KR20180057507A - Device and method for sending money using voice - Google Patents

Abstract

A device according to an embodiment comprises: a memory storing at least one program; a microphone receiving voice; and at least one processor sending money to a payee in response to the voice by executing the at least one program. The processor executes: analyzing the received voice to determine the remittance intention of a user; searching a contact list for a contact stored with the name of the payee; transmitting the name and contact of the payee to a bank server together with the amount of money included in the voice; receiving a breakdown of remittance from the bank server; and authenticating the breakdown of remittance. The device can analyze the voice received on the basis of rules or by using artificial intelligence algorithm when sending money by using the voice. When analyzing the voice by using the artificial intelligence algorithm, the device can analyze the voice by using machine learning, a neural network or a deep learning algorithm.

Description

TECHNICAL FIELD [0001] The present invention relates to a method and apparatus for remitting money using voice,

And more particularly, to a method and apparatus for remitting money using voice.

The present disclosure also relates to an artificial intelligence (AI) system and its application that simulate the functions of the human brain using cognitive learning algorithms.

As multimedia technology and network technology develop, users can receive various services using devices. Particularly, as the speech recognition technology evolves, the user inputs voice to the device, and the device can perform the operation according to the voice of the user.

Users can use financial services using applications provided by banks. For example, a user can remit money to a recipient's account using the device. The user can execute the application, input the account number, the password, etc., and transfer it to the account of the receiver.

In recent years, artificial intelligence systems that implement human-level intelligence have been used in various fields. Artificial intelligence system is a system that the machine learns, judges, and becomes smarter, unlike the existing rule-based smart system. Artificial intelligence systems are becoming more and more recognizable as users use them, and existing rule-based smart systems are increasingly being replaced by deep-run-based artificial intelligence systems.

Artificial intelligence technology consists of elemental technologies that utilize machine learning (eg, deep learning) and machine learning.

Machine learning is an algorithm technology that classifies / learns the characteristics of input data by itself. Element technology is a technology that simulates functions such as recognition and judgment of human brain using machine learning algorithms such as deep learning. Understanding, reasoning / prediction, knowledge representation, and motion control.

The various fields in which artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing, applying, and processing human language / characters, including natural language processing, machine translation, dialogue system, query response, speech recognition / synthesis, and the like. Visual understanding is a technology for recognizing and processing objects as human vision, including object recognition, object tracking, image search, human recognition, scene understanding, spatial understanding, and image enhancement. Inference prediction is a technique for judging and logically inferring and predicting information, including knowledge / probability based reasoning, optimization prediction, preference base planning, and recommendation. Knowledge representation is a technology for automating human experience information into knowledge data, including knowledge building (data generation / classification) and knowledge management (data utilization). The motion control is a technique for controlling the autonomous travel of the vehicle and the motion of the robot, and includes motion control (navigation, collision, traveling), operation control (behavior control), and the like.

A device and method for remitting money to a recipient's account using voice are provided.

A device according to an embodiment includes: a memory in which at least one program is stored; A microphone for receiving voice; And at least one processor for remitting the at least one program to a recipient according to the voice by executing the program, the processor comprising: analyzing the received voice to determine a user's intent to remit; Retrieving a contact stored in the contact list in the name of the recipient; Transmitting the name and the contact of the recipient to a bank server together with an amount included in the voice; Receiving the remittance details from the bank server; and approving the remittance details.

According to an exemplary embodiment, a remittance method includes receiving a voice of a user; Analyzing the received voice to determine the intent of the user to transmit; Retrieving a contact stored in the contact list in the name of the recipient; Transmitting the name and the contact of the recipient to a bank server together with an amount included in the voice; Receiving the remittance details from the bank server; and approving the remittance details.

1 is a diagram for explaining a method of transferring a voice using a user according to an embodiment.
2 is a block diagram illustrating a device according to an embodiment.
3 is a diagram for explaining that a device according to an embodiment learns a pattern.
4 is a diagram for explaining a method for approving remittance details according to an embodiment.
5 is a diagram for explaining a method of selecting one of a plurality of recipients according to an embodiment.
FIG. 6 is a diagram for explaining a method of selecting one bank among a plurality of banks according to an embodiment.
7 is a flowchart for explaining a method of remitting using a voice according to an embodiment.
8 is a diagram for explaining a method of paying by voice according to another embodiment.
9 is a diagram for explaining a device learning a payment pattern according to an embodiment.
FIG. 10 is a flowchart illustrating a method of paying by voice according to an embodiment.
11 is a block diagram of a processor in accordance with one embodiment.
12 is a block diagram of a data learning unit according to an embodiment.
13 and 14 are views showing an example in which a device and a server according to an embodiment learn and recognize data by interlocking with each other.
15 and 16 are flowcharts of a network system using a data recognition model according to some embodiments.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as "comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram for explaining a method of transferring a voice using a user according to an embodiment. Referring to FIG. 1, a user can input a voice to the device 10 and send the voice to the recipient. In particular, even if the user does not input the account number of the recipient, the user can remit the recipient only with the recipient's name.

The device 10 may receive voice from the user. The device 10 may include a microphone, which receives the user's voice.

The device 10 may receive the voice input of the user. The device 10 may receive a voice input of a user input via a microphone by, for example, executing a voice assistant application such as " S voice " and controlling an executed application.

The device 10 recognizes the voice of the user. The device 10 can analyze the voice and determine the intention of the user. For example, when the user receives a voice of '100 million remitted to Samsung', the device 10 can judge whether the user intends to remit money from the user's voice. The device 10 can store the voice inputted when the user sends money in the memory and learn the pattern of the voice inputted when sending money. The device 10 can judge the user's intention more accurately through learning. At the beginning of learning, when the user's voice is input, the device 10 can confirm whether or not to transmit money. The device 10 can more accurately determine the intent of the user's remittance through repeated learning.

As an example, the device 10 may compare the pattern of the stored speech with the pattern of the input speech to determine the intention of the user. The pattern of the stored voice means a pattern of the inputted voice when the user intends to remit it. The device 10 may determine that the user intends to remit money if the pattern of the stored voice and the pattern of the inputted voice are similar or identical. The pattern of the stored speech can be updated or added through learning.

The device 10 confirms the name or the name of the recipient and searches for the name or name stored in the contact list. For example, if the user enters the recipient as 'Samsung', the device 10 searches for 'Samsung' in the contact list. The device 10 confirms the phone number of 'Samsung' in the contact list.

The device 10 transmits the user information, the recipient information, and the amount to the bank server 20. The user information includes the name of the user, the account number, and the like. The recipient information includes the recipient's name, telephone number, and the like. The recipient information may not include the recipient's account number. The amount represents the amount included in the user's voice, and is the amount the user will remit to the recipient.

The device 10 may be a smart phone, a tablet PC, a PC, a smart TV, a mobile phone, a personal digital assistant (PDA), a laptop, a media player, a micro server, Navigation, kiosks, MP3 players, digital cameras, consumer electronics, and other mobile or non-mobile computing devices. In addition, the device 10 may be a wearable device, such as a watch, a pair of glasses, a hair band, and a ring, having a communication function and a data processing function. However, it is not so limited, and the device 10 may include any kind of device capable of receiving a user's voice input and providing an answer message to the user.

