Patent Abstract:
A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of the following earlier-filed U.S. Provisional Application in accordance 35 USC 119: No. 60/888,721 entitled “Fuzzy Matching,” filed Feb. 7, 2007 in the names of Mayer and Narayanan. The entirety of the foregoing application is incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present invention concerns digital data processing software and/or hardware to quickly yet accurately determine if a given computer-readable record is represented, by exact match or pretty close match, in an existing collection of computer-readable records. 
         [0004]    2. Description of the Related Art 
         [0005]    “Fuzzy matching” refers to a well known assortment of techniques to determine whether searched strings approximately match some given pattern string. These techniques are also known by other names such as approximate matching, inexact matching, fuzzy string searching, etc. Each implementation of fuzzy matching uses some similarity function, that is, an algorithm for determining whether the input and searched strings are similar to each other. One common similarity function is Levenshtein distance, and another is n-gram distance. 
         [0006]    The commercial market already contains various products that employ fuzzy matching. One example is the Hunter software of Experian, which is intended to detect fraud in the customer acquisition process. Another example is found in the products of Identity Systems, formerly known as Search Software America, which provides various software products aimed at searching, finding, matching, and grouping identity data, regardless of structure, format, location, duplication, omissions or errors. Other examples are found in the products of IBM Entity Analytic Solutions (EAS), which aims to help organizations recognize the entities with which they are doing business. EAS is said to provide real time recognition and resolution, in context with existing business applications. 
         [0007]    Although these systems provide certain benefits, Fair Isaac Corporation is interested in improving the performance and efficiency of fuzzy matching programs, since various Fair Isaac products do (or could) beneficially employ fuzzy matching. Fair Isaac has identified some areas of possible focus and some potential shortcomings of existing technology. For one, the computational complexity and cost associated with a brute-force, field by field fuzzy matching against each individual record in a reference database (e.g., a fraud file) is prohibitive in practice. Second, existing approaches can give misleading results when strong matches occur on weak data (such as the strong or identical match of a common first name such as “John”). Third, better control over the manner of fuzzy matching is desired. Fourth, the existing approaches are not as modular and easily extensible as some might like. 
         [0008]    In view of these concerns, the existing fuzzy matching products are not completely adequate for all intended applications. 
       SUMMARY OF THE INVENTION 
       [0009]    Broadly, the present disclosure concerns a new technique for fuzzy matching. This works to quickly yet accurately determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match. 
         [0010]    The teachings of this disclosure may be implemented as a method, apparatus, logic circuit, storage medium, or a combination of these. This disclosure provides a number of other advantages and benefits, which should be apparent from the following description. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  is a block diagram of the components and interconnections of a computer system. 
           [0012]      FIG. 2  is a block diagram of a digital data processing machine. 
           [0013]      FIG. 3  shows an exemplary storage medium. 
           [0014]      FIG. 4  is a perspective view of exemplary logic circuitry. 
           [0015]      FIG. 5  is a flowchart of an overall operating sequence. 
           [0016]      FIG. 6  is a more detailed flowchart showing a filtering task in greater detail. 
           [0017]      FIG. 7  is a diagram illustrating the relationship between various record sets. 
       
    
    
     DETAILED DESCRIPTION 
     A. Hardware Components &amp; Interconnections 
     1. Overall Structure 
       [0018]    One aspect of the present disclosure concerns a computer system ( 100 ) with various components that are configured to perform expedited, accurate fuzzy matching. Broadly, the system  100  includes a computer  106 , storage  120 , user input/output (I/O)  108 , and other computers  112 . Various interfaces  110  interconnect these components. Of course, other components may be added to these, but this architecture provides a starting point to illustrate the primary features of this disclosure. 
         [0019]    Computer  106   
         [0020]    Referring to  FIG. 1  in greater detail, the computer  106  includes key generators  106   a - 106   b , a comparison engine  106   c , filter  106   d , analyzer  106   e , scorer  106   f , pruner  106   g , and controller  106   h.    
         [0021]    The computer  106  and its subcomponents  106   a - 106   h  are data processing entities, and these may be implemented by one or more hardware devices, software devices, a portion of one or more hardware or software devices, or a combination of the foregoing. Some examples are discussed below in  FIGS. 2-4 . As one example, the computer  106  may be implemented by a computer workstation, mainframe computer, distributed computing arrangement, personal computer, server, or other computing machine appropriate to the implementation. In this example, the subcomponents  106   a - 106   h  are implemented by processes, subroutines, object oriented programs, Java Applets, processing threads, machine code, or other software programming of the computer  106  hardware. 
