KR102262989B1 - Interactive Flat Panel Display System Performing Artificial Intelligence Control Technology Using Speech Recognition in Cloud Environment - Google Patents

Abstract

The present invention relates to an interactive whiteboard having a large-scaled touch screen, which processes speech recognition information on a user to perform an artificial intelligence (AI) function. The interactive whiteboard of the present invention having a panel unit able to recognize a touch and a sensor unit for identifying a touch mode to perform recognition of a touch in accordance with the touch mode when the panel unit is touched comprises: a microphone array for receiving a speech signal from an external sound source to separate the sound source and eliminate noises; a speech recognition engine for receiving a speech signal, an analog signal whose noises are eliminated from the microphone array to convert the same into digital data, a text signal; a database for storing the received speech signal and the converted text signal repeatedly; a command analysis unit for receiving the converted text signal from the speech recognition engine to analyze the same as a command through machine learning using the database; and a control unit for performing a query search or an internal/external control function in accordance with the analyzed command as an external cloud service.

Description

Electronic blackboard system performing artificial intelligence control technology through speech recognition in a cloud environment {Interactive Flat Panel Display System Performing Artificial Intelligence Control Technology Using Speech Recognition in Cloud Environment}

The present invention relates to an electronic blackboard, and more particularly, to an electronic blackboard system capable of artificial intelligence (AI)-based internal function control processing of voice recognition data and control of external devices connected to the Internet of Things (IoT), and various IoT peripherals In controlling external devices, connected devices can be controlled or monitored through the optimal path, and to protect user privacy in a cloud environment, a database is built using simulated data and the user is authenticated to enhance security. It's about the system.

In recent years, as the classroom or conference room changes to a digital environment, various classroom or meeting tools are replaced with digital equipment, and the communication method has also changed to two-way digital communication. For example, with the spread of digital textbooks and the spread of educational environments using various multimedia devices, in the case of an electronic blackboard, documents of lecture contents or presentation contents are displayed on the screen, and various kinds of lectures necessary for lectures are provided, such as writing and writing functions. It is possible to perform a function, and at this time, it is possible to directly touch a human hand or to detect a touch with a dedicated touch pen.

In addition, recently, with the development of artificial intelligence (AI) equipment and processes, a specific command is transmitted to the device by the user's audio voice recognition instead of a traditional input device (eg, keyboard or mouse, etc.), and various searches of the device are performed. And the technology to control the control function appeared.

The following prior art 1 (KR 2015-0087687 A) is an interactive system, a display apparatus, and a controlling method thereof, and the disclosed display apparatus includes an input unit for receiving a user's utterance voice, an utterance A communication unit that transmits a voice signal for a voice to the voice recognition device, a voice recognition unit that performs voice recognition on the spoken voice, and a reliability value for the first voice information recognized through the voice recognition unit and the second recognized by the voice recognition device and a controller for determining, as an execution command for the spoken voice, voice information having a reliability value greater than or equal to a preset threshold among reliability values for the second voice information. Accordingly, when the display device and the external device simultaneously recognize the user's spoken voice, the display device may select a voice recognition result that is close to the user's intention among the two voice recognition results. On the other hand, another prior art 2 (US 10198246 B2) below is a method and apparatus for voice-activated control of an interactive display, in which communication is possible with an interactive display device. Although it is possible to receive the user's voice data through the highly coupled voice input device, the technical elements of the interactive electronic blackboard including a touch panel screen having a touch sensor are not disclosed.

Therefore, in the present invention, including the voice recognition command in the touch sensor electronic blackboard using the capacitive (PCAP), electromagnetic induction (EMR) or infrared (IR) method, artificial intelligence (AI)-based internal function execution and communication with peripheral devices We would like to propose a method of performing an external control function based on the Internet of Things (IoT) through mutual communication. In this case, an independent individual voice recognition device and voice recognition algorithm are used without being dependent on the dedicated server or communication network of the Internet service provider (telecommunications company). It aims to enable artificial intelligence-based data processing.

Prior Patent 1: Korean Patent Publication No. 10-20152015-0087687 (interactive system, display apparatus and controlling method thereof) Prior Patent 2: US Patent Registration Publication No. 10198246 (Methods and apparatus for voice-activated control of an interactive display) Prior Art 3: Korean Patent Registration Publication No. 10-0919680 (Apparatus and method for delivering data based sensor network)

An object of the present invention is to perform an artificial intelligence (AI)-based cloud search or device control function by processing a user's voice recognition information on an electronic blackboard including a large-area touch screen.

