Abstract:
A method of detecting textual and behavioral commonalities in warranty reported data. Extracting, by a processor, records of verbatim data from a memory storage unit. A first set of basewords is identified for comparison with the extracted records. A binary flag is set in response to an occurrence of a respective baseword in a respective record. An occurrence matrix is generated that includes entries identifying a number of times basewords are identified in each record. The occurrence matrix is formatted to a format as identified by the user.

Description:
BACKGROUND OF INVENTION 
       [0001]    An embodiment relates generally to text mining. 
         [0002]    Service verbatims found in warranty data and service repair procedures are used by various personnel to identify ongoing problems with a part of system. The verbatims include various documents that include customer comments and complaints, service personnel comments, and service personnel corrections information. Due to the number of records of the customer and service verbatims, a person attempting to analyze all the records in attempt to find commonality in any of the records would find it too complex and time consuming. Identifying keywords and then manually searching for those keywords are time consuming and costly due to the personnel&#39;s time involved. Moreover, when higher order analysis is performed, the time and cost increases dramatically. Moreover, after a person analyzes the data and makes a record of their analysis, anyone else utilizing the data must view the data in the form the personnel analyzing the data formatted the output records. As a result, some formats may not be as pleasing or easy to understand due to an individual&#39;s specific liking to a format. As a result, a user would have to reformat the data which may require re-analyzing all the data. 
       SUMMARY OF INVENTION 
       [0003]    An advantage of an embodiment is an automatic identification and visualization of interaction between elements and behaviors with a complex system. The system and techniques as described herein extend text mining capability from identification of terms to identification of relationships between textual terms. The visualization methods described herein advantageously communicate the magnitudes of the differences in relationships based on frequency counts in the data. The analysis of the data also allows for prioritization of work tasks and automatic generation of certain portions of failure mode documents such as DFMEAs and robustness plans. 
         [0004]    An embodiment contemplates a method of detecting textual and behavioral commonalities in warranty reported data. Extracting, by a processor, records of verbatim data from a memory storage unit. A first set of basewords is identified for comparison with the extracted records. A binary flag is set in response to an occurrence of a respective baseword in a respective record. An occurrence matrix is generated that includes entries identifying a number of times basewords are identified in each record. The occurrence matrix is formatted to a format as identified by the user. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0005]      FIG. 1  is a block diagram of a service database mining system. 
           [0006]      FIG. 2  is a process flow for text mining and forming a relationship matrix. 
           [0007]      FIG. 3  is an example of a binary matrix representation correlating verbatim and selected ontology basewords. 
           [0008]      FIG. 4  is an example of a generated frequency mapping matrix. 
           [0009]      FIG. 5  is an exemplary matrix utilizing a heat map technique. 
           [0010]      FIG. 6  is an exemplary matrix utilizing a zero suppression technique. 
           [0011]      FIG. 7  is an exemplary matrix utilizing a Gaussian elimination technique. 
           [0012]      FIG. 8  is an exemplary matrix utilizing a redundant elimination entry technique. 
           [0013]      FIG. 9  is an exemplary matrix illustrating a Pareto technique. 
           [0014]      FIG. 10  is an exemplary matrix utilizing a nesting operation technique. 
           [0015]      FIG. 11  is an illustration of autonomous auto fill technique for a failure mode effects document. 
       
    
    
     DETAILED DESCRIPTION 
       [0016]    There is shown in  FIG. 1  service database mining system  10  for finding textual commonalities in verbatim information. The system  10  utilizes a matrix-based approach for detecting the textual commonalities in the verbatim information. A server  12  includes a microprocessor  14  and a memory storage device  16 . The microprocessor  14  is a multipurpose, programmable device that is capable of receiving input data, processing the information according to readable instructions that are stored in its internal memory, and generating an output that is formatted to the user request. The microprocessor may also utilize the memory of the memory storage device  16  that is external to the microprocessor  16  for temporarily storing data that is used by the microprocessor. The microprocessor  14  as will be discussed later receives document data and applies the data for automatically generating documentation tools that includes, but is not limited to, design failure mode effects and analysis tools. 
