KR20230108894A - Method for verifying identity in untact consultation - Google Patents

Abstract

Disclosed in various embodiments of the present invention is a method for providing a consultation service. The method may comprise: a step of acquiring reference identification information corresponding to a call transmitted from a user terminal; a step of performing a first identity authentication process for authenticating the identity of the user of the user terminal by means of questions and answers after determining the matching degree between the reference identification information and first identification information transmitted from the user terminal in the process of performing a voice consultation session with the user terminal; and a step of performing a second identity authentication process which authenticates the identity of the user of the user terminal by means of voice after performing a voice analysis with respect to first voice data acquired in the process of performing the voice consultation session. Therefore, a more precise personal authentication may be provided.

Description

Method for verifying identity in non-face-to-face counseling {METHOD FOR VERIFYING IDENTITY IN UNTACT CONSULTATION}

The present disclosure relates to identity authentication, and more specifically, to a technique for verifying identity in a non-face-to-face counseling process.

Due to the nature of their work, financial or insurance companies have a fairly high percentage of non-face-to-face business processing. As an example of non-face-to-face business processing, there is business processing according to voice calls, and in the process of conducting such business processing based on voice calls, the identity authentication procedure is considered as an essential element. In this way, before proceeding with finance or insurance business, a person authentication process is performed. Due to the nature of finance or insurance business, there are issues related to money, so specific tasks can be carried out only by the person himself/herself.

Due to the nature of financial or insurance work, when dealing with various customers, there may be attempts to impersonate the person. Issues may be more prominent, and supplements and solutions for such authentication are needed.

As one of the representative complementary methods and solutions for self-authentication, a method of complexly configuring various authentication methods is being discussed. For example, an example is a method of complexly confirming authentication through querying the customer's address and authentication through querying the customer's payment bank. If these various authentication methods are configured in a complex manner, unnecessary time may be required for authentication during the actual process of processing the person's work, which may result in consumption of resources of the customer and the company. Therefore, there is a need for a technique capable of performing non-face-to-face customer authentication while reducing the time required for authentication.

Republic of Korea Patent Publication No. 10-2010-0131095

The present disclosure has been made in response to the above background art, and is intended to provide a non-face-to-face voice counseling service that provides more accurate identity authentication in an efficient manner.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

According to some embodiments of the present disclosure to solve the above problems, a method for providing a counseling service performed by a computing device including one or more processors is disclosed. The method includes: acquiring reference identification information corresponding to a call originating from a user terminal; A primary identity that verifies the identity of the user of the user terminal through a question and answer by comparing the first identification information transmitted from the user terminal with the reference identification information in the process of conducting a voice counseling session with the user terminal. performing a verification process; and performing a second identity verification process of verifying the identity of the user of the user terminal through voice by performing voice analysis on the first voice data acquired in the course of the voice counseling session. there is.

In one embodiment, the first identification information may be generated based on first voice data uttered by the user of the user terminal in response to a query in the process of conducting the voice counseling session. The second identity verification process may utilize the first voice data acquired in the first identity verification process.

In an embodiment, the method may further include determining whether to perform an additional identity verification process with the user terminal based on a verification result of the second identity verification process.

In an embodiment, the additional identity verification process includes a predetermined additional query for a user of the user terminal, and the predetermined additional query includes a predetermined additional query for a user of the user terminal utilized in the primary identity verification process. It may include query contents different from the above query.

In one embodiment, the secondary identity verification process comprises comparing a degree of similarity between the first voice data and reference voice data associated with the user terminal using an artificial intelligence-based voiceprint authentication model.

In one embodiment, the reference voice data may include voice data uttered in the past by a user of the user terminal.

In one embodiment, the voice data uttered in the past is stored in a voiceprint authentication DB including answers of the user of the user terminal corresponding to each of a plurality of queries for authentication.

In one embodiment, the comparing of the degree of similarity between the first voice data and the reference voice data related to the user terminal includes the first voice data including an answer of a user of the user terminal corresponding to a query for identity authentication. It may be compared whether or not the first identification information included in is consistent with the reference identification information stored in the voiceprint authentication DB.

In one embodiment, the comparing of the degree of similarity between the first voice data and the reference voice data related to the user terminal includes the first voice data including an answer of a user of the user terminal corresponding to a query for authentication of the user. At least one of voice rhythm, voice speed, voice timbre, voice intensity, and voice pitch in the data may be compared with the reference voice data.

In one embodiment, the secondary identity verification process determines whether a temporary health change has occurred in a user of the user terminal when a similarity between the first voice data and the reference voice data related to the user terminal is less than a predetermined threshold similarity. generating a health query for and determining whether to perform an additional identity verification process with the user terminal based on an answer of the user of the user terminal obtained in response to the health inquiry.

In one embodiment, the secondary identity verification process determines whether a temporary health change has occurred in a user of the user terminal when a similarity between the first voice data and the reference voice data related to the user terminal is less than a predetermined threshold similarity. generating a health query for changing the predetermined threshold similarity based on an answer of a user of the user terminal obtained in response to the health inquiry; and additionally performing the second identity verification process based on the changed threshold similarity and second voice data included in an answer of the user of the user terminal obtained in response to the health query. .

In an embodiment, the secondary identity verification process may compare similarities between the first voice data and statistical data of other users associated with the user of the user terminal by using an artificial intelligence-based voiceprint authentication model. .

In an embodiment, the statistical data of the other users may include data representing statistical characteristics of voice determined based on the age and gender of the user of the user terminal.

In one embodiment, the secondary identity verification process determines whether the first voice data is voice data representing a characteristic of an age corresponding to a user of the user terminal, and the first voice data is the first voice data of the user terminal. It may be determined whether the voice data represents characteristics of a gender corresponding to the user.

In one embodiment, a computer program stored on a computer readable storage medium is disclosed. The computer program includes instructions for causing a processor of a computing device to perform a method for providing a counseling service, the method comprising: acquiring reference identification information corresponding to a call originating from a user terminal; A primary identity that verifies the identity of the user of the user terminal through a question and answer by comparing the first identification information transmitted from the user terminal with the reference identification information in the process of conducting a voice counseling session with the user terminal. performing a verification process; and performing a second identity verification process of verifying the identity of the user of the user terminal through voice by performing voice analysis on the first voice data acquired in the course of the voice counseling session. there is.

In one embodiment, a computing device for providing counseling services is disclosed. The computing device includes a processor including at least one core; a storage unit for storing a computer program executable by the processor; And it may include a communication unit. The processor obtains reference identification information corresponding to a call originating from a user terminal; A primary identity that verifies the identity of the user of the user terminal through a question and answer by comparing the first identification information transmitted from the user terminal with the reference identification information in the process of conducting a voice counseling session with the user terminal. perform a verification process; In addition, by performing voice analysis on the first voice data obtained in the process of conducting the voice counseling session, a second identity verification process of verifying the identity of the user of the user terminal through voice may be performed.