Another object of the present invention is to perform a function of controlling an external device connected to an Internet of Things (IoT) network by wire or wireless according to voice recognition information in an electronic blackboard including a large-area touch screen.

Another object of the present invention is to control or monitor connected devices through an optimal path in controlling various external devices around IoT, and to build a database to protect user privacy in a cloud environment and to strengthen security by authenticating users The purpose is to perform

The present invention relates to an electronic blackboard comprising a panel unit capable of touch recognition and a sensor unit for performing touch recognition according to a touch method by determining a touch method when the panel unit is touched, receiving a voice signal from an external sound source and receiving a sound source a microphone array to isolate and eliminate noise; a voice recognition engine that receives a voice signal that is an analog signal from which noise has been removed from the microphone array and converts it into digital data that is a text signal; a database for repeatedly storing the received voice signal and the converted text signal; a command interpretation unit that receives the text signal converted from the speech recognition engine and interprets it as a command through machine learning using the database; It provides an electronic blackboard including a control unit that performs a query search or internal and external control function with an external cloud service according to the interpreted command.

Here, the method further includes a voice pattern matching unit that compares the command interpreted by the command interpretation unit with an internal command list to search for a pattern of the voice signal, wherein the voice pattern matching unit performs the voice pattern matching according to a predetermined voice pattern in the first sleep mode. It may be characterized in that it transmits an internal control signal for changing the voice recognition engine to the wake-up mode to the control unit.

Meanwhile, the microphone array may perform acoustic model machine learning for compensating for the distorted voice signal through gain compensation or echo cancellation using a plurality of microphones.

In addition, the control unit may connect the communication to an external cloud server according to the interpreted command, query and search SaaS-based cloud data, and display it on the electronic blackboard.

In addition, the control unit may perform a control function inside the device of the electronic blackboard according to the interpreted command, or may perform a control function of an external peripheral device connected to the electronic blackboard by wire or wireless according to the command.

And when performing a control function of the connected external peripheral devices, the control function is performed through an optimal route recommendation AI algorithm, wherein the algorithm is a data collection step in which the data collection module receives a data set, and the preprocessing module receives the input A preprocessing step of preprocessing a data set, a prediction calculation step in which the prediction module configured through machine learning of an artificial neural network (ANN) applies the preprocessed data set to a prediction algorithm, and the optimization module predicts the model and constraints according to the objective function It may be characterized in that it consists of an optimal route recommendation step of finding an optimal route using

And, the voice signal and the converted text signal use the data of each original signal and k-1 copies of the data, and the database according to the result of processing the hash function process based on the timestamp that is the generation time of the data. By storing it, it may be characterized in that the privacy of the user who ignited the external sound source is protected.

In addition, the hash function uses a Hash-based Message Authentication Code algorithm (HMAC), and a result value Q is generated by Equation 1 below.

<Equation 1>

(TS is timestamp information, K_CNT is a count number based on the k value, and PK is previously shared secret key information)

And the electronic blackboard system is a support vector machine (Support Vector Machine) which is a binary linear classification model of conversion information converted by IP band or user-agent, which is environmental information that the user who uttered the external sound source logs into the system. Machine) may be used to generate a two-class user model to determine whether the user is the user.

The present invention has the effect of learning, interpreting, and processing the user's voice recognition information in an electronic blackboard including a touch sensor to perform an artificial intelligence (AI)-based cloud search or control function.

In addition, the present invention has an effect of controlling an external device connected to an Internet of Things (IoT) network through a user's voice command in an electronic blackboard including a large-area touch screen.

In addition, in performing such artificial intelligence-based input/output processing and IoT control functions, voice data processing is possible through its own independent processing algorithm without being dependent on a dedicated server of a third-party Internet service provider (communication company). It has the effect of being easy to install and use independently regardless of overseas.

On the other hand, in controlling various external devices around the IoT, it is possible to control or monitor a number of connected devices through an optimal path.

Meanwhile, security can be further strengthened by building a database using simulated data to protect privacy in the cloud environment and authenticating the uttering user using SVM (Support Vector Machine).

