U.S. patent application number 11/056102 was filed with the patent office on 2005-07-07 for automated system and method for generating reasons that a court case is cited.
This patent application is currently assigned to LEXIS-NEXIS Group. Invention is credited to Ahmed, Salahuddin, Collias, Spiro G., Harmon, Joseph P., Humphrey, Timothy L., Lu, Xin Allan, Morelock, John T., Parhizgar, Afsar, Wiltshire, James S. JR., Zhang, Paul.
Application Number | 20050149523 11/056102 |
Document ID | / |
Family ID | 34115294 |
Filed Date | 2005-07-07 |
United States Patent
Application |
20050149523 |
Kind Code |
A1 |
Humphrey, Timothy L. ; et
al. |
July 7, 2005 |
Automated system and method for generating reasons that a court
case is cited
Abstract
A computer-automated system and method identify text in a first
"citing" court case, near a "citing instance" (in which a second
"cited" court case is cited), that indicates the reason(s) for
citing (RFC). The automated method of designating text, taken from
a set of citing documents, as reasons for citing (RFC) that are
associated with respective citing instances of a cited document,
has steps including: obtaining contexts of the citing instances in
the respective citing documents (each context including text that
includes the citing instance and text that is near the citing
instance), analyzing the content of the contexts, and selecting
(from the citing instances' context) text that constitutes the RFC,
based on the analyzed content of the contexts. A related
computer-automated system and method selects content words that are
highly related to the reasons a particular document is cited, and
gives them weights that indicate their relative relevance. Another
related computer-automated system and method forms lists of
morphological forms of words. Still another related
computer-automated system and method scores sentences to show their
relevance to the reasons a document is cited. Also, another related
computer-automated system and method generates lists of content
words. In a preferred embodiment, the systems and methods are
applied to legal (especially case law) documents and legal
(especially case law) citations.
Inventors: |
Humphrey, Timothy L.;
(Kettering, OH) ; Lu, Xin Allan; (Springboro,
OH) ; Parhizgar, Afsar; (Dayton, OH) ; Ahmed,
Salahuddin; (Miamisburg, OH) ; Wiltshire, James S.
JR.; (Springboro, OH) ; Morelock, John T.;
(Beavercreek, OH) ; Harmon, Joseph P.;
(Centerville, OH) ; Collias, Spiro G.;
(Springboro, OH) ; Zhang, Paul; (Springboro,
OH) |
Correspondence
Address: |
JACOBSON HOLMAN PLLC
400 SEVENTH STREET N.W.
SUITE 600
WASHINGTON
DC
20004
US
|
Assignee: |
LEXIS-NEXIS Group
|
Family ID: |
34115294 |
Appl. No.: |
11/056102 |
Filed: |
February 14, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11056102 |
Feb 14, 2005 |
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09468785 |
Dec 21, 1999 |
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6856988 |
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Current U.S.
Class: |
1/1 ;
707/999.006; 707/999.007 |
Current CPC
Class: |
G06F 40/268 20200101;
Y10S 707/917 20130101; Y10S 707/99935 20130101; Y10S 707/99933
20130101; Y10S 707/99934 20130101; Y10S 707/942 20130101; G06F
40/20 20200101; Y10S 707/99932 20130101; G06F 16/31 20190101; Y10S
707/99937 20130101; G06F 16/382 20190101 |
Class at
Publication: |
707/007 ;
707/006 |
International
Class: |
G06F 017/30; G06F
007/00 |
Claims
1-8. (canceled)
9. An automated method of finding different morphological forms of
a word, comprising: inputting a word; and stemming the word by
eliminating any letters after the N.sup.th letter from the
beginning of the word, wherein N is a positive integer.
10. The method of claim 9, wherein N=6.
11-23. (canceled)
24. An apparatus of finding different morphological forms of a
word, the apparatus comprising: means for stemming the word, the
stemming means including means for eliminating any letters after
the N.sup.th letter from the beginning of the word; wherein N is a
positive integer.
25. The apparatus of claim 24, wherein N=6.
26-38. (canceled)
39. A computer-readable memory that, when used in conjunction with
a computer, can carry out a method of finding different
morphological forms of a word, the computer-readable memory
comprising: computer-readable code for stemming the word by
eliminating any letters after the N.sup.th letter from the
beginning of the word, wherein N is a positive integer.
40. The computer-readable memory of claim 39, wherein N=6.
41-45. (canceled)
Description
[0001] COPYRIGHT NOTICE. A portion of this disclosure, including
Appendices, is subject to copyright protection. Limited permission
is granted to facsimile reproduction of the patent document or
patent disclosure as it appears in the U.S. Patent and Trademark
Office (PTO) patent file or records, but the copyright owner
reserves all other copyright rights whatsoever.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to systems and methods for
automated text processing, and for automated content and context
analysis. In particular, the present invention relates to automated
systems and methods of identifying sentences near a document
citation (such as a court case citation) that suggest the reason(s)
for citing (RFC).
[0004] 2. Related Art
[0005] In professional writing, people cite other published work to
provide background information, to position the current work in the
established knowledge web, to introduce methodologies, and to
compare results. For example, in the area of scientific research, a
researcher has to cite to demonstrate his contribution to new
knowledge. As another example, in writing court decisions, a judge
has to cite precedent legal doctrine to comply with the common law
tradition of stare decisis. However, the citing in the legal
profession is more precise than that in the scientific research
community.
[0006] Courts deal with legal issues such as points of law or facts
in dispute. Issues arise over differences of opinion as to
definition, interpretation, applicability of specific facts and
acts, prior decisions, legal principles or rules of law. Every
court decision or case involves one or more issues (the reason a
law suit was brought). In addition, in most cases there are usually
several sub-issues that arise from the detailed analysis and
consideration of the issues. Thus, almost every case discusses
multiple issues.
[0007] However, these multiple issues are often not intrinsically
related as one might expect in scientific literature. Rather, the
issues only occur together in a given case because they have a
bearing on the specific factual situation dealt with in that case.
Discussion of each issue or sub-issue is usually supported by
citing relevant legal authorities, which may not be related to one
another.
[0008] For example, People v. Surplice, 203 Cal. App.2d 784, is
frequently cited for the general issue of how the court should
exercise its judicial discretion when the law allows it. But, it is
also frequently cited for the more specific issue that says that it
is reversible error when a judge fails to read and consider a
probation officer's pre-sentence report.
