CN117009476A

CN117009476A - Service interaction processing method and device and computer equipment

Info

Publication number: CN117009476A
Application number: CN202310665770.7A
Authority: CN
Inventors: 刘娟
Original assignee: Bank of China Ltd
Current assignee: Bank of China Ltd
Priority date: 2023-06-06
Filing date: 2023-06-06
Publication date: 2023-11-07

Abstract

The application relates to a business interaction processing method, a business interaction processing device, computer equipment, a computer readable storage medium and a computer program product, which can be used for a product test flow in the financial field. The processing method comprises the following steps: determining the business category of business interaction, acquiring a corresponding input text, inputting the business category into a pre-trained dialogue generation model, outputting a reply text and N recommended questions with highest similarity with the input text by the model, and displaying the reply text and the recommended questions. In this way, the reply text aiming at the user problem can be automatically generated through the dialogue generation model without manual reply, and the user can determine the problem difficulty and obtain corresponding reply according to the recommended problem, so that the waiting time of the user is shortened, and the service processing efficiency is improved.

Description

Service interaction processing method and device and computer equipment

Technical Field

The present disclosure relates to the field of artificial intelligence, and in particular, to a business interaction processing method, apparatus, computer device, computer readable storage medium, and computer program product.

Background

Currently, in many situations, such as customer service area or specific service support area, a user needs to be manually replied to. However, some services are various, the number of users is also large, and it is difficult to solve all the problems in time by manual reply.

In the related art, some machine answer models exist, and corresponding replies can be selected from the question bank according to the problems of the user, but the method can only be used when the problems exist in the question bank, and the method is excessively dependent on the storage amount of the question bank and is not high in usability.

Disclosure of Invention

Based on the above, in order to solve the above technical problems, a business interaction processing method, a business interaction processing device, a computer readable storage medium and a computer program product are provided. The technical scheme of the present disclosure is as follows:

according to an aspect of the embodiments of the present disclosure, there is provided a service interaction processing method, including:

determining a business category of business interaction;

acquiring an input text corresponding to the service class;

inputting the business category and the input text into a pre-trained dialogue generation model, and outputting a reply text and the top N recommendation questions with the highest similarity with the input text through the dialogue generation model; wherein N is a positive integer;

and displaying the reply text and the recommendation problem.

In one embodiment, the training process of the dialog generation model includes:

acquiring historical problems related to business;

classifying the historical problems according to preset business categories to obtain historical problems of a plurality of categories;

screening the historical problems of each category to obtain high-frequency problems with occurrence frequency exceeding a preset threshold;

generating a standard reply corresponding to the high-frequency problem according to the high-frequency problem;

and training the service class, the high-frequency problem and the standard reply as a data set of a model to obtain a dialogue generation model.

In one embodiment, after screening the historical problem of each category to obtain the high-frequency problem with the occurrence frequency exceeding the preset threshold value, the method further includes:

selecting a plurality of history questions as training texts;

taking the training texts as input vectors of the dialogue generating model one by one;

calculating the similarity between any one of the high-frequency problems and the input vector;

and taking the top N high-frequency problems with the highest similarity with the input vector as output vectors of the dialogue generating model.

In one embodiment, before determining the service class of the service interaction, the method further includes:

acquiring basic data of users participating in service interaction;

and storing the basic data into a designated storage area.

displaying service class options;

a text entry box is provided corresponding to each business category option.

In one embodiment, before presenting the reply text and the recommended questions, the method further includes:

transmitting the input text to a designated terminal under the condition that the dialogue generation model does not output a reply text; the appointed terminal is used for providing a reply text corresponding to the input text in a manual mode.

According to another aspect of the embodiments of the present disclosure, there is provided a service interaction processing apparatus, including:

the category determining module is used for determining the service category of service interaction;

the text input module is used for acquiring an input text corresponding to the business category;

the model output module is used for inputting the business category and the input text into a pre-trained dialogue generation model, and outputting a reply text and top N recommendation questions with highest similarity with the input text through the dialogue generation model; wherein N is a positive integer;

and the display module is used for displaying the reply text and the recommendation problem.

According to another aspect of the embodiments of the present disclosure, there is also provided a computer device including a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.

According to another aspect of the disclosed embodiments, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above method.

According to another aspect of the disclosed embodiments, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of the above method.