1 shows a structure in which an artificial intelligence (AI)-based cloud search or control function can be performed by processing a user's voice command 200 information in an electronic blackboard 300 of a touch screen according to an embodiment of the present invention. It is a drawing.
FIG. 2 is a diagram illustrating a situation in which the cloud service 240 is used using the existing communication company's dedicated speaker 210 , the communication network 250 , and the AI server 230 .
3 is a diagram illustrating a situation in which the cloud service 330 is used using its own individual speaker 310 irrespective of a communication company according to an embodiment of the present invention.
Figure 4 is an embodiment of the present invention by processing the user's touch input or voice input (400, 410) information on the electronic blackboard as an embodiment of the present invention (AI) based cloud search 450 or control functions (480, 490) It is a diagram showing a flow chart to perform.

Advantages and features of the present invention and a method of achieving it will become more apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings.

However, the present invention is not limited to the limited number of embodiments disclosed below, but may be implemented in a variety of different forms within substantially the same scope for purposes and effects, and these embodiments are also considered to be included in the technical matters of the present invention. will be able

Examples in the present specification are provided to make the disclosure of the present invention clearer, and to fully understand the scope of the invention to those of ordinary skill in the art to which the present invention pertains.

And the scope of the present invention will be defined by the claims to be described later.

Accordingly, in some embodiments, well-known components, well-known operations, and well-known details are not specifically described to avoid obscuring the present invention.

In addition, the same reference numerals refer to the same elements throughout the specification, and the terms used herein are for the purpose of describing the embodiments only and are not intended to limit the present invention.

In this specification, the singular shall be construed to include the plural as well, unless specifically stated otherwise in the phrase, and an element or action referred to as including (or having) does not exclude the presence and addition of one or more other elements or actions. .

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used with the meaning commonly understood by those of ordinary skill in the art to which the present invention belongs.

In addition, terms defined in general used dictionary shall not be interpreted excessively or ideally unless otherwise defined.

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings.

1 shows a structure in which an artificial intelligence (AI)-based cloud search or control function can be performed by processing a user's voice command 200 information in an electronic blackboard 300 of a touch screen according to an embodiment of the present invention. It is a drawing.

The electronic blackboard 300 including a panel unit (not shown) capable of touch recognition and a sensor unit (not shown) that determines a touch method when the panel unit is touched and performs touch recognition according to the touch method is an external source. A touch input signal 100 or a voice command signal 200 is received from (100, 200).

At this time, according to the input touch method, a pattern of a touch recognized by the touch sensor 310 is displayed on the screen of the electronic blackboard 300 and a response according to other UI input is output.

If the voice command 200 signal is input, the quality of the analog voice signal signal input by the array type microphone module 320 that separates the sound source and removes noise is improved. In this case, the microphone array 320 may implement a gain compensation or echo cancellation function using a plurality of microphones and perform acoustic model machine learning for compensating for the distorted voice signal. Through this, voice recognition can be smoothly processed even from a distance, and even if there is another audio function (eg, music playback) in the electronic board, the voice recognition function must be operated.

Then, a voice signal that is an analog signal from which noise has been removed from the microphone array 320 is received and converted into digital data that is a text signal in the voice recognition engine 340 (Speech to Text), and the voice signal and the converted text signal are converted into a database It is stored repeatedly at (360). The data stored in this way is used as learning data for machine learning based on artificial intelligence for continuous upgrade of S/W in the voice recognition system including the voice recognition engine 340 and the command interpretation unit 350 using its own speaker.

Meanwhile, in the command interpretation unit 350 that receives the text signal converted from the speech recognition engine and interprets it as a command through machine learning using the database 360, the algorithm includes an algorithm for analyzing a voice command pattern, and command control according to the pattern. A protocol matching algorithm, a voice pattern array matching algorithm, and a matching pattern algorithm for granting the same meaning in heterogeneous languages may be performed together.

According to the command interpreted in this way, the control unit 330 may perform a query search function to an external cloud service, such as an Internet search, or perform an internal/external control function.

At this time, as an example of the internal/external control function, first, as an internal function control, the power of the electronic board 300 is turned on or off, the volume of the electronic board 300 is adjusted, or the on-screen control control (screen size adjustment, page movement) is performed. etc.) or an input source (USB, HDMI, etc.) conversion, etc., may perform an internal control function of the electronic board 300 .

And, taking the external function control as an example, there may be a situation in which peripheral devices (eg, lamps, door locks, other electronic blackboards, etc.) located in the Internet of Things (IoT) network connected to the electronic blackboard 300 by wire or wireless are controlled. That is, by using an IoT-related relay and an IR controller, it is possible to switch the lamp lights around and turn them on and off.

