WO2019098875A1 - Détection et classification de l'apparition de réclamations d'utilisateurs dans des dispositifs self-service - Google Patents

Détection et classification de l'apparition de réclamations d'utilisateurs dans des dispositifs self-service Download PDF

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Publication number
WO2019098875A1
WO2019098875A1 PCT/RU2017/000986 RU2017000986W WO2019098875A1 WO 2019098875 A1 WO2019098875 A1 WO 2019098875A1 RU 2017000986 W RU2017000986 W RU 2017000986W WO 2019098875 A1 WO2019098875 A1 WO 2019098875A1
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WIPO (PCT)
Prior art keywords
user
self
complaints
operations
service
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PCT/RU2017/000986
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English (en)
Russian (ru)
Inventor
Валерий Валерьевич ТОЛКАЧЕВ
Марина Игоревна КАЧАЕВА
Сергей Юрьевич САЕНКО
Денис Александрович АРТЮШИН
Екатерина Сергеевна ИВКИНА
Сергей Владимирович АЛПАТОВ
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Публичное Акционерное Общество "Сбербанк России"
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Publication of WO2019098875A1 publication Critical patent/WO2019098875A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/28—Error detection; Error correction; Monitoring by checking the correct order of processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0766—Error or fault reporting or storing
    • G06F11/0778—Dumping, i.e. gathering error/state information after a fault for later diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/079—Root cause analysis, i.e. error or fault diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars

Definitions

  • This technical solution in general, relates to the field of computer technology, and in particular to systems and methods for identifying the causes of claims and incidents in the network of self-service devices.
  • Self-service devices allow customers to carry out various financial transactions, including deposits, money transfers between accounts, payments on bills, balance requests, cash withdrawals, etc.
  • Self-service devices are usually located in places where self-service device customers can quickly and conveniently complete transactions, including transfer of funds.
  • self-service devices are owned by a legal entity (for example, a financial institution), and are managed by another entity, such as a service provider, who has contracted a financial service agency.
  • the service provider is the operator of the self-service device.
  • Operators place self-service devices in places where customers can quickly and conveniently complete transactions for a variety of reasons. For example, a restaurant owner may purchase a self-catering unit to place in a restaurant in order to increase profits at a restaurant’s bar.
  • Financial institutions typically operate large networks of self-service devices that allow customers of financial institutions to have more freedom in making financial transactions. Clients of a financial institution that provides such a large network of self-service devices consider a large number of ATMs as the advantage of doing business with a specific financial institution. Financial institutions see ATMs as another source of potential revenue.
  • an ATM management application is used to provide an ATM operator with a complete set of information about managing an ATM. This information for managing an ATM may allow an ATM operator to increase the profitability of ATMs by increasing availability and reducing ATM funds.
  • the ATM management application can include any number of the following modules: a cash management module, an operation status request module, a courier service module, an automatic balance check module, a deposit check module, a website control module, and a profit management module.
  • the technical problem posed in this technical solution is to identify and classify the causes of the claims of users in self-service devices.
  • the technical result which manifests itself in solving the above problem, is to improve the quality of the analysis of user complaints that have arisen as a result of errors in the operation of the self-service device.
  • An additional technical result achieved when solving the above problem is to improve the accuracy of the analysis of user complaints and the speed of analysis of user complaints by using an automated parser for analyzing operations.
  • This technical result is achieved by implementing a method for identifying and classifying the causes of user complaints in a self-service device, in which they receive a set of user complaints in a chronological order that arose when using a self-service device; allocate a group of users' claims for analysis based on at least one requirement for the type and number of operations, with each claim corresponding to at least one operation; receive at least one operation corresponding to at least one claim from the group of claims selected in the previous step; analyzing at least one operation by applying text syntax, and performing at least one operation line by line; determining at least one client session of the user based on the at least one operation; identify at least one cause of an error in a user operation; Then, users' claims are classified based on user operations for which errors occurred in the self-service device. In some embodiments, a user's photo or video is obtained in conjunction with the user claim set.
  • the resulting set of user claims is filtered based on the type of operation that occurred.
  • a biometric user identification by voice is used to record his claim.
  • user operations are automatically recorded in the event log.
  • a user claims group is identified that relates to failed operations, based on the proportion of affected users or products, or the degree of malfunction.
  • a group of user claims related to failed operations is distinguished based on their severity.
  • the implementation of the cause of the error is determined by a character or numeric identifier that uniquely corresponds to the type of operation and / or the type of error.
  • the implementation of the classification of claims carried out on the basis of errors found on the operating days of the self-service device and / or types of operations and / or types of self-service devices, and / or types of event logs.
  • FIG. 1 shows a block diagram of a method for identifying and classifying the causes of user complaints in a self-service device
  • FIG. 2 shows a diagram of the interaction of the nodes of the system for identifying and classifying the causes of user complaints in the self-service device
  • FIG. 3 shows an exemplary analysis of claims by applying text syntactic analysis
  • FIG. 4 shows an embodiment when several user operations and errors correspond to one user claim.
  • a system 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, well-defined sequence of operations (actions, instructions).
  • a command processing device is an electronic unit or an integrated circuit (microprocessor) that executes machine instructions (programs).
  • the command processing device reads and executes machine instructions (programs) from one or more data storage devices.
  • a storage device can act, but not limited to, hard drives (HDD), flash memory, ROM (read-only memory), solid-state drives (SSD), optical drives.
  • a program is a sequence of instructions intended for execution by a computer control device or command processing device.
  • ATM Automatic Teller Machine
  • ATM - a software and hardware complex designed for the automated issuance and / or receipt of cash with or without payment cards also performing other operations, including payment for goods and services, drawing up documents confirming the relevant operations.
  • Event log (English log) - a standard way for applications and the operating system to record and centrally store information about important software and hardware events.
  • the event log service stores events from various sources in a single event log, which allows the user to monitor the event log, the program interface (API) allows applications to record information in the log and view existing records.
  • API program interface
  • a parser (otherwise a parser) is a program or part of a program that performs parsing.
  • the source text is converted into a data structure, usually a tree, which reflects the syntactic structure of the input sequence of characters and is well suited for further processing.
  • a method for identifying and classifying the causes of user complaints in a self-service device shown in FIG. 1 includes the following steps.
  • Step 101 get a set of user complaints in chronological order, arising from the use of self-service devices;
  • Pre-user 200 accesses the self-service device 201 and performs the banking operations it needs.
  • bank transaction it may be, without limitation, depositing cash into a card account, payment of services in cash, cash withdrawal.
  • the user 200 In case of failures in the operation of the self-service device 201, for example, errors or incorrect operation, or hardware failure in accepting notes, the user 200 usually leaves a claim on the operation of the self-service device 201 and the system as a whole.
  • All user complaints left in the self-service device 201 or by calling the hotline operator come from the self-service device 201 or from the remote server 202 from the user or the hotline operator to the customer complaints and complaints system 203.
  • the user uses the 200 biometric identification of the voice to record his claim.
  • Remote server 202 can be connected to multiple servers 202 a, including, but not limited to, a database server, file server, e-commerce server, content management server, directory server, FTP server, print server, and proxy server with or without a firewall .