         [0022]    Broadly, each of the key generators  106   a - 106   b  acts to receive an input string and compute an output key according to a predetermined computational formula. Under this formula, a given input string will always produce the same output key. However, several input strings (with certain types of similarities, as discussed below) will also produce the same output key. Therefore, the key generators  106   a - 106   b  serve to “fuzzify” input, and provide a “many to one” mapping between input strings and keys. Under this regime, two input strings that produce the same output key must be similar in some ways. These similarities are prescribed by the details of the key computing formula. 
         [0023]    In the illustrated example, the key generator  106   a  applies one fuzzification formula, whereas the key generator  106   b  applies a different formula. Alternatively, the key generator  106   b  may be eliminated, in which case the computer  106  employs a single fuzzification formula. In still another alternative, the system  100  may include three, four, five, or any greater number of key generators. In one example, the system  100  may provide a different key generator for each different field (of current or anticipated records). Operational details of the key generators  106   a - 106   b  are described in appropriate detail below, under the heading “Operation.” 
         [0024]    The comparison engine  106   c  produces an initial pool of candidate records by applying fuzzy matching to a given input record and records of a reference database  122 . In this operation, the engine  106   c  employs the key generator  106   a  to produce new keys for the input record, and as to the existing records ( 122 ) the engine  106   c  uses previous output of the key generator  106   a  stored in a key database  124 . Operational details of the comparison engine  106   c  are described in appropriate detail below, under the heading “Operation.” 
         [0025]    As mentioned above, the comparison engine  106   c  produces a pool of candidate records. The filter  106   d  acts to reduce or “filter” the candidate pool of records by applying various statistical analyses. Operational details of the filter  106   d  are described in appropriate detail below, under the heading “Operation.” 
         [0026]    The analyzer  106   e  analyzes the candidate pool, providing one basis for other components to reduce the candidate pool even further as discussed below. In one embodiment, the analyzer  106   e  applies a second stage of fuzzy matching, which employs keys previously prepared by the key generator  106   a  or employs the key generator  106   b  to produce completely new keys for both input record and reference records ( 122 ). Operational details of the analyzer  106   e  are described in appropriate detail below, under the heading “Operation.” 
         [0027]    The scorer  106   f  applies a predetermined statistical analysis to the filtered, analyzed candidate pool in order to evaluate, score, rank, or otherwise assess these records relative to each other to relative to a predetermined standard. The scorer  106   f  may be omitted, if appropriate to the intended application. For instance, the end user may not care about scoring. Or, scoring may be unnecessary if the computer  106  employs a powerful analyzer  106   e  that limits the final record pool to manageable levels. Operational details of the scorer  106   f  are described in appropriate detail below, under the heading “Operation.” 
         [0028]    The pruner  106   g  acts to reduce the candidate pool even further according to output from the analyzer  106   e  and/or the scorer  106   f . Operational details of the pruner  106   g  are described in appropriate detail below, under the heading “Operation.” 
         [0029]    The controller  106   h  directs the overall operation of the other components  106   a - 106   g , coordinating the various processing stages to produce a final result. The controller  106   h  may perform other functions related to management of the computer  106 , such as managing peripheral hardware, performing functions unrelated to fuzzy matching, etc. 
         [0030]    Interfaces  110   
         [0031]    The system  100  includes one or more interfaces  110  to interface the computer  106  with peripheral hardware and/or software such as user I/O  108 , other computers  112 , and digital data storage  120 . Accordingly, the interfaces  110  include any of the following, as appropriate to serve the architecture and functionality described herein: telephone modems, cable modems, T1 interface, routers, Ethernet cards, IDE or EIDE units, satellite modems, wireless transceivers, USB interfaces, Fire wire ports, PS/2 ports, key ring networks, local area networks, wide area networks, infrared ports, etc. 
         [0032]    User I/O  108   
         [0033]    This component includes hardware and/or software for man-machine interface, such as video display, speakers, keyboard, digitizing pad, trackball, mouse, eye gaze tracking system, foot pedals, dials, buttons, touch screens, brain wave sensing machinery, and the like. 
         [0034]    Other Computers  112   
         [0035]    Optionally, the system  100  may be interfaced with one or more other computers  112  to receive input and/or provide output. As one option, the user I/O  108  may be omitted, with this user input/output occurring at one or more remote computes  112 . Or, the system may work free of user input/output, with input/output coming from external machines  112  instead of humans. 
         [0036]    Storage  120   
         [0037]    The storage  120  provides digital data storage, various embodiments of which are described below in greater detail under the heading “Storage Media.” The storage  120  includes a reference database  122 , key database  124 , and count database  126 . Any or all of the components  120 ,  122 ,  124 ,  126  may be provided by relational databases, linked lists, tables, stacks, queues, or any collection of records that is structured and computer-readable, amenable for a computer program to consult and answer queries. 
