Abstract:
A system and method automatically identify candidate helpdesk problem categories that are most amenable to automated solutions. The system generates a dictionary wherein each word in the text data set is identified, and the number of documents containing these words is counted, and a corresponding count is generated. The documents are partitioned into clusters. For each generated cluster, the system sorts the dictionary terms in order of decreasing occurrence frequency. It then determines a search space by selecting the top dictionary terms as specified by a user defined depth of search. Next, the system chooses a set of terms from the search space as specified by a user-defined value indicating the desired level of detail. For each possible combination of frequent terms in the search space, the system finds the set of examples containing all the terms, and then determines if the frequency is sufficiently high and the overlap sufficiently low for this candidate set of examples to be a frequently asked question.

Description:
CROSS-REFERENCE TO RELATED APPLICATION  
       [0001]    This application is related to co-pending U.S. patent application Ser. No. 09/629,831, filed on Oct. 29, 1999, and titled “System and Method for Interactive Classification and Analysis of Data,” which is assigned to the same assignee as the present invention, and which is incorporated herein by reference. 
     
    
     
       FIELD OF THE INVENTION  
         [0002]    The present invention generally relates to a system and method for classifying and analyzing data, and is particularly applicable to a method for automatically generating a list of “Frequently Asked Questions” or FAQs, by analyzing data sets describing calls and responses received at a help desk.  
         BACKGROUND OF THE INVENTION  
         [0003]    As technology becomes ever more pervasive it has become increasingly common for organizations to provide a helpdesk service to their customers. Typically, a customer will call the helpdesk to ask for information and to seek solutions to problems relating to the operation of products, the performance of services, necessary procedures and forms, etc.  
           [0004]    Typically, helpdesks are staffed by knowledgeable human operators, who often spend considerable time with each caller in order to answer the caller&#39;s questions. As a result, helpdesk operation could be quite expensive to maintain.  
           [0005]    Much of the helpdesk operator&#39;s time is spent solving identical or nearly identical problems over and over again. A need arises for a technique by which the solutions to frequently recurring problems may be automated in order to improve the efficiency of helpdesk operation. In particular, what is needed is a technique that can aid in identification of helpdesk inquiry and problem categories that are most amenable to automated fulfillment or solution.  
         SUMMARY OF THE INVENTION  
         [0006]    The present invention is useful in identifying candidate helpdesk problem categories that are most amenable to automated solutions. In a preferred embodiment, the present invention uses clustering techniques to identify collections of problems from free form text descriptions. It then facilitates a human user&#39;s modifications to collections as appropriate to improve the coherence and usefulness of the classification. Measures such as the level of detail, the depth of search, the confidence level, and overlap levels, are used to help the user determine which set of examples are the best candidates to become a FAQ.  
           [0007]    The present invention describes a method, system, and a computer program product for interactive classification and analysis. In order to carry out the method, a dictionary is generated whereby each word in the text data set is identified, and the number of documents containing these words is counted. The most frequently occurring words in the corpus compose a dictionary. A count of occurrences of each word in the dictionary within each document in the document set is generated. The count may be generated by generating a matrix having rows and columns, each column corresponding to a word in the dictionary, each row corresponding to an example in the text corpus, and each entry representing a number of occurrences of the corresponding word in each example.  
           [0008]    The set of documents may be partitioned by partitioning the set of examples into a plurality of clusters using a k-means partitioning procedure. The k-means partitioning procedure may include determining a distance between a centroid and an example vector using a distance function of:  
             d ( X,Y )=− X.Y/∥X∥.∥Y∥   
           [0009]    wherein X is the centroid, Y is the example vector, and d(X,Y) is the distance between the centroid and the example vector.  
           [0010]    For each of the generated clusters, the present method sorts the dictionary terms in order of decreasing occurrence frequency within the cluster. It then determines a search space by selecting the top (or frequent) S dictionary terms, where S is a user specified value specifying the depth of search. Next, it chooses a set of L terms from the search space, where L is a user-specified value indicating the desired level of detail.  
           [0011]    For each possible combination of L terms in the search space, the present method finds the number of examples containing all L terms. If this number is not null, and if the overlap between this set and all the other sets is less than an overlap value specified by user input, then this set of examples becomes a FAQ.  