  • Step 102 together with the claim of the user 200, a photo or video image of the user is received with one of the cameras installed on the self-service device 201 (in the event that a video surveillance system is installed on the ATM).
  • Step 102 allocate a group of user claims for analysis based on at least one requirement for the type and number of operations, with each claim corresponding to at least one operation;
  • Each recorded user claim is associated with at least one operation for which an error occurred (in other words, a failed operation), as shown in FIG. four.
  • This correspondence may involve recording a claim and an operation into one file, using it with the same numeric or symbol identifier, into a single database entry, etc.
  • All failed operations are recorded automatically in the event log 206 or a log file, which is the system messages generated as a result of the operations that occurred as a result of user actions 200 (Fig. 2) in the self-service device 201, and this data can be filtered on based on the type of operations that occurred.
  • Filter criteria may include predefined conditions and / or conditions developed using machine learning or other adaptive methods. Filter criteria can be implemented on server 204 of system 203 based on production rules automatically configured by the user or dynamically configured using software based on machine learning methods (for example, using an artificial neural network).
  • the event log 206 consists of records stored in the database 205. As the event log 206, you can use, without limitation, PRJ, PRR, PRF and ERL files. In some embodiments, the event log 206 consists of one or more files, each of which includes a specific type of operation or error.
  • Event logs 206 for example, in the * .rg and * .prj format are stored in files corresponding to the ATM operating days, in the form of text files with the names YYYYMMDD, where YYYY is the year, MM is the month and DD is the opening day of the corresponding trading day.
  • the * .erl log can be created every day with the name of the same type YYYYMMDD.
  • the file 20170411.rgg contains a copy of client checks for the ATM operating day, opened on April 1, 2017.
  • the retention periods for event logs, print forms used, trace levels and other characteristics of event logs are ATM software configuration parameters and can be changed by the user, who is, for example, an operator.
  • Records of operations for which errors occurred can be stored for a predetermined time, and then archived.
  • Briefly summary information on event logs can be presented in the form of a table.
  • internal fault conditions can be stored in event log 206 that are not related to user complaints for later analysis, even if fault conditions are never transmitted as a failure signal.
  • Self-service device 201 may transmit failure signals corresponding to failure states not satisfying the filter criteria to remote control server 204 in response to a request for these fault signals or on a periodic basis (for example, daily, weekly, monthly, etc.).
  • a server 204 hosted on the system aggregates information from all testing and user clients that have been configured to use server 204.
  • one server 204 serves all clients.
  • a plurality of servers 204 serve a subset of clients.
  • Server 204 also implements functional logic that provides the user with access to the network to provide detailed information about new events and errors, as well as to analyze existing errors. There are a group of claims on the basis of operations, for example, on the basis of their criticality.
  • operations can be grouped into the following categories: the operation cannot be performed; the operation can be performed partially; the operation can be performed on paper, but requires subsequent manual entry into the system; the operation can be performed completely using a workaround; the operation can be performed completely, the speed of the bank employee is reduced; operation is available - request for revision, consultation, etc.
  • the operations may be grouped together based on the proportion of affected users or products, or the degree of malfunction, without being limited.
  • Step 103 receive at least one operation corresponding to at least one claim from the group of claims outlined in the previous step;
  • the system 203 for registering and registering complaints and claims of users uses the published API 206.
  • This API which has a graphical interface, complaints, claims, and errors in the self-service device 201, due to which claims originated, are unloaded from the system 203.
  • the advantage of the API is that if the user needs the system 203 to record and register customer complaints and complaints, support other types of devices (such as handlers or other testers), the manufacturer of a particular device only needs to publish its own configuration file.
  • This configuration file for system 203 contains different types of events, errors that occur, and their attributes.
  • the system of accounting and registration of complaints and claims of users 203 uses a “web service”, a well-known universal standard that supports interaction between devices and components in a network.
  • the Web Service Front End Interface (WSDL) Language is used in conjunction with the Messaging Protocol (SOAP) and Markup Language (XML) to provide web services over the Internet.
  • SOAP Messaging Protocol
  • XML Markup Language
  • WSDL describes the open web service interface.
  • Step 104 analyze at least one operation by applying text syntax
  • the event log 206 is preliminarily checked for completeness: whether all the necessary event log files 206 are received, whether information on claim transactions contains information. For user claims for which an incompletely received / received set of event logs 206 of the self-service device 201, request a repeated collection of event logs.
  • Step 104a perform line-by-line reading of at least one operation
  • the event log 206 is transmitted to the parser 301, which processes the event log 206 to form a final file, for example, in XML format.
  • the parser 301 can also be implemented using the Java programming language.
  • Event log 206 often includes compilation and execution information that is used in debugging and other maintenance operations on self-service device 201.
  • the parser 301 processes this information by using additional information to create the generated report file in XML format.
  • the parser 301 can extract control characters that are not used during the further steps of the method.
  • the parser 301 is a universal tool that can execute SQL queries for source files (i.e. event logs).
  • SQL queries allow you to import, parse, present, and export a variety of different data from input event logs in various formats (for example, CSV, XML, txt, W3C, IIS, database table, and other data formats).
  • the parser 301 provides filtering of event log entries, searching for data and patterns in files of various data formats. Also, the parser 301 converts event logs from one data format to another data format, creates formatted reports and XML files containing data obtained from different log sources, exports data (all or individual parts of event logs) to database tables (for example , SQL tables), data mining, etc.
  • the 301 parser supports the SUM, COUNT, AVG, MIN, and MAX aggregated functions. It supports the most common operators, such as more (>), IS NULL, LIKE and IS IN. Also, the 301 parser supports most standard SQL queries: SELECT, WHERE, GROUP BY, HAVING, and ORDER BY.
  • the parser 301 allows third-party software developers to add plug-ins to the analyzer 301. For example, to read and analyze the event log 206 of a particular data format, the parser 301 will interact with a plug-in such as a user reader, for example.
  • Step 104 b determine at least one client session of the user based on at least one operation
  • the parameters of the client session can be the beginning of the client session (time of the session beginning), the end of the client session (time of the end of the session).
  • the completion of the client session can be caused due to the fact that the operation is terminated (forced termination session) and due to the fact that there was a forced completion of the transaction (the forced completion of the session).
