WO2020197428A1

WO2020197428A1 - Method and system for checking a set of electronic documents

Info

Publication number: WO2020197428A1
Application number: PCT/RU2019/000197
Authority: WO
Inventors: Евгений Сергеевич ЛАТЫШЕВ; Кирилл Геннадьевич ТАРАСОВ
Original assignee: Публичное Акционерное Общество "Сбербанк России"
Priority date: 2019-03-28
Filing date: 2019-06-06
Publication date: 2020-10-01
Also published as: EA201990647A1; RU2702967C1

Abstract

The proposed technical solution relates in general to the field of image analysis, and more particularly to methods and systems for checking a set of electronic documents, for example, scanned documents of a corporate client of a bank. The technical result is an increase in accuracy of the automatic checking of documents for completeness. This technical result is achieved by virtue of a method for checking a set of electronic documents, implemented by at least one computing device and comprising the following steps: obtaining an image of a document consisting of at least one page; recognizing symbols on the image of a page of the document and converting said symbols into textual information; creating a vector of the page of the document on the basis of the textual information obtained in the previous step; determining the document type and document page type on the basis of said vector of the page of the document; determining a list of pages and at least one attribute of the signer that are to be checked for presence in the given document type; checking the list of pages and the at least one attribute of the signer for presence in the document image obtained in order to determine completeness of the document.

Description

METHOD AND SYSTEM FOR CHECKING ELECTRONIC KIT

DOCUMENTS

FIELD OF TECHNOLOGY

[0001] The presented technical solution relates generally to the field of image analysis, and in particular to methods and systems for checking an electronic set of documents, for example, scanned documents of a corporate client of a bank.

LEVEL OF TECHNOLOGY

[0002] Currently, there is a problem of prompt and high-quality processing of the data of an electronic set of scanned documents in order to check for the presence of mandatory for predefined types of pages of attributes, such as seal and / or signature and / or set of signatures.

[0003] From the prior art, various solutions are known, made with the possibility of processing documents, for example, a client of the Bank, implemented on the basis of ABBYY InfoExtractor software, etc. Also known is a solution for checking a set of documents disclosed in the application US 2011134494 (A1), publ. 06/09/2011, in which read the document having many pages; checking image data of each page of a document having multiple pages, while checking certain areas of the document image for the presence and absence of information. This solution is the closest analogue.

[0004] A significant drawback of the known solutions is the inability to check a set of scanned documents according to the following criteria:

• completeness of the package of documents;

• presence of seals;

• presence and correctness of the composition of signatures. DISCLOSURE OF THE INVENTION

[0005] The technical problem or task posed in this technical solution is the creation of a new effective method for automated verification of a set of documents, for example, documents of a corporate client of the Bank.

[0006] The technical result is to improve the accuracy of automated verification of documents for completeness.

An additional technical result is an increase in the speed of the automated check of documents for their completeness.

[0007] The specified technical result is achieved by implementing a method for checking an electronic set of documents, performed by at least one computing device, and containing the steps in which:

- get an image of the document, consisting of at least one page;

- recognize characters on the image of a page of a document and converts them into text information;

- form the vector of the page of the document based on the text information obtained at the previous stage;

- based on the vector of the document page, the type of the document and the type of its page are determined;

- define a list of pages and at least one signer attribute, the presence of which must be checked in this type of document;

- check the presence of the list of pages and at least one attribute of the signer on the resulting image of the document to determine the completeness of the document.

[0008] In one particular embodiment of the method, the document page vector is formed on the basis of the word values contained in the text information, the structure of word dependencies from each other, and the weight values of said words.

[0009] In another particular embodiment of the method, determining the type of document and the type of its page based on the vector of the document page is carried out by classifying the document according to its belonging to predetermined types of pages and documents, and the mathematical model for classification is implemented by means of machine learning algorithms "random forest".

[0010] In another particular embodiment of the method, the step of checking for the presence of at least one attribute of the signer on the resulting image of the document includes the steps in which:

- detecting at least one attribute of the signer on the image of the page of the document to determine its location;

- define where the signer attribute should be on a given page type;

moreover, checking for the presence of at least one signer's attribute on the obtained document image to determine the completeness of the document is carried out by comparing information about the location of the signer's attribute on the document page image with information indicating where the signer's attribute should be located on this page type.