         [0038]    As mentioned below, one function of the system  100  is to determine if a given input record is represented in a collection of existing records. In this context, the reference database  122  provides the existing collection of records. The reference database  122  provides functional rows and columns representing records and fields, respectively. The database  122  may include virtually any type of data, such as a collection of current customers, past customers, perpetrators of fraud, recipients of a government benefit, etc. Or, apart from people, the database may represent other data concerning machine parts, vehicles, financial transactions, packets of communication, or any other tangible of intangible thing. 
         [0039]    The key database  124  contains keys corresponding to given fields of each record in the reference database  122 . The “given” fields, namely those having keys, may be some or even all fields in the reference database  122 . There is not necessarily a one-to-one relationship between fields and keys, as several fields might be used to generate a single key, or a single field might by itself or in combination with other fields be used to generate several keys. The key database  124  may be incorporated into the reference database  122 , or it may be a separate database (as shown) linked to the reference database by appropriate pointer, reference, or other link ( 124   a ). In the presently illustrated example, the fields of the reference database  122  having keys are those of a set illustrated by item  702  in  FIG. 7 . This is explained in greater detail below. 
         [0040]    The count database  126  contains statistical data concerning (1) the values in the various fields of the reference database  122 , or (2) the occurrence of the keys in the key database  124 , or (3) both of these. The nature of the statistical data is discussed in greater detail below. The count database  126  may be incorporated into the databases  122  and/or  124 , or it may be a separate database (as shown) linked to the respective databases by appropriate pointer, reference, or other links ( 126   a ,  126   b ). 
       2. Exemplary Digital Data Processing Apparatus 
       [0041]    As mentioned above, data processing entities (such as the computer  106  and/or its various subcomponents  106   a - 106   h ) may be implemented in various forms. 
         [0042]    Some examples include a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. 
         [0043]    As a more specific example,  FIG. 2  shows a digital data processing apparatus  200 . The apparatus  200  includes a processor  202 , such as a microprocessor, personal computer, workstation, controller, microcontroller, state machine, or other processing machine, coupled to a digital data storage  204 . In the present example, the storage  204  includes a fast-access storage  206 , as well as nonvolatile storage  208 . The fast-access storage  206  may be used, for example, to store the programming instructions executed by the processor  202 . The storage  206  and  208  may be implemented by various devices, such as those discussed in greater detail in conjunction with  FIGS. 3 and 4 . Many alternatives are possible. For instance, one of the components  206 ,  208  may be eliminated; furthermore, the storage  204 ,  206 , and/or  208  may be provided on-board the processor  202 , or even provided externally to the apparatus  200 . 
         [0044]    The apparatus  200  also includes an input/output  210 , such as a connector, line, bus, cable, buffer, electromagnetic link, network, modem, transducer, IR port, antenna, or other means for the processor  202  to exchange data with other hardware external to the apparatus  200 . 
       3. Storage Media 
       [0045]    As mentioned above, various instances of digital data storage may be used, for example, to provide storage used by the system  100  ( FIG. 1 ), to embody the storage  204  and  208  ( FIG. 2 ), etc. Depending upon its application, this digital data storage may be used for various functions, such as storing data, or to store machine-readable instructions. These instructions may themselves aid in carrying out various processing functions, or they may serve to install a software program upon a computer, where such software program is then executable to perform other functions related to this disclosure. 
         [0046]    In any case, the storage media may be implemented by nearly any mechanism to digitally storage machine-readable signals. One example is optical storage such as CD-ROM, WORM, DVD, digital optical tape, disk storage  300  ( FIG. 3 ), or other optical storage. Another example is direct access storage, such as a conventional “hard drive”, redundant array of inexpensive disks (“RAID”), or another direct access storage device (“DASD”). Another example is serial-access storage such as magnetic or optical tape. Still other examples of digital data storage include electronic memory such as ROM, EPROM, flash PROM, EEPROM, memory registers, battery backed-up RAM, etc. 
         [0047]    An exemplary storage medium is coupled to a processor so the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. In another example, the processor and the storage medium may reside in an ASIC or other integrated circuit. 
       4. Logic Circuitry 
       [0048]    In contrast to storage media that contain machine-executable instructions (as described above), a different embodiment uses logic circuitry to implement processing features such as the computer  106  and/or any one or more of components  106   a - 106   h.    