In addition, the device 10 can communicate with other devices (not shown) through a network to use various context information. The network includes a local area network (LAN), a wide area network (WAN), a value added network (VAN), a mobile radio communication network, a satellite communication network, And is a data communication network having a comprehensive meaning of allowing each network constituent to communicate smoothly with each other, and may include a wired Internet, a wireless Internet, and a mobile wireless communication network. The wireless communication may be performed using, for example, Wi-Fi, Bluetooth, Bluetooth low energy, ZigBee, Wi-Fi Direct, UWB, ), Near Field Communication (NFC), and the like, but the present invention is not limited thereto.

The bank server 20 receives user information and recipient information. The bank server 20 retrieves an account number that matches the user information. The bank server 20 can retrieve the user's account number using the user's name and telephone number. In addition, the bank server 20 may retrieve the account number assigned (or matched) to the unique identification information of the device 10. The device 10 may include unique identification information and the bank server 20 may use the unique identification information of the device 10 to retrieve the account number of the user of the device 10. [ The bank server 20 retrieves the account number matching the recipient information. The bank server 20 retrieves the account number that matches the name and telephone number of the recipient.

The bank server 20 generates the transfer details. The bank server 20 can generate remittance details including the account number of the user, the name of the recipient, the account number of the recipient, and the amount of money. For example, the bank server 20 may transfer 10,000 won to the bank A, 11-1111 (account number), AAA (user name) to bank B, 22-2222 (account number), BBB 'Can be created.

The bank server 20 transmits the remittance details to the device 10.

The device 10 can display the transfer details. The device 10 can display the remittance details so as to confirm from the user whether the intention inputted by the user by voice and the remittance details coincide with each other.

The user approves the transfer details. The user can input voice, fingerprint, iris, vein or password if he / she wants to send money according to the transfer details. The device 10 can perform authentication by determining whether the input voice, fingerprint, iris, vein, or password matches the user's personal information.

The device 10 transmits the authentication result to the bank server 20.

The bank server 20 remits to the recipient according to the authentication result. The bank server 20 sends an error message to the device 10, if it authenticates as a legitimate user, to the device 10, without remitting or otherwise remitting to the recipient.

2 is a block diagram illustrating a device according to an embodiment. 2, the device 10 includes a processor 11, a memory 12, a display 13, and a microphone 14.

The processor 11 may control the memory 12, the display 13 and the microphone 14. [ The processor 11 may store or read data in the memory 12. The processor 11 can determine the image to be displayed on the display 13 and control the display 13 to display the image. The processor 11 controls on / off of the microphone 14 and analyzes the voice input through the microphone 14. [

The memory 12 may store user's personal information, biological information, and the like. For example, the memory 12 may store the user's voice, fingerprint, iris, vein, and password. The memory 12 may store the user's voice for analyzing the user's voice pattern.

The display 13 can display an image or reproduce video under the control of the processor 11. [

The microphone 14 receives voice. The microphone 14 converts the sound generated in the periphery of the device 10 into an electric signal and outputs the electric signal to the processor 11. [

3 is a diagram for explaining that a device according to an embodiment learns a pattern. Referring to FIG. 3, the device 10 may analyze various types of sentences and learn patterns.

The user can speak various types of sentences for remittance. For example, in order to send 100 million won from a user's bank account to Samsung (recipient), the user can say the following type of sentence.

1. Send 100 million won from A bank account to Samsung

2. Remit 100 million won to Samsung

3. Remittance to 100 million Samsung

The device 10 may analyze and learn a pattern of speech that the user speaks to identify a sentence containing the intent to remit by the user.

When there are a plurality of accounts of the user, the device 10 can confirm to the user which account to withdraw. Once an account is designated, the device 10 can withdraw from the designated account from now on, unless there is a separate instruction from the user.

4 is a diagram for explaining a method for approving remittance details according to an embodiment. The user can approve the transfer details using voice, finger print, vein or iris.

The device 10 receives the remittance details from the bank server 20 and displays the remittance details. The transfer details may include the account number of the user, the account number of the recipient, and the amount of money.

The user can confirm the remittance details after confirming the remittance details. When the user approves the transfer details, the user can use voice, finger print, vein or iris. The device 10 sends a message indicating that the input voice, fingerprint or iris matches the user's voice, fingerprint, vein or iris, to the bank server 20.

5 is a diagram for explaining a method of selecting one of a plurality of recipients according to an embodiment. The user can select any one of a plurality of recipients through voice.

The device 10 retrieves from the contact list the name identified as the recipient. If a plurality of recipients including the identified name are found in the contact list, the device 10 displays the names of the plurality of recipients searched. The user can select any one of the names displayed by voice.

The following example describes the case where the following two recipients are searched by the name Samsung.

1. Samsung Electronics

2. Samsung Corporation

The device 10 displays two recipients on the display 13. The user can select either the first or second recipient by voice. For example, a user can select a recipient by inputting a voice such as "remit money 1" or "remit money to Samsung Electronics".

FIG. 6 is a diagram for explaining a method of selecting one bank among a plurality of banks according to an embodiment. The user can select any bank (or account number) from among a plurality of banks (or account numbers) via voice.

The bank server 20 can transmit a plurality of banks (or account numbers) registered in the name of the recipient when transferring the transfer details to the device 10. [ For example, if there are a plurality of account numbers registered in the name of the recipient, the device 10 may display the account numbers to the user to determine which account number to remit. The user can select any one of the account numbers displayed by voice.

The following two account numbers are searched under the name Samsung, for example.

1. Bank A (33-3333)

2. Bank B (55-5555)

The device 10 displays the two account numbers on the display 13. The user can select either the first or second account number by voice. For example, a user can select a bank or account number by entering a voice, such as "Send to A bank", "Send money to 1", or "Send money to account".

7 is a flowchart for explaining a method of remitting using a voice according to an embodiment. Referring to FIG. 7, the user can input the name and amount of the recipient by voice and send it to the recipient.

In step 710, the device 10 receives the user's voice.

At step 720, the device 10 analyzes the received voice to determine the user's intent to send. As a result of analyzing the received voice, if it is determined that there is no intention to send money, the device 10 does not perform the process for transferring money. The voice may include the name, amount, etc. of the recipient. For example, the device 10 may analyze the voice and determine that the user's voice has an intent to remit, if the command, name, amount, etc. are included.

At step 730, the device 10 retrieves the contact stored in the contact list in the name of the recipient. If a contact containing the name of the recipient is not found, the device 10 may display on the display 13 a phrase indicating that the contact is absent. The user can input a contact name with the name of the recipient. The device 10 stores the name of the recipient and the contact in the contact list according to the inputted voice.

In step 740, the device 10 sends the recipient's name and contact information to the bank server 20 along with the amount included in the voice. The contact may be searched by the name of the recipient or entered via the user's voice.

At step 750, the device 10 receives the transfer details from the bank server 20. The bank server 20 searches the account number of the recipient using the name and contact information of the recipient, and transmits the remittance details including the name account number and amount of the recipient to the device 10.