  • the parser 301 uses the parser 301 to detect the number of the self-service device 201, in which the failed operation occurred and the customer complaint, as well as the user's bank card number, are detected.
  • Step 104 c reveal at least one cause of an error in a user operation
  • the parser 301 can perform any combination of different operations in the event log 206. These can be filtering operations, searching data and / or templates in files of various data formats, grouping and / or ordering the extracted information in accordance with the conditions specified in the parser 301 request.
  • the parser 301 detects at least one user operation, according to which errors occurred in the self-service device 201, and generates output data based on the results of the query.
  • the output may be the final results of the conversion of the event log 206 from one data format to another data format. It may also be the creation of formatted reports and XML files containing data obtained from different sources of event logs 206.
  • the parser 301 presents the result of its work to end users (for example, through the display module 303), writes the detected operations into one or more database tables, and / or records the identified operations into a data file of the specified data format.
  • the analysis of the 206 event logs of the self-service device 201 for each claim with entering the results into the parsing file can be performed in the following order:
  • the parser 301 in this step determines at least one cause of the error in the user's operations.
  • the cause of the error is determined by a character or numeric identifier that uniquely corresponds to the type of operation, the type of error.
  • An example of displaying a successful cash withdrawal operation in the PRJ journal is as follows:
  • errors may be recorded when performing a cash out transaction in the event log: a miscalculation error, a hardware failure of the card reader or a card is delayed by timeout, no funds were taken by the client during the timeout, a hardware dispenser failure, software errors, power failure, etc.
  • the timeout is a configurable parameter of the self-service device 201, therefore the values on various devices self-catering can correlate. On average, the value of a custom parameter is 45 seconds.
  • the hardware failure of the bill acceptor is displayed in command 12.
  • the following error options are possible: ⁇ w3 — device hardware failure;
  • the parser 301 searches for the start of the operation, the end of the operation, and also the search for the error string of the operation.
  • the error string in the approximate embodiments may be as follows: HARDWARE FAILURE AT RECEPTION OF COUPLER [date] [time]
  • Step 105 classify user claims based on user operations for which errors occurred in the self-service device.
  • claims from users are automatically classified from the event log file based on the initial rules. Classification of claims can be carried out on the basis of errors found by the operating days of the self-service device and / or by types of operations, or types of self-service devices, or types of event logs.
  • a final or resulting file is generated, for example, the csv format.
  • the result file is archived with or without a password.
  • This file may contain the beginning of the client session, the end of the client session, the status of the end of the client session, as well as the date / time, terminal, user bank card number, transaction, amount; mistake.
  • the format of the resulting file may be as follows:
  • a data processing device that is a computer from a system (or means such as a central / graphics processor or microprocessor) that reads and executes a program recorded on a memory device to perform the functions of the above-described variant (s).
  • the program is provided to a computer, for example, through a network or from a medium for recording various types, serving as a storage device (for example, a machine-readable medium).
  • the data processing device may have additional features or functionality.
  • a data processing device may also include additional data storage devices (removable and non-removable), such as magnetic disks, optical disks or tape, for example.
  • Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or by any technology for storing information, such as computer-readable instructions, data structures, program modules or other data. Storage device, removable storage and non-removable storage are examples of computer storage media.
  • Computer storage media includes, but is not in a limiting sense, random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory or memory made using a different technology, ROM on a CD disk (CD-ROM), digital versatile disks (DVDs) or other optical storage devices, magnetic cassettes, magnetic tapes, storages on magnetic disks or other magnetic storage devices, or any other medium that can be used on to store the desired information and which the data processing device can access. Any such computer storage media can be part of a system for identifying and classifying the causes of user complaints in a self-service device.
  • RAM random access memory
  • ROM read-only memory
  • EEPROM electrically erasable programmable ROM
  • flash memory or memory made using a different technology
  • CD-ROM CD disk
  • DVDs digital versatile disks
  • Any such computer storage media can be part of a system for identifying and classifying the causes of user complaints in a self-service device.
  • the data processing device may also include an input device (s), such as a keyboard, a mouse, a pen, a speech input device, a touch input device, and so on.
  • An output device (s) such as a display, speakers, a printer, and the like may also be included in the system.
  • the data processing device contains communication connections that allow the device to communicate with other computing devices, for example over a network.
  • Networks include local area networks and global networks along with other large scalable networks, including but not limited to corporate networks and extranets.
  • a communication connection is an example of a communication environment.
  • the communication medium can be implemented using computer-readable instructions, data structures, program modules or other data in a modulated information signal, such as a carrier wave, or in another transport mechanism, and includes any medium of information delivery.
  • modulated information signal means a signal that has one or more of its characteristics changed or set to encode information in this signal.
  • communication media includes wired environments such as a wired network or direct-wired connection, and wireless environments such as acoustic, radio frequency, infrared, and other wireless environments.
  • computer readable medium includes both data carriers and communication media.
  • a program may be pre-recorded on a recording medium, such as a hard disk, or a ROM (read-only memory).
  • the program may be temporarily or permanently stored (recorded) on a removable recording medium, such as a floppy disk, CD-ROM (compact disc intended for playback only), MO (magneto-optical) disc, DVD (digital versatile disk) , magnetic disk or semiconductor memory.
  • Removable recording media can be distributed in the form of so-called, sold through a retail network software.
  • the program can be installed from a removable recording medium described above to a computer, or it can be transferred by cable from a download site to a computer or it can be transferred to a computer via network data transmission channels, such as a LAN (local area network) or the Internet.
  • a computer can receive a program transmitted in this way and can install it on a recording medium, such as an internal hard disk.
  • the processes described in this document can be performed sequentially in time, as described, or can be executed in parallel or separately, depending on the processing characteristics of the device performing the processes, or according to need.
  • the system described in this document is a logical set of multiple devices and is not limited to the structure in which these devices are installed in a single package.

