TWI767498B - Cross-channel artificial intelligence dialogue platform integrating machine learning and operation method thereof - Google Patents

Abstract

A cross-channel artificial intelligence dialogue platform comprises three internal servers. The voice input interface of the first internal server receives the first voice signal. The private-data-hiding module deletes the personal data of the first voice signal to generate the second voice signal. The speech-to-text interface and the semantic recognition module of the first internal server respectively converts the second voice signal to the first text signal and obtaining the intent signal according to the first text signal. The dialogue module and the application module respectively generates the response signal and the control instruction according to the intent signal. The text-to-speech interface of the first internal server converts the response signal to the third voice signal. The output interface outputs the third voice signal and the control instruction.

Description

A cross-channel AI conversational platform integrating machine learning and its operation method

The present invention relates to a dialogue-type platform and an operation method of the dialogue-type platform, in particular to a cross-channel artificial intelligence dialogue-type platform and its operation method.

With the popularization of various digital marketing channels, people often hope to get a reply as soon as possible when they encounter any transaction problems.

However, for financial institutions that provide customer service, increasing the number of customer service personnel will inevitably lead to a significant increase in labor costs. In addition, it takes a certain amount of time to train an excellent customer service staff. When a sudden influx of customers comes in, the customer service staff who can deal with various customer problems calmly are often overwhelmed. question. Therefore, for a financial institution that cannot provide good services, the public's evaluation will drop significantly, which will affect the public's trust and willingness to participate in other projects.

In view of this, the present invention proposes a cross-channel artificial intelligence (Artificial Intelligence, AI) conversational platform. The channels include: digital channels, customer service centers, business units, and the like. Through the introduction of speech recognition system, artificial intelligence conversational system and artificial intelligence conversational backend, combined with a new generation of conversational artificial intelligence technology, including: natural language processing (Natural Language Processing, NLP), dynamic learning mechanism, multi-round situational dialogue design and The dynamic information collection mechanism, etc. establishes the customer dialogue analysis background, thereby enhancing the user experience and market influence of the digital channel.

A cross-channel artificial intelligence conversational platform according to an embodiment of the present invention includes: a first internal server, including: a voice input interface, a voice-to-text interface, a semantic recognition interface, a text-to-speech interface, and an output interface, wherein the voice input interface is used to receive a first voice signal; a second internal server, communicating with the first internal server, includes: a client audio database for storing a plurality of audio files, the The content of the audio file corresponds to a plurality of personal data respectively; and a data hiding module is electrically connected to the customer audio database, and the data hiding module is used for dividing the first voice signal into a plurality of voice segments, and When the information hiding module determines that any one of the voice segments matches any one of the audio files, the information hiding module deletes the audio information corresponding to the voice segment from the first voice signal, and send back the first voice signal of the audio data of the deleted voice segment as a second voice signal to the first internal server; wherein the voice-to-text interface of the first internal server is used for according to the second voice signal The voice signal generates a first text signal; the semantic recognition interface is used for generating an intention signal according to the first text signal; and a third internal server communicates with the first internal server, including: a dialogue module for selectively generating a reply signal according to the intention signal; and an application module for generating a control command corresponding to the intention analysis signal; wherein the text-to-speech interface of the first internal server is used for according to the reply The signal generates a third voice signal; the output interface of the first internal server is used for outputting the third voice signal and the control command.

An operation method of a cross-channel artificial intelligence conversational platform according to an embodiment of the present invention includes: receiving a first voice signal through a voice input interface of a first internal server; using a client of a second internal server The audio database stores a plurality of audio files, and the contents of the audio files correspond to a plurality of personal data respectively, wherein the second internal server communicates with the first internal server; hides a piece of information from the second internal server The module divides the first voice signal into a plurality of voice segments, wherein the information hiding module is electrically connected to the customer audio database, and when the information hiding module determines any one of the voice segments When it matches any one of the audio files, use the data hiding module to delete the audio information corresponding to the voice segment from the first voice signal; use the data hiding module to return a second voice signal to the A voice-to-text interface of the first internal server, wherein the second voice signal is a first voice signal for deleting the audio data of the voice segment; the voice-to-text interface generates a first text signal according to the second voice signal; A semantic recognition interface of the first internal server is used to generate an intention signal according to the first text signal; a dialogue module of a third internal server is used to generate a reply signal according to the intention signal, wherein the third internal server The first internal server is communicated and connected; a text-to-speech interface of the first internal server is used to generate a third voice signal according to the reply signal; an application module of the third internal server is used to generate a a control command; and outputting the third voice signal and the control command through an output interface of the first internal server.