At step 760, the device 10 approves the transfer details. The device 10 can approve the transfer details using the user's voice, fingerprint, iris, vein or password. The user can confirm the remittance history, input the voice to be approved into the device 10, or allow the device 10 to recognize the iris, fingerprint, and the like. Further, when the user wears a wearable device such as a smart watch, the user can perform authentication using the vein of the hand using the smart watch. For example, a user can manipulate a smart watch to identify a vein in the back of a hand to authenticate to confirm the transfer details.

8 is a diagram for explaining a method of paying by voice according to another embodiment. Referring to FIG. 8, a user can make a payment using voice.

The device 10 displays a screen for paying for goods or services purchased by the user on the Internet. For example, when the user purchases the Galaxy Note 7, the device 10 may display the message 'Would you like to purchase Galaxy Note 7?'

After checking the payment details, the user can enter a voice and make a payment. For example, if the user inputs 'pay with Samsung card', the device 10 recognizes the user's voice. The user can simply input 'payment is made', and the device 10 can proceed the payment using the card previously paid by the user.

The device 10 transmits the card information and payment information of the user to the card issuer server 30. [ The card information of the user may include a card number, an effective date of the card, a password, and the like. The payment information may include an object or service to be paid, a seller's rating, and the like.

The card issuer server 30 can confirm the card information and proceed with the settlement. The card issuer server 30 can transmit the settlement completion message to the device 10 when payment is completed. The device 10 may display a payment completion message to notify the user that payment has been completed normally.

As an example, if the user wears a smart watch and the user pays for the goods or services, the smart watch can automatically perform the user's biometric authentication. For example, if the user is wearing a smart watch, the smart watch can capture the vein of the user's wrist and perform vein authentication through the captured vein pattern. Therefore, the user can automatically settle the payment through the smart watch without inputting the voice, password, etc. separately. More specifically, the device 10 determines whether the user wears a smart watch when the user touches the settlement button on the Internet. If the user is wearing a smart watch, the device 10 signals the smart watch to perform vein authentication. The smart watch can photograph the user's vein under the control of the device 10 and transmit the result of performing the vein authentication to the device 10. [ The smart watch may also transmit the captured vein image to the device 10 and the device 10 may perform vein authentication. Vein authentication may be to compare a registered vein image (or a vein pattern) with a captured vein image (or a normal pattern). When the user wears the wearable device, the device 10 can proceed the payment without receiving a separate input from the user.

9 is a diagram for explaining a device learning a payment pattern according to an embodiment. Referring to FIG. 9, the device 10 can learn payment patterns by analyzing various types of sentences. The learning of the payment pattern may mean to identify and record the type of voice that the user enters when making a payment.

The user can speak various types of sentences to make a payment. For example, a user can say the following type of sentence:

1. Make a payment with Samsung Card

2. Make a payment with Samsung Card

3. Make a payment with my card

4. Make a payment

The device 10 may store a sentence mainly spoken when the user makes a payment and determine whether the user speaks the same or similar sentence as the stored sentence, and proceed with the settlement.

The device 10 may register the card information of the user at the beginning of learning or request the card information to the user in order to acquire the card information that the user mainly uses. When the user's card information is registered, even if the user simply says " pay with my card ", the device 10 can proceed with payment using the card information of the user registered in the past.

FIG. 10 is a flowchart illustrating a method of paying by voice according to an embodiment. Referring to FIG. 10, a user can settle goods or services using voice.

In step 1010, the device 10 displays the payment history.

In step 1020, the device 10 receives the user's voice. The user can confirm the payment history, and can express whether or not to make a payment or not. For example, a user can say "pay" when making a payment and say "do not pay" when not paying.

In step 1030, the device 10 analyzes the received voice to determine the intent of the user. The device 10 may analyze the voice to determine whether the user wishes to approve the displayed payment details.

In step 1040, the device 10 performs user authentication via voice. The device 10 can perform user authentication by determining whether the voice matches the user's voice. The device 10 can determine whether the registered voice matches the input voice and proceed with the settlement. The device 10 may perform user authentication through not only voice but also fingerprint, iris, vein or password.

At step 1050, the device 10 sends the payment information to the card company. If the authentication is successful, the device 10 transmits the payment information and the card information to the card company. The payment information may include goods, seller information, amount, and the like. The card information may include the user's card number, password, effective date, and the like.

The device 10 may display a payment completion message upon completion of payment.

As described above, when a user purchases goods or services through the Internet, the user can purchase goods or services through voice.

11 is a block diagram of a processor 1300 in accordance with some embodiments.

Referring to FIG. 11, the processor 1300 according to some embodiments may include a data learning unit 1310 and a data recognition unit 1320.

The data learning unit 1310 can learn the criteria for the situation determination. The data learning unit 1310 can learn what data to use to determine a predetermined situation and how to determine the situation using the data. The data learning unit 1310 acquires data to be used for learning, and applies the acquired data to a data recognition model to be described later, so as to learn a criterion for determining a situation.

The data learning unit 1310 may learn a data recognition model using voice or sentence to generate a data recognition model set to estimate the user's intention. At this time, the voice or sentence may include a voice uttered by the user of the device 10 or a voice in which the voice of the user is recognized. Alternatively, the voice or sentence may include a voice uttered by the third party or a voice of the third party.

The data learning unit 1310 can learn a data recognition model by using a map learning method using speech or sentence and a learning entity as learning data.

In one embodiment, the data-aware model may be a model set to estimate a user's intent to send money. In this case, the learning entity may include at least one of user information, recipient information, remittance amount, and remittance intention. The user information may include identification information (e.g., name or alias) of the user or identification information of the user account (e.g., account bank, account name, account alias or account number), and the recipient information may include identification information (E.g., name, alias, or telephone number) or identification information (e.g., an account bank, account name, account alias, or account number) of the recipient account. The intent of the remittance may include whether the user will remit money or not. For example, the remittance intent may include remittance proceedings, remittance reservations, reservation cancellation, remittance remittance, or remittance confirmation.

On the other hand, at least one of the learning entity values may have a value of 'NULL'. In this case, the value 'NULL' may indicate that there is no information on the entity value for the speech or sentence used as learning data.

Specifically, if the voice or sentence for learning is '100 million remittance from A bank account to Samsung', the learning entity is {user information: A bank, recipient information: Samsung, transfer amount: 100 million, remittance instruction: remittance Progress < / RTI > In another example, if the voice or sentence for the learning is '100 million remittance to Samsung', the learning entity will consist of {user information: NULL, recipient information: Samsung, transfer amount: 100 million, transfer command: . As another example, if the voice or sentence for learning is '100 million remittance to Samsung', the learning entity is {user information: NULL, recipient information: Samsung, transfer amount: 100 million, transfer command: ≪ / RTI > As another example, if the voice or sentence is 'cancel reservation for 100 million remittance to Samsung', the learning entity is composed of {user information: NULL, recipient information: Samsung, transfer amount: 100 million, remittance command: reservation cancellation} .