         [0049]    Depending upon the particular requirements of the application in the areas of speed, expense, tooling costs, and the like, this logic may be implemented by constructing an application-specific integrated circuit (ASIC) having thousands of tiny integrated transistors. Such an ASIC may be implemented with CMOS, TTL, VLSI, or another suitable construction. Other alternatives include a digital signal processing chip (DSP), discrete circuitry (such as resistors., capacitors, diodes, inductors, and transistors), field programmable gate array (FPGA), programmable logic array (PLA), programmable logic device (PLD), and the like. 
         [0050]      FIG. 4  shows an example of logic circuitry in the form of an integrated circuit  400 . 
       B. Operation 
       [0051]    Having described the structural features of the present disclosure, the operational aspect of the disclosure will now be described. The steps of any method, process, or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by hardware, or in a combination of the two. 
       1. Introduction 
       [0052]    A basic implementation of fuzzy matching would be to query every field, or collection of fields, of every single record in the reference database  122 , and compute a fuzzy match against every field, or collection of fields, of an input record using any one of various known algorithms. One issue with such an implementation is that it scales poorly, and becomes impractical once the reference database reaches a few ten thousand records. To improve on this, the present disclosure performs a pre-filter of the records that is itself fuzzily compared. 
         [0053]    The present implementation of fuzzy matching pre-computes a condensed key (or signature or token or other computational output) of the actual reference record fields, or collection of fields, to be matched, and stores these pre-computed keys (herein termed the “fuzzed-up fields” even in the case of collections of fields being used to generate the keys) in a separate table ( 124 ). Instead of then having to query the complete reference database  122  and compute a fuzzy match on each record, the computer  106  (in one embodiment) can reduce the list of match candidates from the reference database  122  to only those records that have at least one pre-fuzzed-up field, or collection of fields, (from the key database  124 ) exactly equal to the corresponding fuzzed-up field, or collection of fields, of the application to be matched. Other alternatives to this technique to reduce the list of match candidates are disclosed. Nevertheless, this gain in computational efficiency contributes to making the problem computationally tractable. 
         [0054]    Additionally, the computer  106  can store (along with the fuzzed-up fields) the frequency with how often they occur in the data, since rare keys that match provide more information than matching of a common key. This data ( 126 ) is then available to optionally compute a weighted fuzzy match score by considering all fields used for the match, and summing up the field-level match results. 
         [0055]    In this description, there are widespread references to the act of computing keys from record fields. It is emphasized that this disclosure contemplates, but does not require, a one-to-one relationship between fields and keys. For instance, several fields might be used to generate a single key. Or, a single field might by itself or in combination with other fields be used to generate several keys. Nevertheless, for ease of reading (but without any intended limitation), the following description references the computation of keys from record fields in terms of the basic case, where there is a one-to-one relationship. 
       2. Overall Sequence of Operation 
       [0056]    Introduction 
         [0057]      FIG. 5  shows one example of a fuzzy matching sequence  500 . For ease of explanation, but without any intended limitation, the example  500  is described in the specific context of the system  100  ( FIG. 1 ). 
         [0058]    Select Fields 
         [0059]    In step  502 , system architects select which fields of the reference database  122  will have pre-computed, pre-stored keys. Pre-computing keys for various fields of the reference database  122  will help speed fuzzy matching operations performed later. The designers in step  502  may select any fields that will be likely involved in fuzzy matching. To cite a simplified example, one set of selected fields may be: first name, last name, social security number, street name, city, street address, and home telephone. In  FIG. 7 , these fields (for which keys will be pre-computed and pre-stored) are illustrated by the set  702 . More restrictive sets  706 ,  704  are used later in the sequence  500 , as discussed below. 
         [0060]    Compute Keys 
         [0061]    For ease of discussion, records represented in the reference database  122  will be referred to as “reference records.” In step  504 , the controller  106   h  instructs the key generator  106   a  to compute one key for each of the fields selected in  502  (i.e., the fields  702 ), for each record in the reference database  122 . In the simplified example given above, step  504  starts with a first record in the reference database  122 , and computes a key for each value of first name, last name, social security number, street name, city, street address, and home telephone fields. Step  504  re-performs this operation for every remaining record in the reference database  124 . The controller  106   h  or key generator  106   a  stores the computed keys in the key database  124 . 
         [0062]    As an alternative, instead of using the key generator  106   a  for all fields, the controller  106   h  may use different key generators for different fields or collections of fields. 
         [0063]    As a further alternative, instead of performing step  502  on an existing reference database, step  502  may be performed from the beginning with an empty reference database  122 , whenever new records arrive for storage in the reference database  124 . Or, step  502  may be performed on the set of records in the reference database  122  as of a certain date, and then repeated in real time whenever new records arrive. 