[0009] As a result, when a citing case criticizes a cited case, the
citing case is usually not criticizing the whole case. Most of the
time, the criticism is on a specific legal issue. Similarly, a
citing case may reference a cited case for a specific, supportive
point of law.
[0010] It is not unusual to read a citing case that both agrees
with the cited case on one issue, and disagrees with it on a
different issue. Traditional content analysis techniques that apply
statistical models on whole documents run into difficulty in
pinpointing the exact reason a case is cited.
[0011] Thus, there is a need in the art to provide a technique that
can extract the reason for citing (RFC) at a local region where the
citing instance occurs. However, there do not appear to be any
conventional systems for performing the required task of finding
text near a citing instance that indicates the reason a document is
cited. It is to fulfil this need, among others, that the present
invention is directed. In fulfilling this need, the invention
provides new applications of techniques that are known in the art,
such as word stemming, informetrics and vector space information
retrieval, which are now briefly discussed.
[0012] Porter in [Porter 1980] describes a word stemming algorithm
that strips suffixes from words. This conventional word stemming
algorithm handles many types of suffixes and is not limited by the
length of a word. However, this approach is not computationally
very fast and does not perform well on document sets containing
many long words, such as court opinions and medical journal
articles. However, Applicants have recognized that it is desirable
to use stemming to find morphological variations of words--that is,
words that have different suffixes. Applicants have recognized
that, because many input documents (especially court opinions)
contain many long words, it is valuable to provide a stemming
method that simply shortens them to their first N letters (where N
is a positive integer such as six). Such an inventive stemming
method is described in the Detailed Description.
[0013] Informetrics is a term whose definition is somewhat
ambiguous in the literature. It appears to have been first
introduced in 1979 as general term covering both bibliometrics and
scientometrics [Brookes, 1991]. All three terms have been used
loosely to mean more or less the same thing. Informetrics can be
perceived in its broadest sense as "the study of the quantitative
aspects of information in any form" [Brookes, 1991, p. 1991], or as
"the search for regularities in data associated with the production
and use of recorded information" [Bookstein et al., 1992].
[0014] Small [Small 1978], a bibliometrics researcher, found that
if one examines the text around citing instances of a given
scientific document, one can determine the `particular idea the
citing author is associating with the cited document`. He goes on
to say that the citation of a cited scientific document becomes a
symbol for the ideas expressed in the text of the citing instance.
However, court case opinion citation differs from that of the
scientific community in two fundamental ways.
[0015] First, in the legal profession, a citing instance is
normally for single point-of-law, definition, or fact pattern that
is precisely stated near the citing instance. In contrast, in the
scientific community, a citing instance is often for very general
principles or ideas that are normally not precisely stated near the
citing instance.
[0016] Second, in the legal profession, two citing instances of a
particular case are often for differ points of law, definitions, or
fact patterns [Morse 1998]. In contrast, in the scientific
community two citing instances are generally for the same
principles or ideas that are not clearly stated or imprecisely
stated near the citing instance.
[0017] Therefore, bibliometrics methods that use just the frequency
of citation of documents do not generally work as well when applied
to legal citations as they did when applied to scientific
citations. As an example, take co-citation analysis [Small 1973],
which is the analysis of the frequency that two citations appear in
the same document. One conclusion that co-citation analysis
produces is that two documents citing the same two other documents
have a high probability of being about closely related topics. But
in the legal profession, this is not true as often as it is in the
scientific community.
[0018] For example, if both of two case law documents D1 and D2
cite People v. Surplice, and both documents cite another case for
an issue related to "a probation officer's pre-sentence report",
then co-citation analysis would conclude that these two cases have
similar topics. But, if D1 cites People v. Surplice for the first
very general reason (how the court should exercise its judicial
discretion), and D2 cites it for the 2nd very specific reason
(dealing with a probation officer's pre-sentence report), then D1
and D2 could be about very different topics.
[0019] Accordingly, something more than mere co-citation frequency
counts is needed to determine if two cases are similar in topic. It
is to fulfill this need, among others, that the present invention
is directed.
[0020] Concerning vector space information retrieval, the "Smart"
system [Salton 1989] is an example of an information retrieval
system based on the vector processing model. The goal of the Smart
system is to find the documents that are similar to a "query" (a
list of words). Both queries and documents are represented as word
vectors. In the simple case, each element of a word vector is the
frequency that a specific word appears in the document
collection.
[0021] A simple method of determining the similarity of a document
to a query is to compute the dot product of the document's and
query's word vectors. The dot product is the sum of the products of
corresponding elements from the two word vectors, where
corresponding elements contain the frequency counts of a given
word, either in the document set or the query. Normally this
similarity metric is normalized by taking into account the lengths
of the document and query. The present invention provides, among
other advantages, a new application of the vector processing model
and similarity metric like the one described above.
[0022] U.S. Pat. No. 5,918,236 (Wical; hereinafter "the '236
patent" ) may be considered relevant. The '236 patent discloses a
system that generates and displays "point of view gists" and
"generic gists" for use in a document browsing system. Each "point
of view gist" provides a synopsis or abstract that reflects the
content of a document from a predetermined point of view or slant.
A content processing system analyzes documents to generate a
thematic profile for use by the point of view gist processing.
[0023] The point of view gist processing generates point of view
gists based on the different themes or topics contained in a
document. It accomplishes this task by identifying paragraphs from
the document that include content relating to a theme for which the
point of view gist is based. The '236 patent's Summary of the
Invention discloses that the point of view gist processing
generates point of view gists for different document themes by
relevance-ranking paragraphs that contain a paragraph theme
corresponding to the document theme that was determined by
analyzing document paragraphs and the whole document.
[0024] However, the '236 patent's relevance-ranking does not solve
the problem solved by the present invention--determining which
sentences near a citing instance to determine which sentences are
the best ones to represent the reason for citing (RFC). Thus, there
is a need in to art to provide a system that relevance-ranks
sentences near a citing instance based on the similarity of each
such sentence to typical context of many citing instances for a
given document. Furthermore, there is a need to provide a system to
determine typical context by analyzing the context of many citing
instances for the same case. It is to fulfill these various needs,
among others, that the present invention is directed.
[0025] References:
[0026] 1. Bookstein, A.; O'Neil, E.; Dillion, M.; and Stephens, D.,
1992, "Application of loglinear models to informetrics phenomena",
Information Processing and Management, 28(1), 75.
[0027] 2. Brookes, B., 1991, "Biblio-, sciento-, infometrics???