           [0012]    For each generated FAQ, the present method chooses a name based on the relevant terms in the order in which they occur most often in the text.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0013]    The details of the present invention can be understood by referring to the accompanying drawings, in which like reference numbers and designations refer to like elements, wherein:  
         [0014]    [0014]FIG. 1 is an exemplary block diagram of a networked database system in which the present system and method for defining frequently asked questions (FAQs) in a helpdesk data set, may be implemented according to the present invention;  
         [0015]    [0015]FIG. 2 is an exemplary implementation of the operation of a helpdesk, in which the present system and method may be used;  
         [0016]    [0016]FIG. 3 is an exemplary block diagram of a computer system in which the present system and method may be implemented;  
         [0017]    [0017]FIG. 4 is a snapshot of a graphical user interface where a user provides input parameters using the present system and method;  
         [0018]    [0018]FIG. 5 is a flow diagram illustrating the operation of the system and method of the present invention;  
         [0019]    [0019]FIG. 6 is an exemplary data flow diagram resulting from the operation of FIG. 5; and  
         [0020]    [0020]FIG. 7 is an exemplary report listing predefined parameters for each FAQ, such as its name, size, and so forth, that may be displayed to the user according to the present invention.  
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0021]    An exemplary networked database system is shown in FIG. 1. The system includes database system  104 , network  102 , and a plurality of helpdesk systems  100 A - 100 Z. The helpdesk systems  100 A- 100 Z are communicatively connected to database system  104  by network  102 . The helpdesk systems  100 A- 100 Z keep a log of each question received at the helpdesk and its corresponding solution. These logs are then stored in the database system  104 .  
         [0022]    The helpdesk systems  100 A- 100 Z may, in turn, obtain data from database system  104  by submitting queries through the network  104 . The database system  104  performs the query operation by accessing data that satisfies the query conditions, and transmits the data to the requesting helpdesk system. Many types of data may be stored in database system  104 , including data in the form of documents  106  that contain textual information, such as e-mail messages, categorizations of help desk problem tickets, and logical groupings of solutions by subject. The present invention applies to documents  106  that include information relating to answers and solutions to helpdesk inquiries and problems.  
         [0023]    Documents  106  that are relevant to a particular point may be located by a variety of methods. In the context of providing helpdesk responses, a high level architecture of a system  200  for defining frequently asked questions (FAQs) in a helpdesk data set is illustrated in FIG. 2.  
         [0024]    An inquiry  202 , such as a question about or problem with operation of products, the performance of services, necessary procedures and forms, etc. is sent to the system  200  and is received by the helpdesk operator. Keywords  204  are extracted from the inquiry  202  and input into database system  104 .  
         [0025]    The keywords  204  may be extracted by the operator and entered into the database system  104 . Alternatively, the entire inquiry may be entered and the keywords  204  automatically extracted. Database system  104  uses the input keywords  204  to select one or more classes  208 A- 208 N that relate to FAQs  210 .  
         [0026]    Documents  106  (FIG. 1) are grouped into the plurality of classes  208 A- 208 N according to particular criteria. In the context of a helpdesk, the criteria are typically to classify together documents  106  that are likely to provide answers or solutions to similar helpdesk inquiries.  
         [0027]    As used herein, a “class” of documents is defined by an enumeration or listing of the set of documents  106  contained in the class. A class will also be described generally by a short phase or set of words that are most indicative of the set of documents  106  in the class. This phase or set of words is termed a class name.  
         [0028]    Database system  104  selects one or more classes based on the input keywords  204 . The selected class or classes  208 A- 208 N then define a set of FAQs that are expected to provide the answer or solution  212  to the input inquiry  202 .  
         [0029]    Classes  208 A- 208 N may be generated manually by a human user, who is typically an expert in the subject matter of the documents. However, manual class generation could be time-consuming and expensive. The present invention is a system and method for interactive classification and analysis of textual data, which automates much of the process of generating the classes, and thus provides savings in both time and expense.  