The above description of the present disclosure and the following description of the embodiments are used to demonstrate and explain the spirit and principle of the present invention, and provide further explanation of the scope of the patent application of the present invention.

The detailed features and advantages of the present invention are described in detail in the following embodiments, and the content is sufficient to enable any person skilled in the relevant art to understand the technical content of the present invention and implement it accordingly, and according to the content disclosed in this specification, the scope of the patent application and the drawings , any person skilled in the related art can easily understand the related objects and advantages of the present invention. The following examples further illustrate the viewpoints of the present invention in detail, but do not limit the scope of the present invention in any viewpoint.

Please refer to FIG. 1 , which is an architecture diagram 100 of a cross-channel AI conversational platform according to an embodiment of the present invention. The cross-channel AI conversational platform 100 includes a first internal server 2 , a second internal server 4 and a third internal server 6 . As shown in FIG. 1 , the second internal server 4 and the third internal server 6 are respectively connected to the first internal server 2 for communication. In addition, the components in the first internal server 2 are respectively connected to the client device 91 , the first external server 93 , the second external server 95 and the third external server 97 .

In practice, the first internal server 2 , the second internal server 4 , and the third internal server 6 are, for example, Blade Servers, Rack Servers or stand-up servers in the computer room of a financial institution. Servers (Pedestal Servers), the present invention does not limit the hardware types of the first, second and third internal servers 2, 4 and 6.

The first internal server 2 , the second internal server 4 and the third internal server 6 each have a memory to implement the functions described later. The above-mentioned memory may be, for example, random access memory, read-only memory, or flash memory. In one embodiment, the first internal server 2 , the second internal server 4 and the third internal server 6 further include communication devices supporting wired network, wireless network, mobile network and/or wireless communication. In one embodiment, the first internal server 2 , the second internal server 4 and the third internal server 6 each include a processing circuit, which can perform the functions described later. The processing circuit is, for example, a microcontroller, a microprocessor, a processor, a central processing unit (CPU), a digital signal processor, and an application-specific integrated circuit. (application specific integrated circuit, ASIC), digital logic circuit, field programmable gate array (field programmable gate array, FPGA) and/or other hardware components with computing processing functions, the present invention has no effect on the hardware type of the processing circuit. be restricted.

Please continue to refer to Figure 1. The first internal server 2 includes a voice input interface 21 , a voice-to-text interface 23 , a semantic recognition interface 25 , a text-to-speech interface 27 and an output interface 29 . The voice input interface 21 is communicatively connected to the client device 91 . The client device 91 is, for example, a smart phone or a tablet computer with a mobile banking App (application) installed by the user, or a smart speaker or a smart robot at the counter of a smart branch. chat with the client device 91. In practice, the sound made by the user will be used by the receiver (eg, microphone) of the client device 91 to generate a first voice signal, and then the communication element of the client device 91 will send the first voice signal to the first internal server. 2 of the voice input interface 21. In short, when the user needs to perform financial-related operations, he can directly speak to the client device 91 to generate a first voice signal, and then the first voice signal will be sent to the voice input interface 21 for processing.

Please refer first to the second internal server 4 in FIG. 1 , which includes a client audio database 41 and a data hiding module 43 that are electrically connected to each other. The client audio database 41 stores a plurality of audio files, and the contents of the audio files correspond to a plurality of personal data respectively. In practice, the second internal server may further include a dynamic information learning module. For example, the dynamic information learning module uses the manual customer service recordings of financial institutions as training data in advance, and uses machine learning to learn from the customer service recordings. The audio clips belonging to the customer's personal data are automatically identified and stored in the customer audio database 41 . The dynamic information learning module can further update the records in the customer audio database according to the first voice signal obtained from the voice input interface 21 each time, which is not limited in the present invention.

Please continue to refer to the second internal server 4 of FIG. 1 . The personal information hiding module 43 is electrically connected to the client audio database 41 and is communicatively connected to the speech-to-text interface 23 of the first internal server 2 . The personal information hiding module 43 is used for dividing the first voice signal into a plurality of voice segments, and when the personal information hiding module 43 determines that any one of these voice segments matches the multiple audio files stored in the customer information database 41 When any of the above, the personal information hiding module 43 deletes the audio information corresponding to the voice segment from the first voice signal, and returns the first voice signal of the audio data of the deleted voice segment as a second voice signal to The speech-to-text interface 23 of the first internal server 2 . When the personal information hiding module 43 performs comparison, for example, a fuzzy comparison algorithm may be used. In addition, when the compared user profile is divided into multiple voice segments, the profile hiding module 43 reorganizes these voice segments with the user profile to extract the complete audio belonging to the user profile material. Through the processing mechanism of the above-mentioned personal information hiding module 43, the private information belonging to the user can be restricted to the first internal server 2 and the second internal server 4 set in the computer room of the financial institution, and in the subsequent voice During identification, the user's personal information will not be leaked to the Internet.