According to the technical scheme provided by the embodiment of the disclosure, the service type of service interaction can be determined, the corresponding input text is obtained, the service type is input into a pre-trained dialogue generation model, the model outputs the reply text and N recommendation questions with highest similarity with the input text, and the reply text and the recommendation questions are displayed. In this way, the reply text aiming at the user problem can be automatically generated through the dialogue generation model without manual reply, and the user can determine the problem difficulty and obtain corresponding reply according to the recommended problem, so that the waiting time of the user is shortened, and the service processing efficiency is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the following description will briefly explain the embodiments or the drawings used in the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments described in the present description, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of an application scenario of a business interaction processing method in one embodiment;

FIG. 2 is a flow diagram of a business interaction processing method in one embodiment;

FIG. 3 is a flow diagram of a dialog generation model training method in one embodiment;

FIG. 4 is a flow diagram of a dialog generation model training method in accordance with another embodiment;

FIG. 5 is a flow diagram of a method for obtaining business interaction data in one embodiment;

FIG. 6 is a flow diagram of a method of exposing traffic classes in one embodiment;

FIG. 7 is a flow chart of a business interaction processing method in another embodiment;

FIG. 8 is a schematic structural diagram of a business interaction processing device in one embodiment;

FIG. 9 is a schematic diagram of the internal architecture of a computer device in one embodiment.

Detailed Description

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element. For example, if first, second, etc. words are used to indicate a name, but not any particular order.

The terms "vertical," "horizontal," "left," "right," "upper," "lower," "front," "rear," "circumferential," "direction of travel," and the like as used herein are based on the orientation or positional relationship shown in the drawings and are merely for convenience of description and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore should not be construed as limiting the application.

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or", "at least one of …" as used herein includes any and all combinations of one or more of the associated listed items. The connection, etc. described in the present disclosure may be a direct connection through an interface or a pin between devices, or may be a connection through a wire, or may be a wireless connection (communication connection).

Currently, some business systems usually reply by solving the problem of the product based on the user after pushing out the product or service. In particular, financial institutions such as banks and financial companies often push out a large number of online products, and in order to avoid unexpected problems after the products are online, the online products can be tested and adjusted in combination with users or merchants before being online, and in the process, technical problems encountered in the testing process except basic use guidance of business personnel on the merchants are required to be specifically solved by bank technicians aiming at specific problems. Current banking technicians answer solutions one by one for each specific question each merchant encounters in the joint test. However, as the online products pushed out by the banks are numerous, and after the number of merchants for using the products is large, the workload required by the method is extremely large, the labor cost is high, the problems cannot be solved in time, and the efficiency is low.

In order to solve the above technical problems, according to an aspect of the embodiments of the present disclosure, a service interaction processing method is provided. The business interaction processing method provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that server 104 needs to query or process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In some specific application scenarios, the user may use a product pushed by a banking system through the terminal 102, and the server 104 may be a server where the banking system is located. For example, during a joint debugging test of a product on line by a bank, a user or merchant participating in the test may use the product to be tested through the terminal 102.

In one embodiment, as shown in fig. 2, a service interaction processing method is provided, and the method is applied to the terminal 102 in fig. 1 for illustration, and the processing method includes the following steps:

step S210, determining the business category of business interaction.

The business interactions may include, among other things, a process of receiving a user's questions about a product and replying to the solution. The business category may include a category of a problem, a category of a product, etc., and may be preset in the business system. For example, the business categories may include product type-based categories such as first category products, second category products, etc., and may also include question-based categories such as page loading speed, file upload format, etc.

Specifically, the user interface such as a mobile phone bank or an internet banking can provide options of service categories, and the user can determine the category of service interaction required by the user selection by receiving the user selection. In some other embodiments, a preset category name or serial number may also be displayed to the user, and the category of the service interaction is determined by receiving the name or serial number input by the user.

Step S220, obtaining input text corresponding to the business category.

Specifically, after determining the business category of the business interaction, an input channel such as an input box may be provided on the next interface where the user determines the business category to obtain the input text of the user. For example, when the service class selected by the user is determined to be the file uploading format of the first class product according to the options in the page, the text information input by the user can be continuously acquired through the text input box, so as to obtain the input text under the class.

It should be appreciated that the business categories may include only product-based categories, or only problem-based categories, or both product-based and problem-based categories.

And step S230, inputting the business category and the input text into a pre-trained dialogue generation model, and outputting a reply text and the top N recommended questions with the highest similarity with the input text through the dialogue generation model.

The dialogue generation model can be trained by combining a text generation type model according to historical data such as a manual question-answer log and the like. N is a positive integer.

Specifically, the business category and the input text can be used as the input of a dialogue generating model, the dialogue generating model automatically generates and outputs corresponding reply texts according to the business category and the input text, the dialogue generating model can calculate the similarity between the input text and the database problems, and the dialogue generating model outputs N problems with the highest similarity with the input text. In some other embodiments, the business system may obtain questions in a preset database, and may calculate the similarity between the input text and the questions in the database, where the three questions with the highest similarity are used as the output of the dialog generation model.