What are we talking about?", Informetrics 89/90, edited by Egghe,
L. & Rousseau, R., Amsterdam, Elsevier, 31-44.
[0028] 3. Moor, W. J. 1988, "Citation Context Classification of a
citation classic concerning Citation Context Classification",
Social Studies of Science, 18, pp. 515-521.
[0029] 4. Morse, A. L., 1998, "Citation Sources in Michigan Supreme
Court Opinion", dissertation from University of Michigan, published
by UMI Dissertation Service.
[0030] 5. Porter, M., 1980, "An algorithm for suffix stripping,
Program", Automated Library and Information 14(3), p. 130-137
[0031] 6. Salton, G., 1989, Automatic Text Processing: The
Transformation, Analysis, and Retrieval of Information,
Addison-Wesley Publishing.
[0032] 7. Small, H., 1973, "Co-citation in scientific literature: A
new measure of the relationship between two documents", JASIS
24(4), p. 265-269.
[0033] 8. Small, H., 1978, "Cited Documents as Concept Symbols",
Social Studies of Science, 8, pp.327-340
SUMMARY OF THE INVENTION
[0034] The invention fulfills the various needs described
above.
[0035] The invention provides a computer-automated system and
method for identifying text, near a citing instance, that indicates
the reason(s) for citing (RFC).
[0036] The invention further provides a computer-automated system
and method for selecting content words that are highly related to
the reasons a particular document is cited, and giving them weights
that indicate their relative relevance.
[0037] The invention further provides a computer-automated system
and method for forming lists of morphological forms of words.
[0038] The invention further provides a computer-automated system
and method for scoring sentences to show their relevance to the
reasons a document is cited.
[0039] The invention further provides a computer-automated system
and method for generating lists of content words.
[0040] In a preferred embodiment, the invention is applied to legal
(especially case law) documents and legal (especially case law)
citations.
[0041] Other objects, features and advantages of the present
invention will be apparent to those skilled in the art upon a
reading of this specification including the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0042] The invention is better understood by reading the following
Detailed Description of the Preferred Embodiments with reference to
the accompanying drawing figures, in which like reference numerals
refer to like elements throughout, and in which:
[0043] FIG. 1 illustrates an exemplary hardware configuration in
which the inventive system and method may be implemented.
[0044] FIG. 2 is a high-level flow chart of a preferred
implementation of the RFC (reason for citing) method according to
the present invention.
[0045] FIG. 3A is a flow diagram showing a first exemplary
embodiment of the FIG. 2 step 203 of generating a content word
list.
[0046] FIG. 3B is a flow diagram showing a second exemplary
embodiment of the FIG. 2 step 203 of generating a content word
list. FIG. 3B is like FIG. 3A except that it uses the actual text
of cited document X, and pairs paragraphs of citing instances of X
with paragraphs of X itself.
[0047] FIGS. 3A and 3B may be referred to collectively as "FIG.
3."
[0048] FIG. 4 is a flow diagram showing an exemplary embodiment of
the FIG. 2 step 204 of scoring sentences and selecting those with
highest scores as RFCs.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0049] In describing preferred embodiments of the present invention
illustrated in the drawings, specific terminology is employed for
the sake of clarity. However, the invention is not intended to be
limited to the specific terminology so selected, and it is to be
understood that each specific element includes all technical
equivalents that operate in a similar manner to accomplish a
similar purpose.
[0050] For example, in addition to being applied to legal case law
documents (court opinions), the invention may be applied to any
other type of document that contains citations. Also, what this
specification refers to as a "sentence" may be any text unit that
makes up paragraphs. Likewise, what this specification refers to as
a "paragraph" can refer to any chunk of text that makes up a
document and that are made of "sentence" text units.
[0051] Definitions of terminology. As used in this specification,
the following terms have the following meanings:
[0052] Citing instance--the citation of a "cited" case X found in
another "citing" case Y. For example, when McDougall v. Palo Alto
School District cites Ziganto v. Taylor, the citation is referred
to as "a citing instance of Ziganto in McDougall."
[0053] Content words--words that convey the content of
documents.
[0054] Content word's frequency count--the number of times a
content word is in a paragraph of a citing instance of X.
[0055] Context of the citing instance--text around a citing
instance of X. For example, the paragraph of a citing instance and
the paragraphs before and after it are one example of a "context"
of the citing instance.
[0056] Noise words--words that occur in almost all input documents
and therefore do not convey much about the content of any one
document. Noise words are normally removed when analyzing content.
Appendix C has an exemplary list of noise words.
[0057] Paragraph of a citing instance--the paragraph of some case
that contains a citing instance. For example, the paragraph of
McDougall v. Palo Alto School District that contains a citing
instance of Ziganto v. Taylor would be called a paragraph of a
citing instance of Ziganto.
[0058] RFC--the text, such as sentences in the context of a citing
instance of X, that has the largest calculated content score and
that therefore likely indicates the reason a cited document was
cited.
[0059] With these definitions established, the structure and
operation of preferred embodiments of the invention are now
described.
[0060] Referring to FIG. 1, embodiments of the inventive RFC
generation system may be implemented as a software system including
a series of modules on a conventional computer. An exemplary
hardware platform includes a central processing unit 100. The
central processing unit 100 interacts with a human user through a
user interface 101. The user interface is used for inputting
information into the system and for interaction between the system
and the human user. The user interface includes, for example, a
video display, keyboard and mouse.
[0061] A memory 102 provides storage for data (such as the
documents containing the citing instances, the content word lists,
and the noise word list). It also may provide storage for software
programs (such as the present RFC generation process) that are
executed by the central processing unit. An auxiliary memory 103,
such as a hard disk drive or a tape drive, provides additional
storage capacity and a means for retrieving large batches of
information.
[0062] All components shown in FIG. 1 may be of a type well known
in the art. For example, the system may include a SUN workstation
including the execution platform SPARCsystem 10 and SUN OS Version
5.5.1, available from SUN MICROSYSTEMS of Sunnyvale, Calif. The
software may be written in such programming languages as C, C++ or
Perl. Of course, the system of the present invention may be
implemented on any number of computer systems using any of a
variety of programming languages.
[0063] Exemplary embodiments of the inventive methods provided by
the invention are now described.
[0064] Briefly, in a particular preferred embodiment of the
invention, the text of documents that cite a particular document X
is input. Then, the system extracts from each of these documents,
text around each citing instance of X (that is, the "context" of a
citing instance of X). The system then uses paragraphs containing
the citing instances of X, found in the contexts, to generate a
list of content words. It then uses the list of content words to
calculate a content score for each sentence in each context of each
citing instance of X, and selects the sentences with the highest
score as the RFC for that citing instance of X.
