A method, apparatus, and computer device for generating and managing electronic case files.

Abstract

This application discloses an electronic case file generation and management method, apparatus, and computer equipment. The method includes: receiving paper case files and sending them to a processing workshop; performing image processing on electronic images and OCR text recognition on the processed electronic images to obtain corresponding text content JSON files and two-layer PDF files; cataloging the two-layer PDF files to obtain target categories; categorizing the two-layer PDF files using natural language processing technology and linking the two-layer PDF files to an electronic case file management system; restoring the layout of the text content JSON files and combining it with cataloging information to obtain text case files; and extracting elements and / or analyzing the cause of action of the text case files to obtain data case files. This application provides a complete workflow for generating electronic case files, efficiently realizing paperless case files, and storing paper materials through intelligent storage cabinets to prevent the paper materials from circulating, thus maximizing the security and integrity of the paper materials.

Description

Technical Field

[0001] This application relates to the field of case file technology, and in particular to an electronic case file generation and management method, apparatus and computer equipment. Background Technology

[0002] The simultaneous generation of case files with court records nationwide has further increased the pressure on court staffing. The coexistence of paper and electronic case files has led to confusion in media usage during the transition period. Due to judges' historical work habits, the utilization rate of paper case files remains high, resulting in the overall utilization efficiency of electronic case files consistently falling short of expectations. The lack of unified management of paper case files leads to frequent incidents of lost or missing files. While the core of the simultaneous generation of electronic case files is the electronic file itself, the data carrier characteristics of paper case files still necessitate their management.

[0003] In the traditional model, the circulation of paper case files is not timely and the circulation nodes are not clear, which makes it impossible to form electronic case files in real time. As a result, judges cannot review the case files electronically during the trial or application process, which further leads to judges' resistance to electronic review and their continued use of paper case files. Therefore, paper case files cannot be digitized in a timely manner.

[0004] Traditional electronic case file generation mainly involves using camera or scanner software to take pictures of paper case files and generate corresponding images. The generated images are then processed using image processing tools, and the processed images are then recognized by recognition software. After recognition, the images in different directories are created into different folders for classification. Finally, the corresponding system is opened to transfer the classified images to the corresponding directories.

[0005] Court case files are in multi-volume, multi-page text format. Traditionally, the synchronous generation of case files often uses the default image sorting method. This method can cause logical confusion and disorder in the synchronously generated case files, which increases the difficulty for judges to review the files online.

[0006] In summary, after several years of development, the process of generating litigation case files simultaneously with the case has revealed many challenging issues. Therefore, it is necessary to provide a comprehensive and integrated solution for the simultaneous generation of litigation case files in courts, and to provide strong support for the development and in-depth application of electronic case files in courts. Summary of the Invention

[0007] Based on this, and in response to the aforementioned technical problems, an electronic case file generation and management method, apparatus, and computer equipment are provided to solve the problems of low efficiency in synchronous work of litigation case files and lack of unified management of paper case files in the existing technology.

[0008] An electronic case file generation and management method includes:

[0009] Step S1: Receive paper case files and send them to the processing workshop, where the processing workshop scans the paper case files to generate corresponding electronic images and pushes the paper case files into the intelligent storage cabinet.

[0010] Step S2: Perform image processing on the electronic image, and perform OCR text recognition on the processed electronic image to obtain the corresponding text content JSON file and double-layer PDF file;

[0011] Step S3: Catalog the two-layer PDF file and verify the cataloging results to obtain the target category;

[0012] Step S4: Naming the two-layer PDF files using natural language processing technology and linking the two-layer PDF files to the electronic case file management system;

[0013] Step S5: Restore the layout of the JSON file containing the text content, verify the content, and then combine it with the cataloging information to obtain the text dossier.

[0014] Step S6: Extract elements and / or analyze the cause of action of the text file to obtain a data file.

[0015] Optionally, the method further includes:

[0016] Receive electronic materials and determine the type of said electronic materials;

[0017] When the electronic material is an electronic image, proceed with steps S2-S6;

[0018] When the electronic material is plain text, generate a corresponding two-layer PDF file based on the plain text and execute steps S3-S4; obtain a text file based on the plain text and execute step S6;

[0019] When the electronic material is audio or video, the electronic material is processed to convert it into plain text, and a corresponding two-layer PDF file is generated based on the plain text. Steps S3-S4 are then executed; a text file is obtained based on the plain text, and step S6 is then executed.