Step S240, displaying the reply text and the recommendation question.

Specifically, the reply text and the N recommended questions output by the dialog generation model may be presented to an interface for performing business interactions. For example, the reply text and the recommended questions may be presented in an input box or question-answer box that is business interacted with by the user.

It should be noted that the above processing steps may be performed on a terminal used by a user, or may be performed on a server where a service system is located. When the above steps are performed in the server, the server may acquire the service class and the corresponding input text through communication with the user terminal.

In one embodiment, as shown in fig. 3, the training process of the dialog generation model includes:

step S110, obtain the history problem related to the business.

The historical questions may be pre-accumulated questions related to the business, and may include, for example, a question-and-answer log that previously received the user questions and manually replied to.

In particular, historical questions related to the business may be obtained through a manual question-and-answer log, which may include historical answer records for other products in the business system.

Step S120, classifying the history problems according to preset business categories to obtain history problems of a plurality of categories.

The preset traffic class may be the same as the traffic class in step S210.

Specifically, after the historical problems are obtained, the historical problems can be classified according to preset business categories, and the historical problems are classified into different business categories. For example, the business categories include file upload format, loading speed, etc., and the history problems may be sorted and divided into corresponding business categories.

Step S130, screening the historical problems of each category to obtain high-frequency problems with occurrence frequency exceeding a preset threshold.

The preset threshold may be any value, and may be set to ten times, for example.

Specifically, after obtaining the historical problems of a plurality of categories, the occurrence times of the same problem in each category can be counted, and the problem with the occurrence times exceeding a preset threshold value is screened out as a high-frequency problem.

And step S140, generating standard replies corresponding to the high-frequency problems according to the high-frequency problems.

Specifically, according to the selected high frequency problems, the solution of each high frequency problem can be summarized by the technician and returned as a standard for the high frequency problem. In some other implementations, replies to the questions may also be obtained from the manual question and answer log as standard replies.

And step S150, training the service class, the high-frequency problem and the standard reply as a data set of a model to obtain a dialogue generation model.

Specifically, the service class of the high-frequency problem, the standard reply and the high-frequency problem can be used as a data set, a Seq2Seq (sequence to sequence, sequence-to-sequence) model is selected as an initial model, the service class and the high-frequency problem are used as model inputs, the standard reply is used as model outputs, and a dialogue generating model is obtained after model training iteration. In some other embodiments, the initial model may also employ Long Short-Term Memory (LSTM) networks and/or Attention mechanisms (Attention) on the basis of the Seq2 Seq.

In the above embodiment, the history problems related to the service may be obtained and classified, then the high-frequency problems are screened according to the preset threshold value and corresponding standard replies are generated, the high-frequency problems, the standard replies and the problem category are used as the model data set, the dialogue generating model may be trained and obtained, the model uses the high-frequency problems with higher occurrence frequency in the history problems as the training sample, and the corresponding replies can be automatically generated according to the problems of the user in combination with the manually formulated standard answers, so that the reply accuracy is high.

In one embodiment, as shown in fig. 4, after screening the historical problem of each category to obtain the high-frequency problem with the occurrence frequency exceeding the preset threshold, the method further includes:

step S160, selecting a plurality of history questions as training texts.

Specifically, a certain number of historical questions may be selected from the questions stored in the database as training texts, and the training texts are used for outputting recommended questions by the training model.

Step S170, the training texts are used as input vectors of the dialogue generating model one by one.

Specifically, the history questions as training texts may be sequentially input to the dialog generation model, and each of the input history questions may be an input vector of the model.

Step S180, calculating the similarity between any one of the high frequency problems and the input vector.

The high frequency problem may be stored in a predetermined database.

Specifically, by natural language processing technique (Natural Language Processing, abbreviated as NLP), the similarity of the training text as an input vector to each high frequency problem in the database can be calculated. In some embodiments, the similarity between two questions may be quantified to obtain a similarity score for the input vector and the high frequency questions.

And step S190, taking the top N high-frequency questions with the highest similarity with the input vector as output vectors of the dialogue generating model.

Specifically, the top N high-frequency questions with the highest score may be selected from the high-frequency questions as the output of the model according to the similarity score. By repeating the above process, training texts are continuously input, and similarity scores are manually marked, so that the dialogue generating model has the function of outputting N high-frequency problems according to the input texts.