On the other hand, the command interpreted by the command interpretation unit 350 further includes a voice pattern matching unit (not shown) for searching the pattern of the voice signal by comparing it with the internal command list 370, and the voice pattern matching unit is in the first sleep mode ( In the sleep mode), an internal control signal for changing the voice recognition engine 340 to a wake-up mode according to a predetermined voice pattern is generated. In addition, when there is no additional voice command input for a predetermined time in the wake-up mode, it is switched back to the sleep mode to minimize power and other resource consumption.

In addition, the present invention intends to provide an optimal path of a control process that can reduce data transmission costs for IoT-based connected external devices and maximize IoT data (input, sensing, monitoring) collection.

The optimal path (root) recommendation algorithm can be configured including data acquisition module (data acquisition), preprocessing module (system processes), prediction module (prediction), predictive model learning module (learning module) and optimization module (optimization module) In addition, the collection module receives an input dataset that is actual use data of a peripheral device, and the pre-processing module may pre-process the input data set. In addition, the preprocessing module divides the data set into a training data set and a test data set, and the prediction module applies the preprocessed data set to a prediction algorithm, more specifically, the prediction module converts the test data set into a prediction algorithm can be predicted by applying

On the other hand, the predictive model configuration unit can configure the prediction module by machine learning on an artificial neural network (ANN) using the training data set, and the optimization module selects an optimal route using a predictive model and constraints according to the objective function. can be found

The preprocessing module preprocesses the input data set. In this case, null input can be removed and fields required for prediction can be derived during preprocessing, and the preprocessed data can be supplied to the prediction module. Therefore, the prediction module can calculate the usage prediction model by applying the prediction algorithm having the highest accuracy among various prediction algorithms to the data, and at this time, the prediction algorithm with the best performance is stored and applied to the next step. Accordingly, the optimization module can use some constraints and predictive models and find the most suitable path (root) according to the objective function.

A data set can be created based on the actual peripheral usage frequency and power consumption, and the cost according to the communication cost of the peripheral device (distance between devices and data amount) can be calculated through the model and constraints predicted using the objective function. Minimize and maximize IoT data collection for a specific grid.

FIG. 2 is a diagram illustrating a situation in which the cloud service 240 is used using the existing communication company's dedicated speaker 210 , the communication network 250 , and the AI server 230 .

3, which will be described later, in FIG. 2, in addition to the smartphone 200 and the computer 220, the cloud service ( 240) can be used. In other words, it is necessary to subscribe to a specific communication company and use the AI speaker dedicated to the communication company, and the specialized artificial intelligence service must also be developed by each communication company, so it is not suitable for use for special purposes such as electronic blackboards. In addition, specialized AI functions such as electronic blackboard consume resources due to frequent and many signal requests compared to other services, and there is a limitation that it cannot be applied when using other carriers. And in exceptional circumstances, such as when the telecommunication company's AI server goes down, the entire AI function may not be available. Also, foreign language-based export has disadvantages, and internationalization is difficult due to interests (differences in language implementation, communication service methods, etc.) between telecommunication operators in other countries. In comparison with this, the cloud service usage method of the present invention of FIG. 3 will be described.

3 is a diagram illustrating a situation in which the cloud service 330 is used using its own individual speaker 310 irrespective of a communication company according to an embodiment of the present invention.

In FIG. 3 , there is no dedicated speaker 210 developed and provided by an Internet service provider (communication company) in addition to the smartphone 300 and the computer 320 , and the Internet communication network 340 independent of the communication company which is not the communication company dedicated communication network 250 . We assume the situation of using . In addition, the cloud service 240 can be directly used by using the individual speaker 310 equipped with the voice recognition engine. In other words, there is no need to sign up for a specific carrier and use a dedicated AI speaker for the carrier, and each carrier does not have to develop specialized artificial intelligence services, so individual companies can create custom AI for exclusive use. Therefore, it is suitable for use for special purposes such as electronic blackboards. In addition, specialized AI functions such as electronic blackboards have frequent and many signal requests compared to other services, so less resources can be consumed by installing dedicated S/W to handle them. Since there is no limitation that it cannot be applied when using other carriers, there is no need to worry about whether the carrier is a carrier or not, and there is no need to consider exceptions such as the carrier's AI server down.

In addition, even if it is used for overseas export based on a foreign language other than Korean, it can be developed as a customized service for each exporting country as needed, and it can be developed, maintained, and repaired with AI-specific functions for electronic blackboard, making it compatible with AI of other countries. .

As shown in FIG. 3 , when using the cloud service 330 , the control unit connects communication to an external cloud server according to the command interpreted by the command interpretation unit, and searches for SaaS (Software as a Service)-based cloud data by querying. The corresponding content can be displayed on the screen of the electronic blackboard.