[0020] Optionally, the paper case files may originate from window reception, judge transfer, mailing by the parties, or mailing by lawyers.

[0021] Further, alternatively, the sources of the electronic materials may include window reception, judge transfer, mailing by the parties, mailing by the lawyers, online case filing system, and trial system.

[0022] Further optionally, the categorization of the two-layer PDF files using natural language processing technology specifically includes:

[0023] The two-layer PDF file is parsed, and semantic analysis of the text content is performed using a topic analysis model;

[0024] The analyzed semantics are compared with the semantic database, and it is determined whether the analyzed semantics exist in the semantic database.

[0025] If the analyzed semantics exist in the semantic database, then similarity detection is performed directly; if the analyzed semantics do not exist in the semantic database, then the semantic database is updated, and then similarity detection is performed.

[0026] Based on the semantic and similarity detection results obtained from the analysis, files of the same type in the two-layer PDF files are integrated;

[0027] Feature recognition is performed on the two-layer PDF file;

[0028] Based on the feature recognition results and the target category in the cataloging results, the category to which the two-layer PDF file belongs is determined, and the cataloging of the two-layer PDF file is completed.

[0029] Alternatively, the topic analysis model may be a latent Dirichlet distribution model.

[0030] Optionally, the paper files can be scanned using a browser-based scanner and / or camera to generate corresponding electronic images.

[0031] Secondly, an electronic case file generation and management device includes:

[0032] A paper file receiving module is used to receive paper files and send them to a processing workshop, where the processing workshop scans the paper files to generate corresponding electronic images and pushes the paper files into an intelligent storage cabinet.

[0033] The image processing module is used to process the electronic image and perform OCR text recognition on the processed electronic image to obtain the corresponding text content JSON file and double-layer PDF file;

[0034] The cataloging module is used to catalog the two-layer PDF file and verify the cataloging results to obtain the target category;

[0035] The categorization module is used to categorize the two-layer PDF files using natural language processing technology and to link the two-layer PDF files to the electronic case file management system.

[0036] The layout restoration module is used to restore the layout of the text content JSON file, perform content verification, and then combine it with cataloging information to obtain the text dossier.

[0037] The data file generation module is used to extract elements and / or analyze the cause of action of the text file to obtain the data file.

[0038] Thirdly, a computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described in the first aspect.

[0039] Fourthly, a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0040] The present invention has at least the following beneficial effects:

[0041] This application provides a complete workflow for generating electronic case files. It achieves online electronic generation through scanning, processes electronic images through image processing capabilities, identifies and classifies electronic images through integrated recognition and cataloging capabilities, and automatically matches images of different categories to the corresponding electronic case file directory through natural language processing technology, thereby efficiently realizing paperless case files. At the same time, this application uses intelligent storage cabinets to store paper materials, preventing paper materials from circulating and maximizing the safety and integrity of paper materials.

[0042] This application also ensures the synchronous generation and circulation of electronic materials, laying a solid foundation for the in-depth application of electronic case files. Attached Figure Description

[0043] Figure 1 A flowchart illustrating an electronic case file generation and management method according to one embodiment of this application;

[0044] Figure 2 A detailed flowchart illustrating an electronic case file generation and management method provided in one embodiment of this application;

[0045] Figure 3 This is a schematic diagram illustrating the application of electronic case files in one embodiment of this application;

[0046] Figure 4 This is a schematic diagram illustrating the specific process of destination retrieval in one embodiment of this application;

[0047] Figure 5 This is a flowchart illustrating a material receiving and management method in one embodiment of this application;

[0048] Figure 6 A block diagram of the module architecture of an electronic case file generation and management device provided in one embodiment of this application;

[0049] Figure 7This is an internal structural diagram of a computer device provided in one embodiment of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] In one embodiment, such as Figure 1 As shown, an electronic case file generation and management method is provided, including the following steps:

[0052] Step S1: Receive the paper case file and send it to the processing workshop. The processing workshop scans the paper case file to generate corresponding electronic images and pushes the paper case file into the intelligent storage cabinet.

[0053] The paper case files come from various sources, including window reception, transfer by judges, mailing by litigants, and mailing by lawyers. After the paper case files are stored in the smart storage cabinet, judges can borrow and return them after initiating a borrowing request and passing identity verification.