Techniques according to some embodiments of the present disclosure may provide a non-face-to-face voice counseling service that provides an efficient method of self-authentication.

Effects obtainable in the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. .

Various aspects are now described with reference to the drawings, wherein like reference numbers are used to collectively refer to like elements. In the following embodiments, for explanation purposes, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details.
1 illustratively illustrates a block diagram of a computing device, network, and user terminal in accordance with some embodiments of the present disclosure.
2 is a schematic diagram illustrating a network function according to some embodiments of the present disclosure.
3 exemplarily illustrates a method for providing a counseling service according to some embodiments of the present disclosure.
4 illustratively illustrates an identity verification process according to some embodiments of the present disclosure.
5 illustratively illustrates the association of a voice counseling process and a self-authentication enhancement process according to some embodiments of the present disclosure.
6 depicts a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific details.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure, processor, object, thread of execution, program, and/or computer running on a processor. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component may be distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. Components may be connected, for example, via signals with one or more packets of data (e.g., data and/or signals from one component interacting with another component in a local system, distributed system) to other systems and over a network such as the Internet. data being transmitted) may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or clear from the context, “X employs A or B” is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or, if X uses both A and B, "X uses either A or B" may apply to either of these cases. Also, the term "and/or" as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the features and/or components are present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Also, unless otherwise specified or where the context clearly indicates that a singular form is indicated, the singular in this specification and claims should generally be construed to mean "one or more".

In addition, the term “at least one of A or B” should be interpreted as meaning “when only A is included”, “when only B is included”, and “when A and B are combined”.

Those skilled in the art will further understand that the various illustrative logical blocks, components, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure. The general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention is to be accorded the widest scope consistent with the principles and novel features set forth herein.

1 is a block diagram of a computing device 100, a network 300, and a user terminal 200 for providing a counseling service according to an embodiment of the present disclosure.

The term "consultation service" used in the present disclosure may mean a service including any form of counseling that provides an answer to a part desired by a customer in connection with a call originating from a customer. For example, the counseling service may include counseling for customers conducted in a call center. Hereinafter, a counseling service related to insurance will be described as an example, but the scope of such counseling is not limited to insurance, and counseling related to various domains may also be included within the scope of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100. The computing device 100 in the present disclosure may refer to any type of device having processing capability. As an example, the computing device 100 may be a call center server. As another example, the computing device 100 may be a counselor terminal. Depending on the implementation, the computing device 100 may be a customer terminal.

Computing device 100 may include any type of computer system or computer device, such as, for example, a microprocessor, mainframe computer, digital processor, portable device or device controller, and the like. This computing device 100 may include any type of server and/or user terminal. For example, the user terminal may include a PC (personal computer), a notebook (note book), a mobile terminal, a smart phone (smart phone), a tablet PC (tablet pc), etc. owned by the user, , can include all types of terminals that can access wired/wireless networks.

In one embodiment of the present disclosure, the computing device 100 may include a server for providing counseling services. In another embodiment, the computing device 100 may include a counselor terminal for providing a counseling service.

In one example, computing device 100 may perform an obtaining primary identity verification process, a secondary identity verification process, and/or an additional identity verification process according to embodiments of the present disclosure.

The computing device 100 may include a processor 110 , a storage unit 120 , and a communication unit 130 . The components of the computing device 100 in FIG. 1 are represented for illustrative purposes, and depending on the implementation, additional components may be included in the computing device 100 or only some of the components may be included in the computing device 100. may be included in

The processor 110 may include one or more cores, and includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit), or any type of processor for data processing, data analysis, and/or deep learning. The processor 110 may read a computer program stored in the storage unit 120 and perform a method for providing a counseling service according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in an embodiment of the present disclosure, the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, the computer program executed in the computing device 100 according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

According to an embodiment of the present disclosure, the storage unit 120 may store any type of information generated or determined by the processor 110 and any type of information received by the communication unit 150. In the present disclosure, the storage unit 120 may be used interchangeably with a memory.

According to an embodiment of the present disclosure, the storage unit 120 may be a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Memory) Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs the storage function of the storage unit 120 on the Internet. The above description of the storage unit 120 is only an example, and the present disclosure is not limited thereto.

The storage unit 120 according to an embodiment of the present disclosure may store data required to perform a process of obtaining reference identification information, a primary identity verification process, a secondary identity verification process, and/or an additional identity verification process. .

The network 300 according to an embodiment of the present disclosure may include any wired/wireless communication network capable of transmitting and receiving data and signals of any type and may be included in the network 300 represented in the present disclosure.

The communication unit 130 according to an embodiment of the present disclosure may communicate with any other computing device (eg, the user terminal 200) through the network 300. The communication unit 130 according to an embodiment of the present disclosure may transmit and receive data required to perform a process of obtaining reference identification information, a primary identity verification process, a secondary identity verification process, and/or an additional identity verification process. . For example, the communication unit 130 may transmit a query generated to perform the first identity verification process to the user terminal 200, and an answer (eg, first voice data) corresponding to the query from the user terminal 200. can receive The aforementioned query and answer may be delivered in the form of voice data. As another example, the query and/or answer may be delivered in the form of text data.

The user terminal 200 according to an embodiment of the present disclosure includes a personal computer (PC), a notebook (note book), a mobile terminal (mobile terminal), a smart phone (smart phone), and a tablet PC (tablet pc) owned by the user. ), etc., and may include all types of terminals capable of accessing wired/wireless networks. Although not shown in FIG. 1 , the user terminal 200 may include an output unit, which is necessary to perform the first identity verification process, the second identity verification process, and/or the additional identity verification process according to an embodiment of the present disclosure. Data may be output from the output unit.

2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.

Throughout this specification, artificial intelligence models, artificial intelligence-based models, computational models, neural networks, network functions, and neural networks may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

The artificial intelligence-based voiceprint recognition model in the present disclosure includes an operation of converting voice data into text data, an operation of extracting features of text data, an operation of extracting features of voice data, and an operation between text data. In order to perform an operation of comparing similarity and/or an operation of comparing similarity between voice data, it may be operated based on the artificial intelligence model or neural network described above and later.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may form one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from the above. For example, a layer of nodes may be defined by a distance from a final output node.

