Abstract:
This disclosure relates generally to automatic classification of incident ticket, and more particularly to a system and method for improving the incident ticket classification. An incident ticket classification system to update an incident ticket classification, the system comprising: a memory; and a processor coupled to the memory storing processor executable instructions which when executed by the processor causes the processor to perform operations comprising: identifying one or more user-defined classes from one or more classes assigned to one or more incident tickets; determining, divergence of the one or more user-defined classes from one or more pre-defined classes associated with the incident ticket classification; and updating, the incident ticket classification based on the divergence.

Description:
PRIORITY CLAIM 
       [0001]    This U.S. patent application claims priority under 35 U.S.C. §119 to 3928/CHE/2015, filed Jul. 30, 2015. The aforementioned applications are incorporated herein by reference in their entirety. 
       TECHNICAL FIELD 
       [0002]    This disclosure relates generally to automatic classification of incident ticket, and more particularly to a system and method for improving the incident ticket classification. 
       BACKGROUND 
       [0003]    When an incident ticket is raised classification of the incident tickets may be automated. A user may raise his/her issue in natural language as incident ticket. Currently when the user raises the incident ticket he/she may be presented a few choices representing a class in a historical ticket data. The incident ticket may be classified using the historical ticket data. Typically, the choices which may be displayed to the user do not classify the incident ticket correctly. The historical ticket data may be erroneous. Hence accuracy of classification of the incident ticket may be poor. 
         [0004]    The choices may be retrieved from the historical ticket data by a predictor component. The predictor component may be usually based on positive models. The positive models may return a class if the incident ticket belongs to the class. But as the historical ticket database may at least 30% of incorrect incident tickets the user may expect the classes to be wrongly returned for a significant number of times. Due to this user may be forced to raise a ticket under a wrong class. Hence a delay may be caused in resolution of issue. Hence there is a need to improve the classification for different classes. 
       SUMMARY 
       [0005]    In one embodiment, a method to update an incident ticket classification, the method comprising: identifying, by an incident ticket classification system, one or more user-defined from one or more classes assigned to one or more incident tickets; determining, by the incident ticket classification system, divergence of the one or more user-defined classed from one or more pre-defined classes associated with the incident ticket classification; and updating, by the incident ticket classification system, the incident ticket classification based on the divergence. 
         [0006]    An incident ticket classification system to update an incident ticket classification, the system comprising: a memory; and a processor coupled to the memory storing processor executable instructions which when executed by the processor causes the processor to perform operations comprising: identifying, one or more user-defined from one or more classes assigned to one or more incident tickets; determining divergence of the one or more user-defined classed from one or more pre-defined classes associated with the incident ticket classification; and updating the incident ticket classification based on the divergence. 
         [0007]    It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. 
           [0009]      FIG. 1  illustrates an exemplary diagram for an environment with incident ticket classification system to update an incident ticket classification. 
           [0010]      FIG. 2  illustrates memory  110  which may include a classification analyzer, a score calculator, a divergence module, a negative model generator and an incident ticket classification updater. 
           [0011]      FIG. 3  illustrates an exemplary flow diagram of a method to update an incident ticket classification. 
           [0012]      FIG. 4  is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims. 
         [0014]      FIG. 1  illustrates an exemplary diagram for an environment  100  with incident ticket classification system  102  to update an incident ticket classification. The exemplary environment  100  may include an incident ticket classification system  102 , an incident ticketing portal  104  and a display  106 . The incident ticket classification system  102  may further include a processor  108 , a memory  110 , an input module  112 , and an output module  114  and an incident ticket repository  116 . While not shown, the exemplary environment  100  may include additional components, such as database etc which are well known to those of ordinary skill in the art and thus will not be described here. The incident ticketing portal  104  may be an electronic gateway to a collection of one or more incident tickets raised by a user. One or more incident tickets may be at least one issue or concern or feedback or the like raised by the user in natural language. The display  106  may be peripheral devices used to display result of incident ticket classification system  102  such as a monitor, a projector, a printer. 
