Source: https://patents.google.com/patent/US20030135826
Timestamp: 2018-02-19 00:24:02
Document Index: 470027492

Matched Legal Cases: ['art 300', 'art 300', 'art 500', 'art 500', 'art 600', 'art 600']

US20030135826A1 - Systems, methods, and software for hyperlinking names - Google Patents
US20030135826A1
US20030135826A1 US10171170 US17117002A US2003135826A1 US 20030135826 A1 US20030135826 A1 US 20030135826A1 US 10171170 US10171170 US 10171170 US 17117002 A US17117002 A US 17117002A US 2003135826 A1 US2003135826 A1 US 2003135826A1
US10171170
US7333966B2 (en )
[0013]FIG. 1 is a block diagram of an exemplary system 100 incorporating teachings of the present invention.
[0014]FIG. 2 is a flow chart of an exemplary method of operating system 100 to define expressions or descriptors for use in classifying and linking names.
[0015]FIG. 3 is a flow chart of an exemplary method of operating system 100 to define a hyperlink between names in a document and names in a database, based on the Bayesian inference network trained according to FIGS. 3-5.
[0016]FIG. 4 is a block diagram of an exemplary Bayesian inference system used in operating system 100 to define hyperlinks.
[0017]FIG. 5 is a flow chart of an exemplary method of operating system 100 to train a Bayesian inference network for use in measuring likelihood that a name in a document and a name in a database refer to the same person.
[0018]FIG. 6 is a flow chart of an exemplary search method that incorporates teachings of the present invention.
[0028]FIG. 1 shows a diagram of an exemplary computer system 100 incorporating a system, method, and software for automatically marking one or more portions of a document and defining one or more corresponding hyperlinks for each marked portion. Though the exemplary system is presented as an interconnected ensemble of separate components, some other embodiments implement their functionality using a greater or lesser number of components. Moreover, some embodiments intercouple one or more the components through wired or wireless local- or wide-area networks. Some embodiments implement one or more portions of system 100 using one or more mainframe computers or servers.) Thus, the present invention is not limited to any particular functional partition.
P(name)=P(first name)·P(last name) (1)
where P (first name) denotes the probability of drawing the first name at random from all the first names in the directory and P(last name) similarly denotes the probability of drawing the last name at random from all the last names in the directory. A probability of name uniqueness or rarity is then computed as P  ( nameUniqueness ) = 1 ( H · P  ( name ) ) + 1 ( 2 )
Block 230 determines a probability or likelihood that the extracted information will occur with an in-directory name rather than an out-of-directory name. In the exemplary embodiment, this entails computing the probability of each in-directory unigram and bigram occurring within an eight-word window before and after the out-of-directory names, and the probability of each out-of-directory unigram and bigram occurring within an eight-word window before and after the in-directory names. These co-occurrence probabilities are computed as P  ( unigram / in - directory   rare   name ) = IU NI ( 3 )
where IU=number of times unigram occurs in window with an in-directory rare name and NI=number of in-directory rare names. P  ( unigram / out - of - directory   rare   name ) = OU NO ( 4 )
where OU denotes the number of times unigram occurs in window with an out-of-directory name and NO denotes the number of out-of-directory names. P  ( bigram / in - directory   rare   name ) = IB NI ( 5 )
where IB denotes the number of times bigram occurs in window with in-directory rare name and NI the number of in-directory rare names. P  ( unigram / out - of - directory   rare   name ) = OB NO ( 6 )
To determine the likelihood of unigrams and bigrams co-occurring with an in-directory rare name rather than an out-of-directory name, the exemplary embodiment divides the in-directory rare name co-occurrence probability by the respective the out-of-directory co-occurrence probability. These likelihood formulations are expressed as L  ( unigram ) = P  ( unigram / in - directory   rare   name ) P  ( unigram / out - of - directory   rare   name ) ( 7 ) L  ( bigram ) = P  ( bigram / in - directory   rare   name ) P  ( bigram / out - of - directory   rare   name ) ( 8 )
democratic 14.45 congressman
leader u.s. attorney 37.16
$isVerb forms of verb “to be” is, was
[0069]FIG. 3 shows a flowchart 300 of an exemplary method embodied in linking module 1272. Flowchart 300 includes process blocks 310-370.