In one embodiment of the present disclosure, a set of neurons or nodes may be defined as a layer. In the present disclosure, a set of neurons that receive event data and contract characteristic data may be represented as an input layer, a set of neurons that output scores for each of the expected counseling types may be represented as an output layer, and an input layer. A set of neurons existing between the layer and the output layer may be expressed as a hidden layer.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

In the neural network according to an embodiment of the present disclosure, the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes of the input layer may be less than the number of nodes of the output layer and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the number of nodes increases from the input layer to the hidden layer. can A neural network according to another embodiment of the present disclosure may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted boltzmann machines (RBMs). machine), a deep belief network (DBN), a Q network, a U network, a Siamese network, a Generative Adversarial Network (GAN), and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to dimensions after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

The artificial intelligence-based voiceprint authentication model of the present disclosure can be represented by any of the above-described network structures including an input layer, a hidden layer, and an output layer.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the learning of the neural network, the learning data is repeatedly input into the neural network, the output of the neural network for the training data and the error of the target are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. It is a process of updating the weight of each node of the neural network by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network and a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network output. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate may be used in the early stage of neural network training to increase efficiency by allowing the neural network to quickly obtain a certain level of performance, and a low learning rate may be used in the late stage to increase accuracy.

In neural network learning, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be incremental learning cycles. Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. In order to prevent overfitting, methods such as increasing the training data, regularization, inactivating some nodes in the network during learning, and using a batch normalization layer are methods. can be applied

A computer readable medium storing a data structure according to an embodiment of the present disclosure is disclosed. The above-described data structure may be stored in the storage unit 120 in the present disclosure, executed by the processor 110, and transmitted and received by the communication unit 130.

Data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. Data structure may refer to the organization of data to solve a specific problem (eg, data retrieval, data storage, data modification in the shortest time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements may include a connection relationship between user-defined data elements. A physical relationship between data elements may include an actual relationship between data elements physically stored in a computer-readable storage medium (eg, a persistent storage device). The data structure may specifically include a set of data, a relationship between data, and a function or command applicable to the data. Through an effectively designed data structure, a computing device can perform calculations while using minimal resources of the computing device. Specifically, the computing device can increase the efficiency of operation, reading, insertion, deletion, comparison, exchange, and search through an effectively designed data structure.

The data structure can be divided into a linear data structure and a non-linear data structure according to the shape of the data structure. A linear data structure may be a structure in which only one data is connected after one data. Linear data structures may include lists, stacks, queues, and decks. A list may refer to a series of data sets in which order exists internally. The list may include a linked list. A linked list may be a data structure in which data are connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer can contain information about connection to the next or previous data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circular linked list depending on the form. A stack can be a data enumeration structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (eg, inserted or deleted) at only one end of the data structure. The data stored in the stack may be a LIFO-Last in First Out (Last in First Out) data structure. A queue is a data listing structure that allows limited access to data, and unlike a stack, it can be a data structure (FIFO-First in First Out) in which data stored later comes out later. A deck can be a data structure that can handle data from either end of the data structure.

The nonlinear data structure may be a structure in which a plurality of data are connected after one data. The non-linear data structure may include a graph data structure. A graph data structure can be defined as a vertex and an edge, and an edge can include a line connecting two different vertices. A graph data structure may include a tree data structure. The tree data structure may be a data structure in which one path connects two different vertices among a plurality of vertices included in the tree. That is, it may be a data structure that does not form a loop in a graph data structure.

Throughout this specification, the terms model, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, a neural network is unified and described. The data structure may include a neural network. And the data structure including the neural network may be stored in a computer readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network It may include a loss function for learning of . A data structure including a neural network may include any of the components described above. That is, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network. It may be configured to include all or any combination thereof, such as a loss function for learning of . In addition to the foregoing configurations, the data structure comprising the neural network may include any other information that determines the characteristics of the neural network. In addition, the data structure may include all types of data used or generated in the computational process of the neural network, but is not limited to the above. A computer readable medium may include a computer readable recording medium and/or a computer readable transmission medium. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes.

The data structure may include data input to the neural network. A data structure including data input to the neural network may be stored in a computer readable medium. Data input to the neural network may include training data input during a neural network learning process and/or input data input to a neural network that has been trained. Data input to the neural network may include pre-processed data and/or data subject to pre-processing. Pre-processing may include a data processing process for inputting data to a neural network. Accordingly, the data structure may include data subject to pre-processing and data generated by pre-processing. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used in the same meaning.) Also, a data structure including weights of a neural network may be stored in a computer readable medium. A neural network may include a plurality of weights. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. A data value output from an output node may be determined based on the weight. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

As a non-limiting example, the weights may include weights that are varied during neural network training and/or weights for which neural network training has been completed. The variable weight in the neural network learning process may include a weight at the time the learning cycle starts and/or a variable weight during the learning cycle. The weights for which neural network learning has been completed may include weights for which learning cycles have been completed. Accordingly, the data structure including the weights of the neural network may include a data structure including weights that are variable during the neural network learning process and/or weights for which neural network learning is completed. Therefore, it is assumed that the above-described weights and/or combinations of weights are included in the data structure including the weights of the neural network. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer readable storage medium (eg, a memory or a hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or another computing device and later reconstructed and used. A computing device may serialize data structures to transmit and receive data over a network. The data structure including the weights of the serialized neural network may be reconstructed on the same computing device or another computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure for increasing the efficiency of operation while minimizing the resource of the computing device (for example, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree). The foregoing is only an example, and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of the neural network. Also, the data structure including the hyperparameters of the neural network may be stored in a computer readable medium. A hyperparameter may be a variable variable by a user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle iterations, weight initialization (eg, setting the range of weight values to be targeted for weight initialization), hidden unit number (eg, the number of hidden layers and the number of nodes in the hidden layer). The foregoing data structure is only an example, and the present disclosure is not limited thereto.

3 exemplarily illustrates a method for providing a counseling service according to some embodiments of the present disclosure.

Computing device 100 may recognize an incoming call. In one embodiment, an incoming call may mean that a call is made through a user terminal of a customer to a customer center or call center related to the computing device 100 . For example, when a call originating from a user terminal is connected to a server of a customer center and a voice announcing the start of a call such as ARS is transmitted to the customer's user terminal, the computing device 100 may recognize an incoming call. there is. As another example, while the user terminal is attempting to connect to the server in the customer center (ie, before a voice informing the user of a call initiation such as ARS is transmitted to the customer's user terminal), the computing device 100 receives an incoming call may recognize

Computing device 100 may recognize an incoming call and initiate a voice counseling session for the call (310).

The computing device 100 may obtain reference identification information corresponding to the initiated voice counseling session (310). In one embodiment of the present disclosure, reference identification information may include, for example, customer identification information. Such reference identification information may refer to any type of information for identifying a customer stored in the storage unit.

In one embodiment, the reference identification information may correspond to a customer's phone number and may include any type of information mapped with the customer's phone number. For example, the reference identification information may include a customer's name or ID information of a customer registered in a customer center. For example, the reference identification information may include a customer's ID stored in the customer DB and information on past events of the customer mapped to the customer's ID. For example, the reference identification information may include a customer's ID stored in the customer DB and information on past contract information or past event information of the customer mapped to the customer's ID. As another example, the reference identification information may include any type of information representing a customer's disposition recorded based on a contact with the customer.