         [0017]    The system  10  further includes a service information database  18  and an ontology database  20 . It should be understood that while examples herein may provide details regarding system and components of vehicles, the techniques applied herein can be utilized with any type of warranty reporting system including those non-vehicle related. Moreover, the system is not limited to warranty reporting systems but may include any type of data retrieval system where verbatim are obtained such as product usage and service data. The service information database  18  includes service documents. The service documents may include a single document or a multiple service documents. The documents are service diagnostic procedures or service repair procedures containing verbatim data that are retrieved from the service information database for finding semantic mismatches in the service documents. 
         [0018]    The ontology database  20  includes a list of ontology basewords including terms that are proper names of textual terms used in the verbatim data. The textual terms include names of parts, components, subsystems, systems, defects, or undesirable conditions that are commonly utilized in the verbatim. It should be understood that although one term (e.g., component) is used herein for exemplary purposes, textural terms may further include, but are not limited to, parts, subsystems, and systems, defects, and undesirable conditions which may be substituted herein. 
         [0019]    A report generator  22  may be used to output reports generated by the processor  14  utilizing the techniques described herein. 
         [0020]      FIG. 2  illustrates a process flow for text mining and forming a relationship matrix. 
         [0021]    In block  31 , text mining results are exported from a service information database along with the ontology basewords from the ontology database. The exported results may be obtained directly from a raw database or may be filtered by an interim tool that processes the verbatims into a format that are usable by the system.  FIG. 3  shows an exemplary table illustrating results exported from the service mining database and the ontology database. Verbatims  38  are shown in the form of customer complaints, corrective action comments, and causal comments. The verbatims  38  are listed in rows of tables and are hereinafter referred to as records. It should be understood that the number of records as illustrated are only exemplary to generally show details of the information contained in each record verbatim. Ontology basewords  39  are shown in the columns of the table illustrated in  FIG. 3 . Such basewords are terms selected by the user that have a relationship with the part, component, subsystem, system, defect, or undesirable conditions that is being analyzed by the user via the exported records. The basewords selected may be all the basewords associated with the respective part, component, subsystem, system, defect, or undesirable conditions analyzed or may be filtered utilizing the user&#39;s preferred textual terms. This allows the user to tailor the matrix to a more confined set of textual terms. However, it should be understood that a user has the sole discretion to generate the relationship mapping matrix to any given size as desired. 
         [0022]    In block  32 , the text mining results are converted to a binary matrix representation. The binary matrix representation is illustrated in the table of  FIG. 3 . As described earlier, the ontology basewords  39  are listed in columns and the verbatims  38  are listed in rows within the binary matrix representation. A respective binary representation is illustrated at each cross section for a respective verbatim and baseword. Each respective field identified with a “0” indicates that the baseword identified in the respective column does not occur in the verbatim identified in the respective record row. Each respective field identified with a “1” indicates that the baseword identified in the respective column does occur in the verbatim identified in the respective record row. 
         [0023]    In block  33 , baseword sets are selected for relationship mapping for setting binary flags. 
         [0024]    In block  34 , a relationship occurrence matrix is generated utilizing two sets of basewords. The two sets of basewords may be set up as matrices and the two matrices are multiplied by one another for determining a match. For each multiplication process, one of the baseword set matrices is transposed prior to the multiplication operation. For example, a first baseword set is represented by B 1  and the second baseword set is represented by B 2 . The interaction between the two baseword sets B 1  and B 2  is represented by the following formula: 
         [0000]      (B 1   T )(B 2 ) 
         [0000]    where B 1   T  is a transpose of B 1 . This provides a logical “AND” operation between the flags of the two baseword sets. As a result, a “1” will result only if both baseword sets are flagged as “1” which indicates that match within a record is present. The results are tallied in a mapping between the respective baseword sets. The mapping sums the number of times a match occurred between the respective baseword sets. This is illustrated in  FIG. 4 . In addition, it is shown that in  FIG. 4  that the resulting occurrence matrix is essentially symmetrical, which indicates the same baseword sets were utilized. 
         [0025]    In block  35 , the output of the relationship matrix is converted to an ordered list representation. Formats may be applied to generate reports desired by the user.  FIGS. 5-8  illustrate potential enhancements that may be applied to the resulting matrix. In  FIG. 5 , a heat map is shown. The heat map applies conditional color coding to the matrix elements for indicating those areas having increased interactions for respective basewords. The heat map may be color coded to show those areas that are more heavily concentrated with matches than other areas. Those areas with minimum counts have less intensified coloring or shading than those areas with larger counts. For illustrative purposes in  FIG. 5-8 , the shaded regions indicate regions of increased interaction. Those regions that are more heavily shaded result in increased interaction. Under color schemes, varying degrees of colors may be applied to the matrix with a legend that indicates the degree of interaction that the color represents. 