Name uniqueness (or rarity) record 345 includes a score field which contains an indicator of uniqueness or rarity of its associated name. In the exemplary embodiment, this indicator is a numerical quantity indicative of a prior probability of a name matching a candidate record drawn from a particular directory. More specifically, the exemplary embodiment defines the quantity as the probability of name uniqueness and computes it using P  ( nameUniqueness ) = 1 ( H ′ · P  ( name ) ) + 1 ( 9 )
P(name)=P(first name)·P(last name) (10)
[0089]FIG. 4 show an exemplary inference system 400 which includes input name template 410, and an input candidate record 420, and one or more Bayesian inference engines, such as Bayesian inference engine 430.
Calculation module 435 calculates a similarity score or match probability based on these conditional probabilities and the uniqueness or name-rarity data for the input name template. The exemplary calculation uses the following form of Baye's rule: P  ( M / E ) = P  ( M )  ∏ i = 1 4   P  ( E i / M ) P  ( M )  ∏ i = 1 4   P  ( E i / M ) + P  (  M )  ∏ i = 1 4   P  ( E i /  M ) ( 11 )
where P(M\E) denotes the probability that a template matches a candidate record given a certain set of evidence, such as an input name template and a candidate record. P(M) denotes the prior probability that a template and biography record match (that is, refer to the same person), and P(
M)=1−P(M) (12)
P(Ei\M) is the conditional probability that Ei takes on a particular state given that the input name template matches the candidate record. For example, if E3 denotes location match evidence, then P(E3\M) denotes the probability that the location information in the name template and candidate record have the match state determined by comparison module 433 (exact match, strong fuzzy mach, weak fuzzy match, unknown match, or no match) given that a name template and candidate record match. P(Ei\
M) denotes the conditional probability that Ei takes on a particular state given that a name template does not match any record in the professional directory. For example, P(E3\
[0109]FIG. 5 shows a flowchart 500, which illustrates exemplary structure and operation of training module 1273 to define the conditional probabilities used in the Bayesian inference engine of FIG. 4. Flowchart 500 includes process blocks 510-560.
Block 540 entails determining conditional probabilities for each state of each evidence variable. In the exemplary embodiment, this determination entails determining frequency counts for each comparison state based on the name templates for the manually tagged set of documents. Specifically, for cases in which a tagged name has been manually matched to a candidate record, the embodiment counts the number of times each particular evidence state occurs for each of the evidence variables: name, descriptor, location, and organization. The embodiment then divides the count for each evidence state by the total number of matches to obtain P(Ei\M) that is, the conditional probability values for each state given a match. More precisely, the exemplary embodiment determines the conditional probabilities using a formula, such as P  ( E i = particular   state / M ) = a  y z + 1 - a x ( 13 )
Similarly, the exemplary embodiment counts the number of times each evidence state occurs when the candidate name does not match the template name and divides by the total mismatches to get P(Ei
[0116]FIG. 6 shows that the teachings of the present have applications beyond facilitating generation of hyperlinks for names. In particular, FIG. 6 shows a flowchart 600 of an exemplary method of operating a data retrieval system incorporating teachings of the present invention. Flowchart 600 includes process blocks 610-670.
Block 620 entails determining the ambiguity of the query or one or more terms in the query. In the exemplary embodiment, this entails identifying at least one name in the query and computing a probability of name uniqueness according to P  ( nameUniqueness ) = 1 ( H · P  ( name ) ) + 1 ( 14 )
P(name)=P(first name)·P(last name) (15)
2. The method of claim 1, wherein the name is a person name.
3. The method of claim 1, wherein the rarity indicator is a quantity based on a probability of drawing at least a portion of the name at random from a set of sample names representative of a relevant human population.
4. The method of claim 1, wherein the rarity indicator is a quantity based on a size of a human population, a probability of drawing a first portion of the name at random, and a probability of drawing a second portion of the name at random.
5. The method of claim 1, wherein the first portion is a first name portion of the name and the second portion is a last name portion of the name.
6. The method of claim 1, wherein defining a hyperlink for the name based on the rarity indicator, comprises:
7. The method of claim 6, wherein calculating one or more quantities, each based on the rarity indicator for the person name and the comparison of the non-person-name terms for one of the candidate records, includes using a Bayesian inference engine.
8. The method of claim 6, wherein defining the hyperlink based on the one or more calculated quantities comprises:
9. The method of claim 8, wherein defining the hyperlink based on the greatest one of the quantities exceeding the threshold comprises defining a hyperlink to designate the candidate record corresponding to the greatest one of the quantities.