In one embodiment of the present disclosure, the reference identifying information may include any type of information for verifying the customer's identity. For example, reference identification information may include the customer's address information, the customer's financial account information, the customer's date of birth, the customer's hometown information, the customer's memorable place information, the customer's memorable friend information, the customer's memorable It may include teacher information, customer's school information, and/or customer's work information.

The computing device 100 compares the first identification information transmitted from the user terminal with the reference identification information in the process of conducting a voice counseling session with the user terminal, thereby determining the identity of the user of the user terminal with a query. A first identity verification process for verifying through an answer may be performed (330).

In one embodiment, the computing device 100 may transmit a query for identity verification to the user terminal. The query may include, for example, current address, financial account, date of birth, hometown, memorable place, memorable friend, memorable teacher, school, and/or work related information. Reference identification information associated with such a query may be stored on computing device 100 or another entity external to computing device 100 . For example, reference identification information related to such a query may be stored in a voiceprint authentication DB. For example, reference identification information related to such queries may be customized for each customer and stored in a voiceprint authentication DB. In this example, in the case of a customer who frequently changes residence, other types of queries that are less likely to change, such as a memorable place or date of birth, may be more suitable for the customer than a query related to the current address. Accordingly, in consideration of a change in the customer's identification information, the reference identification information and query may be matched with the customer and stored in the voiceprint authentication DB.

The computing device 100 may compare the reference identification information with a user's answer obtained in response to a query related to the reference identification information through the first identity verification process. For example, based on the comparison between the first voice data included in the answer from the user and the reference identification information, the computing device 100 may perform a first identity verification process. When it is determined that the first voice data and the reference identification information correspond to each other, the computing device 100 may determine that the first identity verification process is successfully completed.

In one embodiment of the present disclosure, the computing device 100 may obtain first identification information from the user terminal in the process of conducting a voice counseling session. Here, the first identification information may be related to first voice data uttered by a user of a user terminal responding to an inquiry in the process of conducting a voice counseling session. For example, when the query transmitted by the computing device 100 to the user terminal is "current residence information", the first voice data refers to information on the current residence uttered by the user, and the first voice data An answer corresponding to the query except for the noise may be extracted as the first identification information. In this example, the user's current residence information may be extracted as the first identification information by excluding words unrelated to the current residence information from the first voice data spoken by the user.

The computing device 100 may perform a first identity verification process for the user terminal by comparing the first identification information with the reference identification information and a corresponding query. If it is determined that the user's identity has not been verified in the first identity verification process, the computing device 100 may determine that the voice counseling session cannot proceed or determine to perform the first identity verification process again.

In one embodiment of the present disclosure, the artificial intelligence-based voiceprint authentication model may also be utilized in the primary identity verification process.

In one embodiment, the computing device 100 may perform text conversion on voice data. The artificial intelligence-based voiceprint authentication model may extract first identification information corresponding to text data from first voice data spoken by a user. In one embodiment, the computing device 100 may generate text data from the first voice data. The computing device 100 may perform pre-processing on text data. For example, the computing device 100 may perform tokenization, which is an operation of dividing text data into units called tokens. A unit of a token may be a morpheme unit, a word unit, a word unit, and/or a sentence unit tokenization, and may be different according to an implementation mode. The computing device 100 may generate text data including temporal information of voice data uttered by a user by using an artificial intelligence-based voiceprint authentication model. For example, it is assumed that first voice data "My home address is the 11th floor of Urban Hive Building, Gangnam-gu, Seoul" is converted into text data. In this family, the first expression is "my house", the second expression is "the address is", the third expression is "Gangnam-gu, Seoul", the fourth expression is "Urban Hive Building", and the fifth expression is "11th floor". There may be a temporal gap between them. The computing device 100 may generate text counseling data 450 from voice data by reflecting the time interval. On the text counseling data 450, such temporal intervals may be expressed as separators such as spaces, semicolons, and/or commas. Accordingly, when the token unit is a word unit, text data may be classified based on the above-described delimiters in the tokenization process, and thus tokenization may be implemented in a word or keyword unit.

In a further embodiment, the computing device 100 excludes "my home", "address is" and "is" from the above example when generating the first identification information, and the first identification information is "Urban Hive Building" It can be configured to include "11th floor".

In a further embodiment, tokenization of units of morphemes may be considered. Morphemes can be largely divided into independent morphemes that can be used on their own regardless of affixes, endings, or postpositions, and dependent morphemes that are used in combination with other morphemes. A free morpheme may itself be a word, and may include, for example, nouns, pronouns, and interjections. Dependent morphemes may include affixes, endings, and particles. Accordingly, the computing device 100 may tokenize the independent morphemes into tokens after removing dependent morphemes, such as affixes, endings, and postpositions, extracting only independent morphemes, by performing tokenization in units of morphemes.

In a further embodiment, computing device 100 may perform vectorization on tokens, such as words or morphemes, by applying one-hot encoding to the tokenized text data.

In this embodiment, vectors generated according to one-hot encoding may be used for comparison between first voice data uttered by a user and reference identification information within an artificial intelligence-based voiceprint authentication model in a primary identity verification process. there is. When text conversion is performed on voice data and the text-converted data is input to an artificial intelligence-based voiceprint authentication model through a method such as one-hot encoding, the voiceprint authentication model generates vectorized output data for the input data. can do. Accordingly, by comparing the vectorized output of the first voice data and the vectorized output of the reference identification information, the computing device 100 compares the first identification information corresponding to the first voice data with the reference identification information, It may be determined whether contents included in both pieces of information are the same.

By performing voice analysis on the first voice data obtained in the process of conducting a voice counseling session, the computing device 100 may perform a second identity verification process of verifying the identity of the user of the user terminal through voice ( 340).

In an embodiment, the computing device 100 may compare first voice data uttered by the user in the first identity verification process with past voice data of the user terminal to determine similarities between the two voice data. For example, when the first voice data is data related to the user's current address, the computing device 100 may compare the first voice data with pre-stored past voice data related to the user's current address. As another example, if the first voice data is data related to the user's current address, the computing device 100 may compare the first voice data with pre-stored past voice data of other contents (eg, financial information, etc.) of the user. there is.

In an embodiment, the computing device 100 may compare similarities between the first voice data and reference voice data (eg, past voice data) related to the user terminal using an AI-based voiceprint authentication model. For example, comparing the similarity of voice data may include comparing voice rhythm, voice speed, voice timbre, voice intensity, and/or voice pitch. Through comparison between the voice data of the corresponding user in the past and the voice data of the user in the current voice counseling session, the computing device 100 may further verify the identity of the user. For example, the computing device 100 may determine the rhythm, speed, timbre, intensity, and/or pitch of first voice data obtained as an answer to a question in the first identity verification process and the rhythm, speed, timbre, and/or pitch of reference voice data. By comparing intensity and/or height, a secondary identification process may be performed.