         [0064]    In order to compute the keys, the key generator  106   a  may use any computational formula such that each key is produced exclusively by a set of input values having certain similarities to each other. Thus, there is a many-to-one mapping from potential input strings to keys. One example of computational formula is the well known Soundex phonetic algorithm (“Russel Soundex”), which is addressed in various issued U.S. patents, technical journal articles, and at least one book. Other examples include the Celko Improved Soundex algorithm, Metaphone algorithm, Double-Metaphone, Daitch-Mokotoff (D-M) Soundex, etc. A further example is the well known NYSIS algorithm developed by the New York State Identification and Intelligence System as an improvement to Soundex. In addition or instead of these, the key generator  106   a  may employ any other key generating formula based on phonics, numerical, double metaphonics, etc. Optionally, different key generators (such as  106   a - 106   b ) may be invoked to vary the key formula for different fields. For example, one of key generators  106   a - 106   b  may act to simply copy-over complete, raw, or original data from the reference record into the key database  124  in the case of certain specified fields. It may prove helpful, for example, to have such field values available for later processing (as discussed below). Also, there may be fields containing unique data (such as social security numbers) for which is it unnecessary or undesirable to compute keys. 
         [0065]    Optionally, at this time, step  505  may statistically analyze the reference database  122  in order to compute and store count data in the database  126 . In this regard, the controller  106   h  in step  505  computes statistical data concerning the values in the various fields of the reference database  122 , and/or the keys in the database  124 . In the case of the reference database  122 , this statistical data includes a statistical breakdown of field values, for example by count (number of occurrences), frequency of occurrence, percentile, or other assessment of values in some or all fields. This data may be broken down further by field. For example, the count database  126  may indicate that a given field value (“Oslo”) occurs one hundred times in the “City Name” field of the database  122 , or that “Oslo” occurs in the “City Name” field of twenty five percent of all records in the reference database  122 . In the case of the key database  124 , this statistical breakdown includes a statistical breakdown of the keys. For example, the count database  126  may indicate that a given key occurs one thousand times in the database  124 , or that the given key constitutes forty percent of all keys in the database  124 , or that the given key occurs four hundred times as to a given reference field. 
         [0066]    Although task  505  may be performed from time to time in batch, alternatives are to calculate statistical data from the beginning with an empty reference database  122 , calculate statistical data whenever new records arrive for storage in the reference database  124 , or perform statistical analysis on a set of records in the reference database  122  as of a certain date, and then repeat in real time for new records that arrive. 
         [0067]    Arrival of Input Record 
         [0068]    In step  506 , the computer  106  (and more particularly, the controller  106   h ) receives a record to evaluate. For ease of explanation, this is interchangeably referred to as the “given” record or “input” record. This record may come from the user I/O  108 , for instance if the record is entered or submitted or identified by a human user such as a customer, system administrator, software user, or other person. Alternatively, the record may be submitted or entered or identified by a remote computer  112 . As another alternative, the computer  106  itself may identify the record. For example, users may enter new records into a cache (not shown), and the computer  106  takes up cached records for processing in order of entry or another order. Or, the computer  106  may self-select records from the reference database  122  for screening or evaluation, unrelated to input of any new record. Step  506  may observe any of these approaches, or a combination. 
         [0069]    Fuzzy Matching 
         [0070]    In step  507 - 508 , the computer  106  performs fuzzy matching upon the input record. In one sense, the pre-computation of keys (from step  504 ) may be considered an early party of fuzzy matching, too. In step  507 , responsive to receiving the input record ( 506 ), the controller  106   h  directs the key generator  106   a  to compute keys for fields of the given record  507 . To expedite the overall process  500 , the key generator  106   a  only computes keys for a limited set of fields of the given record. For example, step  507  may limit key computation to first name, last name, and social security number fields.  FIG. 7  illustrates this limited set at  704 , and shows that this set  704  is a smaller subset of the entire set of fields ( 702 ) for which keys were computed and stored ( 504 ) in the key database  124 . By limiting fuzzy matching to the field set  704  (instead of the entire set  702 ) this expedites the overall process  500 . 
         [0071]    In step  508 , the comparison engine  106   c  compares the keys for the input record (computed in step  507 ) with the corresponding, pre-computed keys (stored in  124 ) of each reference record. In conformance with the limited fuzzy matching strategy, and since step  507  only computed keys for a limited set ( 704 ) of fields, step  508  only compares keys of the limited set  704  of fields as between the given record and the reference records. In the previously introduced example, step  508  will compare the keys for the given record&#39;s first name, last name, and social security number fields to each reference record&#39;s respective keys for those same fields, looking to see if the keys match identically. 