[0011] In another particular embodiment of the method, the detection of at least one signer attribute is carried out only on those images of document pages of the type indicating that the page data contains the signer's attributes.

[0012] In another particular embodiment of the method, at least one signer attribute is additionally classified, and the classification is carried out based on the location information of the signer attribute.

[0013] In another particular embodiment of the method, the signer attribute is a signature and / or a seal.

[0014] In another preferred embodiment of the claimed solution, a system for verifying a set of documents is provided, comprising at least one computing device and at least one memory containing machine-readable instructions that, when executed by at least one computing device, perform the above method. BRIEF DESCRIPTION OF DRAWINGS

[0015] The features and advantages of the present technical solution will become apparent from the following detailed description of the invention and the accompanying drawings, in which:

[0016] in FIG. 1 shows a general diagram of the interaction of system elements for checking a set of documents.

[0017] in FIG. 2 shows an example of a scanned document.

[0018] in FIG. 3 shows an example of a general view of a system for checking a set of documents.

CARRYING OUT THE INVENTION

[0019] The following will describe the concepts and terms necessary to understand this technical solution.

[0020] In this technical solution, the system means, including a computer system, a computer (electronic computer), a CNC (numerical control), a PLC (programmable logic controller), computerized control systems and any other devices capable of performing a given , a well-defined sequence of operations (actions, instructions).

[0021] A command processing device means an electronic unit or an integrated circuit (microprocessor) that executes machine instructions (programs).

[0022] An instruction processor reads and executes machine instructions (programs) from one or more storage devices. Data storage devices can include, but are not limited to, hard drives (HDD), flash memory, ROM (read only memory), solid state drives (SSD), optical drives.

[0023] A program is a sequence of instructions for execution by a computer control device or command processing device.

[0024] Database (DB) - a collection of data organized in accordance with a conceptual structure describing the characteristics of this data and the relationship between them, and such a collection data that supports one or more application areas (ISO / IEC 2382: 2015, 2121423 "database").

[0025] In accordance with the diagram of FIG. 1, the system 1 for checking a set of documents contains interconnected: a data conversion module 10; a page classification module 20, a signer attributes verification module 30 such as signatures and / or stamps, and a document set verification module 40.

[0026] These modules can be implemented on the basis of the software and hardware of the system 1 for checking a set of documents, for example, on the basis of at least one computing device, in particular a microprocessor, and at least one memory containing machine-readable instructions for implementing assigned modules below functions. For example, the data transformation module 10 may contain a vector shaping module 11 and an image filtering module 12, and can be implemented based on the Tesseract opensource tool (Tesseract Open Source OCR Engine) and the TF-IDF algorithm. Page classification module 20 can be implemented on the basis of a pre-trained mathematical model using a mathematical model learning algorithm - a random forest of decision trees. Signer attribute verification module 30 may be implemented on the basis of a YOLOv3 neural network pre-trained on a typical set of signatures and seals. The document set verification module 40 may include at least one database 41 for storing information that may be required to verify the document set.

[0027] In general (see Fig. 3), the system (200) for checking a set of documents contains one or more processors (201), united by a common bus of information exchange, memory means such as RAM (202) and ROM (203), input / output interfaces (204), input / output devices (205), and a device for networking (206).

[0028] The processor (201) (or multiple processors, multi-core processor, etc.) can be selected from a range of devices currently widely used, for example, such manufacturers as: Intel ™, AMD ™, Apple ™, Samsung Exynos ™, MediaTEK ™, Qualcomm Snapdragon ™, etc. Under the processor or one of the used processors in the system (200), it is also necessary to take into account the GPU, for example, NVIDIA GPU with a CUDA-compatible programming model, or Graphcore, the type of which is also suitable for full or partial execution of the method, and can also be used for training and applying machine learning models in various information systems.

[0029] The RAM (202) is a random access memory and is intended for storing machine-readable instructions executed by the processor (201) for performing the necessary operations for logical data processing. RAM (202), as a rule, contains executable instructions of the operating system and corresponding software components (applications, software modules, etc.). In this case, the available memory of the graphics card or graphics processor can act as RAM (202).

[0030] ROM (203) is one or more persistent storage devices such as a hard disk drive (HDD), solid state data storage device (SSD), flash memory (EEPROM, NAND, etc.), optical storage media ( CD-R / RW, DVD-R / RW, BlueRay Disc, MD), etc.