         [0015]    The incident ticket classification system  102  may update an incident ticket classification. The incident ticket classification system  102  may include at least one input device  112  CPU/processor  108 , memory  110 , output module  114 , and incident ticket repository  116 , which may be coupled together by bus  118 , although incident ticket classification system  102  may comprise other types and numbers of elements in other configurations. 
         [0016]    Processor(s)  108  may execute one or more computer-executable instructions stored in the memory  110  for the methods illustrated and described with reference to the examples herein, although the processor(s) can execute other types and numbers of instructions and perform other types and numbers of operations. The processor(s)  108  may comprise one or more central processing units (“CPUs”) or general purpose processors with one or more processing cores, such as AMD) processor(s), although other types of processor(s) could be used (e.g., Intel®). 
         [0017]    The memory  110  may comprise one or more tangible storage media, such as RAM, ROM, flash memory, CD-ROM, floppy disk, hard disk drive(s), solid state memory, DVD, or other memory storage types or devices, including combinations thereof, which are known to those of ordinary skill in the art. The memory  110  may store one or more non-transitory computer-readable instructions of this technology as illustrated and described with reference to the examples herein that may be executed by the one or more processor(s)  108 . 
         [0018]    The input module  112  may receive one or more incident tickets from the incident ticketing portal  104 . The output module  106 , may link the risk determination engine  102  with peripheral devices such as a display  106 . 
         [0019]      FIG. 2  illustrates memory  110  which may include a classification analyzer  202 , a score calculator  204 , a divergence module  206 , a negative model generator  208  and an incident ticket classification updater  210 . The classification analyzer  202  may identify one or more user-defined from one or more classes assigned to one or more incident tickets. An incident ticket classification may comprise one or more classes. The one or more classes may be one or more user-defined classes and one or more pre-defined classes. The one or more incident tickets may be at least one issue or concern or feedback or any text raised by a user in natural language. While the user is rising the one or more incident tickets by the incident ticketing portal  104 , the user may be presented with one or more choices representing one or more pre-defined classes associated with the incident ticket classification. The one or more incident tickets may be assigned to the one or more predefined classes associated with the incident ticket classification, based on the one or more choices selected by the user. The classification analyzer module  202  may identify one or more classes assigned to the one or more incident tickets based on the one or more choices selected by the user. The one or more pre-define classes may be current system assignment CA and historical assignment HA. The one or more user-defined classes may be Manually Assigned User class labelled as MAU or Manually Assigned agent class labelled as MAA. For example, the user may raise one or more incident tickets, but may not select any of the one or more choices. Then the one or more incident tickets, may be assigned to a class MAU. The user may raise one or more incident tickets and may select one of the one or more choices. Then the one or more incident tickets, may be assigned to CA or HA. The one or more incident tickets may be reassigned from a pre-defined class CA to MAA by the user or an agent. 
         [0020]    After identifying the one or more user defined classes, the incident ticket classification system  102  may determine divergence of the one or more user-defined classed from one or more pre-defined classes associated with the incident ticket classification. The divergence of the one or more user-defined classes from the one or more pre-defined class may be determined based on a dampening parameter β, a pre-defined weight assigned to the one or more pre-defined class and number of one or more incident tickets. The pre-defined weight may be assigned to the one or more predefined classes and the one or more classes. For example, in Table A M may have the predefined weight as 1, MAU may have the predefined weight as 0.75, MAA may have the predefined weight as 0.8, CA may have the predefined weight as 0.5 and HA may have the predefined weight as 0.5. 
         [0000]                                              TABLE A                       Source Category   Value                                        M   1           MAU   0.75           MAA   0.8           CA   0.5           HA   0.5                        
The score calculator module  204  may calculate an accuracy score for the one or more one or more classes. The accuracy score may be accuracy of assignment of the one or more incident to the one or more classes. If any of the one or more incident tickets are assigned to more than one class of the one or more classes, average score of the each of the one or more classes assigned to the incident ticket would be considered. The accuracy score for the one or more classes may be calculated as Pre-defined weight minus product of the dampening parameter β and number of the one or more incident tickets.
 