In the above embodiment, the history problem may be input as the training text into the model, and the model may output a plurality of high-frequency problems with the highest similarity by calculating the similarity between the training text and the high-frequency problems in the database. Therefore, the similarity between the historical problem calculation and the high-frequency problem can be calculated, the calculation capacity of the model is improved, the recommended problem with higher similarity to the user problem is output, and the usability is higher.

In one embodiment, as shown in fig. 5, before determining the service class of the service interaction, the method further includes:

step S202, obtaining basic data of users participating in business interaction.

The basic data may include merchant basic information, merchant test environment, and the like, where the merchant test environment may be a product running environment when a merchant uses a business product.

Specifically, the user may be instructed to select or enter basic data such as test environments before determining the traffic class.

And step S204, storing the basic data into a designated storage area.

Specifically, the acquired basic data of the user may be stored in a database designated in advance.

In the above embodiment, when the service interaction is started, the basic data of the user can be obtained in advance, so that deviation of the reply of the service interaction caused by different user factors or test environments is avoided, and the reliability of the service interaction can be improved.

In one embodiment, as shown in fig. 6, before determining the service class of the service interaction, the method further includes:

step S206, the business category options are displayed.

Specifically, service category options related to the service interaction process, such as a first product type, a second product type, and the like, can be displayed in a display interface of an application such as a mobile phone bank, an internet banking, and the like, where the user uses the terminal.

Step S208, a text input box corresponding to each service class option is provided.

Specifically, after the user selects a specific option of the business category, a text input box may be continuously provided to the user in the interface to obtain an input text corresponding to the business category.

In the above embodiment, the service category of the service interaction can be determined by displaying the service category option to the user, so that the relevance between the service interaction problem and the service category is enhanced, and the accuracy of the service interaction reply is improved.

In one embodiment, as shown in fig. 7, before the reply text and the recommended questions are presented, the method further includes:

step S232, in the case that the dialogue generating model does not output the reply text, the input text is sent to the appointed terminal.

The appointed terminal is used for providing a reply text corresponding to the input text in a manual mode.

Specifically, if the dialog generation model does not output a reply text corresponding to the input text, the input text may be sent to the terminal of the technician, and the technician may manually give the reply text.

In the above embodiment, when the dialogue generating model cannot output the reply text corresponding to the user problem, the problem input by the user can be sent to the terminal used by the technician, and the user problem can be timely replied and solved by a manual mode, so that the business interaction process is further improved, and the business interaction efficiency is improved.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

The information related to the user environment and the like possibly related to the embodiments of the application are all data information related to the service, which is actively provided by the user in the service interaction process, is processed based on the reasonable purpose of the service scene according to legal, legal and necessary principles strictly according to the requirements of laws and regulations.

According to another aspect of the embodiments of the present disclosure, as shown in fig. 8, there is also provided a service interaction processing apparatus, including:

a category determining module 310, configured to determine a service category of the service interaction;

a text input module 320, configured to obtain an input text corresponding to the service class;

the model output module 330 is configured to input the business category and the input text into a pre-trained dialogue generation model, and output a reply text and top N recommendation questions with highest similarity to the input text through the dialogue generation model; wherein N is a positive integer;

and the display module 340 is configured to display the reply text and the recommendation question.

The specific limitation of the processing apparatus may be referred to above as limitation of the processing method, and will not be described herein. According to the processing method, the processing device can add the first module, the second module and the like to realize the steps in the corresponding method embodiment. Each of the modules in the processing apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

It should be noted that the business interaction processing method and the processing device of the present application can be used for a product testing process in the financial field, and also can be used in any field other than the financial field, and the application fields of the method and the device of the present application are not limited.

According to another aspect of the embodiments of the present disclosure, there is provided a computer device, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 9. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement the above-described processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

According to another aspect of the disclosed embodiments, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. The business interaction processing method is characterized by comprising the following steps of:

determining a business category of business interaction;

acquiring an input text corresponding to the service class;

and displaying the reply text and the recommendation problem.

2. The method of claim 1, wherein the training process of the dialog generation model comprises:

acquiring historical problems related to business;

3. The method of claim 2, further comprising, after screening the historical problem of each category for high frequency problems with occurrence frequency exceeding a preset threshold value:

selecting a plurality of history questions as training texts;

4. The method of claim 1, further comprising, prior to determining the traffic class for the traffic interaction:

acquiring basic data of users participating in service interaction;

and storing the basic data into a designated storage area.

5. The method of claim 1, further comprising, prior to determining the traffic class for the traffic interaction:

displaying service class options;

a text entry box is provided corresponding to each business category option.

6. The method of claim 1, further comprising, prior to presenting the reply text and the recommended questions:

7. A business interaction processing device, characterized by comprising:

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.

9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.