[0054] Meanwhile, when a case is closed, an application for archiving paper files can be submitted to the smart storage cabinet. Staff will then retrieve, bind, and archive the paper files from the smart storage cabinet.

[0055] In addition, the paper files can be scanned using a browser-based scanner and / or camera to generate corresponding electronic images.

[0056] Step S2: Perform image processing on the electronic image, and then perform OCR text recognition on the processed electronic image to obtain the corresponding text content JSON file and double-layer PDF file.

[0057] Step S3: Catalog the two-layer PDF file and verify the cataloging results to obtain the target category.

[0058] Step S4: Naming the two-layer PDF files using Natural Language Processing (NLP) technology and linking them to the electronic case file management system; navigating means matching files of different categories in the two-layer PDF files to the corresponding electronic case file directories.

[0059] Step S5: Restore the layout of the text content JSON file and perform content verification. Then, combine the cataloging information to obtain the text dossier.

[0060] Step S6: Extract elements and / or analyze the cause of action from the text file to obtain the data file.

[0061] Furthermore, the method also includes:

[0062] (1) Receive electronic materials and determine the type of electronic materials; the sources of electronic materials include window reception, judge transfer, mailing by the parties, mailing by the lawyer, online case filing system and trial system.

[0063] (2) When the electronic material is an electronic image, execute steps S2-S6;

[0064] (3) When the electronic material is plain text, generate a corresponding two-layer PDF file based on the plain text and execute steps S3-S4; obtain the text file based on the plain text and execute step S6;

[0065] (4) When the electronic material is audio or video, the electronic material is processed to convert it into plain text and generate a corresponding two-layer PDF file based on the plain text. Steps S3-S4 are executed; a text file is obtained based on the plain text and step S6 is executed.

[0066] A more detailed flowchart of this method is shown below. Figure 2 As shown, a schematic diagram of the application of the corresponding electronic case file is as follows. Figure 3 As shown.

[0067] Furthermore, such as Figure 4 As shown, the categorization of the two-layer PDF files using natural language processing technology specifically includes:

[0068] (1) The two-layer PDF file is parsed, and the semantic analysis of the text content is performed using a topic analysis model;

[0069] The topic analysis model can be, but is not limited to, the Latent Dirichlet Allocation (LDA) model. The topic analysis model is a technique that uses unsupervised learning to statistically analyze and cluster the implicit semantic structure of documents in order to mine the semantic structure contained in the text. The Latent Dirichlet Allocation (LDA) model is a commonly used method for calculating topic models.

[0070] (2) Compare the semantics obtained from the analysis with the semantic database, and determine whether the semantics obtained from the analysis exist in the semantic database;

[0071] (3) If the semantics obtained from the analysis exist in the semantic database, then the similarity detection is performed directly. If the semantics obtained from the analysis do not exist in the semantic database, the semantic database is updated and then the similarity detection is performed.

[0072] Text similarity is mainly related to the commonalities and differences between texts. The greater the commonalities and the smaller the differences, the higher the similarity; the smaller the commonalities and the greater the differences, the lower the similarity; the highest similarity is when the texts are completely identical.

[0073] Similarity calculation methods can be divided into four main categories: string-based methods, corpus-based methods, knowledge-based methods, and other methods. String-based methods refer to methods that start from the matching degree of strings and use the co-occurrence and repetition of strings as the similarity measurement standard. Corpus-based methods refer to methods that use information obtained from corpora to calculate the similarity of texts. Knowledge-based methods refer to methods that use knowledge bases with standardized organizational systems to calculate the similarity of texts.

[0074] (4) Based on the semantic and similarity detection results obtained from the analysis, files of the same type in the two-layer PDF files are integrated;

[0075] (5) Feature recognition of two-layer PDF files;

[0076] (6) Based on the feature recognition results and the target category in the cataloging results, determine the category to which the two-layer PDF file belongs and complete the cataloging of the two-layer PDF file;

[0077] Text classification is a technical system in which computers automatically read the content of documents and classify them into the corresponding categories according to the document classification system of a specific industry. Its typical processing can be divided into two types: training and operation. That is, the computer reads the documents of each category in advance and extracts features to complete supervised learning training, and in the operation stage, it identifies the content of new documents and completes the classification.