In another embodiment, the data-aware model may be a model set to estimate the user's payment intention. In this case, the learning entity may include at least one of a payment card, a payment item, a payment method, and a payment intention. The payment method may include, for example, a lump sum or installment month number. The payment intention may include whether or not the user makes a payment. For example, the payment intent may include payment proceeding, payment cancellation, payment hold, payment method change, or payment confirmation.

Specifically, if the voice or sentence for the learning is 'make a one-time payment with Samsung Card', the learning entity is composed of {payment means: Samsung card, payment item: NULL, payment method: lump sum, payment instruction: . As another example, if the voice or sentence for learning is 'pay with installment of 10 months', the learning entity consists of {payment method: NULL, payment item: NULL, settlement method: 10 month installment, payment instruction: . As another example, if the voice or sentence for learning is 'canceled a little before payment', the learning entity may be composed of {payment means: NULL, payment item: NULL, payment method: NULL, payment command: cancel payment} .

The data recognition model set to determine the user's remittance intention and the data recognition model set to determine the user's payment intention may be the same recognition model or different recognition models. Alternatively, each of the data recognition models may be composed of a plurality of data recognition models. For example, the intention of the user may be determined using a plurality of data recognition models customized for each environment in consideration of the user's use environment (for example, use time or place of use).

The data recognition unit 1320 can determine the situation based on the data. The data recognition unit 1320 can recognize the situation from predetermined data using the learned data recognition model. The data recognizing unit 1320 can determine a predetermined situation based on predetermined data by acquiring predetermined data according to a predetermined reference by learning and using the acquired data as an input value and using the data recognition model . Further, the resultant value output by the data recognition model with the obtained data as an input value can be used to update the data recognition model.

The data recognition unit 1320 can estimate a user's intention by applying a sentence in which the user's voice or voice is recognized to the data recognition model. For example, the data recognition unit 1320 applies a sentence recognized by the user's voice or voice to the data recognition model to acquire a recognition entity and transmit it to the processor of the device (e.g., the processor 11 of the device 10 of FIG. 2) ). The processor 11 can determine the user's intention to the obtained recognition entity.

In one embodiment, the data-aware model may be a model set to estimate a user's intent to send money. In this case, the data recognition unit 1320 can estimate the user's remittance intention by applying the sentence in which the user's voice or voice is recognized to the data recognition model. For example, the data recognition unit 1320 may acquire a recognition entity from a sentence in which a user's voice or voice is recognized. Recognition entities may include, for example, at least one of user information, recipient information, remittance amounts, and remittance instructions. The data recognition unit 1320 may provide the acquired recognition entity to the processor 11. The processor 11 (or the dialog management module of the processor 11) can determine the user's intention based on the recognition entity.

If it is determined that the intention of the user is based on the recognition entity and there is no intention to send the money, the processor 11 may not perform the process for transferring money. On the other hand, if it is determined that the intention of the user based on the recognition entity is intent to transfer, the processor 11 can perform the process for transferring money.

At this time, when at least one value of the recognition entity is 'NULL', the processor 11 may determine a value corresponding to the 'NULL' value using the user's history information or predetermined information. For example, the processor 11 can determine a value corresponding to the value " NULL " by referring to a recent transfer history. Alternatively, the processor 11 may determine a value corresponding to a value of 'NULL' by referring to information (for example, an account number, an account bank, etc.) preset by the user in the environment setting.

If at least one value of the recognition entity is 'NULL', the processor 11 may request a value corresponding to the value 'NULL' to the user. For example, the processor 11 may display a phrase indicating that there is no information about at least one of the user information, the recipient information, the remittance amount, or the remittance command. When the user inputs at least one piece of information as a voice or a character, the processor 11 executes a processor for remittance using the recognition entity value and user input information obtained from the data recognition unit 1320 .

In another embodiment, the data-aware model may be a model set to estimate the user's payment intention. In this case, the data recognition unit 1320 can estimate the user's payment intention by applying the sentence in which the user's voice or voice is recognized to the data recognition model. For example, the data recognition unit 1320 may acquire a recognition entity from a sentence in which a user's voice or voice is recognized. The recognition entity may include, for example, at least one of a payment means, a payment item, a payment method and a payment instruction. The data recognition unit 1320 may provide the acquired recognition entity to the processor 11. The processor 11 (or the processor 11) can determine the user's intention based on the recognition entity.

If it is determined based on the recognition entity that the intention of the user is not the intention of settlement, the processor 11 may not perform the process for settlement. On the other hand, if it is determined based on the recognition entity that the intention of the user is intention to settle, the processor 11 can perform the process for settlement.

On the other hand, if at least one value of the recognition entity is 'NULL', the processor 11 may determine a value corresponding to the 'NULL' value using the user's history information or predetermined information. Alternatively, the processor 11 may ask the user for a value corresponding to a value of 'NULL'.

At least one of the data learning unit 1310 and the data recognizing unit 1320 may be manufactured in the form of at least one hardware chip and mounted on the electronic device. For example, at least one of the data learning unit 1310 and the data recognition unit 1320 may be fabricated in the form of a dedicated hardware chip for artificial intelligence (AI) or a conventional general purpose processor Or application processor) or a graphics-only processor (e.g., a GPU), and may be mounted on various electronic devices as described above. At this time, the dedicated hardware chip for artificial intelligence is a dedicated processor specialized for probability calculation, and it has a higher parallel processing performance than conventional general purpose processors, so that it can quickly process computation tasks in artificial intelligence such as machine learning.

In this case, the data learning unit 1310 and the data recognizing unit 1320 may be mounted on one electronic device or on separate electronic devices, respectively. For example, one of the data learning unit 1310 and the data recognizing unit 1320 may be included in the electronic device, and the other may be included in the server. The data learning unit 1310 and the data recognition unit 1320 may provide the model information constructed by the data learning unit 1310 to the data recognition unit 1320 via a wire or wireless communication, 1320 may be provided to the data learning unit 1310 as additional learning data.

At least one of the data learning unit 1310 and the data recognition unit 1320 may be implemented as a software module. When at least one of the data learning unit 1310 and the data recognition unit 1320 is implemented as a software module (or a program module including an instruction), the software module can be read by a computer, And may be stored in non-transitory computer readable media. Also, in this case, the at least one software module may be provided by an operating system (OS) or by a predetermined application. Alternatively, some of the at least one software module may be provided by an Operating System (OS), and some of the software modules may be provided by a predetermined application.

12 is a block diagram of a data learning unit 1310 according to some embodiments.

12, a data learning unit 1310 according to some embodiments includes a data obtaining unit 1310-1, a preprocessing unit 1310-2, a learning data selecting unit 1310-3, a model learning unit 1310 -4) and a model evaluation unit 1310-5. In some embodiments, the data learning unit 1310 includes a data acquiring unit 1310-1 and a model learning unit 1310-4, and includes a preprocessing unit 1310-2, a learning data selecting unit 1310- 3) and the model evaluation unit 1310-5, or may not include all of them.

The data acquisition unit 1310-1 can acquire data necessary for learning for the situation determination.

For example, the data acquisition unit 1310-1 may acquire voice data, image data, text data, biometric signal data, and the like. Specifically, the data acquisition unit 1310-1 may acquire a voice or a sentence for performing remittance or settlement. Alternatively, the data acquisition unit 1310-1 may acquire voice data or text data including a voice or a sentence for performing remittance or settlement.