         [0000]      Accuracy Score=Pre-defined weight−β*Number of Incident tickets
 
         [0000]    For example, Let the dampening parameter β be 0.00001. Then the accuracy score for class MAU with 1000 Incident Tickets would be 0.75−(0.00001*1000)=0.74. The accuracy score for class CA with 2000 Incident Tickets would be 0.5−(0.00001*20000)=0.48
 
The dampening parameter β may be used to reduce the pre-defined weight of the one or more classes to save training time and to reduce error oscillations.
 
The divergence module  206  may compare the accuracy score associated with one or more user-defined classes and the accuracy score associated with one or more pre-defined classes. The comparison of the accuracy score associated with one or more user-defined classes and the accuracy score associated with the one or more pre-defined classes may result in a divergence.
 
         [0021]    Once the divergence is determined, the incident ticket classification system  102  may update the incident ticket classification based on the divergence. The negative model generator  208  may generate a negative classification on based on the one or more user-defined classes identified by the classification analyzer module  202 . The generation of the negative classification may be due to: assignment of one or more incident tickets to the one or more user-defined classes MAU; or reassignment of one or more incident tickets from the one or more pre-defined class to the one or more user-defined classes MAA. The one or more predefined-classes may be updated based on the negative classification and the divergence. The incident ticketing classification updater  210  may remove or reprioritize the one or more predefined classes with accuracy score less than the divergence. The reprioritization may cause one or more incident tickets not to be assigned to the one or more predefined classes. The one or more incident tickets from the one or more deprioritized class may not be used for further iterations. The incident ticketing classification updater  210  may add the one or more user defined classes used in with accuracy score above the divergence to the incident ticket classification. 
         [0022]      FIG. 3  illustrates an exemplary flow diagram of a method to update an incident ticket classification. The method may involve identifying, by an incident ticket classification system, one or more user-defined classes from one or more classes assigned to one or more incident tickets at step  302 . An incident ticket classification may comprise one or more classes. The one or more classes may be one or user-defined classes and one or more pre-defined classes. The one or more incident tickets may be at least one issue or concern or feedback or any text raised by a user in natural language. While the user is rising the one or more incident tickets by the incident ticketing portal  104 , the user may be presented with one or more choices representing one or more pre-defined classes associated with the incident ticket classification. The one or more incident tickets may be assigned to the one or more predefined classes associated with the incident ticket classification, based on the one or more choices selected by the user. The classification analyzer module  202  may identify one or more classes assigned to the one or more incident tickets based on the one or more choices selected by the user. The one or more pre-define classes may be current system assigned class CA and historical assigned class HA. The one or more classes may be manually assigned user class MAU or manually assigned agent class MAA. For example, the user may raise one or more incident tickets, but may not select any of the one or more choices. Then the one or more incident tickets, may be assigned to a class MAU. The user may raise one or more incident tickets and may select one of the one or more choices. Then the one or more incident tickets, may be assigned to CA or HA. The one or more incident tickets may be reassigned from a pre-defined class CA to MAA by the user or an agent. 
         [0023]    At step  304 , the incident ticket classification system  102 , may determine a divergence of the one or more user-defined classed from one or more pre-defined classes associated with the incident ticket classification. The divergence of the one or more user-defined classes from the one or more pre-defined class may be determined based on a dampening parameter β, a pre-defined weight assigned to the one or more pre-defined class and number of one or more incident tickets. The pre-defined weight may be assigned to the one or more predefined classes and the one or more classes. For example, in Table A M may have the predefined weight as 1, MAU may have the predefined weight as 0.75, MAA may have the predefined weight as 0.8, CA may have the predefined weight as 0.5 and HA may have the predefined weight as 0.5. 
         [0000]                                              TABLE A                       Source Category   Value                                        M   1           MAU   0.75           MAA   0.8           CA   0.5           HA   0.5                        
The score calculator module  204  may calculate an accuracy score for the one or more one or more classes. The accuracy score may be accuracy of assignment of the one or more incident to the one or more classes. If any of the one or more incident tickets are assigned to more than one class of the one or more classes, average score of the each of the one or more classes assigned to the incident ticket would be considered. The accuracy score for the one or more classes may be calculated as Pre-defined weight minus product of the dampening parameter β and number of the one or more incident tickets.
 
         [0000]      Accuracy Score=Pre-defined weight−β*Number of Incident tickets
 
         [0000]    For example, Let the dampening parameter β be 0.00001. Then the accuracy score for class MAU with 1000 Incident Tickets would be 0.75−(0.00001*1000)=0.74. The accuracy score for class CA with 2000 Incident Tickets would be 0.5−(0.00001*20000)=0.48
 
The dampening parameter β may be used to reduce the pre-defined weight of the one or more classes to save training time and to reduce error oscillations.
 
The accuracy score associated with one or more user-defined classes may be compared with the accuracy score associated with one or more pre-defined classes by the divergence module  206 . The comparison of the accuracy score associated with one or more user-defined classes and the accuracy score associated with the one or more pre-defined classes may result in a divergence.
 