[0065] Embodiments of the inventive method are now described in
greater detail.
[0066] Referring to FIG. 2, a high-level flow chart of the RFC
generation method is shown. Block 200 represents input of the text
of documents (such as court opinions) that cite a document X, which
is by pertinent example a court opinion.
[0067] Block 201 is the step of dividing the documents into
"paragraphs" (or other suitable entity), and dividing each
"paragraph" into "sentences" (or other suitable sub-entity). One
way to divide a case into paragraphs is to assume that blank lines
separate paragraphs. To divide paragraphs into sentences, it may be
assumed that sentences always end with at least four lower case
letters that are immediately followed by a period. These two
assumptions do not divide cases perfectly into paragraphs, nor do
they divide paragraphs perfectly into sentences, but it is an
advantage of the inventive RFC determination method that it does
not require perfect divisions.
[0068] Table 1 illustrates an exemplary way that the text of court
opinions can be input to this invention. Table 1 shows that each
sentence of a case that cites X is assigned
[0069] a) an index for the paragraph it is in, and
[0070] b) a sentence index.
[0071] In the illustrated example, sentences are entered in the
order they appear in the case. In addition, the sentence containing
a citation of X is marked and the citation in the sentence is
marked. For example, in Table 1, sentence 5 contains the citation
of interest, Ziganto v. Taylor, 198 Cal. App. 603, and is marked
with an asterisk in the paragraph number column. Also, the citation
of that sentence is enclosed with sgml tags:
[0072] <citation> . . . </citation>.
1TABLE 1 The "Context" of a citing instance of Ziganto, from
McDougall plus paragraph and sentence indexing Paragraph Sentence
Number Number Sentence Text 1 1 We have not been referred to, nor
have we found, any case upholding the plea of res judicata in the
precise instant situation. 1 2 For the reasons we have given above,
we are persuaded that such plea cannot be availed of "offensively"
in the case before us and that the effect of the original grant
should be determined anew and independently of the earlier action.
2 3 We therefore turn to the original deed of William Paul. 2 4
Since no extrinsic evidence was introduced in the court below, the
construction of the deed presents a question of law. 2* 5 We are
not bound by the trial court's interpretation of it, and we
therefore proceed, as it is our duty, to determine the effect of
its foregoing provisions according to applicable legal principles.
<citation>(Estate of Platt (1942) 21 Cal.2d 343, 352 (131
P.2d 825); Jarrett v. Allstate Ins. Co. (1962) 209 Cal.App.2d 804,
809-810 (26 Cal. Rptr. 231); Ziganto v. Taylor (1961) 198
Cal.App.2d 603, 606 (18 Cal. Rptr. 229); Moffatt v. Tight (1941) 44
Cal.App.2d 643, 648 (112 P.2d 910).)</citation> 3 6
Appellants contend that the deed in question created a fee simple
determinable in the school district with a possibility of reverter
in the original grantor, his heirs and assigns. 3 7 We have
concluded that such contention has merit. *(the asterisk) marks the
paragraph and sentence that contains the citation of interest,
namely, the citation to Ziganto.
[0073] Referring again to FIG. 2, in block 202 the system
determines a "context" (surrounding text) for all citing instances
of X. The context of all citing instances of X is used in steps 203
and 204, discussed below.
[0074] Block 203 represents the step of generating a content word
list. Two exemplary implementations of this step are described
below, with reference to FIGS. 3A and 3B.
[0075] Block 204 represents the step of scoring sentences, and
selecting those sentences with the highest score (or other enhanced
selection technique) as being the desired RFCs. This step is
described in greater detail with reference to FIG. 4.
[0076] Finally, block 205 represents the output of the FIG. 2
process, namely, RFCs for each citing instance of X.
[0077] Next, the process' steps and alternate embodiments thereof
are described in detail, with reference to a particular
example.
[0078] After the text of the legal cases citing X is input (step
200) and parsed into paragraphs and sentences (step 201), the
"context" for all citing instances of X is obtained as follows.
Table 1 shows the text of a case that cites X divided into
paragraphs and sentences. Step 202 uses the X citation marker
(which accompanies the citing sentence in Table 1) to locate the
paragraph containing a citation to X. For each citing instance of
X, an exemplary implementation of step 202 extracts:
[0079] the paragraph containing the citation to X (paragraph 2 in
Table 1);
[0080] the paragraph before the paragraph containing the citation
to X (paragraph 1 in Table 1); and
[0081] the paragraph after the paragraph containing the citation to
X (paragraph 3 in Table 1).
[0082] In this embodiment, these three paragraphs are considered
the "context of the citing instance of X."
[0083] Of course, variations on this choice of context lie within
the scope of the invention. In any implementation, an important
consideration is to have enough context so that sentences that are
in fact relevant to why a case is cited is included in the context.
Also, it is important that there be at least a few sentences in the
context, so that scoring and selecting step 204 has more than one
sentence to score and choose from. Further, it is important for the
context determination step to account for short paragraphs, and
paragraphs of citing instances at the beginning or end of a
document. These are conditions that might otherwise cause the
context to be too small (contain too few sentences).
[0084] Alternative examples of methods of determining the context
are:
[0085] selecting only the paragraph containing the citing instance;
or
[0086] selecting M sentences before the citing instance and N
sentences after the citing instance, where M and N are different
may be variable.
[0087] However the context is determined, the context of each
citing instance of X is used by steps 203 and 204.
[0088] Block 203 represents the step of generating content word
list. Content word list generation step 203 (detailed in flow
diagram in FIGS. 3A and 3B) inputs the context for each citing
instance of X from step 202. Step 203 also uses a
previously-generated "Noise word" list, exemplified in Appendix
C.
[0089] The steps in first and second exemplary embodiments of step
203 are described with reference to FIGS. 3A and 3B,
respectively.
[0090] Referring first to FIG. 3A, in step 300 A paragraphs of
citing instances from the contexts of the instances of X are paired
(associated with each other). Each paragraph of a given citing
instance of X is paired with every other paragraph of a citing
instance of X that is not in the same case as the given citing
instance.