[0031] Various types of I / O interfaces (204) are used to organize the operation of the system components (200) and to organize the operation of external connected devices. The choice of the appropriate interfaces depends on the specific version of the computing device, which can be, but are not limited to: PCI, AGP, PS / 2, IrDa, FireWire, LPT, COM, SATA, IDE, Lightning, USB (2.0, 3.0, 3.1, micro, mini, type C), TRS / Audio jack (2.5, 3.5, 6.35), HDMI, DVI, VGA, Display Port, RJ45, RS232, etc.

[0032] To ensure the interaction of the user with the computing system (200), various means (205) I / O information are used, for example, a keyboard, a display (monitor), a touch display, a touch-pad, a joystick, a mouse manipulator, a light pen, a stylus, touch panel, trackball, speakers, microphone, augmented reality, optical sensors, tablet, light indicators, projector, camera, biometric identification (retina scanner, fingerprint scanner, voice recognition module), etc. [0033] The networking tool (206) provides data transmission via an internal or external computer network, for example, Intranet, Internet, LAN, and the like. One or more means (206) can be used, but not limited to: Ethernet card, GSM modem, GPRS modem, LTE modem, 5G modem, satellite communication module, NFC module, Bluetooth and / or BLE module, Wi-Fi module, and dr.

[0034] Additionally, satellite navigation means can be used as part of the system (200), for example, GPS, GLONASS, BeiDou, Galileo.

[0035] The specific choice of elements of the device (200) for the implementation of various software and hardware architectures can vary while maintaining the required functionality.

[0036] At the first stage of the system 1 operation, the data conversion module 10 receives at least one image of a document, in particular a scanned document, for example, a file in the format of a multipage PDF, JPEG, TIFF or any other known format that can be used for storage in the scanned electronic document set. The document image may come from an image data source 50, in particular directly from a document scanning device such as a scanner, or it may be retrieved from a suitable image database in which the document image data is stored in advance.

[0037] The document, the image of which is supplied to the data conversion module 10, can be any document consisting of at least one page, which may contain the attributes of the signer, and filled in accordance with a known template. The document can be, for example, an agreement concluded between companies "A" and "B", or between a company and an individual, or between individuals, or the document can represent this type of document that is signed by only one signatory - a company or an individual , for example, a power of attorney from a company or from an individual; or etc. [0038] The data conversion unit 10 performs character recognition on at least one image of a document page and converts them into text information. Also, the data conversion module 10 can be configured to preprocess the received text information to reduce the variety of possible texts of recognized document images in order to simplify the operation of the following system modules. At the first stage, textual information is tokenized. The tokenization stage involves the selection of basic text elements (tokens), delimited on both sides by separating characters, spaces or punctuation marks. The elements here are words, numbers, dates, abbreviations, abbreviations, compound prepositions, etc. Tokenization allows you to select discrete units of text, which are the basis for further work at the stages of morphological and syntactic analysis. As a result of tokenization, each element is assigned the appropriate type: word, number, date, address, etc.

[0039] Next, the data conversion unit 10 proceeds to the step of generating document page vectors by the vector generating unit 11. At this stage, the mentioned module for each word obtained after text processing determines the value of the word weight using the statistical measure TF-IDF.

[0040] TF-IDF is a statistical measure used to assess the importance of a word in the context of a document that is part of a document collection or corpus. The weight of a word is proportional to the number of times this word is used in a document, and is inversely proportional to the frequency of using a word in other documents in the collection.

[0041] The TF-IDF measure is often used in text analysis and information retrieval tasks, for example, as one of the criteria for the relevance of a document to a search query, when calculating the measure of document proximity during clustering.

[0042] TF (term frequency) is the ratio of the number of occurrences of a word to the total number of words in the document. The significance of a word within a single document can be determined by the following characteristic:

- where u is the number of occurrences of the word t * in document d; , _k ⁿ _{k is the} total number of words in a given user request and / or document.

[0043] IDF (inverse document frequency) is a value that is inversely proportional to the frequency with which a word occurs in collection documents.

[0044] IDF accounting reduces the weight of commonly used words. There is only one IDF value for each unique word within a particular collection of documents. The IDF characteristic is defined by the following relationship:

- where | £> | - the number of documents in the corpus;

- the number of documents in which t _t occurs.