As described above, in the identity authentication enhancement technique according to an embodiment of the present disclosure, in response to receiving an answer corresponding to a query for identity verification of a user, a first identity verification process is performed based on the corresponding answer And based on the corresponding answer, a secondary identity verification process may be performed. Accordingly, from the user's point of view, only one response to the query is required, and through this one response, a more efficient and accurate identity verification process can be performed.

In one embodiment, the artificial intelligence-based voiceprint authentication model may extract features of voice data and compare features of each of two or more voice data in a vector space. Characteristics of the voice data may include rhythm, speed, timbre, intensity, and/or pitch. In this example, the artificial intelligence-based voiceprint authentication model generates first output data that includes features for the first voice data and second output data that includes features for reference voice data that includes the user's past utterance data. data can be generated. The computing device 100 may perform identity authentication on the first voice data of the user uttered in the voice counseling session by comparing the first output data with the second output data. This identity authentication may be performed based on determining whether the first output data and the second output data have a similarity greater than or equal to a predetermined threshold.

In one embodiment of the present disclosure, voice data uttered in the past may be stored in a voiceprint authentication DB including a plurality of questions for authentication of a person and respective mapped answers. The voiceprint authentication DB may store a data structure in which queries and answers are mapped, and may exist within the computing device 100 or may be stored in an entity external to the computing device 100.

In one embodiment of the present disclosure, the predetermined threshold similarity corresponds to a criterion for comparing a past voice with a current voice. In one embodiment, the predetermined threshold similarity may vary according to the amount of voice data uttered in the past by the user. For example, in the case of a user with a relatively large amount of voice data uttered in the past, a predetermined threshold similarity may be set relatively high. Conversely, in the case of a user with a relatively small amount of voice data spoken in the past, the predetermined threshold similarity may be set relatively low. When a relatively large amount of past utterance voice data is acquired, accuracy of comparison between past voice data and current voice data can be guaranteed. In this case, the computing device 100 may ensure accuracy of identity authentication by increasing the threshold similarity value. As another example, when a relatively small amount of past speech voice data is acquired, accuracy of comparison between past voice data and current voice data may be lowered. In this case, the computing device 100 can prevent inaccurate identity authentication in the case where the person who uttered the two voice data matches by lowering the threshold similarity value. As such, the computing device 100 may consider the secured amount of past speech voice data when determining the threshold similarity value.

In one embodiment of the present disclosure, the computing device 100 may distinguish between first voice data spoken by a user and second voice data spoken by a counselor. In an additional embodiment, the computing device 100 extracts a speaker's unique voice characteristics from voice counseling data based on an artificial intelligence algorithm that may include an artificial intelligence-based voiceprint authentication model, and the like, from voice counseling data. Various speaker recognition or speaker discrimination algorithms, such as a method of recognizing a speaker using a formant of vowels, may be used. In this way, the computing device 100 accurately identifies the first voice data uttered by the user during the course of the counseling session, thereby verifying the identity of the user (eg, a first identity verification process and/or a second identity verification process). ) can increase the accuracy.

In an embodiment of the present disclosure, the computing device 100 performs a secondary identity verification process, when a degree of similarity between first voice data and reference voice data associated with the user terminal is less than a predetermined threshold similarity, the user terminal A health query may be generated to determine whether a temporary health change has occurred to the user of the user. For example, when the rhythm, speed, timbre, intensity, and/or pitch of the first voice data is different from that of the past voice data, the computing device 100 determines whether the change in voice is due to a temporary change in health of the user. can For example, assume a situation in which it is determined that the first identification information extracted from the first voice data and the reference identification information match each other. This situation may mean that the user's speech patterns are similar. For example, based on the comparison between the first identification information and the reference identification information, the computing device 100 determines the user's address address pattern (eg, old address, new address, apartment name and dong number only, city and It is possible to judge the similarity of phrases and dongs together, etc.). If it is determined that the patterns of saying the user's address are the same or if dissimilarities occur between voice data, the computing device 100 may check for temporary health changes before performing an additional identity verification process. For example, the computing device 100 may query whether there is a feeling of cold, and based on an answer to the query (eg, an answer that there is a feeling of a cold), the computing device 100 determines a predetermined threshold similarity. You can change it or decide to receive additional replies from users. Based on the second voice data included in the additional answer from the user, the computing device 100 may perform a comparison between the second voice data and the reference voice data once more. Accordingly, computing device 100 may determine whether to perform an additional identity verification process based on the determination of the temporary health condition for the user. A pattern used in this specification may mean any type of pattern related to a user's speech. For example, depending on the user, when the teacher's name is uttered, "Hong Gil-dong" or "Hong-ja Gil-ja Dong-ja" or "Hong-gil-ja Dong-ja" or "Hong Gil-dong teacher" has the same meaning. Even expressions can have various types of speech patterns. Accordingly, the computing device 100 may increase the accuracy of the identity verification process by analyzing the user's speech pattern. In one embodiment, the determination of the speech pattern may be made in the aforementioned primary identity verification process.

In a further embodiment, the computing device 100 may determine between the reference voice data and the first voice data when the user's answer of the user terminal obtained in response to the health query is "there is no current health abnormality". Since the similarity is low, it may be determined to perform an additional identity verification process of the user terminal.

In a further embodiment, the computing device 100 may determine between the reference voice data and the first voice data when the user's answer of the user terminal obtained in response to the health query is "there is no current health abnormality". Since the similarity is low, it may be determined whether or not to perform an additional identity verification process of the user terminal by additionally comparing the second voice data included in the user's answer indicating that there is no health problem with the reference voice data.

The foregoing includes a personalized similarity comparison algorithm that compares voice data uttered in the past by a user with voice data uttered currently. Hereinafter, embodiments of performing a secondary identity verification process based on a statistical similarity comparison algorithm will be described. In the description of the secondary identity verification process based on the statistical similarity comparison algorithm, a part corresponding to the secondary identity verification process based on the personalized similarity comparison algorithm will be omitted for simplicity of description.

In an embodiment, the computing device 100 may compare a similarity between the first voice data of the user and statistical data of other users related to the user of the user terminal by using an artificial intelligence-based voiceprint authentication model. For example, a user of a user terminal may be assigned to at least one group among a plurality of groups based on user characteristic information. Here, the characteristic information may include the user's age, the user's gender, the user's occupation, and/or the user's residence. The computing device 100 may include the corresponding user in at least one group among a plurality of groups based on the user's characteristic information. These groups may be grouped based on the aforementioned characteristic information. The computing device 100 may generate statistical data based on voice data of other users within a group to which the user belongs. The computing device 100 may perform a second identity verification process of the user by comparing the first voice data with the statistical data. The aforementioned statistical data of other users may include data representing statistical characteristics of voice determined based on the user's age and/or gender. For example, the statistical characteristics of voice may include a frequency range and/or tone of voice of a man in his 50s.

