party device 5100 by using a user account for logging in to the hub device 1000, the third-party function determination model 2348 may be stored in the voice assistant server 2000. For example, when the user registers the third-party device 5100 by logging in using the user account of the hub device 1000, the third-party IoT server 5000 may allow the access right of the IoT server 3000, and the IoT server 3000 may access the third-party IoT server 5000 and may request the third-party function determination model 2348 for obtaining operation information about the detailed operation according to the relationship between the multiple functions of the third-party device 5100 and the detailed operation. The open authorization (oAuth) method may be used to register the third-party device 5100 to the IoT server 3000. The third-party IoT server 500 may provide data of the third-party function determination model 2348 to the IoT server 3000 according to the request, and the IoT server 3000 may provide the third-party function determination model 2348 to the voice assistant server 2000 .

[0335] The third-party IoT server 5000 is a server that stores and manages at least one of the device identification information of the third-party device 5100, the device type of the third-party device 5100, the function execution capability information, the location information, and the status information. The third-party IoT server 5000 can obtain, determine, or generate a control command for controlling the third-party device 5100 by using the device information of the third-party device 5100. The third-party IoT server 5000 can send a second control command to the third-party device 5100 that determines to perform an operation based on the operation information. The third-party IoT server 5000 can be operated by the manufacturer of the third-party device 5100, but is not limited thereto.

[0336] exist Fig.11 In the embodiment, it can be determined that the operation execution device is a third-party device 5100.

[0337] According to an embodiment, in operation S1110, the hub device 1000 sends at least a portion of the text and identification information of the third-party device 5100 to the voice assistant server 2000. In addition to the identification information of the third party 5100, for example, the device ID of the third-party device 5100, being sent to the voice assistant server 2000, operation S1110 is performed in conjunction with Figure 8 The operation S810 is the same as operation S810 of FIG. 1 , and thus a repeated description thereof will not be provided.

[0338] According to an embodiment, in operation S1120, the voice assistant server 2000 selects a function determination model corresponding to the third-party device 5100 by using the received identification information of the third-party device 5100. In an embodiment of the present disclosure, the voice assistant server 2000 may identify the third-party device 5100 based on the identification information of the third-party device 5100 received from the hub device 1000, and may select a third-party function determination model 2348 corresponding to the third-party device 5100 from among a plurality of function determination models 2342, 2344, and 2348.

[0339] According to an embodiment, in operation S1130, the voice assistant server 2000 analyzes the text by using the NLU model 2348a of the third-party function determination model 2348, and determines the intent based on the analysis result. In addition to using the NLU model 2348a of the third-party function determination model 2348, operation S1130 is similar to Figure 8 The operation S830 is the same as that of the operation S830, and thus a repeated description thereof will not be provided here.

[0340] According to an embodiment, in operation S1140, the voice assistant server 2000 obtains operation information about an operation to be performed by the third-party device 5100 based on the intention. In addition to obtaining information about the operation to be performed by the third-party device 5100, operation S1140 is related to Figure 8 The operation S840 is the same as that of the operation S840, so a repeated description thereof will not be provided here.

[0341] According to an embodiment, in operation S1142 , the voice assistant server 2000 transmits identification information of the third-party device 5100 (eg, device ID of the third-party device 5100 ) and operation information to the IoT server 3000 .

[0342] According to an embodiment, in operation S1150, the IoT server 3000 obtains a first control command based on the identification information of the third-party device 5100 and the received operation information. The IoT server 3000 may include a database storing operation information about the third-party device 5100 and control commands according to the operation information. The IoT server 3000 may obtain the operation information of the third-party device 5100 from the third-party IoT server 5000 through the registration method of the third-party device 5100, and may store the obtained operation information in the database. In an embodiment of the present disclosure, the IoT server 3000 may identify the third-party device 5100 based on the identification information of the third-party device 5100, and may generate a first control command by using the operation information of the third-party device 5100.

[0343] The first control command may include an instruction for sequentially performing detailed operations according to the operation information in an execution order when the third party device 5100 performs a function. In an embodiment of the present disclosure, the first control command may not be read by the third party device 5100.

[0344] According to an embodiment, in operation S1152 , the IoT server 3000 sends identification information of the third-party device 5100 and a first control command to the third-party IoT server 5000 .

[0345] According to an embodiment, in operation S1160, the third-party IoT server 5000 converts the first control command into a second control command readable and executable by the third-party device 5100. The third-party IoT server 5000 may convert the first control command into a second control command readable and executable by the third-party device 5100 by using the received identification information of the third-party device 5100.

[0346] According to an embodiment, in operation S1162 , the third party IoT server 5000 transmits a second control command to the third party device 5100 by using identification information of the third party device 5100 .

[0347] According to an embodiment, in operation S1170, the third party device 5100 performs an operation corresponding to the second control command according to the received second control command.

[0348] Fig. 12A 4 is a conceptual diagram illustrating operations of the hub device 1000 and a plurality of devices (eg, a first device 4100a and a second device 4200a) according to an embodiment of the present disclosure.

[0349] Fig. 12A 1 is a block diagram illustrating basic elements for describing the operation of the hub device 1000 and a plurality of devices (eg, a first device 4100a and a second device 4200a). However, the elements of the hub device 1000 and the plurality of devices (4100a and 4200a) are not limited to Fig. 12A components.

[0350] Fig. 12A The arrows indicate movement, transmission, and reception of data including voice signals and text between the hub device 1000 and the first device 4100a. The circled numbers indicate the order in which operations are performed.

[0351] refer to Fig. 12A, the hub device 1000 and multiple devices (for example, the first device 4100a and the second device 4200a) can be connected to each other by using a wired communication method or a wireless communication method, and data communication can be performed. In an embodiment of the present disclosure, the hub device 1000 and multiple devices (for example, the first device 4100a and the second device 4200a) can be directly connected to each other through a communication network, but the present disclosure is not limited thereto. In an embodiment of the present disclosure, although the hub device 1000 and multiple devices (for example, the first device 4100a and the second device 4200a) can be connected to the voice assistant server 2000 (see Figure 3 ), but the hub device 1000 can be connected to multiple devices (e.g., the first device 4100a and the second device 4200a) through the voice assistant server 2000.

[0352] The hub device 1000 and multiple devices (e.g., the first device 4100a and the second device 4200a) can be connected via a LAN, a WAN, a VAN, a mobile radio communication network, a satellite communication network, or a combination thereof. Examples of wireless communication methods may include, but are not limited to, Wi-Fi, Bluetooth, BLE, Zigbee, WFD, UWB, IrDA, and NFC.

[0353] The hub device 1000 is a device that receives a voice signal and controls at least one of a plurality of devices (eg, a first device 4100a and a second device 4200a) based on the received voice signal.

[0354] The plurality of devices (eg, the first device 4100 a and the second device 4200 a ) may be devices that are logged in using the same user account as that of the hub device 1000 and previously registered with the IoT server 3000 by using the user account of the hub device 1000 .

[0355] At least one of the plurality of devices (eg, the first device 4100a and the second device 4200a) may be a listening device that receives a voice input from a user. Fig. 12A In an embodiment of the present disclosure, the first device 4100a may be a listening device that receives a voice input including a user's speech from a user. The listening device may be, but is not limited to, a device that receives only a voice input from a user. In an embodiment of the present disclosure, the listening device may be an operation execution device that receives a control command from the hub device 1000 and performs an operation for a specific function. In an embodiment of the present disclosure, the listening device may receive a voice input related to a function performed by the listening device from a user. For example, the first device 4100a may be an air conditioner, and the first device 4100a may receive a voice input such as "lower the air conditioner temperature to 20°C" from the user through the microphone 4140.

[0356] The first device 4100a as a listening device can receive a voice signal from a voice input received from a user. In an embodiment of the present disclosure, the first device 4100a can convert the sound received by the microphone 4140 into an acoustic signal, and can obtain the voice signal by removing noise (e.g., non-voice components) from the acoustic signal.

[0357] The first device 4100a may transmit a voice signal to the hub device 1000 (step 1).

[0358] The hub device 1000 may receive a voice signal from the first device 4100a and may convert the voice signal into text by performing ASR using data of the ASR module 1310 previously stored in the memory (step 2).

[0359] The memory 1300 of the hub device 1000 (see Figure 2 ) may include a device determination model 1330 that detects intent from text by interpreting the text and determines a device that performs an operation corresponding to the detected intent. The device determination model 1330 may determine an operation execution device from among a plurality of devices (e.g., a first device 4100a and a second device 4200a) registered according to a user account. The hub device 1000 may detect intent from text by interpreting the text using data of a first NLU model 1332 included in the device determination model 1330. The first NLU model 1332 is a model for determining intent by interpreting text and determining an operation execution device based on the intent. The hub device 1000 may determine that the operation execution device that performs the operation corresponding to the intent is the first device 4100a by using data of the device determination model 1330 (step 3).

[0360] The function determination model corresponding to the operation execution device determined by the hub device 1000 may be stored in the memory 1300 of the hub device 1000, may be stored in the first device 4100a itself determined as the operation execution device, or may be stored in the memory 2300 of the voice assistant server 2000 (see Figure 3 The function determination model corresponding to each device is a model for obtaining operation information about detailed operations of performing operations according to the determined functions of the device and the relationships between the detailed operations.

