US007877403B2
`
`(12) Ulllted States Patent
`Schwalb
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 7,877,403 B2
`Jan. 25, 2011
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`(54) SYSTEM AND METHOD FOR DATABASE
`SEARCHING USING FUZZY RULES
`
`2004/0139072 A1 *
`2007/0011154 A1 *
`2007/0179776 Al *
`
`................ .. 707/4
`7/2004 Broder et al.
`
`.. 707/5
`1/2007 Musgrove et al.
`8/2007 Segond et al.
`................ .. 704/9
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`(75)
`
`Inventor: Edward M. Schwalb, Irvine, CA (US)
`
`OTHER PUBLICATIONS
`
`03>
`
`( * ) Notice:
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`LLC’ Santa Ana’ CA (US)
`
`Language Processing, l988.*
`
`Subject to any disclaimer, the term of this
`%atSeIg i1S5:il)(::E)iI:3(:]e(:6O9rd2;l(:/Jisllsted under 35
`'
`'
`'
`'
`
`* Cited by examillef
`Primary Examiner—Tim T. V0
`Assistant Examiner—Aaron Sanders
`
`(21) Appl. No.: 11/751,186
`
`(22)
`
`Filed:
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`May 21, 2007
`
`(65)
`
`Prior Publication Data
`Us 2008/0294616 A1
`NOV. 27, 2008
`
`(51)
`
`Int CL
`(200601)
`G06F 7/00
`(52) U.s. Cl.
`......................................... .. 707/767; 704/9
`(58) Field of Classification Search ..................... .. None
`See application file for Complete Search history.
`
`(56)
`
`References Cited
`
`(74) Attorney, Agent, or Firm—Snell & Wilmer L.L.P.
`
`ABSTRACT
`(57)
`An apparatus and method for database searching using fuzzy
`rules is presented. The apparatus and method may accept a
`word or word phrase such as a persons name and returns fuzzy
`rules for database searching. Applicable search rules are
`selected and word or word phrase equivalents are displayed to
`a user. The user accepts or rejects each of the word or word
`Phrase Th? Wofd Or Word Phiase aiong W11 the use“ accep-
`tame or reiecuon are Stored In a Sample database‘ The fuzzy
`rules are modified according to the data in the sample data-
`base. The database is filtered by training and testing portions
`of the database for accuracy and purging the least accurate
`
`U.S. PATENT DOCUMENTS
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`portions‘
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`5,369,577 A * 11/1994 Kadashevich et al.
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`........ .. 704/9
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`16 Claims, 5 Drawing Sheets
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`2
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`TRAINING
`SET DATABASE
`206
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`312
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`SAMPLES
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` 208
`TRAINING
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`ENGINE
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`US 7,877,403 B2
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`REGARDED AS MISMATCHES
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`UPDATE TRAINING SET DATABASE
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`UPDATE RULES
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`FIG. 4
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`FOLD CROSS-VALIDATION
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`FROM THE PARTITION HAVING THE HIGHEST ACCURACY
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`FIG. 5
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`1
`SYSTEM AND METHOD FOR DATABASE
`SEARCHING USING FUZZY RULES
`
`BACKGROUND
`
`1. Field of the Invention
`
`The present invention relates to database searches. More
`particularly, the present invention relates to the application of
`fuzzy rules to database searches.
`2. Related Art
`
`Electronic database records are often indexed by keywords
`or phrases that aid search engines in locating documents.
`These keywords or phrases occasionally have alternate spell-
`ings, alternate word orderings, or multiple spellings. For
`example, the keyword or phrase may be a person’s name. In
`some instances, a data entry person may enter the name
`incorrectly into an electronic database by misspelling the
`name or by an inadvertent word reordering. For example, Bill
`Smith may be incorrectly spelled as Bill Smitth or Tam
`Nguyen may be incorrectly entered as Nguyen Tam. A simple
`word phrase search of an electronic database for Bill Smith or
`Tam Nguyen may end up producing poor results because of
`the incorrectly spelled Bill Smitth or incorrectly entered
`Nguyen Tam.
`Electronic name searches are particularly difficult since
`computers do not normally apply the intuitive rules which
`people apply when they deal with narnes.A person named Joe
`Smith may also be known as Joseph Smith, Joseph A. Smith,
`Joseph Allen Smith, Joseph Allen Smith III, Joey Smith, etc.
`Some surnames are translations from languages without an
`equivalent alphabet and may be spelled phonetically in a
`variety ofways. Social customs in some countries may dictate
`that the surname be listed first and the given name last. A
`person from one of these countries may be referred to differ-
`ently depending on which social custom they happen to be
`observing at the time.