The data acquisition unit 1310-1 may receive data through an input device (e.g., a microphone, a camera, a sensor, or the like) of the electronic device. Alternatively, the data acquisition unit 1310-1 may acquire data via an external device (e.g., a server) that communicates with the device.

The preprocessing unit 1310-2 can preprocess the acquired data so that the acquired data can be used for learning for the situation determination. The preprocessing unit 1310-2 can process the acquired data into a predetermined format so that the model learning unit 1310-4, which will be described later, can use the data acquired for learning for the situation determination. For example, the preprocessing unit 1310-2 may extract learning entity values from the voice data according to a predetermined format. For example, if the predetermined format is composed of {user information, recipient information, transfer amount, transfer command}, or if the predetermined format is composed of {payment means, payment item, payment method, payment instruction} 2) may extract a learning entity value according to the format from the voice data. At this time, if the learning entity value is not extracted, the preprocessing unit 1310-2 can display the specific entity value as 'NULL'.

The learning data selection unit 1310-3 can select data necessary for learning from the preprocessed data. The selected data may be provided to the model learning unit 1310-4. In this case, data acquired by the data acquisition unit 1310-1 or data processed by the preprocessing unit 1310-2 may be provided to the model learning unit 1310-4 as learning data. The learning data selection unit 1310-3 can select data necessary for learning from the preprocessed data according to a predetermined criterion for the situation determination. The predetermined criteria can be determined, for example, in consideration of at least one of the attributes of the data, the generation time of the data, the creator of the data, the reliability of the data, the object of the data, the generation area of the data and the size of the data. Alternatively, the learning data selection unit 1310-3 may select data according to a predetermined reference by learning by the model learning unit 1310-4, which will be described later.

The model learning unit 1310-4 can learn a criterion on how to determine the situation based on the learning data. Further, the model learning unit 1310-4 may learn a criterion as to which learning data should be used for the situation determination. For example, the model learning unit 1310-4 can learn a judgment model in accordance with a map learning method or a non-map learning method to generate a data recognition model for predicting, determining, or estimating. The data recognition model may be, for example, a model set for estimating the remittance intention of the user, or a model set for estimating the user's intention of payment.

Also, the model learning unit 1310-4 can learn the data recognition model used for the situation determination using the learning data. The data recognition model may be a pre-built model. For example, the data recognition model may be a pre-built model that receives basic learning data (e.g., sample data, etc.).

The data recognition model can be constructed considering the application field of the recognition model, the purpose of learning, or the computer performance of the device. The data recognition model may be, for example, a model based on a neural network. The data recognition model can be designed to simulate the human brain structure on a computer. The data recognition model may include a plurality of weighted network nodes that simulate a neuron of a human neural network. A plurality of network nodes may each establish a connection relationship to simulate synaptic activity of neurons sending and receiving signals via synapses. The data recognition model may include, for example, a neural network model or a deep learning model developed in a neural network model. In the deep learning model, a plurality of network nodes are located at different depths (or layers) and can exchange data according to a convolution connection relationship. For example, models such as Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Bidirectional Recurrent Deep Neural Network (BRDNN) may be used as a data recognition model, but the present invention is not limited thereto.

According to various embodiments, when there are a plurality of data recognition models that are built in advance, the model learning unit 1310-4 may use a data recognition model to learn a data recognition model having a large relation between input learning data and basic learning data You can decide. In this case, the basic learning data may be pre-classified according to the type of data, and the data recognition model may be pre-built for each data type. For example, the basic learning data may be pre-classified by various criteria such as an area where the learning data is generated, a time at which the learning data is generated, a size of the learning data, a genre of the learning data, a creator of the learning data, .

In addition, the model learning unit 1310-4 can learn a data recognition model using, for example, a learning algorithm including an error back-propagation method or a gradient descent .

Also, the model learning unit 1310-4 can learn the data recognition model through, for example, supervised learning using the learning data as an input value. Also, the model learning unit 1310-4 learns, for example, the type of data necessary for the situation judgment without any guidance, and by doing unsupervised learning to find a criterion for the situation judgment, The data recognition model can be learned. Also, the model learning unit 1310-4 can learn the data recognition model through reinforcement learning using, for example, feedback as to whether the result of the situation judgment based on learning is correct.

The learning data may include a voice of a user or a third party, a sentence in which a voice of a user or a third party is recognized, a sentence entered by a user or a third party, and the like. Further, the learning data may comprise learning entities associated with the speech or sentence. Various examples of learning entities are described in detail with reference to FIG. 11, and redundant explanations are omitted.

Further, when the data recognition model is learned, the model learning unit 1310-4 can store the learned data recognition model. In this case, the model learning unit 1310-4 can store the learned data recognition model in the memory of the electronic device including the data recognition unit 1320 (for example, the device 10 described above). Alternatively, the model learning unit 1310-4 may store the learned data recognition model in a memory of the electronic device including the data recognition unit 1320 to be described later. Alternatively, the model learning unit 1310-4 may store the learned data recognition model in an electronic device (for example, the above-described device 10) and a memory of a server connected via a wired or wireless network.

In this case, the memory in which the learned data recognition model is stored may also store, for example, instructions or data associated with at least one other component of the electronic device. The memory may also store software and / or programs. The program may include, for example, a kernel, a middleware, an application programming interface (API), and / or an application program (or "application").

The model evaluating unit 1310-5 inputs the evaluation data to the data recognition model. If the recognition result output from the evaluation data does not satisfy the predetermined criterion, the model evaluating unit 1310-5 can allow the model learning unit 1310-4 to learn again have. In this case, the evaluation data may be predetermined data for evaluating the data recognition model.

For example, when the number or the ratio of the evaluation data in which the recognition result is not correct is greater than a preset threshold value, among the recognition results of the learned data recognition model for the evaluation data, the model evaluation unit 1310-5 It can be evaluated as unsatisfactory. For example, when the predetermined criterion is defined as a ratio of 2%, when the learned data recognition model outputs an incorrect recognition result for evaluation data exceeding 20 out of a total of 1000 evaluation data, Can be estimated that the learned data recognition model is not suitable.

On the other hand, when there are a plurality of learned data recognition models, the model evaluation unit 1310-5 evaluates whether each of the learned data recognition models satisfies a predetermined criterion, Can be determined as a model. In this case, when there are a plurality of models satisfying the predetermined criterion, the model evaluating unit 1310-5 can determine any one or a predetermined number of models previously set in the order of higher evaluation scores as the final data recognition model.

The data acquisition unit 1310-1, the preprocessing unit 1310-2, the learning data selection unit 1310-3, the model learning unit 1310-4, and the model evaluation unit 1310-4 in the data learning unit 1310, -5) may be fabricated in at least one hardware chip form and mounted on an electronic device. For example, at least one of the data acquisition unit 1310-1, the preprocessing unit 1310-2, the learning data selection unit 1310-3, the model learning unit 1310-4, and the model evaluation unit 1310-5 One may be made in the form of a dedicated hardware chip for artificial intelligence (AI), or may be fabricated as part of a conventional general purpose processor (e.g., a CPU or application processor) or a graphics dedicated processor (e.g., GPU) And may be mounted on various electronic devices.