         [0072]    If at least one key of the given record matches a key for the same field of the reference record, that reference record is added to a “candidate pool.” For example, if the given record&#39;s key for last name matches a particular reference record&#39;s key for last name, the reference record is added to the candidate pool. In one embodiment, where each reference record is given a unique record number in the database  122 , addition of a record to the candidate pool may be carried out by recording the record&#39;s number in a list. 
         [0073]    Filtering 
         [0074]    In step  509 , the filter  106   d  hones the candidate pool by removing records from the candidate pool whose fuzzy match with the input record is weak (according to predetermined criteria). Broadly stated, this is carried out by statistically analyzing the nature of the matches found in  508 . For greater speed, this process utilizes the statistical data stored in the count database  126 . Filtering ( 509 ) is discussed in greater detail below, with reference to the sequence  600  ( FIG. 6 ). 
         [0075]    Analysis 
         [0076]    After filtering (step  509 ), the next step in the process  500  is to analyze the candidate pool ( 510 ). Broadly, in step  510  the analyzer  106   e  performs more comprehensive fuzzy matching than was conducted in steps  507 - 508 . Now that the process  500  has narrowed the reference records down to a candidate pool (step  508 ) and further filtered that pool (step  509 ), it is possible to perform more comprehensive or comprehensive fuzzy matching without great sacrifice in computational effort. Thus, in step  510  the analyzer  106   e  performs fuzzy matching as between the input record and the records of the filtered and pruned candidate pool. Renewed fuzzy matching ( 510 ) may be carried out in various ways, two of which are described as follows. 
         [0077]    Step  513  describes one exemplary technique. Here, the last part of the previously conducted fuzzy matching is repeated, but applied to a broader set of fields. In other words, and as compared to the fuzzy matching of steps  507 - 508  (based upon fields of the set  704  shown in  FIG. 7 ), the renewed fuzzy matching of step  513  involves a greater number of fields (for example, the set  706  or even the set  702 ). Advantageously, then, the field set  706  is substantially greater than the field set  704 . In the present example, the field set  706  includes first name, last name, social security number, street name, and city. Fuzzy matching of step  513  employs the same keys computed in steps  504 ,  507  along with additional keys that must be computed for the input record (for fields not having keys computed in  507  but are part of the fuzzy matching  513 ). For computing these added keys, the key generator  106   a  is used. 
         [0078]    In contrast to step  513 , steps  511 - 512  describe an alternative technique for technique. This technique employs the key generator  106   b  instead of the key generator  106   a . For each record in the filtered pool, step  511  retrieves the complete, original, or raw record from the reference database  122  (or at minimum, the complete, original, or raw values of the fields  706 ). Then, step  512  computes new keys upon these field and the corresponding fields of the input record. Using these, step  512  performs fuzzy matching on all fields of the field set  706 , as between the input record and the records of the filtered candidate pool. In this example, then, the approach of steps  511 - 512  is enhanced relative to the earlier fuzzy matching (steps  504 ,  507 ,  508 ) because it considers a greater number of fields ( 706  or even  702 ) than the field set ( 704 ) used in steps  504 ,  507 ,  508 . Additionally, the approach of steps  511 - 512  is further enhanced because it employs an enhanced fuzzy matching formula, namely that of the key generator  106   b  rather than  106   a . As to the fuzzy matching formula, this may use a similar key formula as discussed before (e.g., Soundex, NYSIS, etc.) but with different resolution, bit sampling, comparison or combination of multiple formulas, etc. Alternatively, the fuzzy matching formula of step  512  may conduct analysis unrelated to keys, with one example being the Levenshtein edit distance. 
         [0079]    Instead of using the key generator  106   b  for all fields in step  512 , the following is one alternative. As an example, this may be used in the embodiment (described above) where step  504  used different key generators for different fields or collections of fields. Like step  502 , step  512  may use different key generators for different fields. However, in this example, the set of key generators used here is different than the set of key generators used in step  514 . 
         [0080]    Ultimately, step  510  produces a list of records referred to as a final candidate pool. 
         [0081]    Scoring 
         [0082]    Next, in step  514  the scorer  106   f  scores the candidate pool according to the analysis of step  510 . Broadly, the scorer  106   f  analyzes records of the analyzed candidate pool to evaluate, score, rank, or otherwise assess these records relative to each other or to relative to a predetermined standard. In one example, scoring may consider factors such as inverse term frequency, i.e., terms that occur more often are given a lower score contribution than terms that occur infrequently with more significance. As a different example, step  514  may act to compute a weighted fuzzy match score by considering all fields used for the match, and summing up the field-level match results. Scoring may be implemented using these, or a combination of these, or a variety of different known techniques described in the numerous patents and patent publications of Fair Isaac Corporation. Optionally, step  514  may also produce a reason code, indicating an explanation for a given record&#39;s score. 