         [0030]    The person who operates the present system  200  in order to interactively classify and analyze textual data is referred to herein as the “user,” while the person who uses the resulting classification and analysis in order to operate a system such as a helpdesk is referred to as an “operator” of the helpdesk or another system.  
         [0031]    An exemplary system  200  in which the present invention may be implemented is shown in FIG. 3. System  200  includes central processing unit (CPU)  302 , which is connected to random access memory (RAM)  304 , read-only memory (ROM)  306 , input/output devices (I/O)  308  and the database system  104 . CPU  302  may include, for example, a microprocessor, a mini-computer, or a mainframe processor.  
         [0032]    RAM  304  and ROM  306  store program instructions that are executed by CPU  302  and data that is used during program execution. I/O devices  308  may include any device used to input data to system  200 , such as a keyboard, mouse, trackpad, trackball and graphics tablet, or to output data from system  200 , such as a display and a printer, and to both input and output data, such as a modem and a network adapter.  
         [0033]    The database system  104  includes stores data used by system  200 , may comprise, for example, a random-access memory, a magnetic disk and/or optical disk, or a magnetic tape.  
         [0034]    Database system  104  stores documents (or document set)  312 , which includes one or more documents  106 A- 106 Z. Each of these documents  106 A- 106 Z is typically stored as one or more files. Each document  106 A- 106 Z typically contains at least some text, but may also contain other types of information, such as numeric data, graphics, images, audio, or video.  
         [0035]    Database system  104  further includes a dictionary  316  that contains a subset of the words contained in document set  312 . A cluster module  318  partitions the documents set  312  into a plurality of clusters  320 A- 320 Z. As used herein, a cluster is a grouping of documents in the document set  312  (i.e., documents  106 A- 106 Z) containing similar words. Clusters are partitioned from among all of the documents  106 A- 106 Z. A cluster quality/relation table  322  includes a cohesion score, distinctness score and size for each cluster, and allows the user to see quickly which clusters have the best or worst quality, and how the clusters  320 A- 320 Z relate to each other.  
         [0036]    Database system  104  further includes processing routines  324  that comprise program software routines executed by CPU  302 , and that implement the processes of the present system  200 , as it will be described later in connection with FIG. 5. The database system  104  also includes one or more operating systems  326 .  
         [0037]    An exemplary snapshot  400  of a graphical user interface used by the user to input parameters affecting the generation of FAQs is shown in FIG. 4. Information such as the level of detail, the depth of search, the allowable FAQ overlap will be used to generate the FAQs.  
         [0038]    [0038]FIG. 5 illustrates a method of operation  500  of the system  200 . FIG. 5 is best viewed in conjunction with FIG. 6 that represents a data flow diagram  600  illustrating the information processing sequence according to the present invention.  
         [0039]    Illustrated in FIG. 6, is a set of documents  312 , wherein each document ( 106 A- 106 A) may be wholly or partially composed of text. At step  502  of FIG. 5, the dictionary  316  (FIG. 3) of frequently used words contained in the set of documents  312  is generated by identifying each word in the text of document set  312  and counting the number of documents  106 A- 106 Z in which each word occurs. The most frequently occurring words are used to form dictionary  316 .  
         [0040]    At step  504  of FIG. 5, occurrences of words in common dictionary  316  are counted, resulting in the creation of a matrix  606  that is shown in FIG. 6. In matrix  606 , each column, such as column  608 , corresponds to a word in the dictionary  316 , and each row, such as row  610 , corresponds to an example.  
         [0041]    Each entry, such as entry  612 , in matrix  606  represents the number of times each dictionary word occurs in each example. Since many of these entries will, under normal circumstances, have a value of zero, the matrix  606  is termed sparse. In a preferred embodiment, this property of sparseness may be used by a compression scheme to greatly decrease the amount of storage required to hold the matrix in memory, while incurring only a relatively small cost in retrieval speed.  