In an embodiment, the secondary identity verification process determines whether the first voice data is voice data representing a characteristic of an age corresponding to a user of the user terminal, and determines whether the first voice data corresponds to a user of the user terminal. It may be determined whether the voice data represents characteristics of the gender to be determined. The computing device 100 may compare similarities between the first voice data and statistical data of other users related to the user of the user terminal by using an artificial intelligence-based voiceprint authentication model. Accordingly, when it is determined that the first voice data obtained from a user corresponding to a specific gender and a specific age is out of the range of voice data that the specific gender and/or age can have, the computing device 100 provides a second voice data. Through the identity verification process, it may be decided to proceed with an additional identity verification process for the corresponding user.

In one embodiment of the present disclosure, an additional identity verification process refers to any form of process for further verifying a user's identity, which may include, for example, forwarding a more personalized query to the user. In this example, the computing device 100 may forward a query to the user terminal, for example, about the user's “zodiac sign”. As in this example, the computing device 100 may perform an additional identity verification process by transmitting a query different from that used in the first identity verification process to the user.

In one embodiment, the computing device 100 may set a predetermined threshold time in proceeding with the additional identity verification process. In the additional identity verification process, when an answer to the user is delivered to the computing device 100 after a predetermined threshold time elapses, the computing device 100 may determine that the user's identity has not been verified.

As described above, by automating the primary identity verification process, the secondary identity verification process, and the additional identity verification process, the reliability of real-time authentication through the customer's voice can be increased.

In an additional embodiment, the computing device 100 may additionally use the identity authentication technique used in at least one of the primary identity verification process and the secondary identity verification process in the additional identity verification process. For example, after generating and delivering a random question to the user, an additional identity verification process may be performed to determine whether the user answers frequently used words or combinations of words.

4 illustratively illustrates an identity verification process according to some embodiments of the present disclosure.

Identity verification process 400 in FIG. 4 may be performed by computing device 100 .

As shown in FIG. 4 , identity verification process 400 may include a primary identity verification process 410 , a secondary identity verification process 420 and an additional identity verification process 430 .

Among the first identity verification process 410, the second identity verification process 420, and the additional identity verification process 430 described in FIG. 4, overlapping parts with those described in FIG. 3 are omitted for simplicity of description. do it with

In one embodiment, the primary identity verification process 410 may refer to a process of verifying a user's identity through questions and answers.

In one embodiment, the primary identity verification process 410 may be performed by comparing reference identification information with text data obtained from voice data. When text data converted from voice data uttered by a user is analyzed, patterns of words used by each user may be analyzed. Therefore, the first identity verification process 410 analyzes the user's speech pattern as well as whether the information spoken by the user and the reference identification information match, and determines the identity of the user based on the user's speech sequence and/or frequently spoken words. Identity can be accurately and quickly determined based on text data.

In one embodiment, the primary identity verification process 520 uses an artificial intelligence-based voiceprint recognition model to generate a degree of similarity between first voice data 560 uttered by a user and reference voice data corresponding to the user. can do. For example, an artificial intelligence-based voiceprint recognition model removes noise that may occur in a call from first voice data, performs tokenization by dividing the first voice data into a plurality of tokens, and each of the plurality of tokens. A preprocessing module that performs one-hot encoding on , one or more input layers including neurons receiving the result of the preprocessing module and reference speech data, a plurality of hidden layers, and a similarity between the first speech data and the reference speech data It may include an output layer that outputs.

In a further embodiment, the artificial intelligence-based voiceprint recognition model removes noise that may occur in a call from the first voice data, performs tokenization to divide the first voice data into a plurality of tokens, and divides the first voice data into a plurality of tokens. A preprocessing module that performs one-hot encoding for each of the preprocessing modules, one or more input layers including neurons receiving results of the preprocessing module, a plurality of hidden layers, and an output layer that outputs a first feature of the first voice data can include In addition, the artificial intelligence-based voiceprint recognition model removes noise that may occur on a call from the reference voice data, performs tokenization by dividing the reference voice data into a plurality of tokens, and performs one-one for each of the plurality of tokens. It may include a preprocessing module that performs hot encoding, one or more input layers including neurons receiving results of the preprocessing module, a plurality of hidden layers, and an output layer that outputs a second feature of the reference speech data. The artificial intelligence-based voiceprint recognition model may compare the first voice data with the reference voice data by comparing the first feature and the second feature. As an example, the first feature and the second feature may be expressed as vector values in a vector space.

In one embodiment, the secondary identity verification process 420 may refer to a process of verifying the user's identity through the user's voice. As an example, the secondary identity verification process 420 may be performed when the primary identity verification process 410 is successfully completed. If the primary identity verification process 410 is not passed, the voice counseling session with the corresponding user may be terminated.

In one embodiment, the secondary identity verification process 420 may utilize accents in the voice data to compare the first voice data with the reference voice data. For example, the intonation in the voice data may include a state of pitch or a type of variation of the spoken voice data. For example, in a phrase, the melody of voice, which is indicated by the rise and fall of voice, the stress of voice (eg, the alternation of long and short sounds or the alternation of words with and without stress), the speed of voice indicating the speed and slowness of speech, the emotion or A tone that represents the color of the voice giving psychological nuance can be a component of intonation. The computing device 100 may increase the accuracy of verifying the user's identity by performing the comparison based on the accent in the secondary identity verification process 420 comparing voice data.

In one embodiment, when it is determined that the secondary identity verification process 420 has been passed, a voice counseling session with the corresponding user may be normally performed.

In one embodiment, the secondary identity verification process 420 may be performed based on a personalized similarity comparison algorithm and/or a statistical similarity comparison algorithm. For example, the personalized similarity comparison algorithm may include an algorithm for verifying an identity through comparison between past speech data and current speech data of a specific individual. For example, the statistical similarity comparison algorithm may include an algorithm for verifying an identity through comparison between statistical voice data of other users within a group to which a specific individual belongs and current speech data of the individual.

In one embodiment, an additional identity verification process 430 may be performed if the secondary identity verification process 420 is not passed. In another embodiment, an additional identity verification process 430 may be performed if the primary identity verification process 410 and the secondary identity verification process 420 pass to increase the security of identity authentication.

In one embodiment, additional identity verification process 430 may utilize queries not used in primary identity verification process 410 . The query used in the additional identity verification process 430 may include highly personalized content. A timer may be assigned to the query according to the additional identity verification process 430, for example, so that if the user answers the query after the timer has elapsed, the additional identity verification process 430 succeeds. It may be determined that it is not complete.

In one embodiment, at least one of the primary identity verification process 410, the secondary identity verification process 420, and the additional identity verification process 430 may be performed using an AI-based voiceprint recognition model. .