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Abstract

L'invention concerne des systèmes et des procédés pour détecter et les raisons d'apparition de réclamations et d'incidents dans des réseaux de dispositifs self-service. Dans ce procédé de détection et classification de l'apparition de réclamations d'utilisateurs dans des dispositifs self-service on reçoit un ensemble de réclamations d'utilisateurs selon un ordre chronologique. On isole un groupe de réclamations pour l'analyse sur la base des exigences portant sur le type et le nombre d'opérations. On reçoit l'opération correspondant à la réclamation parmi les groupes de réclamations isolés au stade précédent et on effectue une analyse syntaxique du texte. On effectue la lecture ligne par ligne de l'opération et on détermine une session client utilisateur sur la base de l'opération. On détecte la raison de l'erreur concernant l'opération utilisateur et on effectue un classement des réclamations utilisateurs sur la base des opérations utilisateurs qui sont concernés par l'erreur dans le dispositif self-service.
PCT/RU2017/000986 2017-11-20 2017-12-27 Détection et classification de l'apparition de réclamations d'utilisateurs dans des dispositifs self-service WO2019098875A1 (fr)

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RU2017140234 2017-11-20
RU2017140234A RU2673001C1 (ru) 2017-11-20 2017-11-20 Способ и система выявления и классификации причин возникновения претензий пользователей в устройствах самообслуживания

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Publication number Priority date Publication date Assignee Title
CN113496338A (zh) * 2020-04-08 2021-10-12 中国移动通信集团广东有限公司 网络质差原因的分析方法、系统及装置
CN113496338B (zh) * 2020-04-08 2023-08-22 中国移动通信集团广东有限公司 网络质差原因的分析方法、系统及装置

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