[0361] The hub device 1000 itself may store a function determination model corresponding to at least one of the plurality of devices (e.g., the first device 4100a and the second device 4200a). For example, when the hub device 1000 is a voice assistant speaker, the hub device 1000 may store a speaker function determination model 1352 for obtaining operation information about detailed operations for performing the functions of the voice assistant speaker and the relationship between the detailed operations.

[0362] The hub device 1000 may also store a function determination model corresponding to another device. For example, the hub device 1000 may store a refrigerator function determination model 1356 for obtaining operation information about detailed operations corresponding to the refrigerator and the relationship between the detailed operations. The refrigerator may be a device previously registered to the IoT server 3000 by using the same user account as the user account of the hub device 1000.

[0363] The speaker function determination model 1352 and the refrigerator function determination model 1356 may include second NLU models 1352a and 1356a and action plan management modules 1352b and 1356b, respectively. Fig. 12A In the embodiment of FIG. 1 , the hub device 1000 includes a second NLU model 1352a and an action plan management module 1352b for a speaker, and includes a second NLU module 1356b and an action plan management module 1356b for a refrigerator. In addition to the device type being changed from a TV to a refrigerator, the second NLU model 1352a and the action plan management module 1352b are respectively included in Figure 2 The second NLU models 1352a and 1354a and the action plan management modules 1352b and 1354b in the speaker function determination model 1352 and the TV function determination model 1354 are the same, so a repeated description will not be given.

[0364] The hub device 1000 can identify a device storing a function determination model 4132 by using data of the function determination device determination module 1340, and the function determination model 4132 corresponds to the first device 4100a determined as the operation execution device. In an embodiment of the present disclosure, the hub device 1000 can obtain information about the function determination model from each of the multiple devices (e.g., the first device 4100a and the second device 4200a). The term "information about the function determination model" refers to information about whether each of the multiple devices (e.g., the first device 4100a and the second device 4200a) itself stores a function determination model for obtaining operation information about detailed operations according to the function execution operation and the relationship between the detailed operations. In an embodiment of the present disclosure, when the hub device 1000 receives information about the function determination model from each of the multiple devices (e.g., the first device 4100a and the second device 4200a), the hub device 1000 can also obtain information about the storage location of the function determination model of each of the multiple devices (e.g., device identification information, IP address, or MAC address).

[0365] In another embodiment of the present disclosure, the hub device 1000 may determine the function model of the device by using the database 1360 (see Figure 2 ) to identify a device in which a function determination model corresponding to an operation execution device is stored from the hub device 1000, the first device 4100a, the second device 4200a, and the voice assistant server 2000. In an embodiment of the present disclosure, the hub device 1000 may search the database 1360 according to the device identification information of the first device 4100a determined as the operation execution device by using data of the function determination device determination module 1340, and may obtain information about the storage location of the function determination model 4132 corresponding to the first device 4100a based on the search results of the database 1360.

[0366] The hub device 1000 may transmit at least a portion of the text to the first device 4100a identified as storing the function determination model 4132 corresponding to the operation execution device by using the function determination device determination module 1340 (step 4).

[0367] The first device 4100a may receive at least a portion of the text from the hub device 1000, and may interpret at least a portion of the text by using a function determination model 4132 previously stored in the memory 4130. In an embodiment of the present disclosure, the first device 4100a may analyze at least a portion of the text by using an NLU model 4134 included in the function determination model 4132, and may obtain operation information about an operation to be performed by the first device 4100a based on the analysis result of at least a portion of the text. The function determination model 4132 may include an action plan management module 4136 configured to manage operation information related to detailed operations of the device so as to generate detailed operations to be performed by the first device 4100a and an execution order of the detailed operations. The action plan management module 4136 may manage operation information about detailed operations of the first device 4100a and a relationship between the detailed operations. The action plan management module 4136 may plan detailed operations to be performed by the first device 4100a and an execution order of the detailed operations based on the analysis result of at least a portion of the text. The first device 4100a may plan detailed operations and an execution order of the detailed operations based on an analysis result of at least a portion of the text by the function determination model 4132, and may perform the operations based on the planning result (step 5).

[0368] However, the first device 4100a of the present disclosure is not limited to performing an operation based on at least a portion of the text received from the hub device 1000. In another embodiment of the present disclosure, the first device 4100a may receive a voice input from a user by using a microphone 4140, may convert the received voice input into text by using an ASR model 4138, and may perform an operation corresponding to the text by interpreting the text by using a function determination model 4132. That is, even without the participation of the hub device 1000, the first device 4100a may independently perform an operation.

[0369] Fig. 12A An embodiment is illustrated in which the first device 4100a as a listening device receiving a voice input from a user and the operation execution device to perform an operation related to the voice input through the hub device 1000 are the same. For example, when the first device 4100a receives a voice input saying "lower the air conditioner temperature to 20°C" from the user, the hub device 1000 can receive the user's voice input from the first device 4100a as the listening device, and can determine the first device 4100a as the air conditioner as the operation execution device related to the voice input. The first device 4100a can receive text related to the voice input from the hub device 1000, and can perform the air conditioner temperature adjustment operation based on the text.

[0370] and Fig. 12ADifferent, the listening device and the operation execution device can be different from each other, which will refer to Fig. 12B Give a description.

[0371] Fig. 12B 4 is a conceptual diagram illustrating operations of the hub device 1000 and a plurality of devices (eg, a first device 4100a and a second device 4200a) according to an embodiment of the present disclosure.

[0372] Fig. 12B 1 is a block diagram illustrating basic elements for describing the operation of the hub device 1000 and a plurality of devices (eg, the first device 4100a and the second device 4200a). Fig. 12A The hub device 1000 and the plurality of devices (eg, the first device 4100a and the second device 4200a) may have the same elements.

[0373] refer to Fig. 12B , the second device 4200a may be a listening device that receives a voice input from a user. In an embodiment of the present disclosure, although the second device 4200a may be a listening device, the second device 4200a may not be an operation execution device that receives a control command from the hub device 1000 or performs an operation for a specific function. Fig. 12B In the embodiment of the present invention, the second device 4200a as a TV can receive voice input unrelated to the TV through the microphone 4240, such as "lower the air conditioner temperature to 20°C".

[0374] The second device 4200a may obtain a voice signal from a voice input received through the microphone 4240 and may transmit the voice signal to the hub device 1000 (step 1).

[0375] The hub device 1000 may receive a voice signal from the second device 4200a and convert the voice signal into text by performing ASR using data of the ASR module 1310 previously stored in the memory (step 2).

[0376] The hub device 1000 may determine the intent by interpreting the text using the first NLU model 1332, and may determine the operation execution device associated with the intent by using the data of the device determination model 1330 (step 3). Fig. 12B In the embodiment of the present invention, the hub device 1000 can determine that the operation execution device that performs the operation corresponding to the intention is the first device 4100a by using the data of the device determination model 1330. The first device 4100a can be a device different from the listening device that receives the voice input from the user. For example, the first device 4100a can be an air conditioner.

[0377] The hub device 1000 can identify a device in which a function determination model 4132 corresponding to the first device 4100a determined as the operation execution device is stored by using data of the function determination device determination module 1340, and can send at least a portion of the text to the identified first device 4100a (step 4).

[0378] The first device 4100a can receive at least a portion of the text from the hub device 1000, can analyze at least a portion of the text by using the function determination model 4132, can plan detailed operations and the execution order of the detailed operations based on the analysis results, and can perform operations based on the planned results (step 5).

[0379] Since the function determination model 4232 that can interpret information about detailed operations related to the functions of the second device 4200a and the execution order of the detailed operations is stored in the memory 4230 of the second device 4200a, the second device 4200a may not interpret text related to the operations of the first device 4100a. Fig. 12B In the embodiment of , because the second device 4200a is a TV and the function determination model 4232 corresponding to the TV is stored in the memory 4230, the second device 4200a may not interpret the voice input of "lower the air conditioner temperature to 20°C". In this case, the second device 4200a as a listening device may send the voice input received from the user to the hub device 1000, and the hub device 1000 may send the text related to the operation to the first device 4100a determined as the operation execution device. The first device 4100a may perform the air conditioner temperature adjustment operation based on the text received from the hub device 1000.

[0380] refer to Fig. 12A and Fig. 12BIn an embodiment, a user may not send a voice command related to an operation to be performed to a specific device that performs an operation related to a specific function, but may send a voice command to any one of a plurality of devices (e.g., the first device 4100a and the second device 4200a) connected via a wired / wireless communication network by using a user account. For example, when a user generates a voice command saying "lower the air conditioner temperature to 20°C", the user may send the voice command to an air conditioner that performs a function related to "cooling temperature setting", or may send a voice command related to cooling temperature adjustment to a TV that is not related to the air conditioner at all. A listening device that receives a voice input from a user among a plurality of devices (e.g., the first device 4100a and the second device 4200a) may send the voice input to the hub device 1000, and the hub device 1000 may determine an operation execution device corresponding to the user's speech intention included in the voice input, and may control the operation execution device to perform the operation.

[0381] exist Fig. 12A and Fig. 12B In the embodiment, since the user does not need to directly specify a specific device related to the function from among a plurality of devices (e.g., the first device 4100a and the second device 4200a) and send a voice command to the specific device, and the operation execution device for executing the operation related to the function is automatically determined, user convenience can be improved. In addition, since the hub device 1000 determines the operation execution device without involving an external server such as the voice assistant server 2000, it is not necessary to use a network, and thus network usage fees can be reduced.