`Similar problems exist with searching electronic databases
`for addresses. A particular address may, for example, have
`been subject to a zip code change or a street renaming. A
`particular address may also be located in one municipality for
`tax purposes and another municipality for mail service.
`Searches of an electronic database using the current street
`address may lead to misleading or inaccurate results depend-
`ing on the purpose of the search.
`Many database search programs use fuzzy search algo-
`rithms which include phonetic matching and variation gen-
`erators to expand the name search from a simple name to
`include name equivalents. A problem with these name search
`algorithms is that they are overbroad for some applications
`and too narrow for others. The algorithms generally only
`capture the explicit knowledge of the developer and do not
`capture the knowledge and work patterns of individuals in
`specific industries.
`Many industries have a large economic interest in being
`able to execute fuzzy electronic data searches customized to
`their industry. For example, title search companies may have
`an interest in searching recording databases for outstanding
`mortgages or a chain of title. These searches are frequently
`conducted by an ab stractor who may employ many definitive
`and intuitive rules, acquired through years of experience, in
`searching for the relevant documents. The searches are time
`consuming and the abstractor may have to make many
`searches to acquire all the relevant documents. Since the
`abstractor’s search skills are partly intuitive, the accuracy of
`the search is dependent on the experience of the abstractor.
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`Capturing the explicit and intuitive knowledge of a skilled
`abstractor may be of significant economic interest in the title
`search industry.
`Accordingly, there is a general need for a system and
`method of document retrieval that uses fuzzy rules that are
`adaptable to the needs of a specific industry, application or
`user. Applicant’s invention addresses this need as well as
`others.
`
`SUMMARY
`
`According to the present invention, a computer (or com-
`puter software) is supplied with fuzzy rules for conducting
`index or keyword searches. An index or keyword is also
`supplied to a computer. The computer executes a search on
`the index or keyword using fuzzy rules. The results of the
`search are presented to a user. The user accepts or rejects each
`of the search results. The selected search rules as well as the
`
`search results are saved as a historic search sample in a
`database. The fuzzy rules are modified according to the his-
`toric search samples in the database. This process may train
`the computer. The computer learns by adapting its fuzzy rules
`to generalize the training examples specified by the users. The
`computer learns the intuitive search knowledge of the user
`over time.
`
`the
`To avoid unbounded acquisition of new samples,
`present invention filters the sample database and removes the
`least informative samples. The database is filtered by parti-
`tioning the database into one or more datasets. Each dataset is
`then further partitioned into a training sample subset and a
`testing sample subset. The training samples in each of the
`datasets or partitions are then used to train a corresponding
`fuzzy rule set. The testing samples in each of the datasets or
`partitions are then used to estimate the accuracy of the newly
`trained fuzzy rule sets. The least accurate rule set or sets may
`then be purged from the database. Thus, the database is sys-
`tematically purged ofthe least accurate data samples creating
`room in the database for more representative data samples.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`The features, objects, and advantages of the present inven-
`tion will become more apparent from the detailed description
`set forth below when taken in conjunction with the drawings,
`wherein:
`
`FIG. 1 shows a block diagram of a training view of a fuzzy
`name search apparatus according to an embodiment of the
`present invention.
`FIG. 2 shows a block diagram of a learning view of a fuzzy
`name search apparatus according to an embodiment of the
`present invention.
`FIG. 3 shows a block diagram ofthe training set repository
`of FIG. 2 according to an embodiment of the present inven-
`tion.
`
`FIG. 4 shows a flow diagram of a method of searching a
`database using fuzzy rules according to an embodiment ofthe
`present invention.
`FIG. 5 shows a flow diagram of a method of filtering or
`refreshing a fuzzy search training set repository according to
`an embodiment of the present invention.
`
`DETAILED DESCRIPTION
`
`The method or algorithm described in connection with the
`examples disclosed herein may be embodied directly in hard-
`ware, in a software module executed by a processor, or in a
`combination of the two. A software module may reside in
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`flash memory, ROM memory, EPROM
`RAM memory,
`memory, EEPROM memory, registers, hard disk, a remov-
`able disk, a CD-ROM, or any other form of storage medium
`known in the art. An exemplary storage medium is coupled to
`the processor such that the processor can read information
`from, and write information to, the storage medium. In the
`alternative, the storage medium may be integral to the pro-
`cessor. The processor and the storage medium may reside in
`an Application Specific Integrated Circuit (ASIC). The pro-
`cessor may be any processing device such as a controller, a
`microprocessor, a software and/or hardware module, etc.