The data acquisition unit 1310-1, the preprocessing unit 1310-2, the learning data selection unit 1310-3, the model learning unit 1310-4, Or may be mounted on separate electronic devices, respectively. For example, some of the data acquisition unit 1310-1, the preprocessing unit 1310-2, the learning data selection unit 1310-3, the model learning unit 1310-4, and the model evaluation unit 1310-5 May be included in the electronic device, and the rest may be included in the server.

At least one of the data acquisition unit 1310-1, the preprocessing unit 1310-2, the learning data selection unit 1310-3, the model learning unit 1310-4, and the model evaluation unit 1310-5 Software module. At least one of the data acquisition unit 1310-1, the preprocessing unit 1310-2, the learning data selection unit 1310-3, the model learning unit 1310-4, and the model evaluation unit 1310-5, (Or a program module including an instruction), the software module may be stored in a computer-readable, readable non-transitory computer readable media. Also, in this case, the at least one software module may be provided by an operating system (OS) or by a predetermined application. Alternatively, some of the at least one software module may be provided by an Operating System (OS), and some of the software modules may be provided by a predetermined application.

13 is a block diagram of a data recognition unit 1320 according to some embodiments.

13, the data recognition unit 1320 according to some embodiments includes a data acquisition unit 1320-1, a preprocessing unit 1320-2, a recognition data selection unit 1320-3, 1320-4 and a model updating unit 1320-5. In some embodiments, the data recognizing unit 1320 includes a data acquiring unit 1320-1 and a recognition result providing unit 1320-4, and includes a preprocessing unit 1320-2, a recognition data selecting unit 1320-2, -3) and a model update unit 1320-5.

The data acquisition unit 1320-1 can acquire data necessary for the situation determination. For example, the data acquisition unit 1320-1 may acquire a sentence in which the user's voice or voice is recognized. Specifically, the data acquisition unit 1320-1 may acquire a voice or a sentence of the user for performing remittance or settlement. Alternatively, the data acquisition unit 1320-1 may acquire voice data or text data including a voice or a sentence of the user for performing remittance or settlement.

The preprocessing unit 1320-2 can preprocess the acquired data so that the acquired data can be used for the situation determination. The preprocessing unit 1320-2 can process the acquired data into a predetermined format so that the recognition result providing unit 1320-4, which will be described later, can use the acquired data for the situation determination. For example, the preprocessing unit 1320-2 may extract learning entity values from the voice data according to a predetermined format. For example, the preprocessing unit 1320-2 can extract a learning entity value according to a format of {user information, recipient information, transfer amount, transfer command} or {payment means, payment item, payment method, payment command}.

The recognition data selection unit 1320-3 can select data necessary for the situation determination from the preprocessed data. The selected data may be provided to the recognition result provider 1320-4. The recognition data selection unit 1320-3 can select some or all of the preprocessed data according to a predetermined criterion for the situation determination. The predetermined criteria can be determined, for example, in consideration of at least one of the attributes of the data, the generation time of the data, the creator of the data, the reliability of the data, the object of the data, the generation area of the data and the size of the data. Alternatively, the recognition data selection unit 1320-3 may select data according to a predetermined criterion by learning by the model learning unit 1310-4.

The recognition result provider 1320-4 can apply the selected data to the data recognition model to determine the situation. The recognition result providing unit 1320-4 can provide the recognition result according to the data recognition purpose. The recognition result providing unit 1320-4 can apply the selected data to the data recognition model by using the data selected by the recognition data selecting unit 1320-3 as an input value. In addition, the recognition result can be determined by the data recognition model.

For example, when the data recognition model is a model set for estimating the remittance intention of the user, the recognition result providing unit 1320-4 applies the sentence in which the user's voice or voice is recognized to the data recognition model, Can be estimated, inferred or predicted. Alternatively, when the data recognition model is a model set for estimating the user's payment intention, the recognition result providing unit 1320-4 applies the sentence in which the user's voice or voice is recognized to the data recognition model, (Or inferred or predicted).

The recognition result providing unit 1320-4 can acquire the recognition entity as a result of estimating the user's intention. The recognition result provider 1320-4 may provide the acquired recognition entity to a processor (e.g., processor 11 of device 10 of FIG. 2). The processor 100 may determine the user's intention based on the recognition entity and proceed with the process for remitting or settlement.

The model updating unit 1320-5 can update the data recognition model based on the evaluation of the recognition result provided by the recognition result providing unit 1320-4. For example, the model updating unit 1320-5 provides the recognition result provided by the recognition result providing unit 1320-4 to the model learning unit 1310-4 so that the model learning unit 1310-4 The data recognition model can be updated.

Alternatively, the model updating unit 1320-5 may receive evaluation (or feedback) on the recognition result from the processor (for example, the processor 11 of the device 10 in Fig. 2). For example, the device 10 can display the remittance history according to the user's remittance intention by applying the voice inputted by the user to the data recognition model.

The user may approve the transfer details or refuse the approval. For example, if the user approves the transfer details, the user may enter voice, fingerprints, iris, vein, or password. On the other hand, when the user rejects the approval of the transfer details, the user may select the cancel button, enter a voice requesting cancellation, or may not perform a separate input for a predetermined time or more.

In this case, the user feedback in accordance with the approval or rejection of the user can be provided to the model updating unit 1320-5 as an evaluation of the recognition result. That is, the user feedback may include information indicating whether the determination result of the data recognition unit 1320 is false or information indicating that the determination result is true. The model updating unit 1320-5 can update the judgment model using the obtained user feedback.

The data acquisition unit 1320-1, the preprocessing unit 1320-2, the recognition data selection unit 1320-3, the recognition result providing unit 1320-4, and the model updating unit 1320-4 in the data recognizing unit 1320 1320-5 may be fabricated in at least one hardware chip form and mounted on an electronic device. For example, among the data acquisition unit 1320-1, the preprocessing unit 1320-2, the recognition data selection unit 1320-3, the recognition result providing unit 1320-4, and the model updating unit 1320-5 At least one may be made in the form of a dedicated hardware chip for artificial intelligence (AI) or as part of a conventional general purpose processor (e.g. CPU or application processor) or a graphics dedicated processor (e.g. GPU) It may be mounted on one electronic device.

The data acquiring unit 1320-1, the preprocessing unit 1320-2, the recognition data selecting unit 1320-3, the recognition result providing unit 1320-4, and the model updating unit 1320-5 May be mounted on an electronic device, or may be mounted on separate electronic devices, respectively. For example, among the data acquisition unit 1320-1, the preprocessing unit 1320-2, the recognition data selection unit 1320-3, the recognition result providing unit 1320-4, and the model updating unit 1320-5 Some may be included in the electronic device, and the rest may be included in the server.