[0382] Fig.13 is a flowchart illustrating a method in which the hub device 1000 determines an operation execution device based on a voice signal received from a listening device and transmits a text to a device storing a function determination model corresponding to the operation execution device according to an embodiment of the present disclosure.

[0383] According to an embodiment, in operation S1310, the hub device 1000 receives a voice signal from a listening device. The listening device may be any of a plurality of devices that are logged in by using the same user account as the user account of the hub device 1000 and previously registered to the IoT server 3000 by using the same user account as the user account of the hub device 1000. The listening device may be connected to the hub device 1000 via a wired or wireless communication network. The listening device may be, but is not limited to, a device that receives only voice input from a user. In an embodiment of the present disclosure, the listening device may be an operation execution device that receives a control command from the hub device 1000 and performs an operation for a specific function.

[0384] In the embodiment of the present disclosure, the processor 1200 (see Figure 2 ) can be connected via the communication interface 1400 (see Figure 2 ) receives a voice signal from a listening device.

[0385] According to an embodiment, in operation S1320, the hub device 1000 converts the received voice signal into text by performing ASR. In an embodiment of the present disclosure, the hub device 1000 may perform ASR by using a predefined model such as AM or LM to convert the voice signal received from the listening device into a computer-readable text. When the hub device 1000 receives an acoustic signal from the listening device without removing noise, the hub device 1000 may obtain a voice signal by removing noise from the received acoustic signal, and may perform ASR on the voice signal.

[0386] According to an embodiment, in operation S1330, the hub device 1000 analyzes the text by using the first NLU model, and determines the operation execution device corresponding to the analyzed text by using the device determination model. In addition to the listening device being included in the device candidates that can be determined as the operation execution device, operation S1330 is related to Figure 6 The operation S630 is the same as that of the operation S630, and thus no repeated description will be given.

[0387] According to an embodiment, in operation S1340, the hub device 1000 identifies a device storing a function determination model corresponding to an operation execution device determined from among the listening device and the operation execution device. The function determination model corresponding to the operation execution device determined by the hub device 1000 may be stored in the memory 1300 of the hub device 1000 (see Figure 2 ), may be stored in the internal memory of the operation execution device itself, or may be stored in the memory 2300 (see Figure 3 ). When the determined operation execution device and the listening device are not the same, the function determination model corresponding to the operation execution device can be stored in the listening device. The function determination model corresponding to the operation execution device is a model used by the device determined as the operation execution device to obtain operation information about detailed operations of performing operations according to the determined functions of the operation execution device and the relationship between the detailed operations.

[0388] In the embodiment of the present disclosure, the processor 1200 (see Figure 2 ) can be determined by using the function determination device determination module 1340 (see Figure 2) program code or data to identify the device storing the function determination model corresponding to the operation execution device. In an embodiment of the present disclosure, the hub device 1000 can obtain the storage information of the function determination model from each of the multiple devices connected to the hub device 1000 through a wired or wireless communication network and logged in by using the same user account as the hub device 1000. The term "storage information of the function determination model" refers to information about whether each of the multiple devices itself stores a function determination model, wherein the function determination model is used to obtain operation information about detailed operations according to the function execution operation and the relationship between the detailed operations. In an embodiment of the present disclosure, when the hub device 1000 receives information about the function determination model from the multiple devices, the hub device 1000 can also obtain information about the storage location of the function determination model of each of the multiple devices (for example, device identification information, IP address or MAC address). When the operation execution device and the listening device are not the same, the hub device 1000 can receive information about the function determination model from the listening device, and can determine whether the function determination model corresponding to the operation execution device is stored in the internal memory of the listening device based on the received information about the function determination model.

[0389] In another embodiment of the present disclosure, the hub device 1000 can be configured to receive the data from the server by using the database 1360 (see Figure 2 ) to identify a device in which a function determination model corresponding to the operation execution device is stored from among the hub device 1000, the operation execution device, and the voice assistant server 2000, the database 1360 includes information about the function determination model stored in the memory 1300 (see Figure 2 ). In an embodiment of the present disclosure, the hub device 1000 may search the database 1360 according to the device identification information of the operation execution device by using the program code or data of the function determination device determination module 1340, and may obtain information about the storage location of the function determination model corresponding to the operation execution device based on the search results of the database 1360. When the operation execution device and the listening device are not the same, the hub device 1000 may search the database 1360 for the identification information of the listening device, and may determine whether the function determination model corresponding to the operation execution device is stored in the internal memory of the listening device based on the search results of the database 1360.

[0390] Will refer to Fig.14 Operation S1340 is described.

[0391] According to an embodiment, in operation S1350, the hub device 1000 sends at least a portion of the text to the recognized device. The hub device 1000 may transmit the text to the recognized device by using the communication interface 1400 (see Figure 2) sends at least a portion of the text to the recognized device. For example, based on determining that the function determination model corresponding to the operation execution device is stored in the internal memory of the operation execution device, the hub device 1000 can send at least a portion of the text to the operation execution device by using the communication interface 1400. When the listening device and the operation execution device are the same, the hub device 1000 can send at least a portion of the text to the listening device. As another example, based on determining that the function determination model corresponding to the operation execution device is not stored in the listening device and the operation execution device, but is stored in the memory 2300 (see Figure 3 ), the hub device 1000 can send at least a portion of the text to the voice assistant server 2000 by using the communication interface 1400.

[0392] In an embodiment of the present disclosure, the hub device 1000 can control the communication interface to separate the part about the name of the operation execution device from the text, and send only the remaining part of the text to the identified device. For example, when the device identified as storing the function determination model corresponding to the operation execution device is "air conditioner" and the text is "lower the set temperature to 20°C in the air conditioner", it is not necessary to send "in the air conditioner" when the text is sent to the air conditioner. In this case, the hub device 1000 can parse the text in units of words or phrases, can identify words or phrases that specify the name, common name, installation location, etc. of the device, and can provide the remaining part of the text except the words or phrases identified in the text to the device (e.g., air conditioner).

[0393] Fig.14 is a flowchart illustrating a method in which the hub device 1000 transmits a text to a device storing a function determination model corresponding to an operation execution device according to an embodiment of the present disclosure. Fig.14 Picture shows Fig.13 Operations S1340 and S1350. Fig.14 Operations S1410 to S1450 are Fig.13 Detailed embodiment of operation S1340, and operations S1460 to S1480 are Fig.13 Detailed embodiment of operation S1350.

[0394] In execution Fig.13 Operation S1410 is performed after operation S1330. In operation S1410, the hub device 1000 determines whether the listening device and the determined operation execution device are the same. In an embodiment of the present disclosure, the processor 1200 of the hub device 1000 may obtain the operation performed in operation S1330 (see Fig.13) and the device identification information of the listening device, and then it can be determined whether the listening device and the operation execution device are the same by comparing the obtained device identification information of the operation execution device with the obtained device identification information of the listening device.

[0395] Based on the determination in operation S1410 that the listening device and the operation execution device are the same (yes), the hub device 1000 may obtain storage information of the function determination model from the listening device (S1420). In an embodiment of the present disclosure, the hub device 1000 may obtain storage information of the function determination model by using the communication interface 1400 (see Figure 2 ) receives storage information of a function determination model from a listening device. The storage information of a function determination model refers to information about whether the listening device itself stores a function determination model in an internal memory, and the function determination model is used to obtain operation information about detailed operations of performing operations according to functions and the relationship between the detailed operations. In an embodiment of the present disclosure, when the hub device 1000 receives information about the function determination model from the listening device, the hub device 1000 can also obtain information about the storage location of the function determination model (for example, identification information, IP address, or MAC address of the listening device).

[0396] In operation S1430, the hub device 1000 determines whether a function determination model corresponding to the determined operation execution device is stored in an internal memory of the listening device based on the storage information of the function determination model of the listening device. Figure 2 ) can be determined by using the function determination device determination module 1340 (see Figure 2 ) to determine whether the function determination model corresponding to the operation execution device is stored in the internal memory of the listening device. In an embodiment of the present disclosure, the processor 1200 may determine whether the function determination model stored in the internal memory of the listening device is the same as the function determination model corresponding to the operation execution device based on the storage information of the function determination model of the listening device obtained.

[0397] In another embodiment of the present disclosure, the processor 1200 may store information about the function determination models of a plurality of devices by using a database 1360 (see Figure 2) determines whether the function determination model corresponding to the operation execution device is stored in the internal memory of the listening device. In an embodiment of the present disclosure, the processor 1200 may search the database 1360 according to the device identification information of the listening device by using the program code or data of the function determination device determination module 1340, and may obtain information on whether the function determination model corresponding to the operation execution device is stored in the listening device based on the search result of the database 1360.

[0398] Based on determining in operation S1430 that the function determination model corresponding to the operation execution device is stored in the internal memory of the listening device (Yes), the hub device 1000 sends at least a portion of the text to the listening device (S1460). In an embodiment of the present disclosure, the processor 1200 may send at least a portion of the text to the function determination model previously stored in the internal memory of the listening device by using the communication interface 1400.