[0045] Thus, the TF-IDF measure is the product of two factors: tf · idf (t, d, D) = tf (t, d) x idf (t, D).

[0046] A high TF-IDF weight is given to words with high frequency within a particular document and with low frequency in other documents.

[0047] The TF-IDF measure is often used to represent collection documents as numerical vectors reflecting the importance of using each word from a set of words (the number of words in a set determines the dimension of the vector) in each document. Such a model is called a vector model and makes it possible to compare texts by comparing the vectors that represent them in some metric (Euclidean distance, cosine measure, Manhattan distance, Chebyshev distance, etc.), that is, by performing cluster analysis.

[0048] Next, the module 11 for generating vectors based on the word values obtained after preprocessing, the structure of the dependence of words on each friend in the text information and the values of the weights of the mentioned words forms the vector of the document page. The generated document page vector is sent to the page classification module 20 to determine the document type and the document page type, i.e. to classify a document as belonging to predefined types of pages and documents.

[0049] Accordingly, if the scanned document contains two or more pages, the vector generating unit 11 generates in the same way for each page of the document a document page vector, which are also sent to the page classification unit 20.

[0050] Module 20 classification of pages for determining the type of document and types of its pages contains a mathematical model, the input of which is received data on the vectors of the pages of the document. The mathematical model can be implemented through machine learning algorithms "random forest", which consists in using a committee (ensemble) of decision trees. The classification of objects is carried out by voting: each committee tree assigns the object to be classified, in this case a document page, to one of the classes characterizing the page type and document type, and the class for which the largest number of trees voted wins. The optimal number of trees is selected in such a way as to minimize the classifier error on the test sample.

[0051] Accordingly, after processing the data on the document page vector, the output of the mathematical model comes from each decision tree pointers of the document type and the page type. The page classification module 20 analyzes the number of said pointers obtained at the output of said model, and determines the document type and page type based on the document type indicator, the number of which is greater at the output of the mathematical model, i.e. for which the largest number of trees voted. For example, if the largest number of trees voted for the fact that the document, on the basis of the page image of which the document page vector was formed, is an Agreement concluded between companies "A" and "B", and the page is a page of the Agreement with the attributes of signers, then the document type will be determined as a "contract", and the data on the page type will indicate that the page in the image is a page of the contract, which must contain the attributes of signers in the form of signatures and seals of companies "A" and "B" located in the specified areas on the page (for example , in areas 105 and 106, see Fig. 2). Also, the document type can be defined, for example, as a power of attorney from company "A" or from an individual, and the page type is a page of a power of attorney with the signer's attributes, for example, in the form of a signature and a seal in a specified area of the page, for example, area 106, if the power of attorney from the company, or only in the form of a signature in the specified area of the page, if the power of attorney is from an individual.

[0052] If the scanned document consists of two or more pages, then the input of the mathematical model receives data on two or more vectors of document pages. The page classification module 20 similarly analyzes the number of the mentioned pointers at the output of the mathematical model and determines the type of document and the list of its pages based on that document type indicator, the number of which is greater at the output of the mathematical model. In this case, the document type is determined based on the vectors of all its pages. For example, based on the vectors of the pages of the document, the page classification module 20 can determine the type of the document as an agreement between companies "A" and "B", consisting of 4 pages, and the data on the types of pages may indicate that the first page is a page of the Agreement, not containing the signer's attributes, the second page is the contract page with the signer's attributes in the specified areas, and pages 3 and 4 are applications that do not contain the signer's attributes.

[0053] The data on the types of the document and the types of its pages is sent by the page classification module 20 to the document set verification module 40 and to the image filtering module 12, which determines the types of pages with at least one signer attribute, extracts the corresponding page images with at least one the signer attribute from the document image, and sends the page image data to the signer attribute validator 30 for further analysis. Thus, since not the entire image of the document is sent to the module 30 for checking the attributes of the signer, but only images of document pages, the type of which assumes the presence of at least one signer's attribute on these pages, reduces the computational load and increases the speed of image processing by the module 30 for detecting images of the signer's attributes, thereby increasing the speed of automated verification of documents for their completeness.