         [0083]    Step  514  is optional, however, and may omitted without departing from this disclosure. As a further alternative, scoring  514  may performed at a different occasion in the sequence  500 . One example is between steps  508 - 509 , in which case operation  509  may utilize scores in performing filtering. As another example, scoring may be performed between steps  509 - 510 , or during step  510 . Thus, step  510  may utilize scoring information in performing its analysis. 
         [0084]    Pruning &amp; Output 
         [0085]    After the optional scoring (step  514 ), the sequence  500  presents two options  514   a - 514   b . In option  514   a , the pruner  106   g  prunes the candidate pool (step  515 ) according to output from the analysis (step  510 ) and scoring (step  514 , if applicable). To provide some examples, some examples of pruning include setting a score threshold and removing all candidates receiving a score below it, or setting several score thresholds to be used based on which fields did match, or limiting the absolute number of candidates and removing all but the highest scoring ones in case this number was exceeded, or any combination of these, or no pruning at all. 
         [0086]    After step  515 , the controller  106   h  in step  516  provides an output identifying the records of the pruned pool and/or the computed scores of the pruned records. Also in step  516 , the controller  106   h  renders this output to a site such as the user I/O  108  or another computer  112 . As an alternative, the controller  106   h  may cache the output for retrieval on demand by a user, remote machine, or automated process. 
         [0087]    In contrast to option  514   a , in option  514   b  the sequence  500  skips step  514   a  and performs step  516  as discussed above, only with regard to the un-pruned candidate pool. 
       3. Filtering 
       [0088]      FIG. 6  shows one exemplary process  600  for conducting filtering as per step  509  ( FIG. 5 ). The process  600  is carried out by the filter  106   d . The process  600  works by “picking” certain candidates to retain in the candidate pool and excluding the rest. 
         [0089]    As mentioned above, steps  507 - 508  perform fuzzy matching upon the input record as to the reference records. If at least one key of the input record matches a key for the same field of the reference record, that reference record is added to the candidate pool. For example, if the given record&#39;s key for last name matches a particular reference record&#39;s key for last name, the reference record is added to the candidate pool. Accordingly, a reference record may be qualified to enter the candidate pool for numerous reasons, i.e., multiple fields that demonstrate a fuzzy match with a corresponding field of the input record. 
         [0090]    With this in mind, step  602  begins with a sub-group of the candidate pool. Namely, step  602  begins with a set of all reference records that entered the candidate pool (step  508 ) due to a fuzzy match occurring in a first field. The first field may be selected on any appropriate basis, such as arbitrarily, field order, alphabetic order of field names, etc. The field under discussion at any one iteration of the process  600  is referred to as the “current” field (for brevity the term “current field” is used even in case of a collection of fields). 
         [0091]    Next, step  604  considers frequency data for the current field of the input record. As mentioned above, the sub-group of candidates under discussion qualified for the candidate pool for at least the following reason—the current field of the reference record was a fuzzy match to the current field of the input record. This is why the current field of the input record is examined in step  604 . More particularly, step  604  references the database  126  to determine the count of the data from the current field of the input record. 
         [0092]    For example, where the current field is a “City” field, step  604  may reveal that the current field of the input record is “Oslo” and this occurs in 1,263 records of the reference database  122 . If the input record also qualified for the candidate pool based on a fuzzy match of another field, or another collection of fields, this is irrelevant for the present analysis. 
         [0093]    Next, step  606  asks whether the number from step  604  is greater than a prescribed threshold (“NMAX”). If so, step  607  refrains from “picking” the current sub-group of reference records. In the present example, NMAX is set to one thousand. Thus, step  606  is satisfied in the present example because Oslo occurs in over one thousand reference records. 
         [0094]    In a different example, if the threshold (NMAX) were set to 5,000, then step  606  would not be satisfied, and the process  600  would advance to step  608 . Step  608  asks whether the number from step  604  is less than a second threshold (NKEEP). The threshold NKEEP is set sufficiently low (e.g., fifty records) so as to identify highly meaningful matches. Thus, the filter gives precedence to less common matches, which are statistically more relevant. In the present case, step  608  is not satisfied since Oslo occurs in more than fifty records. Thus, step  612  (discussed below) is performed. On the other hand, if Oslo occurred in less than fifty records, step  608  would be satisfied, and step  610  would occur. Step  610  “picks” the current sub-group of reference records. The utility of “picking” record groups is discussed in greater detail below. 