Please refer to Figure 1. The speech-to-text interface 23 of the first internal server 2 is communicatively connected to the data hiding module 43 of the second internal server 4 and the first external server 93 respectively, and the semantic recognition interface 25 is communicatively connected to the second external server 95 . The speech-to-text interface 23 generates a first text signal according to the second voice signal, and the semantic recognition interface 25 generates an intent signal according to the first text signal. In other words, the speech-to-text interface 23 converts the audio data including the user's personal information into text, and the semantic recognition interface 25 interprets the user's intention from the text. For example, when the first text signal is: "I want to transfer one thousand dollars", the semantic recognition module 25 can learn the two intentions of "the user wants to transfer money" and "the transfer amount is one thousand dollars". . In practice, the speech-to-text interface 23 and the semantic recognition interface 25 are, for example, application programming interfaces (APIs), and the first external server 93 is, for example, a Google Cloud speech-to-text (STT) external server. device. The second external server 95 is, for example, an IBM Watson external server, which can provide various Watson cognitive computing services, including natural language processing (NLP) services for judging customer intentions. ) machine learning mechanism to improve semantic understanding accuracy.

Please refer to Figure 1. The third internal server 6 includes a dialogue module 61 and an application module 63 that are communicatively connected to the semantic recognition interface 25 . The dialogue module 61 selectively generates a reply signal according to the intention signal. The application module 63 can generate control commands corresponding to the intended analysis signal. In practice, the dialogue module 61 of the third internal server 6 can provide a dynamic learning mechanism through the machine learning model, thus greatly improving the maintenance efficiency. The dialogue module 61 is designed with multiple rounds of situational dialogue. In practice, for example, a dialogue analysis model is trained on the Watson platform by pre-training the human customer service records to be analyzed, and then the trained dialogue analysis model is stored in the database of the dialogue module 61, so that the dialogue module 61 can provide Interactive situational dialogue design, and has the effect of context coherence. For example, when the user says: "I want to transfer one thousand dollars", the dialogue module 61 obtains information from the semantic recognition interface 25 that includes "the user wants to transfer money" and "the transfer amount is one thousand dollars". In addition to the intention signal of an intention, it can also provide the user with reply signals such as "ask the user's account to transfer the account" and "ask the user's account to be used to transfer the account", so as to facilitate the client device The mobile banking app running on 91 can collect enough information to complete subsequent transfer operations. In addition, the dialogue module 61 has a dynamic information collection mechanism, which can quickly set parameters for rapid deployment and improve user experience. When the dialogue module 61 cannot identify the user's intention signal, the dialogue module 61 can be transferred to the manual customer service system, and the online customer service personnel will respond to the user's question.

Please continue to refer to Figure 1. The text-to-speech interface 27 of the first internal server 2 is respectively connected to the dialogue module 61 of the third internal server 6 and the third external server 97 for communication. The text-to-speech interface 27 is used to generate a third voice signal according to the reply signal; in other words, the reply generated by the dialogue module 61 is converted into a computer voice that the user can understand, and then output by the output interface 29 of the first internal server 2 The third voice signal is sent to the client device 91 so that the speaker or speaker of the client device 91 can play the third voice signal for the user to listen to. In practice, the third external server 97 is, for example, a text-to-speech web service external server of the Industrial Technology Research Institute, which can provide a text-to-speech (TTS) web service (web service), and the server provides SOAP (Simple Object Access Protocol) protocol network service, which converts the input text into voice for output. It should be noted that although the first, second and third external servers 93, 95 and 97 of the present invention are the first internal server 2 connected to each cloud service through the Internet in the above-mentioned embodiment, However, in another embodiment, the above-mentioned external server can also be purchased by a financial institution with a text-to-speech interconversion function and a server with semantic understanding function and set up in the local computer room. The present invention does not limit the first to third external servers. Servers 93~97 must be connected to the cloud to achieve the above functions.