[0054] After receiving images of pages with at least one signer's attribute, the module 30 for checking the signer's attributes proceeds to the step of detecting at least one image of the signer's attribute on each received image of the document page to determine its location on the document page. For example, the signer attribute validator 30 may determine that the signer's attribute image is a signature and / or seal image in document area 105 or 106 (see FIG. 2). Accordingly, area 101 of document 100 may contain information about the Agreement number, area 102 - the name of the city, area 103 - the date of the Agreement, and area 104 - the text of the Agreement.

[0055] The well-known algorithms of the neural network of the YOLOv3 architecture trained on a selected dataset of signatures and seals are used to detect images of the signer's attributes, disclosed, for example, in an article published on the Internet at: https://pireddie.com/media/files /papers/YQLOv3.pdf. The data on the detected attributes of the signer, in particular information on their location on the document page, is transmitted to the module 40 for checking the set of documents.

[0056] Module 40 verification of a set of documents in the course of its work checks for the presence of mandatory for this type of document list of pages and attributes of signers, such as a seal and / or signature and / or a set of signatures, in the specified areas of the pages. To determine the attributes of the signer, the presence of which must be checked, the module 40 for checking the set of documents can be equipped with a corresponding database 41 with information about the templates of documents, their list of pages, and the attributes of the signers, the presence of which must be checked in a given area of pages from the list of pages of a given type of document. Since the compilation is Page vectors are based on textual information, which may include the names of one or more companies, or the names of one or more individuals, then information about the type of pages will also determine in which area of the page the attributes should be located 5 signed on the image of the document page.

[0057] For example, if the document type information indicates that the document is a Contract 200 (see Fig. 2), consisting of 1 page of the Contract, which must be signed by only one signer, then the module 40 for checking the set of documents in In accordance with the document template, checks the area of page 105 or 106, depending on the type of document and the type of page, for the presence of a signer attribute, in particular his signature and / or seal. If the document type information indicates that the document is a contract consisting of 2 contract pages, and the second page of the Contract in accordance with the document template must be signed by two signers, then the document set verification module 40 checks areas 105 or 106 of the second page for the presence of attributes of signers, and the location of the attributes of the first and second signers in these areas is determined by the type of document and the types of its pages.

20 [0058] To check the document, the module 40 for checking the set of documents based on the data on the type of the document received from the module 20 classifying pages, searches the database 41 for a template of this type of document, on the basis of which the module 40 will check the set of documents, and extracts information about the types of pages of this 25 document template. For example, if the page classification module 20 determines that the scanned document is an Agreement between companies "A" and "B", then based on this information about the type of document, the module 40 for checking the set of documents finds in the database a template of the Agreement between companies "A" and "B" »And retrieves information about the types of pages present in the template of the Agreement. If the Agreement is executed on 1 sheet, then, as a rule, the signer's attributes should be located on the first page of the document. The information that the signer's attributes should be on the first page, as well as their location on the page, can be contained in the page type information, according to which the package verification module 40 will check for the presence of signers' attributes on the first page of the contract.

[0059] If the Contract consists of two or more pages, then, for example, information about the type of the last page of the document may contain information that the attributes of the signer should be in a given area (for example, in areas 105 or 106) on this page. Also, information about the type of the first page or the type of document can contain information that the signer's attributes should be in the specified areas on the second or other page in the document.

[0060] If the page type information obtained from the page classification unit 20 does not match the page type information of the document template, then the document set check unit 40 decides that the document set is incomplete. For example, according to the document template, this scanned document is an Agreement concluded with an individual, consisting of 3 pages, where the first 2 pages are the pages of the Agreement, and the third page is a scan of the passport. Thus, if the scanned Agreement does not contain a scan of the passport or instead of a scan of the passport another document is attached, the image of which will be processed by system 1, then the information on the type of the third page received from module 20 will not coincide with the information on the type of pages of the document template.

[0061] Information that the scanned set of documents is incomplete, for example, in the form of a message "missing passport scan", can be output to the I / O means (205).