[0091] As an example, consider a hypothetical situation in which
there are four citing instances of case X--one citing instance in
case A, two citing instances in case B, and one citing instance in
case C. The citing instances may be denoted as:
[0092] 1A, 2B, 3B, 4C
[0093] where the letter in the denotation indicates the citing
case. If this denotation is used to label the four paragraphs
containing these four citing instances, then the pairs created by
step 300A would be:
[0094] 1A-2B
[0095] 1A-3B
[0096] 1A-4C
[0097] 2B-4C
[0098] 3B-4C
[0099] Paragraphs 2B and 3B are not paired because they are in the
same case.
[0100] The following is an example of one pair of paragraphs for
citing instances of Ziganto v. Taylor. The citing cases are
McDougall v. Palo Alto School District, 212 Cal. App. 3d 422, and
Jarrett v. Allstate Ins. Co., 209 Cal. App. 2d 804.
[0101] Ziganto in McDougall: We therefore turn to the original deed
of William Paul. Since no extrinsic evidence was introduced in the
court below, the construction of the deed presents a question of
law. We are not bound by the trial court's interpretation of it,
and we therefore proceed, as it is our duty, to determine the
effect of its foregoing provisions according to applicable legal
principles. (Estate of Platt (1942) 21 Cal.2d 343, 352 (131 P.2d
825); Jarrett v. Allstate Ins. Co. (1962) 209 Cal.App.2d 804,
809-810 (26 Cal. Rptr. 231); Ziganto v. Taylor (1961) 198
Cal.App.2d 603, 606 (18 Cal. Rptr. 229); Moffatt v. Tight (1941) 44
Cal.App.2d 643, 648 (112 P.2d 910).)
[0102] Ziganto in Jarrett: The construction of the instant contract
is one of law because it is based upon the terms of the insurance
contract without the aid of extrinsic evidence. Accordingly, we are
not bound by the trial court's interpretation of it, but it is our
duty to make the final determination in accordance with the
applicable principles of law. (Estate of Platt, 21 Cal.2d 343, 352
(131 P.2d 825); Ziganto v. Taylor, 198 Cal.App.2d 603, 606 (18 Cal.
Rptr. 229).) Our interpretation does, however, coincide with that
made by the trial court.
[0103] Step 301 is the step of removing anything that is not a
word, from both paragraphs of a pair. In this example, step 301
results in the following two lists of words:
[0104] Ziganto in McDougall: We therefore turn to the original deed
of William Paul Since no extrinsic evidence was introduced in the
court below the construction of the deed presents a question of law
We are not bound by the trial court interpretation of it and we
therefore proceed as it is our duty to determine the effect of its
foregoing provisions according to applicable legal principles
[0105] Ziganto in Jarrett: The construction of the instant contract
is one of law because it is based upon the terms of the insurance
contract without the aid of extrinsic evidence Accordingly we are
not bound by the trial court interpretation of it but it is our
duty to make the final determination in accordance with the
applicable principles of Our interpretation does however coincide
with that made by the trial court
[0106] Step 302 is the step of inputting (or referring to
previously-input) noise words from a noise word list. Appendix C
illustrates a noise word list that may be used in this
embodiment.
[0107] Step 303 is the step of removing noise words from both
paragraphs. For this example, step 303 results in the following two
lists of non-noise words:
[0108] Ziganto in McDougall: turn original deed William Paul Since
extrinsic introduced below construction deed presents bound
interpretation proceed duty determine effect foregoing provisions
according applicable legal principles
[0109] Ziganto in Jarrett: construction instant contract based
terms insurance contract aid extrinsic bound interpretation duty
make final determination accordance applicable principles
interpretation however coincide made
[0110] Step 304 is the step of stemming the remaining non-noise
words of both paragraphs by shortening them to their first N
letters (N is a positive integer) when any has more than N letters
to begin with. (The choice of exactly six letters is somewhat
arbitrary, and the exact number of letters may of course be varied
while still remaining within the scope of the present invention.)
Then, the resulting stemmed words are alphabetized. For this
example, stemming step 304 results in the following two lists of
stemmed non-noise words:
[0111] Ziganto in McDougall: accord applic below bound constr deed
deed determ duty effect extrin forego interp introd legal origin
Paul presen princi procee provis Since turn Willia
[0112] Ziganto in Jarrett: accord aid applic based bound coinci
constr contra contra determ duty extrin final howeve instan insura
interp interp made make princi terms
[0113] Step 305 is the step of determining the "common" stemmed,
non-noise words--those stemmed, non-noise words that are in both
paragraphs of a pair. In this example, step 305 results in the
following list of stemmed non-noise words that are common to the
two paragraphs:
[0114] accord applic bound constr determ duty extrin interp
princi
[0115] Step 306 is the step of tallying each common, stemmed,
non-noise word's frequency count by adding one to its frequency
count for each paragraph in the pair that has not been processed by
this process. Because the paragraphs in the example are the first
two paragraphs processed by this step, each of the above stems has
a frequency count of exactly 2 because each is in both paragraphs
in the pair. However, as paragraphs after the first two paragraphs
are processed, the numbers of some of the stems grow to higher than
2 as the stems are again encountered.
[0116] Step 307 is the step of designating as content words, the
non-noise words whose stems are the common stemmed non-noise words.
In this example, these words are:
[0117] accordance according, applicable, bound, construction,
determination determine, duty, extrinsic, interpretation,
principles
[0118] In the above list of words, different morphological forms of
the same word ("accordance" and "according" ) are separated by a
space and not by a comma. These forms are associated because they
have the same first six letters.
[0119] This completes discussion of this one application of FIG. 3A
to a single pair of paragraphs. Appendix A shows a complete list of
content words and associated tallied frequency counts generated by
the FIG. 3A embodiment when applied to all paragraphs of citing
instances.
[0120] The invention provides that the content word list may be
supplemented and/or restricted by additional techniques. Such
supplementation and/or restriction of the content word list
constitute optional steps shown schematically as optional step
308.
[0121] For example, the content word list may be supplemented with
specific words and phrases that often indicate legally significant
text. For example, words that might specifically indicate concise
expression of rules of law, or words indicating how the citing case
is treating the cited case, are meaningful and may thus be included
in content word lists. Such words include, for example,
"following," "overruling," "questioning," and so forth.
[0122] Conversely, the content word list can be restricted by other
techniques. For example, it is possible to require a non-noise word
to be in more than a given number M paragraphs of citing instances
(M>2, for example). Words in the content word list that do not
meet this criteria are removed from the list.