[0399] Based on the determination in operation S1430 that the function determination model corresponding to the operation execution device is not stored in the internal memory of the listening device (No), the hub device 1000 sends at least a portion of the text to the voice assistant server 2000 (S1480). In an embodiment of the present disclosure, the processor 1200 may send at least a portion of the text to the memory 1300 (see FIG. 140) previously stored in the voice assistant server 2000 by using the communication interface 1400. Figure 3 ) in the plurality of function determination models 2342, 2344, 2346 and 2348 corresponding to the operation execution device.

[0400] In operations S1460 and S1480, the processor 1200 may transmit only at least a portion of the text instead of the entire text, which is similar to the reference Fig.13 The descriptions are the same, so no repeated description will be given.

[0401] Based on the determination in operation S1410 that the listening device and the operation execution device are different from each other (No), the hub device 1000 obtains storage information of the function determination model from the operation execution device (S1440). In an embodiment of the present disclosure, the hub device 1000 can obtain storage information of the function determination model by using the communication interface 1400 (see Figure 2 ) receives storage information of the function determination model from the operation execution device. In an embodiment of the present disclosure, when the hub device 1000 receives information about the function determination model from the operation execution device, the hub device 1000 can also obtain information of the storage location of the function determination model (for example, identification information, IP address or MAC address of the operation execution device).

[0402] In operation S1450, the hub device 1000 determines whether the function determination model is stored in the internal memory of the operation execution device. In an embodiment of the present disclosure, the processor 1200 can determine whether the function determination model corresponding to the operation execution device is stored in the internal memory of the operation execution device by using the program code or data of the function determination device determination module 1340. In another embodiment of the present disclosure, the processor 1200 can determine whether the function determination model corresponding to the operation execution device is stored in the internal memory of the operation execution device by using a database 1360 storing information about the function determination models of multiple devices. In an embodiment of the present disclosure, the processor 1200 can search the database 1360 according to the device identification information of the operation execution device by using the program code or data of the function determination device determination module 1340, and can obtain information about whether the function determination model corresponding to the operation execution device is stored in the internal memory of the operation execution device itself based on the search results of the database 1360.

[0403] Based on determining in operation S1450 that the function determination model corresponding to the operation execution device is stored in the internal memory of the operation execution device (yes), the hub device 1000 sends at least a portion of the text to the operation execution device (operation S1470). In an embodiment of the present disclosure, the processor 1200 may send at least a portion of the text to the function determination model previously stored in the internal memory of the operation execution device by using the communication interface 1400.

[0404] Based on determining in operation S1450 that the function determination model corresponding to the operation execution device is not stored in the operation execution device (No), the hub device 1000 transmits at least a portion of the text to the voice assistant server 2000 (S1480).

[0405] Fig.15 is a flowchart illustrating a method of operating the hub device 1000, the voice assistant server 2000, and the listening device 4000b according to an embodiment of the present disclosure. Fig.15 It is illustrated that the listening device 4000 b is determined as the operation execution device by the hub device 1000 via natural language interpretation.

[0406] refer to Fig.15 , the hub device 1000 may include an ASR module 1310, an NLG module 1320, a device determination model 1330, a function determination device determination module 1340, and a plurality of function determination models (e.g., 1352, 1354, and 1356). However, the present disclosure is not limited thereto, and the hub device 1000 may not store a function determination model, or may not store a function determination model corresponding to an operating device.

[0407] The voice assistant server 2000 may store a plurality of function determination models 2342, 2344, and 2346. For example, the function determination model 2342 as the first function determination model stored in the voice assistant server 2000 may be a model for determining the function of the air conditioner and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations. For example, the function determination model 2344 as the second function determination model may be a model for determining the function of the TV and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations, and the function determination model 2346 as the third function determination model may be a model for determining the function of the washing machine and obtaining operation information about detailed operations related to the determined function and the relationship between the detailed operations.

[0408] The listening device 4000b is a device that receives a voice input from a user. However, the present disclosure is not limited thereto, and in an embodiment of the present disclosure, the listening device 4000b may be an operation execution device that receives a control command from the hub device 1000 and performs an operation for a specific function. Fig.15 In the embodiment of the present invention, the listening device 4000b may include a function determination model 4032b, an ASR module 4038b, and a microphone 4040b. The function determination model 4032b may include an NLU model 4034b and an action plan management module 4036b.

[0409] In operation S1510, the listening device 4000b receives a voice input from the user. In an embodiment of the present disclosure, the listening device 4000b may receive a voice input related to a function performed by the listening device 4000b from the user through the microphone 4040b. For example, when the listening device 4000b is an air conditioner, the listening device 4000b may receive a voice input such as "lower the air conditioner temperature to 20°C" from the user through the microphone 4040b.

[0410] In an embodiment of the present disclosure, the listening device 4000b may obtain a voice signal from a voice input received from a user. In an embodiment of the present disclosure, the listening device 4000b may convert the sound received by the microphone 4040b into an acoustic signal, and may obtain a voice signal by removing noise (e.g., non-voice components) from the acoustic signal.

[0411] In operation S1520, the listening device 4000b determines whether the device determination model is stored in the memory of the listening device 4000b. In an embodiment of the present disclosure, the listening device 4000b may determine whether a program code or data corresponding to the device determination model is stored by scanning the memory. However, the present disclosure is not limited thereto, and the listening device 4000b may obtain device specification information based on device identification information (e.g., device ID information), and may determine whether the device determination model is stored in the internal memory of the listening device 4000b by using the device specification information.

[0412] Based on the determination device determining that the model is not stored in the memory of the listening device 4000b (No), the listening device 4000b transmits the voice signal to the hub device 1000 (S1522).

[0413] Based on the determination that the device determines that the mode is stored in the memory of the listening device 4000b (yes), the listening device 4000b converts the speech signal into text by performing ASR (S1530). The listening device 4000b can convert the speech signal into computer-readable text by performing ASR using the ASR module 4038b. When the listening device 4000b receives an acoustic signal from which noise is not removed, the listening device 4000b can obtain a speech signal by removing noise from the received acoustic signal, and can perform ASR on the speech signal.

[0414] According to an embodiment, in operation S1540, the listening device 4000b determines the listening device 4000b as an operation execution device by interpreting the text using the device determination model. In an embodiment of the present disclosure, the device determination model included in the listening device 4000b may include an NLU model, and the listening device 4000b may determine the listening device 4000b as an operation execution device by interpreting the text using the NLU model. For example, when the text converted from the voice signal is "lower the air conditioner temperature to 20°C", the listening device 4000b may determine the listening device 4000b as an air conditioner as an operation execution device by interpreting the text using the NLU model.

[0415] In operation S1550, the listening device 4000b provides the text to the function determination model 4032. For example, the function determination model 4032, which is a function determination model of the air conditioner, can be a model for obtaining operation information about detailed operations of performing operations according to the functions of the air conditioner and the relationship between the detailed operations. The function determination model 4032 may include an NLU model 4034b, which is configured to obtain operation information related to the operation to be performed by the listening device 4000b based on the analysis result of at least a part of the text. The function determination model 4032b may include an action plan management module 4036b, which manages the operation information related to the detailed operation of the device to generate the detailed operation to be performed by the listening device 4000b and the execution order of the detailed operation. The action plan management module 4036b can plan the detailed operation to be performed by the listening device 4000b and the execution order of the detailed operation based on the analysis result of at least a part of the text.

[0416] In an embodiment of the present disclosure, the listening device 4000b can provide a text representing "lower the air conditioning temperature to 20°C" to the function determination model 4032b for the air conditioner, and can obtain operation information to be performed by the air conditioner by interpreting the text using the NLU model 4034b, for example, information about the operation of "setting the temperature to lower", and can plan the detailed operations for performing the operation of "setting the temperature to lower" and the execution order of the detailed operations by using the action plan management module 4036b.

[0417] In operation S1522, the hub device 1000 receives a voice signal from the listening device 4000b.

[0418] According to an embodiment, in operation S1560, the hub device 1000 converts the voice signal into text by performing ASR. Figure 6 The method is the same as that described in operation S620, and thus a detailed description will not be given.

[0419] According to an embodiment, in operation S1570, the hub device 1000 interprets the text by using the first NLU model 1332, and determines the listening device 4000b as an operation determination device related to the text by using the device determination model 1330. For example, the hub device 1000 can obtain the intent corresponding to "air conditioner setting temperature reduction" by interpreting the text "lower the air conditioner temperature to 20°C" by using the first NLU model 1332, and can determine the listening device 4000b as an air conditioner as an operation execution device based on the obtained intent by using the device determination model 1330. The detailed method of determining the operation execution device by the hub device 1000 is as follows Figure 6The method is the same as that described in operation S630, and thus a detailed description will not be given.

[0420] According to an embodiment, in operation S1580, the hub device 1000 determines whether the function determination model is stored in the listening device 4000b. In an embodiment of the present disclosure, the processor 1200 (see Figure 2 ) determines whether the function determination model corresponding to the listening device 4000b is stored in the internal memory of the listening device 4000b based on the storage information of the function determination model obtained from the listening device 4000b. In another embodiment of the present disclosure, the processor 1200 may store information about the function determination model of multiple devices by using the database 1360 (see Figure 2 ) determines whether the function determination model corresponding to the listening device 4000b is stored in the internal memory of the listening device 4000b. The detailed method of determining whether the function determination model corresponding to the listening device 4000b is stored in the internal memory of the listening device 4000b is as follows Fig.14 The method is the same as that described in operation S1430 of , and thus no repeated description will be given.