In one embodiment, when at least one of the primary identity verification process 410, the secondary identity verification process 420, and the additional identity verification process 430 is performed, the computing device 100 is configured in the counseling voice data. noise can be removed. For example, when voice data consists of a user's voice and background sound (eg, ambient sound), the background sound has a spectrum different from that of the user's voice, such as a multiple of the fundamental frequency in the frequency domain, unlike the user's voice. can have The computing device 100 may improve the performance of the artificial intelligence-based voiceprint recognition model by removing voice data having a different spectrum from the user's voice as noise. As another example, the computing device 100 may remove noise from voice data based on parameters related to the user's voice, such as frequency corresponding to voice, type (voiced or unvoiced, etc.), sound velocity, and pitch.

5 illustratively illustrates the association of a voice counseling process and a self-authentication enhancement process according to some embodiments of the present disclosure.

The voice counseling process 500A illustrated in FIG. 5 may refer to a voice counseling process between the computing device 100 and the user terminal. The voice counseling process 500A may consist of initiation of a counseling session 510 , a primary identity verification process 520 , an additional identity verification process 530 and an ongoing voice consultation 540 . The identity authentication strengthening process 500B may be performed by the computing device 100 or by a device different from the device performing the voice counseling process 500A.

The identity authentication strengthening process 500B shown in FIG. 5 may refer to a separate process for increasing accuracy of identity authentication that proceeds in parallel with the voice counseling process 500A. The identity authentication enhancement process 500B may include a secondary identity verification process 550 .

In one embodiment, the first voice data 560 may be obtained from the primary identity verification process 520 . The computing device 100 may additionally utilize the first voice data 560 obtained during the first identity verification process 520 in the second identity verification process 550 . In one embodiment, the first voice data 560 is passed to the secondary identity verification process 550 of the identity authentication enhancement process 500B to perform a personalized similarity comparison algorithm 550A and/or a statistical similarity comparison algorithm 550B. can be analyzed through

According to an embodiment of the present disclosure, the first voice data 560 obtained during the voice counseling process 500A is included in the first identity verification process 520 as well as the identity authentication strengthening process 500B. Since it can be used in the secondary identity verification process 550, the user has the advantage of not having to go through multiple procedures for identity verification, and the advantage of being able to perform more accurate identity authentication or identity verification in terms of accuracy of the authentication process. exist. User authentication can be processed more quickly and accurately through not only the first identity verification process 520 consisting of questions and answers, but also the second identity verification process 550 that additionally determines the similarity of voices, and furthermore, the additional identity verification process Through step 530, the accuracy of identity authentication can be further increased.

In one embodiment, the secondary identity verification process 550 may use an artificial intelligence-based voiceprint recognition model to vectorize or graph the shape of the voice. The artificial intelligence-based voiceprint recognition model can extract phonetic features of incoming voice data by vectorizing or graphing the frequency and/or intensity of voice. The computing device 100 may perform phonetic comparison between the first voice data and the reference voice data through vector or graph comparison by using such an artificial intelligence-based voiceprint recognition model. Based on this phonetic comparison, a secondary identity verification process 550 may be performed. In one embodiment, the above-described denoising technique in voice data may be utilized in performing phonetic comparison.

The secondary identity verification process 550 may include a personalized similarity comparison algorithm 550A and a statistical similarity comparison algorithm 550B.

In one embodiment, the personalized similarity comparison algorithm 550A represents a user-specific similarity comparison algorithm. For example, the personalized similarity comparison algorithm 550A may compare past voice data and current voice data of a specific user to verify the identity of the corresponding user.

In one embodiment, the statistical similarity comparison algorithm 550B is an algorithm for verifying the identity of a user using statistical information. The computing device 100 may group a plurality of users according to characteristics and then generate voice data for each group. The computing device 100 may compare voice data for each group with current voice data of a specific user to determine whether the voice data of the corresponding user deviate from the characteristics of the group.

In a further embodiment of the present disclosure, the query in the primary identity verification process 520 to verify the identity of the user through queries and answers may be generated in a variety of ways. For example, the query may be generated based on event information and/or contract information of a specific customer. Here, the event information may refer to data representing past activity details of a customer in relation to a customer center or an insurance company (or a financial company). For example, assume that two weeks ago, customer A inquired about whether a certain disease was covered by the insurance company. In this case, the event information corresponding to customer A may be expressed as “confirmation of coverage for a specific disease”. As another example, it is assumed that an insurance company sent a specific guide mail to customer B a month ago. In this case, event information corresponding to customer B may be expressed as "receiving a specific guide mail". As another example, assume that customer C inquired about an insurance policy loan three months ago. In this case, the event information corresponding to the customer C may be expressed as “terms and conditions loan inquiry”. Examples of the above event information are used for the purpose of explaining the present disclosure, and the event information of the present disclosure is data representing any type of activity record occurring between a customer and an insurance company (or financial company). can be used as Accordingly, when the first identity verification process 520 is performed, the computing device 100 may generate a query asking what was inquired last week and request an answer thereto. Here, the contract information may refer to any type of data derived from a contract concluded between a customer and an insurance company. For example, assume that a customer of company D entered into a corporate insurance contract with an insurance company at the beginning of this year. In this case, contract information assigned to customer D may be expressed as a corporate insurance contract. Additionally, the contract information may include any type of data indicating the type or characteristics of insurance purchased by the customer. In this case, when the first identity verification process 520 is performed, the computing device 100 may create a query asking what type of insurance the user has recently subscribed to and request an answer thereto. Accordingly, the query generated in the above manner may be matched with the reference identification information of the present disclosure.

In one embodiment of the present disclosure, additional identity verification process 530 may proceed if at least one of secondary identity verification process 550 and/or primary identity verification process 520 is not passed. This additional identity verification process 530 may, for example, consist of a question and answer. The query used in the additional identity verification process 530 needs to be more customer-specific than the query used in the primary identity verification process 520 in order to more accurately verify the customer's identity. Accordingly, the query used in the additional identity verification process 530 may be generated based on the aforementioned event information and/or contract information.

6 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above as being generally embodied by a computing device, those skilled in the art will understand that the present disclosure may be combined with computer-executable instructions and/or other program modules that may be executed on one or more computers and/or may be implemented in hardware and software. It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will understand that the methods of the present disclosure can be applied to single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like ( It will be appreciated that each of these may be implemented with other computer system configurations, including those that may be operative in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Computer readable media can be any medium that can be accessed by a computer, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media. Computer readable storage media are volatile and nonvolatile media, transitory and non-transitory, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store desired information.

A computer readable transmission medium typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Including all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information within the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer readable transmission media.