[0078] In addition, this application also provides a method for material receiving and management, the process of which is as follows: Figure 5 As shown, by establishing a unified collection, transfer, and centralized storage mechanism for paper materials, the paper materials are no longer circulated. The Internet of Things (IoT) technology is used to manage storage devices online, ensuring the safety and integrity of paper materials to the greatest extent possible.

[0079] This application provides a complete workflow for generating electronic case files. It achieves online electronic generation through browser-based scanners and camera embedding; processes electronic images using image processing capabilities; classifies electronic images using integrated recognition and cataloging capabilities; and automatically matches images of different categories to the corresponding electronic case file directory using natural language processing technology. This efficiently achieves paperless case file generation, reducing the generation time by 50%. Simultaneously, this application uses intelligent storage cabinets to store paper materials, preventing their circulation and maximizing their safety and integrity.

[0080] This application standardizes the production and processing of all types of materials, ensuring the synchronous generation and circulation of electronic materials, facilitating rapid location and retrieval, and enabling the visual utilization of material content, thus laying a solid foundation for the in-depth application of electronic case files.

[0081] It should be understood that, although Figure 1-5 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1-5 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0082] In one embodiment, such as Figure 6 As shown, an electronic case file generation and management device is provided, comprising the following modules:

[0083] The paper file receiving module 601 is used to receive paper files and send them to the processing workshop. The processing workshop scans the paper files to generate corresponding electronic images and pushes the paper files into the intelligent storage cabinet.

[0084] Image processing module 602 is used to process electronic images and perform OCR text recognition on the processed electronic images to obtain corresponding text content JSON files and two-layer PDF files;

[0085] Cataloging module 603 is used to catalog two-layer PDF files and verify the cataloging results to obtain the target category;

[0086] The categorization module 604 is used to categorize two-layer PDF files using natural language processing technology and to link the two-layer PDF files to the electronic case file management system.

[0087] The layout restoration module 605 is used to restore the layout of the text content JSON file, perform content verification, and then combine it with the cataloging information to obtain the text dossier.

[0088] The data file generation module 606 is used to extract elements and / or analyze the cause of action from text files to obtain data files.

[0089] For specific limitations regarding an electronic case file generation and management device, please refer to the limitations of an electronic case file generation and management method described above, which will not be repeated here. Each module in the aforementioned electronic case file generation and management device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0090] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an electronic case file generation and management method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0091] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0092] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program relating to all or part of the processes in the methods of the above embodiments.

[0093] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon relating to all or part of the processes in the methods of the above embodiments.

[0094] In one embodiment, a task allocation system is also provided, comprising:

[0095] The planned work acquisition module is used to acquire all types of planned work from various business systems through data interfaces;

[0096] The standard quantification module is used to set uniform standards for all different planned work tasks and to identify the key elements of a single task.

[0097] The intelligent assignment module is used to intelligently assign all planned tasks after the system obtains the information of the on-duty dispatcher.

[0098] The monitoring and alarm module is used to monitor the number of tasks each dispatcher is responsible for and the current number of tasks in real time. When the number of tasks exceeds a certain level, or when the unevenness reaches a certain level, an alarm will be issued to remind the dispatcher to adjust the workload.

[0099] Work initiated and planned for completion is output to the standard quantification module. In-process planned work that ends outside of designated shifts is sent to the administrator for assignment based on workload. The standard quantification module identifies the key elements of a single task, analyzes the weight of each element, quantifies the workload of each task, and calculates the equivalent workload for each task. These key elements include work content data, number of operations data, operation time data, work unit data, and peak scheduling data. During the intelligent assignment module's intelligent assignment of all planned work, planned work for the same process is assigned to the same employee; no multiple tasks are started by the same employee at the same time; and the workload difference between different employees does not exceed 10%.

[0100] This task allocation system automatically and evenly distributes employee workloads. It can be used in conjunction with the aforementioned electronic case file generation management method, integrating previously independent tasks into a single online system. This workflow transformation has increased the efficiency of frontline staff involved in electronic case file generation by 80%, reduced the error rate by 30%, and decreased electronic case file generation time by 50%.