[0421] Based on determining in operation S1580 that the function determination model is stored in the listening device 4000b (Yes), the hub device 1000 transmits the text to the function determination model of the listening device 4000b (S1592).

[0422] Based on the determination in operation S1580 that the function determination model is not stored in the listening device 4000b (No), the hub device 1000 sends the text to the function determination model corresponding to the listening device 4000b of the voice assistant server 2000 (S1594). When the listening device 4000b is an air conditioner and it is determined that the function determination model for the air conditioner is not stored in the internal memory of the listening device 4000b, the hub device 1000 may send the text to the function determination model 2342 that is previously stored in the memory 2300 of the voice assistant server 2000 (see Figure 3 ) among the multiple function determination models 2342, 2344, m and 2346 for air conditioning.

[0423] In an embodiment of the present disclosure, operations S1530 to S1550 performed by the listening device 4000b and operations S1560 to S1594 performed by the hub device 1000 can be performed in parallel by separate entities. In addition, operations S1530 to S1550 performed by the listening device 4000b can be performed independently, and have nothing to do with operations S1560 to S1594 performed by the hub device 1000. That is, although the listening device 4000b can be determined as an operation execution device by the hub device 1000 and can receive at least a portion of the text, the present disclosure is not limited thereto. In an embodiment of the present disclosure, the listening device 4000b can determine the operation execution device by interpreting the user's voice input, and can provide at least a portion of the text to the function determination model 4032b.

[0424] Fig.16 is a flowchart illustrating a method of operating the hub device 1000, the voice assistant server 2000, the operation execution device 4000a, and the listening device 4000b according to an embodiment of the present disclosure. Fig.16 It is illustrated that the listening device 4000b and the operation execution device 4000a are different from each other. For example, the listening device 4000b may be a TV, and the operation execution device 4000a may be an air conditioner.

[0425] refer to Fig.16 , the hub device 1000 may include an ASR module 1310, an NLG module 1320, a device determination model 1330, a function determination device determination module 1340, and a plurality of function determination models (eg, 1352, 1354, and 1356).

[0426] The voice assistant server 2000 may store a plurality of function determination models 2342 , 2344 , and 2346 .

[0427] Fig.16 The hub device 1000 and the voice assistant server 2000 are respectively connected to Fig.15 The hub device 1000 and the voice assistant server 2000 are the same, so no repeated description will be given.

[0428] and Fig.15 different, Fig.16 The listening device 4000b may include only the microphone 4040b. The listening device 4000b may receive the user's voice input through the microphone 4040b, and may send the received voice input to the hub device 1000. In an embodiment of the present disclosure, when the listening device 4000b receives the user's voice input, the listening device 4000b may be preset to perform an operation of sending the voice input to the hub device 1000.

[0429] The operation execution device 4000a itself may store the function determination model 4032. The operation execution device 4000a itself may analyze the text, and the function determination model 4032 for performing the operation according to the user's intention based on the analysis result of the text may be stored in the memory of the operation execution device 4000a. For example, when the operation execution device 4000a is "air conditioner", the function determination model 4032 stored in the memory of the operation execution device 4000a may be a model for determining the function of the air conditioner and obtaining operation information about the detailed operation of the determined function and the relationship between the detailed operations.

[0430] According to an embodiment, in operation S1610, the listening device 4000b receives a voice input from a user. Operation S1610 is related to Fig.15 The operation S1510 is the same as that of the operation S1510, and thus no repeated description will be given.

[0431] According to an embodiment, in operation S1620, the listening device 4000b determines a device including the device determination model 1330 as the hub device 1000 from among a plurality of devices previously registered by using a user account. In an embodiment of the present disclosure, the listening device 4000b may obtain device determination model information from a plurality of devices connected through a wired or wireless communication network and logged in by using the same user account, and may determine the device storing the device determination model 1330 as the hub device 1000 based on the obtained device determination model information. The device determination model information may include at least one of information on whether a device determination model for each of a plurality of devices exists, identification information of a device / server in which the device determination model is stored, and information on an IP address of the stored device / server or a MAC address of the stored device / server.

[0432] In operation S1630 , the listening device 4000 b transmits a voice signal to the hub device 1000 .

[0433] According to an embodiment, in operation S1640, the hub device 1000 converts the voice signal into text by performing ASR.

[0434] According to an embodiment, in operation S1650 , the hub device 1000 interprets the text by using the first NLU model 1332 , and determines an operation execution device related to the text by using the device determination model 1330 .

[0435] Operations S1640 and S1650 are respectively related to Figure 6 Operations S620 and S630 are the same, and thus repeated description will not be given.

[0436] According to an embodiment, in operation S1660, the hub device 1000 determines whether the function determination model is stored in the operation execution device. In an embodiment of the present disclosure, the processor 1200 (see Figure 2 ) determines whether the function determination model 4032 corresponding to the operation execution device 4000a is stored in the internal memory of the operation execution device 4000a based on the storage information of the function determination model 4032 obtained from the operation execution device 4000a. In another embodiment of the present disclosure, the processor 1200 may store information about the function determination model of multiple devices by using the database 1360 (see Figure 2 ) determines whether the function determination model corresponding to the operation execution device 4000a is stored in the internal memory of the operation execution device 4000a. The detailed method of determining whether the function determination model corresponding to the operation execution device 4000a is stored in the internal memory of the operation execution device 4000a is as follows Fig.14 The method is the same as that described in operation S1450 of , and thus a repeated description will not be given.

[0437] Based on determining in operation S1660 that the function determination model 4032 is stored in the operation execution device 4000a (Yes), the hub device 1000 sends the text to the operation execution device 4000a (S1672). For example, when the operation execution device 4000a is an air conditioner and it is determined that the function determination model 4032 stored in the memory of the operation execution device 4000a is a model for obtaining the operation information of the air conditioner, the hub device 1000 may send at least a portion of the text to the function determination model 4032 stored in the operation execution device 4000a.

[0438] Based on the determination in operation S1660 that the function determination model is not stored in the operation execution device 4000a (No), the hub device 1000 transmits the text to the voice assistant server 2000 (S1674). For example, when the operation execution device 4000a is an air conditioner but it is determined that the function determination model 4032 stored in the memory of the operation execution device 4000a is a model for obtaining operation information of a TV, the hub device 1000 may transmit at least a portion of the text to the function determination model 2342 for obtaining operation information of an air conditioner among the plurality of function determination models 2342, 2344, and 2346 stored in the voice assistant server 2000.

[0439] Fig.17 is a diagram illustrating an example in which the operation execution device 4000 a updates a function determination model according to an embodiment of the present disclosure.

[0440] refer to Fig.17, the voice assistant server 2000 may include a device determination model 2330 and a plurality of function determination models 2342, 2344, and 2346. The operation execution device 4000a may include a function determination model 4032, and the function determination model 4032 may include an NLU model 4034 and an action plan management module 4036.

[0441] The device determination model 2330 included in the voice assistant server 2000 may be a model configured to determine the latest version of the operation execution device 4000a by interpreting the voice input for the latest function or the latest device. The plurality of function determination models 2342, 2344, and 2346 included in the voice assistant server 2000 may be models configured to interpret the voice input of the latest functions related to, for example, air conditioners, refrigerators, and TVs and generate operation information as the latest version of the analysis result.

[0442] The function determination model 4032 included in the operation execution device 4000a is configured to interpret the text converted from the voice input and generate operation information for executing a specific function. The function determination model 4032 may include an NLU model 4034 trained to interpret only the text about a limited number of functions and an action plan management module 4036 trained to generate operation information from the text interpreted by the NLU model 4034. In an embodiment of the present disclosure, the function determination model 4032 included in the operation execution device 4000a may not be the latest version, and therefore may not be able to interpret the text about the latest function and may not be able to generate operation information about the latest function. When receiving the text about the latest function, because the function determination model 4032 may not be able to interpret the text and may not be able to determine the function even when using the NLU model 4034, the function determination model 4032 may not be able to generate the operation information even when using the action plan management module 4036. In this case, the operation execution device 4000a cannot determine the function.

[0443] According to an embodiment, in operation S1710, the operation execution device 4000a transmits device information and update request information of the function determination model to the voice assistant server 2000. The update request information of the function determination model may be information for requesting that the version of the function determination model 4032 stored in the memory of the operation execution device 4000a be synchronized with the version of the function determination model corresponding to the operation execution device 4000a among the plurality of function determination models 2342, 2344, and 2346 stored in the voice assistant server 2000.

[0444] In an embodiment of the present disclosure, the operation execution device 4000a may periodically send update request information of the function determination model to the voice assistant server 2000 at a specific time interval, a specific date, etc. However, the present disclosure is not limited thereto, and when an application or firmware is updated, the operation execution device 4000a may send update request information of the function determination model to the voice assistant server 2000. In another embodiment of the present disclosure, when the operation execution device 4000a receives a control command from the IoT server 3000, the operation execution device 4000a may send update request information of the function determination model to the voice assistant server 2000.

[0445] According to an embodiment, in operation S1720, the voice assistant server 2000 transmits update data of the function determination model.