10. A machine-readable medium comprising machine executable instructions for performing the method of claim 1.
11. A machine-readable medium comprising machine executable instructions for performing the method of claim 9.
18. The method of claim 17, wherein identifying person names in a set of documents comprises:
19. The method of claim 17, wherein calculating for each of the plurality of person names a quantity based on the probability of drawing its last name at random from a set of last names, includes:
20. The method of claim 17, wherein identifying descriptive language from one or more documents, based on the identified names comprises identifying appositives related to the identified names:
21. The method of claim 17, wherein identifying descriptive language from one or more documents, based on the identified names in the set of documents comprises:
22. The method of claim 17, wherein the one or more first terms includes one or more parts of speech and one or more of the second terms includes one or more parts of speech.
23. A machine-readable medium comprising machine executable instructions for performing the method of claim 16.
29. The method of claim 28, wherein obtaining additional information to assist in answering the query in response to the determined measure comprises:
31. The method of claim 28, wherein requesting additional information comprises requesting information is related to a profession, a location, and/or an organization.
32. The method of claim 28, wherein obtaining additional information to assist in answering the query in response to the determined measure comprises:
33. The method of claim 28, wherein updating or supplementing the query comprises:
US10171170 2001-12-21 2002-06-13 Systems, methods, and software for hyperlinking names Active 2024-06-28 US7333966B2 (en)
US34295601 true 2001-12-21 2001-12-21
US10171170 US7333966B2 (en) 2001-12-21 2002-06-13 Systems, methods, and software for hyperlinking names
CA 2924140 CA2924140A1 (en) 2001-12-21 2002-12-20 Systems, methods and software for hyperlinking names
CA 2754006 CA2754006C (en) 2001-12-21 2002-12-20 Systems, methods, and software for hyperlinking names
PCT/US2002/041144 WO2003060767A3 (en) 2001-12-21 2002-12-20 System, method and software for automatic hyperlinking of persons’ names in documents to professional directories
ES02799974T ES2378653T3 (en) 2001-12-21 2002-12-20 Systems, methods and software for automatic hyperlinks of names of people in documents for professional directories.
CA 2471592 CA2471592C (en) 2001-12-21 2002-12-20 Systems, methods and software for hyperlinking names
EP20100012722 EP2328101A1 (en) 2001-12-21 2002-12-20 System, method and software for automatic hyperlinking of persons' names in documents to professional directories
CN 02828266 CN1623146B (en) 2001-12-21 2002-12-20 Systems, methods and software for hyperlinking names
EP20100012705 EP2336903A1 (en) 2001-12-21 2002-12-20 System, method and software for automatic hyperlinking of person's names in documents to professional directories
EP20020799974 EP1474759B1 (en) 2001-12-21 2002-12-20 System, method, and software for automatic hyperlinking of persons' names in documents to professional directories
JP2003560793A JP4436909B2 (en) 2001-12-21 2002-12-20 System in order to hyperlink the name, method, and software
US11960054 US9002764B2 (en) 2001-12-21 2007-12-19 Systems, methods, and software for hyperlinking names
JP2009105735A JP5252725B2 (en) 2001-12-21 2009-04-23 System in order to hyperlink the name, method, and software
US11960054 Continuation US9002764B2 (en) 2001-12-21 2007-12-19 Systems, methods, and software for hyperlinking names
US20030135826A1 true true US20030135826A1 (en) 2003-07-17
US7333966B2 US7333966B2 (en) 2008-02-19
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US10171170 Active 2024-06-28 US7333966B2 (en) 2001-12-21 2002-06-13 Systems, methods, and software for hyperlinking names
US11960054 Active 2026-06-08 US9002764B2 (en) 2001-12-21 2007-12-19 Systems, methods, and software for hyperlinking names
US (2) US7333966B2 (en)
JP (2) JP4436909B2 (en)
CN (1) CN1623146B (en)
CA (3) CA2924140A1 (en)
EP (3) EP2328101A1 (en)
ES (1) ES2378653T3 (en)
WO (1) WO2003060767A3 (en)
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JP2009259252A (en) 2009-11-05 application
US20080301074A1 (en) 2008-12-04 application
WO2003060767A3 (en) 2004-08-26 application
JP5252725B2 (en) 2013-07-31 grant
EP2336903A1 (en) 2011-06-22 application
CA2471592A1 (en) 2003-07-24 application
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JP4436909B2 (en) 2010-03-24 grant
ES2378653T3 (en) 2012-04-16 grant
EP1474759A2 (en) 2004-11-10 application
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EP2328101A1 (en) 2011-06-01 application
WO2003060767A2 (en) 2003-07-24 application
US7333966B2 (en) 2008-02-19 grant
US9002764B2 (en) 2015-04-07 grant
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CA2754006A1 (en) 2003-07-24 application
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