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0102] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for generating and managing electronic case files, characterized in that, include: Step S1: Receive paper case files and send them to the processing workshop, where the processing workshop scans the paper case files to generate corresponding electronic images and pushes the paper case files into the intelligent storage cabinet. Step S2: Perform image processing on the electronic image, and perform OCR text recognition on the processed electronic image to obtain the corresponding text content JSON file and double-layer PDF file; Step S3: Catalog the two-layer PDF file and verify the cataloging results to obtain the target category; Step S4: The two-layer PDF files are categorized using natural language processing technology, and then linked to the electronic case file management system. Specifically, categorizing the two-layer PDF files using natural language processing technology includes: The two-layer PDF file is parsed, and a topic analysis model is used to perform semantic analysis of the text content. The topic analysis model uses unsupervised learning to statistically analyze and cluster the implicit semantic structure of the document in order to mine the semantic structure contained in the text. The analyzed semantics are compared with the semantic database, and it is determined whether the analyzed semantics exist in the semantic database. If the analyzed semantics exist in the semantic database, then similarity detection is performed directly; if the analyzed semantics do not exist in the semantic database, then the semantic database is updated, and then similarity detection is performed. Based on the semantic and similarity detection results obtained from the analysis, files of the same type in the two-layer PDF files are integrated; Feature recognition is performed on the two-layer PDF file; Based on the feature recognition results and the target category in the cataloging results, the category to which the two-layer PDF file belongs is determined, and the cataloging of the two-layer PDF file is completed. Step S5: Restore the layout of the JSON file containing the text content, perform content verification, and then combine it with the cataloging information to obtain the text dossier. Step S6: Extract elements and / or analyze the cause of action of the text file to obtain a data file.

2. The electronic case file generation and management method according to claim 1, characterized in that, The method further includes: Receive electronic materials and determine the type of said electronic materials; When the electronic material is an electronic image, proceed with steps S2-S6; When the electronic material is plain text, generate a corresponding two-layer PDF file based on the plain text and execute steps S3-S4; obtain a text file based on the plain text and execute step S6; When the electronic material is audio or video, the electronic material is processed to convert it into plain text, and a corresponding two-layer PDF file is generated based on the plain text. Steps S3-S4 are then executed; a text file is obtained based on the plain text, and step S6 is then executed.

3. The electronic case file generation and management method according to claim 1, characterized in that, The paper case files were obtained from various sources, including window reception, judge transfer, mailing by the parties involved, and mailing by lawyers.

4. The electronic case file generation and management method according to claim 2, characterized in that, The sources of the electronic materials include window reception, judge transfer, mailing by the parties, mailing by the lawyers, online case filing system and trial system.

5. The electronic case file generation and management method according to claim 1, characterized in that, The topic analysis model is the implicit Dirichlet distribution model.

6. The electronic case file generation and management method according to claim 1, characterized in that, The paper files are scanned using a browser-based scanner and / or camera to generate corresponding electronic images.

7. An electronic case file generation and management device, characterized in that, include: A paper file receiving module is used to receive paper files and send them to a processing workshop, where the processing workshop scans the paper files to generate corresponding electronic images and pushes the paper files into an intelligent storage cabinet. The image processing module is used to process the electronic image and perform OCR text recognition on the processed electronic image to obtain the corresponding text content JSON file and double-layer PDF file; The cataloging module is used to catalog the two-layer PDF file and verify the cataloging results to obtain the target category; The categorization module is used to categorize the two-layer PDF files using natural language processing technology and to link the two-layer PDF files to the electronic case file management system; wherein, the categorization of the two-layer PDF files using natural language processing technology specifically includes: The two-layer PDF file is parsed, and a topic analysis model is used to perform semantic analysis of the text content. The topic analysis model uses unsupervised learning to statistically analyze and cluster the implicit semantic structure of the document in order to mine the semantic structure contained in the text. The analyzed semantics are compared with the semantic database, and it is determined whether the analyzed semantics exist in the semantic database. If the analyzed semantics exist in the semantic database, then similarity detection is performed directly; if the analyzed semantics do not exist in the semantic database, then the semantic database is updated, and then similarity detection is performed. Based on the semantic and similarity detection results obtained from the analysis, files of the same type in the two-layer PDF files are integrated; Feature recognition is performed on the two-layer PDF file; Based on the feature recognition results and the target category in the cataloging results, the category to which the two-layer PDF file belongs is determined, and the cataloging of the two-layer PDF file is completed. The layout restoration module is used to restore the layout of the text content JSON file, perform content verification, and then combine it with cataloging information to obtain the text dossier. The data file generation module is used to extract elements and / or analyze the cause of action of the text file to obtain the data file.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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