At least one of the data acquisition unit 1320-1, the preprocessing unit 1320-2, the recognition data selection unit 1320-3, the recognition result providing unit 1320-4, and the model updating unit 1320-5 May be implemented as a software module. At least one of the data acquisition unit 1320-1, the preprocessing unit 1320-2, the recognition data selection unit 1320-3, the recognition result providing unit 1320-4, and the model updating unit 1320-5, When implemented in a module (or program module including an instruction), the software module may be stored in a computer-readable, non-transitory computer readable medium. Also, in this case, the at least one software module may be provided by an operating system (OS) or by a predetermined application. Alternatively, some of the at least one software module may be provided by an Operating System (OS), and some of the software modules may be provided by a predetermined application.

14 is a diagram showing an example of learning and recognizing data by interlocking the device 1000 and the server 2000 according to some embodiments.

The device 1000 may correspond to the device 10 of Fig. 2, for example. The data acquisition unit 1320-1, the preprocessing unit 1320-2, the recognition data selection unit 1320-3, the recognition result providing unit 1320-2, and the like, which are components of the data recognition unit 1320 of the device 1000, -4 and the model updating unit 1320-5 are respectively connected to the data obtaining unit 1320-1, the preprocessing unit 1320-2, the recognition data selecting unit 1320-3 ), The recognition result providing unit 1320-4, and the model updating unit 1320-5. The data acquisition unit 2310, the preprocessing unit 2320, the learning data selection unit 2330, the model learning unit 2340, and the model evaluation unit 2350, which are components of the data learning unit 2300 of the server 2000, Each include a data acquiring unit 1310-1, a preprocessing unit 1310-2, a learning data selecting unit 1310-3, a model learning unit 1310-4, And may correspond to the model evaluation unit 1310-5.

The device 1000 may interwork with the server 2000 via local or remote communication. The fact that the device 1000 and the server 2000 are connected to each other means that the device 1000 and the server 2000 are directly connected or connected to each other by other components (e.g., a third component, an access point (AP), a hub ), A relay device, a base station, a router, and a gateway).

Referring to FIG. 14, the server 2000 can learn a criterion for determining a situation, and the device 1000 can determine a situation based on a learning result by the server 2000. [

In this case, the model learning unit 2340 of the server 2000 can perform the function of the data learning unit 1310 shown in Fig. The model learning unit 2340 of the server 2000 can learn what data to use to determine a predetermined situation and how to determine the situation using data. The model learning unit 2340 acquires data to be used for learning, and applies the acquired data to the data recognition model to learn the criteria for the situation determination. For example, the model learning unit 2340 can learn a data recognition model using speech or sentence, and can generate a data recognition model set to estimate the user's intention. The generated data recognition model may be, for example, a model set for estimating at least one of a user's remittance intention and settlement intention.

The recognition result providing unit 1320-4 of the device 1000 can determine the situation by applying the data selected by the recognition data selecting unit 1320-3 to the data recognition model generated by the server 2000. [ For example, the recognition result providing unit 1320-4 transmits the data selected by the recognition data selecting unit 1320-3 to the server 2000. When the server 2000 selects the recognition data selecting unit 1320-3, To the data recognition model to determine the situation. In addition, the recognition result providing unit 1320-4 can receive from the server 2000 information on the status determined by the server 2000. [ For example, when the selected data includes a sentence in which the user's voice or voice is recognized, the server 2000 applies the selected data to the data recognition model set to estimate the user's intention, Can be obtained. The server 2000 may provide the acquired entity to the recognition result provider 1320-4 as information on the determined status.

As another example, the recognition result provider 1320-4 of the device 1000 may receive the recognition model generated by the server 2000 from the server 2000 and determine the situation using the received recognition model . In this case, the recognition result providing unit 1320-4 of the device 1000 can apply the data selected by the recognition data selecting unit 1320-3 to the data recognition model received from the server 2000 to determine the situation have. For example, when the selected data includes a sentence in which the user's voice or voice is recognized, the recognition result provisioning unit 1320-4 of the device 1000 may estimate the user's intention received from the server 2000 To a data recognition model set up to acquire a recognition entity including a user's intention. The device 1000 may then provide the acquired entity to the processor (e.g., processor 11 of FIG. 2) as information about the determined situation.

The processor 11 can determine the intent of the user's remittance or settlement intention based on the recognition entity, and perform the process for remitting or settlement.

The device 10 according to one embodiment can remit to a recipient only by voice input.

The device 10 according to one embodiment can send the recipient's name, contact, and money to the bank server 20 and send it to the recipient without the recipient's account number.

The device 10 according to one embodiment can make a settlement only by voice input.

15 and 16 are flowcharts of a network system using a data recognition model according to some embodiments.

In Figures 15 and 16, the network system may include a first component 1501, 1601 and a second component 1502, 1602. Here, the first components 1501 and 1601 may be the device 1000, and the second components 1502 and 1602 may be the server 2000 storing the data analysis model. Alternatively, the first component 1501, 1601 may be a general purpose processor and the second component 1502, 1602 may be an artificial intelligence dedicated processor. Alternatively, the first component 1501, 1601 may be at least one application, and the second component 1502, 1602 may be an operating system (OS). That is, the second component 1502, 1602 may be implemented as a component that is more integrated, dedicated, delay less, performance dominated, or has more resources than the first component 1501, 1601 , And can be a component that can process many operations required for creating, updating, or applying the data recognition model more quickly and effectively than the first components 1501 and 1601.

In this case, an interface for transmitting / receiving data between the first component 1501 and 1601 and the second component 1502 and 1602 may be defined.

For example, an application program interface (API) having an argument value (or an intermediate value or a transfer value) of learning data to be applied to the data recognition model may be defined. An API is defined as a set of subroutines or functions that can be called for any processing of any protocol (e.g., a protocol defined in the device 1000) to another protocol (e.g., a protocol defined in the server 2000) . That is, an environment in which the operation of another protocol can be performed through any one of the protocols through the API can be provided.

In FIG. 15, the first component 1501 may analyze a user's intent to transfer using a data recognition model.

In step 1511, the first component 1501 may receive the user's voice that speaks with the intent of remitting.

In step 1513, the first component 1501 may send a sentence to the second component 1502 that recognizes the received voice or voice. For example, the first component 1501 may apply a voice or sentence as an argument value of an API function provided for use of the data recognition model. In this case, the API function may transmit the voice or sentence to the second component 1502 as recognition data to be applied to the data recognition model. At this time, the voice or sentence can be changed and transmitted according to the promised communication format.

At step 1515, the second component 1502 may apply the received voice or sentence to a data recognition model set to estimate the user's transfer intent.

As a result of the application, in step 1517, the second component 1502 may obtain a recognition entity. For example, the recognition entity may include at least one of user information, recipient information (e.g., recipient's name), remittance amount, and remittance command.

In step 1519, the second component 1502 may send the recognition entity to the first component 1501. At this time, the recognition entity can be changed and transmitted according to the promised communication format.

In step 1521, the first component 1501 can determine, based on the recognizing entity, that the user's voice has intent to remit. For example, the first component 1501 may determine that the user's voice has an intent to send a remittance if the 'remittance progress', the name of the recipient, and the remittance amount are included in the remitting command value of the recognizing entity.