         [0095]    As mentioned above, if step  608  is not satisfied, then step  612  occurs. Step  612  reduces the candidate pool belonging to the current field given that the size of this pool is larger than NKEEP but smaller or equal to NMAX. This reduction may be based on rules or statistical analysis, may be random, or a combination thereof. Some examples of this include:
       (a) Random selection of NKEEP many records,   (b) Sort the records in increasing order by how many other fields have matched as well.   (c) Assuming that all fields are sorted by a preset importance value, sort the records from the pool by whether they match also in the most important other field, or in the second-most important other field, etc.   (d) Using pre-assigned weights for each field, for each record in the candidate pool compute a match score by summing the weights of those fields that match. Then sort the candidate records by their achieved sum of weights.   (e) Require that the candidate records also match at least a certain number (e.g., one or two) of other fields.   (f) Require that the candidate records also match a specific selection of (e.g., one or two) other fields.
 
For methods (b) through (f), take the top NKEEP many records. Tie breaks may be resolved randomly, or with the use of some of the other rules.
       
 
         [0102]    After any of steps  607 ,  610 ,  612   a , or  612   b  complete, step  616  asks whether there are any remaining candidate sub-groups to consider. Namely, step  616  asks whether the process  604 - 612   b  has progressed through all fields, or all collection of fields, and corresponding keys used in step  508 . If not, step  618  advances to the next candidate group (based upon the next field of fuzzy matching), and then re-performs step  604  on this basis. 
         [0103]    On the other hand, when step  616  finds that all candidate groups have been considered, step  620  carries out a filtering operation. Namely, step  620  creates a filtered candidate pool consisting of those reference records of the candidate pool that were “picked” in steps  610  or  612   a . The process  600  ends in step  622 . 
       4. Applications 
       [0104]    The disclosed system  100  and processes  500 ,  600  may be applied in a number of different contexts. Without any intended limitation, the following is a sampling of different applications. 
         [0105]    In one example, these techniques are employed in fraud screening. During processing of credit applications, for example, there is usually some kind of fraud screening. Credit applications are often compared against previously identified frauds. Since the fraudsters are aware of this customary check, they tend to vary their application data slightly, maybe change a spelling here or there, or change some components of the address or other information. The challenge is then to still match the application against the fraud file in a non-exact, or fuzzy, manner. Of course, the matching of applications does not have to be limited to matching against the previously identified fraud file, one can also match against previous applications, or the customer master file. In fact, fuzzy matching does not necessarily concern applications, but could be used for any kind of string data. 
         [0106]    In another context, these techniques may be employed when people apply for government assistance, to conduct same entity analysis to see if they are already receiving assistance or another disqualifying benefit. In another context, a corporation may employ the system  100  to periodically conduct redundancy analysis in its databases, by recognizing records that (despite trivial differences) are really represent the same person or entity. For instance, a club or association may reduce magazine mailing costs by periodically screening its subscription list to identify cases where the same people mistakenly appear twice, causing two magazines to be sent to the same person. Furthermore, the system  100  is useful in numerous computing applications that seek to perform some type of same entity analysis. 
       C. Other Embodiments 
       [0107]    While the foregoing disclosure shows a number of illustrative embodiments, it will be apparent to those skilled in the art that various changes and modifications can be made herein without departing from the scope of the invention as defined by the appended claims. Accordingly, the disclosed embodiment are representative of the subject matter which is broadly contemplated by the present invention, and the scope of the present invention fully encompasses other embodiments which may become obvious to those skilled in the art, and that the scope of the present invention is accordingly to be limited by nothing other than the appended claims. 
         [0108]    All structural and functional equivalents to the elements of the above-described embodiments that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the present claims. Moreover, it is not necessary for a device or method to address each and every problem sought to be solved by the present invention, for it to be encompassed by the present claims. Furthermore, no element, component, or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element, component, or method step is explicitly recited in the claims. No claim element herein is to be construed under the provisions of 35 USC 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the phrase “step for”. 
         [0109]    Furthermore, although elements of the invention may be described or claimed in the singular, reference to an element in the singular is not intended to mean “one and only one” unless explicitly so stated, but shall mean “one or more”. Additionally, ordinarily skilled artisans will recognize that operational sequences must be set forth in some specific order for the purpose of explanation and claiming, but the present invention contemplates various changes beyond such specific order. 
         [0110]    In addition, those of ordinary skill in the relevant art will understand that information and signals may be represented using a variety of different technologies and techniques. For example, any data, instructions, commands, information, signals, bits, symbols, and chips referenced herein may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, other items, or a combination of the foregoing. 
         [0111]    Moreover, ordinarily skilled artisans will appreciate that any illustrative logical blocks, modules, circuits, and process steps described herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. 
         [0112]    The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Technology Classification (CPC): 6