`FIG. 1 shows a block diagram of a training View of a fuzzy
`name search apparatus 100 according to an embodiment of
`the present invention. A name or phrase 1 02 may be entered or
`input into a computer (e.g., a desktop, PDA, cell phone,
`software and/or hardware module, etc.) that performs a rule
`driven search 104. The rule driven search 104 may search
`databases (e.g., third party databases or local databases), the
`Internet, etc. for information related to the name or phrase
`102. A search engine 106 using fuzzy name search rules
`identifies fuzzy matches 108. The fuzzy matches 108 are
`passed through a rule filter 110 that filters the fuzzy matches
`108 by applying learned rules 112. The rule filter 110 outputs
`one or more matches 114 that are displayed on a name match
`display 116 for a user to make match selections. The user may
`use a touch pad, a touch screen or a keyboard to make the
`match selections. The selected matches 120 are input into a
`report generator 124. The report generator 124 generates
`appropriate reports 126.
`For example, the user may wish to search a database for
`encumbrances on a property at 1135A Greenacre Drive. The
`user may enter the name or address 102 as 1135 Greenacre
`Drive into a computer to perform a rule driven search 104.
`The search engine 106 may return a number of fuzzy matches
`such as 1135A Green Acre Drive, 1125A Green Acre Drive,
`1135 Greenacre Cove, 1135B Greenacre, etc. The rule filter
`110 would then filter the names using learned rules 112. The
`learned rules 112 may include a number ofrules such as reject
`all addresses having less than a perfect match for a street
`number. In the example, the address 1 125A GreenAcre Drive
`would be filtered out by the rule filter 110 because it does not
`have a perfect match for the street number. The matches 114
`may be displayed on the name match display 116 for the user
`to select the matches 114 the user desires. The user may, for
`example, realize that Greenacre and Green Acre are alternate
`spellings ofthe same street. The user may therefore select the
`match 1135B Greenacre among other matches the user finds
`relevant.
`
`FIG. 2 shows a block diagram of a learning view of a fuzzy
`name search apparatus 200 according to an embodiment of
`the present invention. A name or phrase 202 is entered or
`input into a computer that performs a rule driven search 104.
`The rule driven search 104 may search databases (e.g., third
`party databases or local databases), the Internet, etc. for infor-
`mation related to the name or phrase 202. The rule driven
`search 104 results in matches 114 that are displayed on a
`name match display 116 for a user to make match selections.
`The name match display 116 displays all fuzzy logic matches.
`The learned rules 112 (shown in FIG. 1 as being a part of the
`rule driven search 104) may be applied with any proposed
`filtered matches displayed to the user as “stricken” matches.
`The user may accept or reject any of the fuzzy logic matches
`including the “stricken” matches.
`The user selections 118 are forwarded as selected matches
`
`120 to a training set repository 204. The training set reposi-
`tory 204 may include a training set database 206, which stores
`the selected matches. The training set repository 204 may also
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`include a training engine 208 that receives candidate samples
`210 from the training set database 206. The training engine
`208 identifies the candidate samples 210 that should be fil-
`tered from the training set database 206. The candidate
`samples 210, identified to be filtered, are filtered using a filter
`212. The training set repository 204 also feeds a rule search
`generator 214 that modifies the search rules for subsequent
`rule driven searches 104 and sample management functions
`(explained hereinafter) of the training set repository 204.
`The learning view of the fuzzy name search apparatus 200
`has at least two components that distinguish it from the train-
`ing view of the fuzzy name search apparatus 100. The first
`component is the training set repository 204. The training set
`repository 204 has a training set database 206 that stores user
`choices. The training set database 206 provides the informa-
`tion needed to allow the fuzzy name search apparatus 200 to
`learn.
`
`The second component is the rule search generator 214.
`The rule search generator 214 uses the training set database
`206 to modify the fuzzy search rules based on user selections.
`The rule search generator 214 modifies the rules based on user
`selections. The rules are updated for future searches and to
`select historic samples for elimination.
`FIG. 3 shows a block diagram ofthe training set repository
`204 of FIG. 2 according to an embodiment of the present
`invention. The selected matches 120 are input into a new
`samples module 302 ofthe training set database 206. The new
`samples module 302 ofthe training set database 206 sends the
`selected matches 120 to a historic samples module 304 of the
`training set database 206. At various times, m samples 306
`from the new samples module 302 and n samples 308 from
`the historic samples module 304 are selected as candidate
`samples 310 for elimination from the training set database
`206. The candidate samples 310 are partitioned into y parti-
`tions 312 that are inputs to the training engine 208. The
`training engine 208 receives search rules 314 from the rule
`search generator 214. The search rules 314 are replicated y
`times.