         [0026]      FIG. 6  illustrates a technique where those interactions that resulted in “0” are suppressed from the matrix (e.g., left blank in the matrix). This may be more visually pleasing to a user to allow the user to identify and readily focus on those interactions that resulted in matching interaction. As illustrated in  FIG. 6 , all the “0” are suppressed by removing them from the matrix and only the interactions where at least one match was recorded remain in the matrix. 
         [0027]      FIG. 7  illustrates a resulting matrix where a Gaussian elimination technique is applied to cluster the results to respective portion of the matrix, typically the upper left portion of the matrix. Those interactions which resulted in a “0” are forced to the lower portion of the matrix and those interactions with at least one interaction are forced to the upper left portion of the matrix. It should be understood that the interaction number for distinguishing whether an entry are forced to a respective region may be a predetermined number other than “0” if desired by the user. 
         [0028]      FIG. 8  illustrates a resulting matrix where redundant entries are eliminated (i.e., left blank in the matrix). Since a matrix is essentially symmetric, entries on one portion of the symmetric matrix may be eliminated. An imaginary diagonal line extends from an upper left corner of the matrix to a lower right corner of the matrix. Values on one side of the imaginary diagonal line are maintained while values on an opposite side of the imaginary diagonal line are suppressed. 
         [0029]      FIG. 9  illustrates a table where the interaction counts from binary matrix representation are displayed in a list format. The list format may be sorted in an increasing or decreasing order of frequency to illustrate a Pareto distribution of interactions. As noted  FIG. 9 , the exemplary Pareto as illustrated identifies that the ordered frequency occurrences from highest to lowest. 
         [0030]      FIG. 10  illustrates a nesting operation where pair wise interactions from the Pareto table are concatenated and used as basewords to generate additional matrices illustrating new heat maps for higher order interactions. As shown in  FIG. 10 , the baseword nesting allows for generation of two-dimensional reports providing additional details illustrating higher order illustrations. This may be performed by correlating the basewords (e.g., additional multiplication operations) originally selected or basewords from the occurrence matrix that were found to exist in the records with a next set of basewords that provide enhanced detail of the warranty claims such symptoms or causal factors (e.g. defect, fault, undesirable appearance, undesirable operation/function). It should be understood that the respective basewords selected from a previously generated occurrence matrix may include a single baseword (e.g. tambour door) or a combination baseword (e.g., tambour door &amp; latch) for correlation with the next set of basewords (e.g., damaged, hard to move, not attached). 
         [0031]      FIG. 11  illustrates an auto fill technique for a failure mode effects document. A Pareto table  40  identifies interfaces components, symptoms, and the frequency count for interactions between the interface device and the symptoms. A design failure mode effects and analysis (DFMEA) worksheet  42  is a tool for evaluating a design for robustness against potential failures and is often the first step of a system reliability study. A plurality of many components, assemblies, and subsystems are evaluated to identify failure modes, and their causes and effects. For each respective component of an assembly (or step of a process), the failure modes and their resulting effects on the rest of the system are recorded in a specific FMEA worksheet. 
         [0032]    As illustrated in  FIG. 11 , the respective components, symptoms, and frequency counts identified in the Pareto table  40  may be autonomously copied and entered into the FMEA worksheet  42 . For example, interface components  44  of the Pareto table  40  are autonomously entered into a parts field  46  of the DFMEA worksheet  42 . Similarly, symptoms  48  from the Pareto table  40  are autonomously entered into potential failure modes field  50  and potential effects field  52  of the DFMEA worksheet  42 . In additional, a count field  54  from the Pareto table  40  is autonomously entered into an occurrence field  56  in the DFMEA worksheet  42 . The data may be copied and entered utilizing the processor and memory described in  FIG. 1 , as well as outputting the DFMEA worksheet utilizing the report generator. 
         [0033]    While certain embodiments of the present invention have been described in detail, those familiar with the art to which this invention relates will recognize various alternative designs and embodiments for practicing the invention as defined by the following claims.