[0446] According to an embodiment, in operation S1730, the operation execution device 4000a updates the function determination model to the latest version by using the update data. In an embodiment of the present disclosure, the operation execution device 4000a can overwrite and update the previously stored function determination model 4032 by using the data of the latest version of the function determination model received from the voice assistant server 2000. The operation execution device 4000a can perform training to interpret the text about the latest function added, modified or deleted by updating the function determination model 4032, and generate operation information according to the interpretation result.

[0447] According to an embodiment, in operation S1740, the voice assistant server 2000 updates the device determination model 2330. In an embodiment of the present disclosure, when the voice assistant server 2000 interprets the text related to the latest function and determines the device for performing an operation on the latest function, the operation execution device 4000a may be updated so that the device determination model 2330 is also included in the device candidates.

[0448] according to Fig.17 In an embodiment, because the function determination model 4032 stored in the internal memory of the operation execution device 4000a itself is synchronized with the latest function determination model of the voice assistant server 2000, when the user speaks to execute the latest function, even if the voice assistant server 2000 is not accessed by using a communication network, the operation can be performed through the function determination model 4032 of the operation execution device 4000a, thereby reducing network usage fees and improving server operation efficiency.

[0449] Fig.18 is a flowchart illustrating a method of operating the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the operation execution device 4000a according to an embodiment of the present disclosure. Fig.18It is illustrated that when a control command is received from the IoT server 3000 , the operation execution device 4000 a updates the function determination model 4032 to the latest version.

[0450] refer to Fig.18 , the hub device 1000 may include an ASR module 1310, an NLG module 1320, a device determination model 1330, a function determination device determination module 1340, and a plurality of function determination models (eg, 1352, 1354, and 1356).

[0451] The operation execution device 4000 a may include a function determination model 4032 , and the function determination model 4032 may include an NLU model 4034 and an action plan management module 4036 .

[0452] Fig.18 The hub device 1000 and the operation execution device 4000a are respectively connected to Fig.16 The hub device 1000 and the operation execution device 4000a are the same, so a repeated description will not be given.

[0453] The voice assistant server 2000 may include a device determination model 2330 and a plurality of function determination models 2342, 2344, and 2346. The device determination model 2330 and Figure 3 The device determination model 2330 is the same, so no repeated description will be given.

[0454] In operation S1810 , the IoT server 3000 transmits a control command to the operation execution device 4000 a .

[0455] In operation S1820, the operation execution device 4000a determines whether the operation information corresponding to the control command is included in the function determination model 4032. In an embodiment of the present disclosure, the operation execution device 4000a may determine whether information about a function of performing an operation according to a control command received from the IoT server 3000 is included in the function determination model 4032. For example, when the operation execution device 4000a is an air conditioner and receives a control command of a dehumidification mode, the operation execution device 4000a may determine whether information about a detailed operation of a dehumidification function of the dehumidification mode and an execution order of the detailed operations is stored in the function determination model 4032.

[0456] Based on the determination in operation S1820 that the operation information corresponding to the control command is stored in the function determination model 4032 (Yes), the operation execution device 4000a executes the operation according to the control command (S1830).

[0457] Based on the determination in operation S1820 that the operation information corresponding to the control command is not stored in the function determination model 4032 (No), the operation execution device 4000a transmits update request information and device information of the function determination model 4032 to the voice assistant server 2000 (S1840). The device information may include at least one of device identification information (e.g., device ID information) of the operation execution device 4000a, storage information of the function determination model 4032 of the operation execution device 4000a, and version information of the function determination model 4032. The version information of the function determination model 4032 may include information on the properties, type, or number of functions that can be identified by using the function determination model 4032.

[0458] In operation S1842, the voice assistant server 2000 transmits update data of the function determination model to the operation execution device 4000a.

[0459] In operation S1850, the operation execution device 4000a updates the function determination model 4032 to the latest version by using the update data. Operation S1850 is Fig.17 The operation S1730 is the same as that of the operation S1730, and thus no repeated description will be given.

[0460] In operation S1860, the operation execution device 4000a transmits update information of the function determination model 4032 to the voice assistant server 2000. In an embodiment of the present disclosure, the operation execution device 4000a may transmit at least one of information about the version of the function determination model 4032 after the update and information about the properties, types, or quantities of functions that can be identified by using the function determination model 4032 after the update to the voice assistant server 2000.

[0461] In operation S1870, the voice assistant server 2000 updates the device determination model 2330 based on the update information of the function determination model 4032 of the operation execution device. In an embodiment of the present disclosure, the voice assistant server 2000 may update the device determination model 2330 based on the updated version information of the function determination model 4032 and at least one of information about the properties, types, and quantities of functions that can be identified by using the updated function determination model 4032 received from the operation execution device 4000a, so that the operation execution device 4000a is included in the device candidate list for determining the device for executing the operation of the latest function.

[0462] In operation S1880 , the voice assistant server 2000 transmits update data of the device determination model 2330 to the hub device 1000 .

[0463] In operation S1890, the hub device 1000 updates the device determination model 1330 based on the received update data. The device determination model 1330 can be synchronized with the device determination model 2330 of the voice assistant server 2000 by being updated. Therefore, when the user's voice input related to the latest function is received through the hub device 1000, because the hub device 1000 itself can update the device determination model 1330 by using the data stored in the internal memory 1300 (see Figure 2 ) in the device determination model 1330 to determine the operation execution device 4000a without having to access the voice assistant server 2000 by using the communication network, so the network usage fee can be reduced, the processing time can be reduced, and thus the response speed can be improved.

[0464] Fig.19 is a flowchart illustrating a method of operating the hub device 1000, the voice assistant server 2000, the IoT server 3000, and the new device 4000c.

[0465] refer to Fig.19 , the hub device 1000 may include an ASR module 1310 , an NLG module 1320 , a device determination model 1330 , and a function determination device determination module 1340 .

[0466] The voice assistant server 2000 may include a device determination model 2330 and a plurality of function determination models 2342, 2344, and 2346. The device determination model 2330 and Figure 3 The device determination model 2330 is the same, so no repeated description will be given.

[0467] The new device 4000c is a target device that is logged in by using the same user account as the user account of the hub device 1000 and is expected to be registered in the user account of the voice assistant server 2000. The new device 4000c can be connected to the hub device 1000, the voice assistant server 2000, and the IoT server 3000 through a wired or wireless communication network.

[0468] exist Fig.19 In an embodiment of the present invention, the new device 4000c itself can analyze the text, and a function determination model 4032c for performing an operation according to the user's intention based on the analysis result of the text can be stored in the memory of the new device 4000c. For example, when the new device 4000c is an "air purifier", the function determination model 4032c stored in the memory of the new device 4000c can be a model for determining the function of the air conditioner and obtaining operation information about the detailed operation of the determined function and the relationship between the detailed operations. The function determination model 4032c can include an NLU model 4034c and an action plan management module 4036c.

[0469] However, the present disclosure is not limited thereto, and the new device 4000c may not include the function determination model 4032c. In another embodiment of the present disclosure, the new device 4000c may include a device determination model.

[0470] In operation S1910 , the new device 4000 c obtains user account information by logging in. The user account information includes a user ID and a password. The obtained user account information may be the same as the user account information of the hub device 1000 .

[0471] In operation S1920, the hub device 1000 provides the IoT server 3000 with the device identification information and determination capability information of the device determination model 1330. The term "device determination capability information" refers to information about the ability of the hub device 1000 to interpret text by using the first NLU model 1332 included in the device determination model 1330 and determine the device for performing an operation according to the interpretation result of the text by using the device determination model 1330. The capability information may include information about device candidates used by the device determination model 1330 to determine the operation execution device. For example, the device determination model 1330 of the hub device 1000 may be a model trained to interpret only text related to air conditioners, TVs, and refrigerators and determine only one device among the air conditioners, TVs, and refrigerators as the operation execution device according to the interpretation result. In this case, the candidate devices may be "air conditioners, TVs, and refrigerators."

[0472] In operation S1930, the new device 4000c provides the IoT server 3000 with user account information, identification information (e.g., device id information) of the new device 4000c, and storage information of the device determination model and the function determination model. The term "storage information of the device determination model" refers to information about whether the new device 4000c itself stores a device determination model for determining an operation execution device in an internal memory. The term "storage information of the function determination model" refers to information about whether the new device 4000c stores a function determination model for obtaining operation information about detailed operations according to function execution operations and the relationship between the detailed operations in an internal memory. The storage information of the function determination model may include information about whether not only the function determination model corresponding to the new device 4000c but also the function determination model corresponding to devices other than the new device 4000c is stored.

[0473] In operation S1940, the IoT server 3000 adds the new device 4000c to the device list corresponding to the user account. The IoT server 3000 may store the device identification information and storage information of the device determination model and the function determination model of the new device 4000c in the device list registered according to each user account. Operation S1940 may be an operation of registering the new device 4000c. When operation S1940 is performed, the new device 4000c is a registered device.

[0474] In operation S1950 , the IoT server 3000 provides the user account, the device list, and storage information of the device determination model and the function determination model of each device candidate included in the device list to the voice assistant server 2000 .

[0475] In operation S1960, the voice assistant server 2000 determines a device storing a device determination model among a plurality of devices registered in the user account as the hub device 1000. In an embodiment of the present disclosure, the voice assistant server 2000 may identify a device storing a device determination model based on storage information of a device determination model of each of a plurality of devices registered according to the user account obtained from the IoT server 3000, and may determine the identified device as the hub device 1000.