`
`The candidate sample partitions 312 are further partitioned
`into training samples that are used to train respective rule sets
`R1 to Ry 314 using machine learning algorithms RG1 to RGy
`313 and testing samples that are inputs to respective test
`databases 316. The training engine 208 applies cross valida-
`tion techniques to estimate the accuracy of the learning for
`each candidate sample partition 312. The candidate sample
`partition 3 12 with the most accurate learning is retained as the
`new training set. For example, if the training set contains
`11,000 samples, 10 subsets of 10,000 samples each are gen-
`erated, training and cross validation is performed using each
`partition, and the accuracy is calculated for each of the 10
`partitions. The 10,000 samples ofthe partition with best accu-
`racy are retained as the new training set, thus reducing the
`total number of samples from 11,000 down to 10,000. The
`training engine 208 uses the test samples to generate accuracy
`indices A1 to Ay 318. The accuracy indices 318 are used by the
`training set repository 204 to filter samples 212 from the
`historic samples module 304 of the training set database 206.
`FIG. 4 shows a flow diagram of a method of searching a
`database using fuzzy rules according to an embodiment ofthe
`present invention. First, a name search request is received for
`a fuzzy name search (402). The name may be a person’s
`name, an address, a zip code, any combination thereof, or any
`word or word phrase for searching a database. The name may
`also include search attributes for searching a relational data-
`base. User attributes, application attributes or any other indi-
`cia on which machine learning may be accomplished may
`also be received. The name or other data may be received in a
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`digital computer or equivalent. Rules may also be presented
`to the user for selection. The rules may, for example, have
`rules that return all matches having common nicknames for a
`given name.
`A rule driven search may also be performed (404). Learned
`rules are applied and items regarded as potential mismatches
`are “stricken” (405). The matches may be displayed on a
`display screen (406). For example, the search may have been
`on Richard James and the fuzzy results show records for
`equivalent names such as Ricky James, Rick James, Rich
`James, James Richard, James Richards, Jimmy Richards,
`Jimmy Rick, etc. Some of the matches may be stricken based
`on application of learned rules. The user may then select the
`desired matches “stricken” or not (408). For example, the user
`may decide that records for Ricky James, Rick James, Rich
`James, and James Richard are likely to be relevant while those
`for James Richards, Jimmy Richards and Jimmy Rick are
`likely to be irrelevant.
`A search report may then be generated (410). The search
`reports may be tailored to a particular industry such as the title
`search industry. The search reports may, for example, contain
`chain of title information or recorded land encumbrances for
`each relevant record.
`
`The training set database 206 may be updated with the user
`selections (412). The search rules may then be updated (414)
`according to the user selections and historic user selections
`stored in the training set database 206. For example, it may be
`noted that for virtually all of the samples in the training set
`database 206, the user has selected all equivalent names: 1)
`where a nickname associated with the given name is used
`with the correct last name and 2) where the first name and the
`last name have been transposed have been selected; but the
`user has rejected all equivalent names with a nickname and
`the last name where the nickname and the equivalent name
`have been transposed. In this case, the system may learn a new
`rule or set of rules.
`
`FIG. 5 shows a flow diagram of a method of filtering or
`refreshing a fuzzy search training set repository according to
`an embodiment of the present invention. Matches and user
`selections are received (502). The matches and user selections
`may be used to update a sample database (504). The database
`may store search samples, user selections, and any applicable
`attributes of the fuzzy matches or the search. The sample
`database may contain historic as well as new samples.
`The candidate samples 310 may be partitioned into Y sets
`of candidate samples (508). Each of the Y sets may contain
`the target maximum number of samples configured for the
`system. Each sample set may be further partitioned or divided
`into training and testing samples (510).A set ofY test rule sets
`may be created. Y copies of the rule set may be generated.
`Each of the rule sets may be trained using respective training
`samples. The training engine 208 may generateY trained rule
`sets using theY training samples (512). The accuracy of each
`trained rule-set may be tested using k-fold accuracy valida-
`tion or cross-validation (514). The sample database may then
`be replaced with samples from the partition having the high-
`est accuracy (516).
`The description of the disclosed examples is provided to
`enable any person of ordinary skill in the art to make and use
`the disclosed methods and apparatus. Various modifications
`to these examples will be readily apparent to those skilled in
`the art, and the principles defined herein may be applied to
`other examples without departing from the spirit and scope of
`the disclosed method and apparatus. The described embodi-
`ments are to be considered in all respects only as illustrative
`and not restrictive and the scope of the present invention is,
`therefore, indicated by the appended claims rather than by the
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`foregoing description. All changes which come within the
`meaning and range of equivalency of the claims are to be
`embraced within their scope.