[0123] Further, it is possible to remove from the content word
list, non-noise words to be in at least M paragraphs of citing
instances (M.gtoreq.2, for example), along with W other non-noise
words. For example, if M=2 and W=3, then the non-noise word
"injury" would be a content word because it is in two paragraphs of
citing instances with the other three non-noise words "insured",
"vehicle", and "coverage".) Words in the content word list that do
not meet this criteria are removed from the list.
[0124] Variations of the content word generation method lie within
the contemplation of the invention, based on at least the following
observations.
[0125] The FIG. 3A method of generating a list of content words
(which includes comparing the text of each paragraph of a citing
instance of X to the text of other paragraphs of citing instances
of X), results in the same list of content words as taking all the
non-noise words that have occurred in at least two paragraphs of
citing instances of X. However, by viewing the process as taking
words in common that result from a comparison of two sets of
paragraphs, the resulting content words could be very different if
the two sets of paragraphs are very different.
[0126] Also, referring now to FIG. 3B, a second embodiment of the
method of generating content words compares paragraphs of citing
instances of X to paragraphs in the Majority Opinion of X itself.
One situation in which it is advisable to use the second embodiment
to generate content words is when case X has not been cited often.
In this situation, there will be few paragraphs of citing instances
to compare.
[0127] Still another alternative embodiment involves combining
paragraphs of citing instances with paragraphs from the Majority
Opinion of X, and comparing each paragraph of a citing instance
with both.
[0128] The second embodiment of FIG. 2 step 203 is now described,
with reference to its decomposed flow diagram in FIG. 3B. Input
used by this alternative embodiment is different from that used by
FIG. 3A, and includes the context for each citing instance of X and
the text of the legal case X itself. As in FIG. 3A, the final
output of method of FIG. 3B is a list of content words.
[0129] Briefly, the second embodiment of the method of generating a
list of content words includes comparing the text of each paragraph
of a citing instance of X to the text of each paragraph in the
Majority Opinion of X. Like the first embodiment, each time two
paragraphs are compared, the result is a list of words they have in
common, and these common words are the words that become the
content words.
[0130] Comparing two paragraphs in the FIG. 3B embodiment may be
chosen to be generally the same as the comparing process in the
FIG. 3A embodiment. For the FIG. 3B method, each paragraph of X
itself is paired with each paragraph of a citing instance of X, as
shown in step 300B which is the only step different from its
corresponding step in FIG. 3A. As an example, consider the
hypothetical situation in which there are:
[0131] three citing instances of case X; and
[0132] four paragraphs in the Majority Opinion of X.
[0133] In this situation, each of the three paragraphs of the three
citing instances are paired with each of the four paragraphs of the
Majority Opinion of X, yielding 3.times.4=12 pairs of
paragraphs.
[0134] The description of the second embodiment is abbreviated, it
being understood that the foregoing discussion of FIG. 3A applies
to corresponding steps in FIG. 3B.
[0135] Applying this technique to the concrete example includes
pairing the citing paragraph in McDougall to the second paragraph
of the Majority Opinion of Ziganto:
[0136] McDougall: We therefore turn to the original deed of William
Paul. Since no extrinsic evidence was introduced in the court
below, the construction of the deed presents a question of law. We
are not bound by the trial court's interpretation of it, and we
therefore proceed, as it is our duty, to determine the effect of
its foregoing provisions according to applicable legal principles.
(Estate of Platt (1942) 21 Cal.2d 343, 352 (131 P.2d 825); Jarrett
v. Allstate Ins. Co. (1962) 209 Cal.App.2d 804, 809-810 (26 Cal.
Rptr. 231); Ziganto v. Taylor (1961) 198 Cal.App.2d 603, 606 (18
Cal. Rptr. 229); Moffatt v. Tight (1941) 44 Cal.App.2d 643, 648
(112 P.2d 910).)
[0137] Ziganto 2.sup.nd paragraph: Appellant is the owner of a lot
in Palo Alto upon which he arranged for the construction of an
apartment house by a general contractor. During the course of
construction respondent, a subcontractor and materialman, at the
request of the contractor furnished certain cabinets and other
materials of a claimed value of $5,075.21 which were used in the
building. On Jan. 26, 1959, respondent filed for record his claim
of lien in the above amount.
[0138] After removing everything not a word, removing noise words,
and shortening to their first N=6 letters those words having more
than six letters, the potential content words in McDougall and
Ziganto are:
[0139] McDougall: accord applic below bound constr deed deed determ
duty effect extrin forego interp introd legal origin Paul presen
princi procee provis Since turn Willia
[0140] Ziganto 2.sup.nd paragraph: above Alto amount apartm arrang
buildi cabine certai claim claime constr constr contra contra
course During furnis house Januar lien lot materi materi owner Palo
record reques respon respon subcon used value
[0141] The following is the "list" of words in common (in this
case, a list of one word) that therefore becomes the sole
contribution of this pair of paragraphs to the content word
list:
[0142] Construction
[0143] A complete list of content words generated for this example
by all paragraphs processed by the FIG. 3B embodiment is provided
in Appendix B.
[0144] Of course, it is envisioned that still further methods, and
variations of methods, may be used to generate lists of content
words, in addition to those shown in FIGS. 3A and 3B.
[0145] Referring again to FIG. 2, step 204 represents the step of
scoring text (such as sentences) and selecting those with the
highest score(s) as the RFC. An RFC may be one or more sentences.
Step 204's decomposed flow diagram is shown in FIG. 4.
[0146] The following describes calculation of a content score
using, as an example, the first sentence in the context of the
citing instance of Ziganto in McDougall. The first sentence in this
context (the first row in the body of Table 2) is the focus of
discussion of individual steps in FIG. 4. Table 2 shows the
sentences of this example's context, along with the values
calculated by the steps in FIG. 4.
[0147] In Table 2, there are seven sentences, one in each row.
There are seven columns in Table 2:
[0148] 1) The column labeled "Sentence . . . ", contains:
[0149] a) the text of sentences in the context,
[0150] b) each content word found in the sentences, and
[0151] c) each content word's respective frequency count,
determined from the content word list such as one or more of those
shown in Appendix A or Appendix B.
[0152] 2) The column labeled W shows the number of words in the
sentence.
[0153] 3) The column labeled ICS shows the sentence's initial
content score.
[0154] 4) The column labeled NICS shows the normalized initial
content score.
[0155] 5) The column labeled D shows the sentence's distance, in
number of sentences, from the citing instance of Ziganto, which in
this case is the fifth sentence.