[0476] In operation S1970 , the voice assistant server 2000 transmits device identification information and storage information of the device determination model and the function determination model of the new device 4000 c to the hub device 1000 .

[0477] In operation S1980, the hub device 1000 updates the device determination model 1330 to add the new device 4000c to the device candidates that can be determined as the operation execution device by the device determination model 1330. Through the update, the device determination model 1330 can interpret the text related to the new device 4000c by using the first NLU model 1332, and the device determination model 1330 can determine the new device 4000c as the operation execution device according to the interpretation result.

[0478] For example, when the new device 4000c is an air purifier, before the device determination model 1330 is updated, the device determination model 1330 may not be able to interpret text such as "operate fine dust purification mode", may not be able to determine the operation execution device, and thus may output a "failure" message. When the new device 4000c is added to the device candidate by updating the device determination model 1330, the device determination model 1330 can interpret the text of "operate fine dust purification mode" by using the updated first NLU model 1332, and can determine the air purifier as the new device 4000c as the operation execution device based on the interpretation result of the text.

[0479] Fig. 20 is a diagram illustrating a network environment including a hub device 1000 , a plurality of devices 4000 , and a voice assistant server 2000 .

[0480] refer to Fig. 20 , the hub device 1000, the plurality of devices 4000, the voice assistant server 2000, and the IoT server 3000 may be connected to each other by using a wired communication or wireless communication method, and may perform communication. In an embodiment of the present disclosure, the hub device 1000 and the plurality of devices 4000 may be directly connected to each other through a communication network, but the present disclosure is not limited thereto.

[0481] The hub device 1000 and the plurality of devices 4000 may be connected to the voice assistant server 2000, and the hub device 1000 may be connected to the plurality of devices 4000 through the server. In addition, the hub device 1000 and the plurality of devices 4000 may be connected to the IoT server 3000. In another embodiment of the present disclosure, the hub device 1000 and the plurality of devices 4000 may both be connected to the voice assistant server 2000 through a communication network, and may be connected to the IoT server 30000 through the voice assistant server 2000. In another embodiment of the present disclosure, the hub device 1000 may be connected to the plurality of devices 4000, and the hub device 1000 may be connected to the plurality of devices 4000 through one or more nearby access points. In addition, the hub device 1000 may be connected to the plurality of devices 4000 in a state where the hub device 1000 is connected to the voice assistant server 2000 or the IoT server 3000.

[0482] The hub device 1000, the plurality of devices 4000, the voice assistant server 2000, and the IoT server 3000 may be connected via a LAN, a WAN, a VAN, a mobile radio communication network, a satellite communication network, or a combination thereof. Examples of wireless communication methods may include, but are not limited to, Wi-Fi, Bluetooth, BLE, Zigbee, WFD, UWB, IrDA, and NFC.

[0483] In an embodiment of the present disclosure, the hub device 1000 may receive a user's voice input. At least one of the multiple devices 4000 may be a target device that receives a control command from the voice assistant server 2000 and / or the IoT server 3000 and performs a specific operation. At least one of the multiple devices 4000 may be controlled to perform a specific operation based on the user's voice input received by the hub device 1000. In an embodiment of the present disclosure, at least one of the multiple devices 4000 may receive a control command from the hub device 1000 without receiving a control command from the voice assistant server 2000 and / or the IoT server 3000.

[0484] The hub device 1000 may receive voice input (e.g., speech) from a user. In an embodiment of the present disclosure, the hub device 1000 may include an ASR model. In an embodiment of the present disclosure, the hub device 1000 may include an ASR model with limited functionality. For example, the hub device 1000 may include an ASR model having a function of detecting a specified voice input (e.g., a wake-up input such as “Hi, Bixby” or “OK, Google”) or a function of pre-processing a voice signal obtained from a partial voice input. Although the hub device 1000 is Fig. 20 , but the present disclosure is not limited thereto. In an embodiment of the present disclosure, one of the multiple devices 4000 may be the hub device 1000. In addition, the hub device 1000 may include a first NLU model, a second NLU model, and a natural language generation model. In this case, the hub device 1000 may receive the user's voice input through a microphone, or may receive the user's voice input from at least one of the multiple devices 4000. When receiving the user's voice input, the hub device 1000 may process the user's voice input by using the ASR model, the first NLU model, the second NLU model, and the natural language generation model, and may provide a response to the user's voice input.

[0485] The hub device 1000 may determine the type of target device for performing the operation intended by the user based on the received voice signal. The hub device 1000 may receive the voice signal as an analog signal, and may convert the voice portion into a computer-readable text by performing ASR. The hub device 1000 may interpret the text by using a first NLU model, and may determine the target device based on the interpretation result. The hub device 1000 may determine at least one of the multiple devices 4000 as the target device. The hub device 1000 may select a second NLU model corresponding to the determined target device from among the multiple stored second NLU models. The hub device 1000 may determine the operation to be performed by the target device requested by the user by using the selected second NLU model. Based on determining that there is no second NLU model corresponding to the determined target device among the multiple stored second NLU models, the hub device 1000 may send at least a portion of the text to at least one of the multiple devices 4000 and the voice assistant server 2000. The hub device 1000 sends information about the determined operation to the target device so that the determined target device performs the determined operation.

[0486] The hub device 1000 may receive information of the plurality of devices 4000 from the IoT server 3000. The hub device 1000 may determine a target device by using the received information of the plurality of devices 4000. In addition, the hub device 1000 may control the target device to perform a determined operation by using the IoT server 3000 as a relay server for transmitting information about the determined operation.

[0487] The hub device 1000 may receive a user's voice input through a microphone, and may send the received voice input to the voice assistant server 2000. In an embodiment of the present disclosure, the hub device 1000 may obtain a voice signal from the received voice input, and may send the voice signal to the voice assistant server 2000.

[0488] exist Fig. 20 In the embodiment of the present disclosure, the plurality of devices 4000 include but are not limited to the first device 4100 as an air conditioner, the second device 4200 as a TV, the third device 4300 as a washing machine, and the fourth device 4400 as a refrigerator. For example, the plurality of devices 4000 may include at least one of a smart phone, a tablet PC, a tablet PC, a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop PC, a netbook computer, a workstation, a server, a PDA, a PMP, an MP3 player, a mobile medical device, a camera, and a wearable device. In an embodiment of the present disclosure, the plurality of devices 4000 may be household appliances. The household appliances may include at least one of the following items: a TV, a digital video disk (DVD) player, an audio device, a refrigerator, an air conditioner, a vacuum cleaner, an oven, a microwave oven, a washing machine, an air purifier, a set-top box, a home automation control panel, a security control panel, a game console, an electronic key, a camera, and an electronic photo frame.

[0489] The voice assistant server 2000 may determine the type of target device for performing the operation intended by the user based on the received voice signal. The voice assistant server 2000 may receive a voice signal as an analog signal from the hub device 1000, and may convert the voice portion into a computer-readable text by performing ASR. The voice assistant server 2000 may interpret the text by using a first NLU model, and may determine the target device based on the interpretation result. In addition, the voice assistant server 2000 may receive at least a portion of the text and information about the target device determined by the hub device 1000 from the hub device 1000. In this case, the hub device 1000 converts the user voice signal into text by using the ASR model and the first NLU model of the hub device 1000, and determines the target device by interpreting the text. In addition, the hub device 1000 sends at least a portion of the text and information about the determined target device to the voice assistant server 2000.

[0490] The voice assistant server 2000 may determine the operation to be performed by the target device requested by the user by using a second NLU model corresponding to the determined target device. The voice assistant server 2000 may receive information of a plurality of devices 4000 from the IoT server 3000. The voice assistant server 2000 may determine the target device by using the received information of the plurality of devices 4000. In addition, the voice assistant server 2000 may control the target device to perform the determined operation by using the IoT server 3000 as a relay server for sending information about the determined operation. The IoT server 3000 may store information about a plurality of devices 4000 connected through a network and previously registered. In an embodiment of the present disclosure, the IoT server 3000 may store at least one of identification information (e.g., device id information) of a plurality of devices 4000, a device type of each of the plurality of devices 4000, and function execution capability information of each of the plurality of devices 4000.

[0491] In an embodiment of the present disclosure, the IoT server 3000 may store state information about power on / off or an operation being performed for each of the plurality of devices 4000. The IoT server 3000 may send a control command for performing a determined operation to a target device among the plurality of devices 4000. The IoT server 3000 may receive information about the determined target device and information about the determined operation from the voice assistant server 2000, and may send a control command to the target device based on the received information.

[0492] Fig.21A and Fig. 21B is a diagram illustrating a voice assistant model 200 that may be executed by a hub device 1000 and a voice assistant server 2000 according to an embodiment of the present disclosure.

[0493] refer to Fig.21A and Fig. 21B , the voice assistant model 200 is implemented as software. The voice assistant model 200 may be configured to determine the user's intention from the user's voice input and control a target device related to the user's intention. When a device controlled by the voice assistant model 200 is added, the voice assistant model 200 may include a first assistant model 200a configured to update an existing model to a new model through learning, etc., and a second assistant model 200b configured to add a model corresponding to the added device to the existing model.