`What is claimed is:
`
`1. A machine storage medium for performing a name
`search comprising instructions that upon execution cause a
`processor to:
`receive a name of a person;
`apply a plurality of fuzzy search rules to the name to
`generate name equivalents;
`apply a plurality of learned rules to the name equivalents to
`generate at least one stricken name equivalent and at
`least one non-stricken name equivalent, wherein the at
`least one stricken name equivalent has been previously
`rejected by a user;
`display the at least one stricken name equivalent and the at
`least one non-stricken name equivalent;
`receive a rejection for the at least one stricken name equiva-
`lent or the at least one non-stricken name equivalent; and
`modify at least one ofthe plurality of learned rules based on
`the rejection.
`2. The storage medium of claim 1 further comprising
`instructions that upon execution cause the processor to store
`the name equivalents and corresponding rejection in a data-
`base.
`
`3. The storage medium of claim 2 further comprising
`instructions that upon execution cause the processor to
`modify at least one of the plurality of rules based on the
`contents of the database.
`
`4. The storage medium of claim 2 wherein the contents of
`the database are filtered.
`
`5. The storage medium of claim 1 further comprising
`instructions that upon execution cause the processor to
`receive attributes of the name search.
`
`6. The storage medium of claim 5 wherein the plurality of
`learned rules applied to the name depends on the attributes of
`the name search.
`
`7. The storage medium of claim 1 further comprising
`instructions that upon execution cause the processor to dis-
`play two or more learned rules from the plurality of learned
`rules.
`
`8. The storage medium of claim 7 further comprising
`instructions that upon execution cause the processor to select
`at least one of the two or more learned rules.
`
`9. The storage medium of claim 8 wherein the plurality of
`learned rules are modified based on the selection of at least
`one of the two or more learned rules.
`
`10. A readable storage medium for training a computer to
`perform a fuzzy name search comprising instructions that
`upon execution cause the computer to:
`receive a name from a list of names;
`apply a plurality of fuzzy search rules to the name to
`generate name equivalents;
`apply a plurality of learned rules to the name equivalents to
`generate at least one stricken name equivalent and at
`least one non-stricken name equivalent, wherein the at
`least one stricken name equivalent has been previously
`rejected by a user;
`display the at least one stricken name equivalent and the at
`least one non-stricken name equivalent;
`accept or reject the at least one stricken name equivalent
`and the at least one non-stricken name equivalent; and
`modify the plurality oflearned rules based on the accepting
`or rejecting.
`11. The storage medium of claim 10 further comprising
`instructions that upon execution cause the computer to repeat
`the steps a pre-determined number of times.
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`12. The storage medium of claim 10 further comprising
`instructions that upon execution cause the computer to store
`the names and aeeepled er releeted name equiValem5 in 3
`database.
`
`13. The storage medium of claim 12 further comprising 5
`instructions that upon execution cause the computer to
`modify the plurality of learned rules based on the contents of
`the database.
`14. The storage medium of claim 13 further comprising
`instructions that upon execution cause the computer to filter 10
`the Contents Ofthe database‘
`15. The storage medium of claim 1 further comprising
`instructions that upon execution cause the processor to:
`generate a plurality of training Samples based on the rej ec_
`t1on;
`generate a plurality of accuracy lpdlces by applying a
`machine learning algorithm and a plurality of search
`rules to the plurality of training samples; and
`
`15
`
`8
`eliminate at least one stricken-name equivalent or at least
`one n0n_stricken nalne equivalent from the plurality of
`learned rules based on the plurality of accuracy indices.
`16. The storage medium of claim 10 further comprising
`instructions that upon execution cause the computer to:
`
`generate a plurality Of training Samples based 011 the
`rejected stricken name equivalent and non-stricken
`name equivalent;
`generate a plurality cf accuracy lrrdlces by applying a
`machine learmng algorithm and a plurality of search
`rules to the plurality Oftralnlng Samples. and
`
`eliminate at least one stricken-name equivalent or at least
`one non-stricken name equivalent from the plurality of
`l
`l
`.
`learned rules based on the plurality of accuracy 1nd1ces.
`
`*
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`*
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`*
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`*
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`*
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`ROTHSCHILD EXHIBIT 1006
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`Page 10 of 10
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`ROTHSCHILD EXHIBIT 1006