Please continue to refer to Figure 1. The output interface 29 of the first internal server 2 is communicatively connected to the application module 63 of the third internal server 6 and the client device 91 . In addition to outputting the third voice signal, the output interface 29 also outputs the control commands generated by the application module 63 . The control instruction is, for example, an instruction to control the mobile banking App to complete the transfer operation.

According to the content of the above-mentioned cross-channel artificial intelligence conversational platform 100, in practice, the cross-channel artificial intelligence conversational platform 100 can be communicatively connected to the user's smartphone or the smart speaker at the smart branch counter. In this way, the user can complete the desired financial transaction operation item through dialogue with the client device 91 .

Please refer to Figure 1 and Figure 2 together. FIG. 2 illustrates an operation method of the cross-channel artificial intelligence conversational platform according to an embodiment of the present invention, which is applicable to the aforementioned cross-channel artificial intelligence conversational platform 100 . Please refer to step S11, the voice input interface 21 receives the first voice signal. To be more specific, the client device 91 transmits the user's voice through wired or wireless communication, and is then received by the voice input interface 21 of the first internal server 2 . Referring to step S21, the personal information hiding module 43 of the second internal server 4 divides the first voice signal into a plurality of voice segments. Please refer to step S23 , the personal information hiding module 43 compares the voice segment with the audio file in the customer audio database 41 . Referring to step S25, the personal information hiding module 43 determines whether any voice segment matches any audio file. If so, continue to step S27. Otherwise, go back to step S23. Please refer to step S27, the personal information hiding module 43 returns a second voice signal S27, the second voice signal is the first part of the audio data (for example, the waveform signal representing the user's personal data) of the audio segment deleted. a voice signal. Referring to step S13, the voice-to-text interface 23 of the first internal server 2 generates a first text signal according to the second voice signal, and specifically, converts the audio data without the user's personal data into text data. Referring to step S15, the semantic recognition interface 25 of the first internal server 2 generates an intention signal S15 according to the first text signal, for example, the text signal is analyzed by a semantic understanding server providing cloud services to obtain the user's intention. Please refer to step S31, the dialogue module 61 of the third internal server 6 generates a reply signal according to the intention signal. In other words, the dialogue module 61 can identify the user's intention from the reply signal to provide personalized service or response . In practice, if the customer's question is not within the response range of the dialogue module 61, the dialogue module 61 can transfer to the manual customer service system for subsequent customer service. Referring to step S17, the text-to-speech interface 27 of the first internal server 2 generates a third voice signal S17 according to the reply signal, which converts the reply given to the user by the system into a computer voice audible to the user. Please refer to step S33, the application module 63 generates the control command S33 according to the intention signal. The control command is used to perform an operation corresponding to the user's voice on the client device. Please refer to step S35, the output interface 29 outputs the third voice signal and the control command to the client device 91, for example, playing the system reply to the user for listening, realizing the dialogue interaction with the user and obtaining the operation required by the user to perform the operation. other parameters, or execute control instructions to complete the financial transaction operation items desired by the user.

To sum up the above, the cross-channel artificial intelligence dialogue platform disclosed in the present invention provides the service of completing financial transaction operations through dialogue with the user, so that the customer can feel the best experience and service, and can prevent the user's personal Private information is leaked to the cloud to protect the security of users' personal information. In addition, by connecting the cross-channel artificial intelligence dialogue platform to the mobile banking app, smart branch counter or smart personal financial management service, it can also reduce the additional manpower required by financial institutions to hire and train personnel who can provide the above financial services. with time cost.

Although the present invention is disclosed in the foregoing embodiments, it is not intended to limit the present invention. Changes and modifications made without departing from the spirit and scope of the present invention belong to the scope of patent protection of the present invention. For the protection scope defined by the present invention, please refer to the attached patent application scope.

100: A Cross-Channel AI Conversational Platform 2: The first internal server 21: Voice input interface 23: Speech-to-text interface 25: Semantic recognition interface 27: Text-to-speech interface 29: Output interface 4: Second internal server 41:Customer audio database 43: Personal information hidden module 6: Third internal server 61: Dialogue Mods 63: Application Mods 91: Client Device 93:First external server 95: Second external server 97: Third external server S11~S35: Steps

FIG. 1 is a structural diagram of a cross-channel artificial intelligence conversational platform according to an embodiment of the present invention. FIG. 2 is a flowchart illustrating an operation method of a cross-channel AI conversational platform according to an embodiment of the present invention.