[0494] The first assistant model 200a is a model that determines the target device related to the user's intention by analyzing the user's voice input. The first assistant model 200a may include an ASR model 202, an NLG model 204, a first NLU model 300a, and a device determination model 310. In an embodiment of the present disclosure, the device determination model 310 may include the first NLU model 300a. In another embodiment of the present disclosure, the device determination model 310 and the first NLU model 300a may be configured as separate elements.

[0495] The device determination model 310 is a model for performing an operation of determining a target device by using the analysis result of the first NLU model 300a. The device determination model 310 may include a plurality of detailed models, and one of the plurality of detailed models may be the first NLU model 300a. The first NLU model 300a or the device determination model 310 may be an AI model.

[0496] When a device controlled by the voice assistant model 200 is added, the first assistant model 200a may update at least the device determination model 310 and the first NLU model 300a by learning. Learning may refer to learning using both the training data used to train the existing device determination model and the first NLU model and the additional training data related to the added device. In addition, learning may refer to updating the device determination model and the first NLU model by using only the additional training data related to the added device.

[0497] The second assistant model 200b, which is a model dedicated to a specific device, is a model that determines an operation to be performed by a target device corresponding to a user's voice input from among a plurality of operations that can be performed by the specific device. Fig.21A In the embodiment of the present disclosure, the second assistant model 200b may include multiple second NLU models 300b, an NLG model 206, and an action plan management module 210. Multiple second NLU models 300b may correspond to multiple different devices respectively. The second NLU model, the NLG model, and the action plan management module may be models implemented by a rule-based system. In an embodiment of the present disclosure, the second NLU model, the NLG model, and the action plan management model may be AI models. Multiple second NLU models may be elements of multiple function determination models.

[0498] When a device controlled by the voice assistant model 200 is added, the second assistant model 200b can be configured to add a second NLU model corresponding to the added device. That is, in addition to the existing multiple second NLU models 300b, the second assistant model 200b can also include a second NLU model corresponding to the added device. In this case, the second assistant model 200b can be configured to select a second NLU model corresponding to the determined target device from among multiple second NLU models including the added second NLU model by using information about the target device determined by the first assistant model 200a.

[0499] refer to Fig. 21B , the second assistant model 200b may include multiple action plan management models and multiple NLG models. Fig. 21B In the example, the plurality of second NLU models included in the second assistant model 200b may be respectively Fig.21A Each of the plurality of NLG models included in the second assistant model 200b may correspond to the second NLU model 300b of Fig.21A The NLG model 206 corresponds to the NLG model 206, and each of the plurality of action plan management models included in the second assistant model 200b can be Fig.21A Corresponding to the action plan management module 210.

[0500] exist Fig. 21B In the embodiment of the present disclosure, multiple action plan management models may be configured to correspond to multiple second NLU models, respectively. In addition, multiple NLG models may be configured to correspond to multiple second NLU models, respectively. In another embodiment of the present disclosure, one NLG model may be configured to correspond to multiple second NLU models, and one action plan management model may be configured to correspond to multiple second NLU models.

[0501] exist Fig. 21B In the example, when a device controlled by the voice assistant model 200 is added, the second assistant model 200b can be configured to add a second NLU model, NLG model, and action plan management model corresponding to the added device.

[0502] exist Fig. 21BIn the embodiment, when a device controlled by the voice assistant model 200 is added, the first NLU model 300a can be configured to be updated to a new model through learning, etc. In addition, when the device determination model 310 includes the first NLU model 300a, the device determination model 310 can be configured so that when a device controlled by the voice assistant model 200 is added, the existing model is completely updated to a new model through learning, etc. The first NLU model 300a or the device determination model 310 can be an AI model. Learning can refer to learning using both the training data used to train the existing device determination model and the first NLU model and the additional training data related to the added device. In addition, learning can refer to updating the device determination model and the first NLU model by using only the additional training data related to the added device.

[0503] exist Fig. 21B In the example, when a device controlled by the voice assistant model 200 is added, the second assistant model 200b can be updated by adding the second NLU model, NLG model, and action plan management model corresponding to the added device to the existing model. The second NLU model, NLG model, and action plan management model can be models implemented by a rule-based system.

[0504] exist Fig. 21B In the embodiment of the present invention, the second NLU model, the NLG model and the action plan management model can be AI models. The second NLU model, the NLG model and the action plan management model can all be managed as one device according to the corresponding device. In this case, the second assistant model 200b may include a plurality of second assistant models 200b-1, 200b-2 and 200b-3 corresponding to a plurality of devices, respectively. For example, the second NLU model corresponding to TV, the NLG model corresponding to TV and the action plan management model corresponding to TV can be managed as the second assistant model 200b-1. In addition, the second NLU model corresponding to the speaker, the NLG model corresponding to the speaker and the action plan management model corresponding to the speaker can be managed as the second assistant model 200b-2 corresponding to the speaker. In addition, the second NLU model corresponding to the refrigerator, the NLG model corresponding to the refrigerator and the action plan management model corresponding to the refrigerator can be managed as the second assistant model 200b-3 corresponding to the refrigerator.

[0505] When a device controlled by the voice assistant model 200 is added, the second assistant model 200b may be configured to add a second assistant model corresponding to the added device. That is, in addition to the existing multiple second assistant models 200b-1 to 200b-3, the second assistant model 200b may also include a second assistant model corresponding to the added device. In this case, the second assistant model 200b may be configured to select a second assistant model corresponding to the determined target device from among multiple second assistant models including the second assistant model corresponding to the added device, by using information about the target device determined by the first assistant model 200a.

[0506] The program executed by the hub device 1000, the voice assistant server 2000, and the plurality of devices 4000 according to the present disclosure may be implemented as a hardware component, a software component, and / or a combination of a hardware component and a software component. The program may be executed by any system capable of executing computer-readable instructions.

[0507] Software may include a computer program, code, instructions, or a combination of one or more thereof, and may configure a processing device to operate as desired or to command the processing device, either individually or collectively.

[0508] Software may be implemented in a computer program including instructions stored in a computer-readable storage medium. Computer-readable storage media may include, for example, magnetic storage media (e.g., ROM, RAM, floppy disk, hard disk, etc.) and optical reading media (e.g., compact disk (CD)-ROM, DVD, etc.). Computer-readable recording m...

Claims

1. A method comprising: Based on receiving a user's voice input by a hub device in the home network environment: converting the received voice input into text by the hub device by performing automatic speech recognition (ASR); identifying, by the hub device, a device capable of performing an operation corresponding to the text from among a plurality of devices connected to the hub device in the home network environment; identifying which device, from among the hub device and a plurality of other devices connected to the hub device, stores a function determination model corresponding to a device capable of performing an operation corresponding to the text; as well as The device that determines the model based on the recognized storage function is a device different from the hub device, and at least a portion of the text is sent to the recognized device.

2. The method according to claim 1, wherein: Identify devices capable of performing the described operations including: Analyzing the text using a first natural language understanding (NLU) model; and A device capable of performing the operation is determined based on an analysis result of the text, wherein a first natural language understanding (NLU) model is included in the device determination model.

3. The method according to claim 1, further comprising: At least a portion of the text is analyzed using a second natural language understanding (NLU) model included in the function determination model, and operation information related to an operation corresponding to the text is obtained based on the analysis result of at least a portion of the text.

4. The method according to claim 1, further comprising: Information about the functionality determination model stored in at least one device is obtained from at least one device storing the functionality determination model.

5. The method according to claim 4, wherein: Identifying which device stores the function determination model includes identifying a device storing the function determination model corresponding to the identified device capable of performing the operation based on the obtained information about the function determination model.

6. A hub device for controlling devices based on voice input in a home network environment, the hub device comprising: a communication interface configured to perform data communication with at least one of a plurality of devices connected to the hub device, a voice assistant server, and an Internet of Things (IoT) server in the home network environment; A microphone configured to receive a user's voice input; a memory configured to store a program including one or more instructions; as well as A processor configured to execute one or more instructions of a program stored in the memory to: Converts speech input received through a microphone into text by performing automatic speech recognition (ASR), identifying a device from the plurality of devices that is capable of performing an operation corresponding to the text, identifying which device stores a function determination model corresponding to a device capable of performing an operation corresponding to the text from among the hub device and a plurality of other devices connected to the hub device, and The device based on the recognized storage function determination model is a device different from the hub device, and the communication interface is controlled to send at least a portion of the text to the device of the recognized storage function determination model.

7. The hub device according to claim 6, wherein: The processor is also configured to identify devices capable of performing operations corresponding to the text using a device determination model including a first natural language understanding (NLU) model configured to analyze the text, and determine devices capable of performing operations corresponding to the text based on analysis results of the text.

8. The hub device according to claim 6, wherein: The function determination model includes a second natural language understanding (NLU) model configured to analyze at least a portion of the text and obtain operation information related to an operation to be performed by a device capable of performing an operation corresponding to the text based on the analysis result of at least a portion of the text.

9. The hub device according to claim 6, wherein: The processor is also configured to execute one or more instructions to control the communication interface to obtain information about a function determination model stored in at least one device from at least one device storing the function determination model among multiple devices, wherein the function determination model is used to determine functions associated with each of the multiple devices.

10. The hub device of claim 9, wherein: The processor is further configured to execute one or more instructions to identify which device stores a function determination model corresponding to a device capable of performing an operation corresponding to the text based on the obtained information about the function determination model. 11 . A non-transitory computer-readable recording medium having recorded thereon a program for executing the method according to claim 1 on a computer.

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