[0156] 6) The column labeled MAD shows the modified absolute value
of distance D after it has been modified by steps 403 and 404 (FIG.
4).
[0157] 7) The column labeled CS shows each sentence's calculated
content score.
2TABLE 2 Sentence, content words in sentence, and each content
word's frequency count W ICS NICS D MAD CS We have not been
referred to, nor have we found, 23 3 0.02 -4 6 0.01 any case
upholding the plea of res judicata in the precise instant
situation. (instant(3)) For the reasons we have given above, we are
41 8 0.02 -3 5 0.01 persuaded that such plea cannot be availed of
"offensively" in the case before us and that the effect of the
original grant should be determined anew and independently of the
earlier action. (determined(8)) We therefore turn to the original
deed of William 10 0 0.00 -2 2 0.00 Paul. Since no extrinsic
evidence was introduced in the 20 21 0.13 -1 1 0.13 court below,
the construction of the deed presents a question of law.
(extrinsic(7) below(3) construction(6) presents(5)) We are not
bound by the trial court's interpretation 33 52 0.19 0 0 0.19 of
it, and we therefore proceed, as it is our duty, to determine the
effect of its foregoing provisions according to applicable legal
principles. (Estate of Platt (1942) 21 Cal.2d 343, 352 (131 P.2d
825); Jarrett v. Allstate Ins. Co. (1962) 209 Cal.App.2d 804,
809-810 (26 Cal. Rptr. 231); Ziganto v. Taylor (1961) 198
Cal.App.2d 603, 606 (18 Cal. Rptr. 229); Moffatt v. Tight (1941) 44
Cal.App.2d 643, 648 (112 P.2d 910).) (bound(7) interpretation(8)
duty(6) determine(8) provisions(4) according(6) applicable(7)
principles(6)) Appellants contend that the deed in question created
29 8 0.04 1 5 0.02 a fee simple determinable in the school district
with a possibility of reverter in the original grantor, his heirs
and assigns. (determinable(8)) We have concluded that such
contention has merit. 8 5 0.08 2 6 0.03 (concluded(5))
[0158] Referring to FIG. 4, step 400 is the step of calculating an
initial content score (ICS) for the sentence as the sum of the
frequency counts of all content words in the sentence. In the
example in Table 2, the only content word in the first sentence is
`instant`, whose frequency count (from Appendix A) is 3. Therefore,
the initial content score (ICS) for the first sentence is 3, which
is entered in the ICS column of the first row of Table 2. As
another example, the fourth sentence has four content words whose
frequency counts total 7+3+6+5=21, so that 21 is listed in the ICS
column of row 4.
[0159] The ICS may be normalized to provide a fairer and more
meaningful contribution to the final content score CS that is
ultimately calculated.
[0160] Block 401 is the optional step of normalizing the initial
content scores (ICSs) to arrive at normalized initial content
scores (NICSs). In a preferred embodiment, normalization is
accomplished by dividing the ICS by the product of the number of
words in the sentence (W) and by the largest frequency count of any
content word in the content word list (Appendix A). In the first
row of Table 2, the number of words in the sentence is 23 and the
largest frequency count in the list of content words of Appendix A
is 8. Therefore, the NICS (rounded to 2 decimal places) is 3/(8*23)
or 0.02, which is entered in the first row of the NICS column in
Table 2.
[0161] Block 402 is the step of determining the number of sentences
between the present sentence and the closest citing instance of X.
This number of sentences is the distance D for the present
sentence. Sentences before the closest citing instance are assigned
negative numbers, and sentences after the citing instance are
assigned positive numbers. In the example of Table 2, the distance
D of the first sentence is -4, which is entered in the first row of
column D of Table 2.
[0162] The distance D may be modified according to strategic
criteria to provide a more meaningful contribution to the final
content score CS that is ultimately calculated.
[0163] Sentences that are a greater distance D from the citing
instance are initially assumed to be less relevant as reasons for
citing. To enhance the meaning of the distance measurement, the
invention envisions optional steps that take the absolute value of
the distance, and enhance the absolute distance based on one or
more strategic criteria. The criteria relate to predetermined
statistical observations of the implications of placement of a
sentence in the citing document relative to the citing instance.
The modification of the raw distance measurement D to arrive at a
Modified Absolute Distance (MAD) figure is described with reference
to steps 403 and 404.
[0164] Block 403 is the step of adding some penalty number, such as
2, to the absolute value of the distance D--if the sentence is not
in the paragraph containing the citing instance of X. In the
example of Table 2, the first sentence is not in the paragraph
containing the citing instance of Ziganto, but is in the paragraph
before the paragraph of the citing instance. Therefore, MAD, the
modified absolute value of its distance D, becomes 6 after step 403
is executed.
[0165] Block 404 is the further step of adding another penalty,
such as 2, to the MAD--if the sentence is after the citing instance
of X. In the example of Table 2, the absolute value of the distance
does not change for the first sentence because it is before, not
after, the citing instance of Ziganto. Thus, in Table 2, MAD
remains 6 after step 404.
[0166] The invention encompasses means of modifying the distance D
to arrive at a modified absolute distance MAD, based on criteria
other than the foregoing criteria (whether the sentence of interest
is in a different paragraph as the citing instance, or is recited
after the citing sentence). Also, the size of the "penalty" may be
a value other than 2. Moreover, a number may be subtracted from the
absolute distance so as to function, not as a penalty, but as a
bonus. Thus, steps 403 and 404 are not only optional, but are
exemplary and non-limiting.
[0167] Block 405 is the step of calculating the content score CS of
the sentences. This calculation may be accomplished in a variety of
ways. However, the following way incorporates a balancing of the
value of the content word scores (reflected in the value of NICS)
and the sentence's distance from the citing instance (reflected in
the value of MAD). In this exemplary method of calculating CS:
[0168] if MAD>2, CS is calculated by dividing NICS by
MAD.sup.0.5.
[0169] if MAD<2, CS is simply chosen as NICS.
[0170] In the first sentence of Table 2, the absolute value of the
distance is 6, which is greater than 2. Therefore, its content
score CS (rounded to 2 decimal places) is 0.02/6.sup.0.5 or 0.01,
which is entered into the CS column in the first row of Table
2.
[0171] Block 406 represents the RFC selecting step, in which the
one or more sentence(s) with the largest content score(s) are
determined to be the RFC. In the example of Table 2, the fifth
sentence has the highest content score (0.19). Therefore, if only
one sentence is selected, the fifth sentence would be the RFC.