[0062] If the page type information received from the page classification module 20 matches the page type information of the document template, then the document set verification module 40 extracts from the database 41 information about the location of at least one signer's attribute on at least one page according to the document template for those types of pages that must contain at least one signer attribute. The mentioned information about the location of at least one attribute of the signer can be obtained experimentally based on the data on the average coordinates of the location of signatures and seals in document templates. [0063] For example, if the scanned document is an Agreement between companies "A" and "B", consisting of 1 page, then the data classification module 20 extracts from the database 41 information about the location of signatures and / or seals (i.e. about the attributes of the signer ) on this page 5 of the document in accordance with the template. In particular, DB 41 can be stored as a type of document template, in which the location information will indicate that the attributes of the signer of company "A" should be in the area of page 105 of the Agreement 100, and the attributes of the signer of company B - in the area 106 Agreement 100, and the type of document template, in which the attributes of the signatory of the company "B" should be in area 105 of the Agreement 100, and the attributes of the signer of the company "A" - in area 106 of the Agreement 100.

[0064] Accordingly, the information about the location of at least one attribute of the signer 15 extracted from the database 41 in the previous step is compared by the module 40 for checking the set of documents with the information about the location of at least one attribute on the document page received from the module 30. If the extracted from the database 41, said location information of at least one signer's attribute does not match the location information of at least one attribute 20 on a document page received from module 30, then the document set verification module 40 decides that the document set is incomplete. Information that the scanned set of documents is incomplete, for example, in the form of a message "there is no client signature on page 3", can be displayed on the means (205) I / O 25 information

[0065] If the location information retrieved from the database 41 matches the location information received from the module 30, then the document set verification module 40 decides that the document set meets the specified completeness requirements. Information that the scanned document set is complete can also be output to the I / O means (205).

[0066] Thus, due to the fact that the type of the document and the types of pages of the document included in its composition are determined based on vectors pages formed on the basis of textual information contained on the page, the accuracy of determining the type of document and the list of its pages, as well as the list of pages and attributes of the signer, the presence of which must be checked on the image of the document, increases. the accuracy of the automated check of documents for their completeness is improved. Formation of page vectors, taking into account the meanings of words contained in the text information, the structure of the dependence of words from each other and the values of the weight of the mentioned words will additionally increase the accuracy in determining the type of document and types of document pages, as well as the list of pages and attributes of the signer, the presence of which must be checked on the image document.

[0067] Additionally, the document set verification module 40 may be configured to classify at least one signer attribute based on the signer's attribute location information. To provide this possibility, DB 41 additionally contains information about which side of the Agreement the signer's attribute belongs to, depending on its location on the page. For example, the database 41 may contain information that in the area of page 105 of the Agreement 100 there is an attribute of the signer of the client, and in the area of page 106 - the executor of the Agreement. Thus, by comparing the signer's attribute location information received from module 30 with the signer's attribute location information from DB 41, the document suite verification module 40 classifies the signer's attribute image, for example, as a signer's attribute client, if the signer's attribute is located in the page area 105. or as an attribute of the signer of the Contractor if the signer attribute is located in the page 106 area.

[0068] Modifications and improvements to the above-described embodiments of the present technical solution will be clear to those skilled in the art. The foregoing description is provided by way of example only and is not intended to be limiting. Thus, the scope of the present technical solution is limited only by the scope of the attached claims.

Claims

Claim.

1. A method for checking an electronic set of documents, performed by at least one computing device, comprising the steps at which:

- get an image of the document, consisting of at least one page;

2. The method according to claim 1, characterized in that the vector of the document page is formed on the basis of the values of the words contained in the text information, the structure of the dependencies of words from each other and the values of the weight of said words.

3. The method according to claim 1, characterized in that the definition of the type of the document and the type of its page based on the vector of the document page is carried out by classifying the document according to its belonging to predetermined types of pages and documents, and the mathematical model for classification is implemented using machine learning algorithms "random forest".

4. The method according to claim 1, characterized in that the step of checking for the presence of at least one attribute of the signer on the obtained image of the document includes the steps at which: - detecting at least one attribute of the signer on the image of the page of the document to determine its location;

- define where the signer attribute should be on a given page type;

5. The method according to claim. 4, characterized in that the detection of at least one attribute of the signer is carried out only on those images of pages of documents, the type that indicates that the page data contains the attributes of the signer.

6. The method according to claim 4, characterized in that it comprises the step of classifying at least one attribute of the signer, which is carried out on the basis of information about the location of the attribute of the signer.

7. A method according to claim 1, characterized in that the signer attribute is a signature and / or a seal.

8. A system for checking a set of documents containing at least one computing device and at least one memory containing machine-readable instructions, which, when executed by at least one computing device, perform the method according to any one of claims. 1-7.