         [0042]    At step  506  of FIG. 5, the documents  106 A- 106 Z are partitioned into clusters, such as clusters  320 A- 320 N of FIG. 6, using known or available partitioning techniques. In a preferred embodiment, the well-known “k-means” procedure is used. In the k-means procedure, “k” is the number of clusters produced. This procedure provides automatic classification of data when no classification is known. Preferably, a means procedure is implemented using a cosine distance metric to determine the distance (d) between a centroid (X) and a document vector (Y):  
           d ( X,Y )=−X.Y/∥X∥.∥Y∥ 
         [0043]    The number of clusters to produce, k, is an input to the procedure. This input may be provided by the user, or it may be generated based on the size of the document set or dictionary.  
         [0044]    At step  508  of FIG. 5, the dictionary terms are sorted in order of decreasing relative occurrence frequency, F, within the cluster. This step is represented by blocks  616 A- 616 N in FIG. 6. The relative occurrence frequency, F(T,C) of a dictionary term T within a cluster is calculated as follows:  
           F ( T,C )= Tc/Nc−T/N,    
         [0045]    where Tc is the number of documents in cluster C that contain term T, NC is the total number of documents in cluster C, T is the number of documents term T occurs in the entire document set, and N is the size of the entire document set. The terms are now sorted in decreasing order by this numeric F value. The order of the terms will thus be different in every cluster.  
         [0046]    At step  510  of FIG. 5, the search space is determined by selecting the top S dictionary terms  618 A- 618 N of FIG. 6. The value the user gave for S determines how many terms get selected for each cluster. These will be the first S terms in the ordering calculated at step  508  of FIG. 5, for each cluster.  
         [0047]    At step  512  of FIG. 5, method  500  selects all possible combinations of L terms in the search space S, where L is the level of detail that is selected by the user. An exemplary data flow diagram is presented in blocks  620 A- 620 C and  620 X- 620 Y of FIG. 6.  
         [0048]    At step  514  of FIG. 5, method  500  determines the number of examples, E, containing each combination of L terms as determined at step  512 .  
         [0049]    At decision step  516 , method  500  determines if the set of examples generated at step  514  is eligible to become a FAQ. If E is at least one, and if the overlap between this set and all other sets is less than P, where P is a value specified by the user, then this set of examples becomes a FAQ. Eventually. Only the FAQs that reach a user-defined frequency (confidence) will be retained in the report  700 . Blocks  622 A- 622 B and  622 X- 622 Y of FIG. 6 represent the equivalent step in the data flow diagram.  
         [0050]    At step  518  of FIG. 5, method  500  names the FAQ based on the combination of L terms, listing those terms in the order in which they most frequently occur in the text. Method  500  then returns to step  508  and repeats steps  508 - 518 , as described above, for each cluster, for the number of clusters.  
         [0051]    At step  520 , method  500  writes a report listing the name of each FAQ generated and its size. An exemplary report  700  is illustrated in FIG. 7. The report  700  will only include the FAQs that occur with a frequency higher than a user specified minimum. Alternatively, the user may specify a fixed number of FAQs and then the report  700  will include the most frequent FAQs up to that number.  
         [0052]    A particularly advantageous aspect of the present invention is the inclusion of similarity information in the display of individual documents in the cluster. The documents are sorted based on their similarity to the other documents in the cluster, or, alternatively, to a “typical” or “template” document in the cluster. Preferably, this is determined by calculating the distance of each document in the cluster to the centroid of the cluster. Documents may be sorted in order of descending distance, causing the more typical documents to be displayed first. This allows the user to determine the content of the documents in the cluster without having to look at all of the documents in order to do so. This saves the user considerable time, and expense.  
         [0053]    It is important to note that while the documents discussed in this invention are helpdesk problem reports, labeled as examples in the figures, the present invention is applicable to other types of documents as well. Also, it is important to note that while the present invention has been described in the context of a fully functioning data processing system, those of ordinary skill in the art will appreciate that the processes of the present invention are capable of being distributed in the form of a computer readable medium of instructions and a variety of forms and that the present invention applies regardless of the particular type of signal bearing media actually used to carry out the distribution. Examples of computer readable media include recordable-type media such as floppy disc, a hard disk drive, RAM, and CD-ROMs, as well as transmission-type media, such as digital and analog communications links.  
         [0054]    Although specific embodiments of the present invention have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the invention is not to be limited by the specific illustrated embodiments, but only by the scope of the appended claims.