Meanwhile, as cloud services have become more popular in recent years, various cloud service providers are emerging. Various types of services can be provided on cloud platforms, and among them, storage-based cloud services are the most widely used service type, and the market is expected to continue to expand in the future. Currently, in many cloud storage environments, the deduplication function that does not store the same data as the existing data is applied to manage large-capacity storage servers due to the nature of the cloud environment. However, if the deduplication technology is used, there is a structural privacy problem.

That is, in order to apply the deduplication technology, the mapping structure between users and files is stored as meta information, and in this process, a list of users who uploaded a specific file can be secured through meta-analysis on the server.

In addition, as big data technology develops, a technology for tracking location information of a specific individual or device is also becoming important. Specific location information of an individual or device can be expressed as latitude and longitude, which are GPS coordinate information. In the case of an application that utilizes location tracking information, it is possible to easily output the movement route of a specific individual by time by matching the map information. However, if these applications that provide a location tracking function continuously store location information, a serious privacy problem may occur.

As a security technique that can be used at this time, the Q value, which is a hash function value, may be calculated by Equation 1 using the K-anonymity model and data disturbance. Q values, which are HMAC values for time information, may be calculated based on k values (eg, k=3) preset for K-anonymization.

<Equation 1>

(HMAC is a Hash-based Message Authentication Code algorithm, TS is the timestamp information of time information, K_CNT is the count number based on the k value, and PK is the previously shared secret key information)

K-anonymity is one of the privacy protection models of data de-identification, and since there is a risk of individual identification when only specific single data is stored, it is a method of lowering the possibility of identification by simultaneously storing data with a value of k or more when storing data. For example, if k is 3, when saving 1 IoT data, 3 data are stored at the same time. In this case, one piece of data is real data, and the other two pieces of data are virtual fake data. At this time, you need to know the PK corresponding to the pre-shared secret key by an authorized administrator, etc., so that you can determine which is the real data.

TS stands for Time Stamp, and as mentioned above, when data is input at a preset period, it means a specific time at which data is input or the time at which IoT data is generated. K_CNT corresponds to a number counted according to a preset value of k. For example, when k is 3, the value of K_CNT has values of 1, 2, and 3. In this case, when the K_CNT value is 1, it is original data, and when it is 2 or 3, it can be treated as fake (imitation) data.

As in Equation 1 above, the specific timestamp (TS) information is converted to the Q value of the character data using the HMAC algorithm based on the PK information, and at the same time, all the output values corresponding to the k K_CNT values are calculated and k Q value can be calculated. Time information is not included in the database stored in this way, and only k Q values are stored. Therefore, even if the database is hacked, the time information is not exposed, and even if the Q-value information is hacked, if the secret key (PK) information is not known, it is impossible to know which values are the original data, so it is practically impossible to hack the original data. will do

Figure 4 is an embodiment of the present invention by processing the user's touch input or voice input (400, 410) information on the electronic blackboard as an embodiment of the present invention (AI) based cloud search 450 or control functions (480, 490) It is a diagram showing a flow chart to perform.

Referring to FIG. 4 , first, it is determined that there is an input 400 of a touch input or a voice input ( 410 ), and when a voice signal is input, a voice recognition engine provided in the electronic blackboard converts an analog voice signal into a digital signal. Converts to a text signal (speech to text) (420).

Then, in interpreting the converted text data (430), the previously analyzed data and the previously classified voice signal through machine learning are called from the database and compared repeatedly. As time passes and the amount of data to be processed increases, more Sophisticated interpretation is possible. In this way, by using AI-based learning and interpretation, the speech recognition rate can be increased to more than 90%, and the operation according to the speech recognition can be implemented.

Next, it is determined whether the interpreted command is a request such as an Internet search using a cloud service or a request to control the device (440), and in the case of an Internet search request (450), the content screen corresponding to the search result is displayed (450). It can be displayed on the electronic blackboard (460).

Or, in the case of a request to control the device (470), it is a command to control the internal functions (power, volume, screen, source, etc.) of the electronic board or control functions of external devices connected by wire or wireless (lamps, curtains, door locks, other electronic devices). The external device is controlled (480) or an internal function of the electronic blackboard is executed (490) depending on whether the command is to be executed (blackboard, etc.).

Meanwhile, as a method of authenticating a user who has uttered an external sound source, the following steps may be performed.