An exemplary environment 1100 implementing various aspects of the present disclosure is shown including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106 , to processing unit 1104 . Processing unit 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

System bus 1108 may be any of several types of bus structures that may additionally be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, or EEPROM, and is a basic set of information that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also include an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes—a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM) for reading disc 1122 or reading from or writing to other high capacity optical media such as DVDs). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to the system bus 1108 by a hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art can use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible media readable by the computer, such as the like, may also be used in the exemplary operating environment and any such media may include computer executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored on the drive and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, a parallel port, IEEE 1394 serial port, game port, USB port, IR interface, may be connected by other interfaces such as the like.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, handheld computer, microprocessor-based entertainment device, peer device, or other common network node, and generally includes It includes many or all of the components described for, but for simplicity, only memory storage device 1150 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and corporations and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, computer 1102 connects to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communications to LAN 1152, which also includes a wireless access point installed therein to communicate with wireless adapter 1156. When used in a WAN networking environment, computer 1102 may include a modem 1158, be connected to a communicating computing device on WAN 1154, or establish communications over WAN 1154, such as over the Internet. have other means. A modem 1158, which may be internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored on remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between computers may be used.

Computer 1102 is any wireless device or entity that is deployed and operating in wireless communication, eg, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, wireless detectable tags associated with It operates to communicate with arbitrary equipment or places and telephones. This includes at least Wi-Fi and Bluetooth wireless technologies. Thus, the communication may be a predefined structure as in conventional networks or simply an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet without wires. Wi-Fi is a wireless technology, such as a cell phone, that allows such devices, eg, computers, to transmit and receive data both indoors and outdoors, i.e. anywhere within coverage of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band) .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields s or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are electronic hardware, (for convenience) , may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory devices (eg, EEPROM, cards, sticks, key drives, etc.), but are not limited thereto. Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure, and the general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be interpreted in the widest scope consistent with the principles and novel features presented herein.

Claims (16)

A method for providing a counseling service performed by a computing device including one or more processors, comprising:
obtaining reference identification information corresponding to a call originating from a user terminal;
In the process of conducting a voice counseling session with the user terminal, by comparing the first identification information transmitted from the user terminal with the reference identification information, the identity of the user of the user terminal can be inquired and answered. performing a first identity verification process verified through; and
performing a second identity verification process of verifying the identity of the user of the user terminal through voice by performing voice analysis on the first voice data acquired in the course of the voice counseling session;
including,
Methods for providing counseling services.
According to claim 1,
The first identification information is generated based on first voice data uttered by the user of the user terminal in response to the inquiry in the process of conducting the voice counseling session, and
The second identity verification process utilizes the first voice data obtained in the first identity verification process.
Methods for providing counseling services. According to claim 1,
determining whether to perform an additional identity verification process with the user terminal based on a verification result of the second identity verification process;
Including more,
Methods for providing counseling services.
According to claim 3,
The additional identity verification process,
A predetermined additional query for a user of the user terminal, and the predetermined additional query includes query content different from the query for the user of the user terminal utilized in the primary identity verification process.
Methods for providing counseling services.
According to claim 1,
The second identity verification process,
Comprising a similarity between the first voice data and reference voice data related to the user terminal using an artificial intelligence-based voiceprint authentication model,
Methods for providing counseling services.
According to claim 5,
The reference voice data,
Including past uttered voice data of the user of the user terminal,
Methods for providing counseling services.
According to claim 6,
The voice data spoken in the past,
Stored in a voiceprint authentication DB containing answers of the user of the user terminal corresponding to each of a plurality of queries for authentication,
Methods for providing counseling services.
According to claim 7,
Comparing the similarity between the first voice data and the reference voice data related to the user terminal,
Comparing whether the first identification information included in the first voice data including the user's answer of the user terminal corresponding to the query for identity authentication matches the reference identification information stored in the voiceprint authentication DB,
Methods for providing counseling services.
According to claim 5,
Comparing the similarity between the first voice data and the reference voice data related to the user terminal,
Refer to at least one of voice rhythm, voice speed, voice timbre, voice intensity, and voice pitch in the first voice data including an answer of a user of the user terminal corresponding to a query for identity authentication. compared to audio data,
Methods for providing counseling services.
According to claim 8,
The second identity verification process,
generating a health query for determining whether a temporary health change has occurred in a user of the user terminal when the similarity between the first voice data and the reference voice data related to the user terminal is less than a predetermined threshold similarity; and
determining whether to perform an additional identity verification process with the user terminal based on an answer of a user of the user terminal obtained in response to the health inquiry;
Including more,
Methods for providing counseling services.
According to claim 8,
The second identity verification process,
generating a health query for determining whether a temporary health change has occurred in a user of the user terminal when the similarity between the first voice data and the reference voice data related to the user terminal is less than a predetermined threshold similarity;
changing the predetermined threshold similarity based on an answer of a user of the user terminal obtained in response to the health inquiry; and
further performing the secondary identity verification process based on the changed threshold similarity and second voice data included in the user's answer of the user terminal obtained in response to the health query;
Including more,
Methods for providing counseling services.
According to claim 1,
The second identity verification process,
Comparing the similarity between the first voice data and statistical data of other users related to the user of the user terminal using an artificial intelligence-based voiceprint authentication model,
Methods for providing counseling services.
According to claim 12,
Statistical data of the other users,
Including data representing statistical characteristics of voice determined based on the age and gender of the user of the user terminal,
Methods for providing counseling services.
According to claim 13,
The second identity verification process,
determining whether the first voice data is voice data representing characteristics of an age corresponding to a user of the user terminal; and
Determining whether the first voice data is voice data representing characteristics of a gender corresponding to the user of the user terminal;
Methods for providing counseling services.
A computer program stored on a computer readable storage medium,
The computer program includes instructions for causing a processor of a computing device to perform a method for providing a counseling service, the method comprising:
obtaining reference identification information corresponding to a call originating from a user terminal;
A primary identity that verifies the identity of the user of the user terminal through a question and answer by comparing the first identification information transmitted from the user terminal with the reference identification information in the process of conducting a voice counseling session with the user terminal. performing a verification process; and
performing a second identity verification process of verifying the identity of the user of the user terminal through voice by performing voice analysis on the first voice data acquired in the course of the voice counseling session;
including,
A computer program stored on a computer readable storage medium.
A computing device for providing a consultation service,
a processor comprising at least one core;
a storage unit for storing a computer program executable by the processor; and
communications department;
including,
the processor,
obtaining reference identification information corresponding to a call originating from a user terminal;
A primary identity that verifies the identity of the user of the user terminal through a question and answer by comparing the first identification information transmitted from the user terminal with the reference identification information in the process of conducting a voice counseling session with the user terminal. perform a verification process; and
Performing a second identity verification process of verifying the identity of the user of the user terminal through voice by performing voice analysis on the first voice data obtained in the process of conducting the voice counseling session,
computing device.

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