         [0024]    At step  306 , the incident ticket classification system  102  may update the incident ticket classification based on the divergence. A negative classification may be generated, by the negative model generator  208  on based on the one or more user-defined classes identified by the classification analyzer module  202 . The generation of the negative classification may be due to: assignment of one or more incident tickets to the one or more user-defined classes MAU; or reassignment of one or more incident tickets from the one or more pre-defined class to the one or more user-defined classes MAA. The one or more predefined-classes may be updated based on the negative classification and the divergence. The one or more predefined classes with accuracy score less than the divergence may be removed or reprioritized from the incident ticketing classification by the incident ticketing classification updater  210 . The reprioritization may cause one or more incident tickets not to be assigned to the one or more predefined classes. The one or more incident tickets from the one or more deprioritized class may not be used for further iterations. The one or more user defined classes used in with accuracy score above the divergence may be added to the incident ticket classification by the incident ticketing classification updater  210 . Hence the incident ticket classification may be updated. 
       Computer System 
       [0025]      FIG. 4  is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. Variations of computer system  401  may be used for implementing, incident ticket classification system  102  and Incident Ticketing Portal  104 . Computer system  401  may comprise a central processing unit (“CPU” or “processor”)  402 . Processor  402  may comprise at least one data processor for executing program components for executing user- or system-generated requests. A user may include a person, a person using a device such as such as those included in this disclosure, or such a device itself. The processor may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM&#39;s application, embedded or secure processors, IBM PowerPC, Intel&#39;s Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor  402  may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc. 
         [0026]    Processor  402  may be disposed in communication with one or more input/output (I/O) devices via I/O interface  403 . The I/O interface  403  may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n/b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc. 
         [0027]    Using the I/O interface  403 , the computer system  401  may communicate with one or more I/O devices. For example, the input device  404  may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device/source, visors, etc. Output device  405  may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, or the like), audio speaker, etc. In some embodiments, a transceiver  406  may be disposed in connection with the processor  402 . The transceiver may facilitate various types of wireless transmission or reception. For example, the transceiver may include an antenna operatively connected to a transceiver chip (e.g., Texas Instruments WiLink WL1283, Broadcom BCM4750IUB8, Infineon Technologies X-Gold 618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system (GPS), 2G/3G HSDPA/HSUPA communications, etc. 
         [0028]    In some embodiments, the processor  402  may be disposed in communication with a communication network  408  via a network interface  407 . The network interface  407  may communicate with the communication network  408 . The network interface may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network  408  may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface  407  and the communication network  408 , the computer system  401  may communicate with devices  410 ,  411 , and  412 . These devices may include, without limitation, personal computer(s), server(s), fax machines, printers, scanners, various mobile devices such as cellular telephones, smartphones (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), tablet computers, eBook readers (Amazon Kindle, Nook, etc.), laptop computers, notebooks, gaming consoles (Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. In some embodiments, the computer system  401  may itself embody one or more of these devices. 
         [0029]    In some embodiments, the processor  402  may be disposed in communication with one or more memory devices (e.g., RAM  413 , ROM  414 , etc.) via a storage interface  412 . The storage interface may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, solid-state drives, etc. 
         [0030]    The memory devices may store a collection of program or database components, including, without limitation, an operating system  416 , user interface application  417 , web browser  418 , mail server  419 , mail client  420 , user/application data  421  (e.g., any data variables or data records discussed in this disclosure), etc. The operating system  416  may facilitate resource management and operation of the computer system  401 . Examples of operating systems include, without limitation, Apple Macintosh OS X, Unix, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like. User interface  417  may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system  401 , such as cursors, icons, check boxes, menus, scrollers, windows, widgets, etc. Graphical user interfaces (GUls) may be employed, including, without limitation, Apple Macintosh operating systems&#39; Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, Javascript, AJAX, HTML, Adobe Flash, etc.), or the like. 
         [0031]    In some embodiments, the computer system  401  may implement a web browser  418  stored program component. The web browser may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure web browsing may be provided using HTTPS (secure hypertext transport protocol), secure sockets layer (SSL), Transport Layer Security (TLS), etc. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, application programming interfaces (APIs), etc. In some embodiments, the computer system  401  may implement a mail server  419  stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ActiveX, ANSI C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as internet message access protocol (IMAP), messaging application programming interface (MAPI), Microsoft Exchange, post office protocol (POP), simple mail transfer protocol (SMTP), or the like. In some embodiments, the computer system  401  may implement a mail client  420  stored program component. The mail client may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, Mozilla Thunderbird, etc. 
         [0032]    In some embodiments, computer system  401  may store user/application data  421 , such as the data, variables, records, etc. (e.g., classification data) as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase. Alternatively, such databases may be implemented using standardized data structures, such as an array, hash, linked list, struct, structured text file (e.g., XML), table, or as object-oriented databases (e.g., using ObjectStore, Poet, Zope, etc.). Such databases may be consolidated or distributed, sometimes among the various computer systems discussed above in this disclosure. It is to be understood that the structure and operation of the any computer or database component may be combined, consolidated, or distributed in any working combination. 
         [0033]    The specification has described a system and method for improving the incident ticket classification. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. 
         [0034]    Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media. 
         [0035]    It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.