First, the user's login environment information is extracted. The user's login environment information may be an IP band and a User-Agent when the user logs in to an Internet site. Next, the extracted log-in environment information is converted into a One-Hot-Encoding method to generate conversion information, and a number is assigned to unique values for each environment information from 1 to label encoding.

Next, a two-class user model is generated using the conversion information using a support vector machine (SVM). SVM (Support Vector Machine) is one of the machine learning algorithms and is mainly used for pattern recognition.

SVM (Support Vector Machine) creates a non-stochastic binary linear classification model that determines which category the new data belongs to based on the given data set when a set of data belonging to one of two categories is given. In the user authentication method, a two-class user model is generated by using SVM for conversion information of the entire system user, and when a user logs in, the two-class user model is applied to determine whether the user is the identity. Therefore, when an illegal user steals the ID and password of the original owner to log in to an IoT peripheral device or an Internet site, it is possible to detect and deny access, thereby improving security.

The present invention is not limited to the specific preferred embodiments described above, and without departing from the gist of the present invention as claimed in the claims, anyone with ordinary skill in the art to which the invention pertains can make various modifications. And it will be obvious that such changes are within the scope of the claims.

100: touch input
200 : voice command
300: electronic blackboard
310: touch sensor
320: microphone array
330: control unit
340: speech recognition engine
350: command interpretation unit
360: database
370 : command list

Claims (9)

In the electronic blackboard, comprising: a panel unit capable of touch recognition; and a sensor unit that determines a touch method when the panel unit is touched and performs touch recognition according to the touch method,
a microphone array that receives a voice signal from an external sound source, separates the sound source, and removes noise;
a voice recognition engine that receives a voice signal that is an analog signal from which noise has been removed from the microphone array and converts it into digital data that is a text signal;
a database for repeatedly storing the received voice signal and the converted text signal;
a command interpretation unit that receives the text signal converted from the speech recognition engine and interprets it as a command through machine learning using the database;
Includes a control unit that performs a query search or internal and external control function to an external cloud service according to the interpreted command,
The control unit connects communication to an external cloud server according to the interpreted command, searches for SaaS-based cloud data and displays it on the electronic board, and controls the internal control function of the electronic board according to the interpreted command. or performs a control function of external peripheral devices connected to the electronic blackboard by wire or wireless according to the command,
When performing a control function of the connected external peripheral devices,
Performs the control function through an optimal route recommendation AI algorithm that finds the most suitable route that minimizes communication cost with the external peripheral devices while maximizing IoT data collection for control of the external peripheral devices,
The algorithm includes a data collection step in which the data collection module receives a data set based on the frequency of use and power consumption of the external peripheral device, a pre-processing step in which the pre-processing module pre-processes the input data set, ANN (Artificial Neural Network) A prediction calculation step in which the prediction module constructed through machine learning of the preprocessed data set is applied to the prediction algorithm, and the optimum route recommendation step in which the optimization module finds the optimum route using the prediction model and constraints according to the objective function Features an electronic blackboard system. delete delete delete delete delete In the electronic blackboard, comprising: a panel unit capable of touch recognition; and a sensor unit that determines a touch method when the panel unit is touched and performs touch recognition according to the touch method,
a microphone array that receives a voice signal from an external sound source, separates the sound source, and removes noise;
a voice recognition engine that receives a voice signal that is an analog signal from which noise has been removed from the microphone array and converts it into digital data that is a text signal;
a database for repeatedly storing the received voice signal and the converted text signal;
a command interpretation unit that receives the text signal converted from the speech recognition engine and interprets it as a command through machine learning using the database;
Includes a control unit that performs a query search or internal and external control function to an external cloud service according to the interpreted command,
The voice signal and the converted text signal are
Using k-1 simulated data based on the data of each original signal and the k value preset for perturbation of the data,
The electronic blackboard system according to claim 1, wherein the privacy of the user who uttered the external sound source is protected by storing the database according to the result of processing the hash function process based on the timestamp that is the time of generation of the data. 8. The method of claim 7,
The hash function uses a Hash-based Message Authentication Code algorithm (HMAC), and the result value Q is generated by Equation 1 below.
<Equation 1>

(TS is timestamp information, K_CNT is a count number based on the k value, and PK is previously shared secret key information) 8. The method of claim 7,
The system uses a support vector machine, which is a binary linear classification model, for conversion information converted from IP band or user-agent, which is environmental information that a user who has uttered the external sound source logs in to the system. The electronic blackboard system, characterized in that it is determined whether the user is the user by generating a two-class user model using the

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