100: A Cross-Channel AI Conversational Platform

2: The first internal server

21: Voice input interface

23: Speech-to-text interface

25: Semantic recognition interface

27: Text-to-speech interface

29: Output interface

4: Second internal server

41:Customer audio database

43: Personal information hidden module

6: Third internal server

61: Dialogue Mods

63: Application module

91: Client Device

93:First external server

95: Second external server

97: Third external server

Claims (3)

A cross-channel artificial intelligence conversational platform integrating machine learning, comprising: a first internal server, including: a voice input interface, a voice-to-text interface, a semantic recognition interface, a text-to-speech interface, and an output interface, wherein The voice input interface is used for receiving a first voice signal; a second internal server, communicating with the first internal server, includes: a client audio database for storing a plurality of audio files, among which the audio files are The content corresponds to a plurality of personal data respectively; and a data hiding module is electrically connected to the customer audio database, and the data hiding module is used for dividing the first voice signal into a plurality of voice segments, and when the When the data hiding module determines that any one of the voice clips matches any of the audio files, the data hiding module deletes the audio information corresponding to the voice clip from the first voice signal, and will be Deleting the first voice signal of the audio data of the voice segment is sent back to the first internal server as a second voice signal; wherein the voice-to-text interface of the first internal server is used for generating according to the second voice signal a first text signal; the semantic recognition interface is used for generating an intention signal according to the first text signal; and a third internal server, communicating with the first internal server, including: a dialogue module for selectively A reply signal is generated according to the intention signal; and an application module is used for generating a control command corresponding to the intention analysis signal; wherein the text-to-speech interface of the first internal server is used for generating a reply signal according to the reply signal a third voice signal; the output interface of the first internal server is used for outputting the third voice signal and the control command; wherein the first internal server, the second internal server and the third internal server are Internal servers are blade servers, rack servers, or stand-up servers; the speech-to-text interface is communicatively connected to a Google Cloud speech-to-text external server, and the semantic recognition interface is communicatively connected to an IBM Watson external server , and the text-to-speech interface is communicatively connected to the ITRI text-to-speech web service external server; the second internal server further includes a dynamic information learning module, which is used for recording according to a customer service Recording and extracting the audio files associated with the personal data from the customer service recording in a machine learning manner, and storing the audio files in the customer audio database. The cross-channel artificial intelligence conversational platform integrating machine learning as claimed in claim 1, wherein when the information hiding module determines that the first voice signal has a plurality of voice segments respectively corresponding to the plurality of voice segments in the customer audio database When the audio file is generated, the data hiding module is further used for recombining the voice clips to extract a complete personal data among the personal data. An operation method of a cross-channel artificial intelligence conversational platform integrating machine learning, comprising: receiving a first voice signal through a voice input interface of a first internal server; hiding a module with an information of the second internal server Divide the first voice signal into a plurality of voice segments, wherein the information hiding module is electrically connected to the client audio database, and when the information hiding module determines that any one of the voice segments conforms to the When any one of the plurality of audio files stored in a client audio database of the second internal server is used, the data hiding module is used to delete the audio information corresponding to the audio segment from the first audio signal, wherein the audio information corresponding to the audio segment is deleted from the first audio signal. Two internal servers are connected to the first internal server in communication, and the contents of the audio files correspond to a plurality of personal data respectively; the data hiding module returns a second voice signal to one of the first internal servers A voice-to-text interface, wherein the second voice signal is the first voice signal of the audio data of the voice segment deleted; a first text signal is generated by the voice-to-text interface according to the second voice signal; the first internal server is used A semantic recognition interface generates an intent signal according to the first text signal; a dialogue module of a third internal server generates a reply signal according to the intent signal, wherein the third internal server is communicatively connected to the first internal server using a text-to-speech interface of the first internal server to generate a third voice signal according to the reply signal; using an application module of the third internal server to generate a control command according to the intention signal; and using the first An output interface of an internal server outputs the third voice signal and the control command; wherein the first internal server, the second internal server and the third internal server are internal servers which are blade servers, machine server or stand-up server; the speech-to-text interface is communicatively connected to a Google Cloud speech-to-text external server, the semantic recognition interface is communicatively connected to an IBM Watson external server, and the text-to-speech interface is communicative Connecting to an external server of text-to-speech web service of ITRI; before receiving the first voice signal, further comprising obtaining from a customer service recording record by means of machine learning with a dynamic information learning module of the second internal server The audio files associated with the personal data; and using the dynamic information learning module to store the audio files to the client audio database.

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