Here, steps 1513 to 1521 may correspond to an embodiment of the process of the device 10 in the step 720 of FIG. 2 described above to analyze the received voice to determine the intent of the user to transfer.

If it is determined in step 1523 that the user's voice has the intent of remitting, the first component 1501 may retrieve the contact stored in the contact list in the name of the recipient included in the recognition entity.

In steps 1525 through 1529, the first component 1501 may approve the transfer of the money to the account number of the recipient based on the retrieved recipient's contact information. The corresponding process corresponds to steps 740 to 760 of FIG. 7, and a duplicate description will be omitted.

In FIG. 16, the first component 1601 may analyze the user's billing intent using a data-aware model.

In step 1611, the first component 1601 may provide a payment history. For example, the first component 1601 may display the payment details on the screen or output the payment details by voice.

The user can check the payment history displayed on the screen, and can express whether or not to make a payment or not.

In step 1613, the first component 1601 may receive the user's voice.

In step 1615, the first component 1601 may send a sentence to the second component 1602 that recognizes the received voice or voice. For example, the first component 1601 may apply a voice or a sentence as an argument value of an API function provided for use of the data recognition model. In this case, the API function may transmit the voice or sentence to the second component 1602 as recognition data to be applied to the data recognition model. At this time, the voice or sentence can be changed and transmitted according to the promised communication format.

In step 1617, the second component 1602 may apply the received voice or sentence to a data awareness model that is set up to estimate the user's payment intent.

As a result of the application, in step 1619, the second component 1602 may obtain a recognition entity. For example, the recognition entity may include at least one of a payment means, a payment item, a payment method and a payment command.

In step 1621, the second component 1602 may send the recognition entity to the first component 1601. At this time, the recognition entity can be changed and transmitted according to the promised communication format.

In step 1623, the first component 1601 may determine, based on the recognition entity, that the user's voice has intent to settle. For example, if 'payment cancellation' is included in the payment command value of the settlement entity, the first component 1601 may determine that the intention is not to proceed to the user's voice. On the other hand, if 'settlement progress' is included in the settlement command value of the settlement entity, the first component 1601 may determine that the intention is to proceed with settlement of the user's voice.

Here, steps 1615 to 1623 may correspond to an embodiment of the process in which the device 10 analyzes the received voice to determine the user's payment intention in step 1030 of FIG. 10 described above.

If it is determined that the user's voice has the intention of payment, the first component 1601 can transmit the payment information to the card company in step 1625 through step 1627, if the user authentication through voice is successful. The corresponding process corresponds to steps 1040 to 1050 of FIG. 10, and redundant description is omitted.

Some embodiments may also be implemented in the form of a recording medium including instructions executable by a computer, such as program modules, being executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable medium may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, 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. Communication media typically includes any information delivery media, including computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism.

Further, the method according to the disclosed embodiments may be provided as a computer program product.

The computer program product may include a S / W program, a computer readable storage medium storing the S / W program, or a merchandise traded between the merchant and the purchaser.

For example, the computer program product may be a product of the S / W program type distributed electronically via the device 10 or the manufacturer of the device 10 or an electronic marketplace (e.g. Google Play Store, App Store) Double app). For electronic distribution, at least a portion of the S / W program may be stored on a storage medium or may be created temporarily. In this case, the storage medium may be a manufacturer or a server of an electronic market, or a storage medium of a relay server.

Also, in this specification, the term " part " may be a hardware component such as a processor or a circuit, and / or a software component executed by a hardware component such as a processor.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

10: Device 11: Processor
12: Memory 13: Display
14: microphone

Claims (19)

A memory in which at least one program is stored;
A microphone for receiving voice; And
And at least one processor for transferring to the recipient according to the voice by executing the at least one program,
The processor comprising:
Analyzing the received voice to determine the intent of the user to transmit;
Retrieving a contact stored in the contact list in the name of the recipient;
Transmitting the name and the contact of the recipient to a bank server together with an amount included in the voice;
Receiving remittance details from the bank server; and
And accepting the transfer details.
The method of claim 1, wherein the step of determining the intent of remitting the user includes:
And learning the pattern through the voice input by the user when making a remittance.
2. The apparatus of claim 1,
Further comprising authenticating that the voice input to the microphone is a user of the device,
And when the voice input to the microphone is a user of the device, the device determines the intent of remitting the user.
2. The apparatus of claim 1,
Further comprising the step of displaying the remittance details including the account number of the recipient.
The method according to claim 1, wherein the step of approving the transfer details comprises:
And confirms the transfer details using the fingerprint, iris or voice of the user.
The method according to claim 1, wherein the step of approving the transfer details comprises:
And confirms the transfer details using the vein pattern received from the wearable device worn by the user.
The method of claim 1, wherein the step of determining the intent of remitting the user includes:
And applying the received voice to a data recognition model set to estimate a user's transfer intent to determine the user's transfer intention.
8. The data recognition method according to claim 7,
Based on artificial intelligence algorithms, the model is learned using speech or text as learning data and learning entities,
Wherein the learning entity comprises at least one of user information, recipient information, remittance amounts and remittance instructions.
8. The method of claim 7,
The method of claim 1,
Determining a user's transfer intention based on a recognition entity obtained as a result of applying the data recognition model to the received voice,
Wherein the recognition entity comprises at least one of user information, recipient information, remittance amount and remittance instructions.
Receiving a voice of the user;
Analyzing the received voice to determine the intent of the user to transmit;
Searching a contact list for a contact stored in a name of a recipient included in the voice;
Transmitting the name and the contact of the recipient to a bank server together with an amount included in the voice;
Receiving remittance details from the bank server; and
And accepting the transfer details.
The method as claimed in claim 10, wherein the step of determining the intent of remitting the user includes:
And learning a pattern through a voice inputted when the user makes a remittance.
11. The method of claim 10,
Further comprising authenticating that the voice is a user of the device,
And when the voice is a user of the device, determines the intent of remitting the user.
11. The method of claim 10,
Further comprising the step of displaying the remittance history including the account number of the recipient.
The method as claimed in claim 10,
And said transfer details are approved using said fingerprint, iris or voice of said user.
The method as claimed in claim 10,
Wherein the transferring unit approves the transfer details using the vein pattern received from the wearable device worn by the user.
The method as claimed in claim 10, wherein the step of determining the intent of remitting the user includes:
Applying the received voice to a data recognition model set to estimate a user's remittance intention to determine the remittance intention of the user.
17. The data recognition method of claim 16,
Based on artificial intelligence algorithms, the model is learned using speech or text as learning data and learning entities,
Wherein the learning entity includes at least one of user information, recipient information, remittance amount, and remittance instruction.
17. The method of claim 16,
The method of claim 1,
Determining a user's transfer intention based on a recognition entity obtained as a result of applying the data recognition model to the received voice,
Wherein the recognizing entity includes at least one of user information, recipient information, remittance amount, and remittance command.
Analyzing the user's voice to determine a user's intent to send the message;
Searching a contact list for a contact stored in a name of a recipient included in the voice;
Transmitting the name and the contact of the recipient to a bank server together with an amount included in the voice;
Receiving remittance details from the bank server; and
Accepting the transfer details; and instructions for causing the device to execute.

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