[0172] In an alternative embodiment in which more than one sentence
is selected as the RFC, the one or more sentences with the
next-higher content scores would be selected as the RFC (for
example, starting with the fourth sentence of Table 2, which has a
CS of 0.13). As a still further alternative, specific sentences may
always be included as part of an RFC (for example, the sentence
containing the citing instance and/or the sentence immediately
before the citing instance's sentence.) Of course, strategies may
be combined to form new strategies for selecting the RFC. Thus, the
scope of the invention should not be limited to the particular
selection criteria described above.
[0173] The invention envisions enhancements, improvements, and
alternate embodiments of the scoring and selection process in FIG.
4. For example, when the normalized initial content score NICS of
every sentence of a context is small, or when the sentence with the
highest scoring sentence is far from the citing instance, RFC
sentence selection may be improved by one or more of the following
techniques.
[0174] For example, the invention provides for using a different
content word list, or using two or more content word lists
generated by different methods (such as the respective methods
shown in FIGS. 3A and 3B). When the normalized initial content
scores of all sentences are small when using a only one list of
content words, the scores may not all be small when using another
content word list or when using more than one content word
list.
[0175] Alternatively, if the sentence with the highest CS is too
far from the citing instance, a closer sentence whose score is not
as high, but still acceptable, is selected.
[0176] The inventive methods having been described above, the
invention also encompasses apparatus (especially programmable
computers) for carrying out the methods. Further, the invention
encompasses articles of manufacture, specifically,
computer-readable memory on which computer-readable code embodying
the methods may be stored, so that, when the code is used in
conjunction with a computer, the computer can carry out the
methods.
[0177] A non-limiting, illustrative example of an apparatus that
the invention envisions is described above and illustrated in FIG.
1. The apparatus may constitute a computer or other programmable
apparatus whose actions are directed by a computer program or other
software.
[0178] Non-limiting, illustrative articles of manufacture (storage
media with executable code) may include the disk memory 103 (FIG.
1), other magnetic disks, optical disks, "flash" memories,
conventional 3.5-inch, 1.44 MB "floppy" diskettes, "ZIP" disks or
other magnetic diskettes, magnetic tapes, and the like. Each
constitutes a computer readable memory that can be used to direct
the computer to function in a particular manner when used by the
computer.
[0179] Those skilled in the art, given the preceding description of
the inventive methods, are readily capable of using knowledge of
hardware, of operating systems and software platforms, of
programming languages, and of storage media, to make and use
apparatus for carrying out the foregoing methods, as well as
computer readable memory articles of manufacture that can be used
in conjunction with a computer to carry out the inventive methods.
Thus, the invention's scope includes not only the methods
themselves, but related apparatus and articles of manufacture.
Appendices
[0180] Concerning the content of the following Appendices, see the
copyright notice at the beginning of the specification.
[0181] Appendix A--List of "Content Words" generated by the method
in FIG. 3A
[0182] Appendix B--List of "Content Words" generated by the method
in FIG. 3B
[0183] Appendix C--List of "Noise Words"
3APPENDIX A List of "Content Words" and respective frequency counts
generated by the method of FIG. 3A 3 absence 5 conclude 2
expiration 2 months 5 accept 5 concluded 7 extrinsic 2 omitted 5
accepted 5 conclusion 4 february 2 order 6 accordance 5 conclusions
4 final 7 period 6 accorded 2 conflict 2 findings 2 plain 6
according 2 conflicting 4 first 5 present 2 added 2 conflicts 2 fn
5 presented 2 administrative 2 consent 4 followed 5 presents 2
administratively 4 consider 4 following 6 principles 2 adopted 4
consideration 3 footnotes 2 procedure 2 adoption 4 considered 3
generally 5 provide 2 agency 2 constitute 2 given 5 provided 2
agreement 2 constituted 2 haley 5 provides 4 aid 6 construction 2
hand 4 provision 7 applicability 6 constructions 2 holiday 4
provisions 7 applicable 3 contract 3 identical 2 refused 7
application 2 count 2 inferences 2 release 2 april 6 date 2 inquiry
2 released 2 august 8 day 3 instant 2 resort 3 based 8
determination 2 instrument 2 resorted 2 basis 8 determine 8
interpretation 2 respect 2 begun 8 determining 8 interpretations 2
respectively 3 below 2 drawn 8 interpreted 2 respondents 7 bound 2
during 2 introduced 3 six 2 calculating 6 duty 3 issue 2 stated 2
child 3 erroneous 2 italics 2 support 2 civil 2 establish 2
language 2 supported 4 commenced 2 established 2 legal 6 terms 4
commencement 2 establishes 2 likewise 2 then 4 commences 4 event 4
made 2 therefrom 4 commencing 7 exclude 5 make 2 thus 7 computation
7 excluded 3 making 7 time 7 computed 7 excludes 3 meaning 2 unless
7 computing 7 excluding 3 month 2 urges 3 written
[0184]
4APPENDIX B List of "Content Words" and respective frequency counts
generated by the method of FIG. 3B 2 above 2 continued 2 necessary
6 accordance 2 continuously 2 new 6 accorded 3 contract 2 order 6
according 3 contractor 2 parties 2 added 7 date 8 period 3
agreement 9 day 3 present 2 allegation 2 days 3 presented 2
allegations 2 decision 3 procedure 3 april 2 decisions 2 properly 2
argument 4 determination 2 property 3 august 4 determine 6 provide
2 between 4 determined 6 provided 2 certain 4 determining 6
provides 3 civil 2 entered 6 providing 2 claim 5 event 3 provision
2 claimed 2 excluded 3 provisions 4 commenced 2 excludes 2
reasonable 4 commencement 2 excluding 2 request 4 commences 2
executed 2 requested 2 complained 2 execution 3 respondent 2
complaint 3 expiration 3 respondents 2 computation 3 first 2 same 2
computed 5 followed 2 stipulated 6 conclude 5 following 2
stipulation 6 concluded 3 given 3 terms 6 conclusion 2 include 3
then 6 conclusions 2 instrument 2 thereof 5 construction 2 issues 8
time 5 constructions 3 language 3 unless 2 contained 2 mentioned 2
used 2 contains 2 necessarily 3 written 2 ziganto
[0185]
5APPENDIX C List of "Noise Words" a but few me probably therefore
about by fewer mere proceeding these accordingly cal filed merely
proper they act can footnote might pursuant this acts cannot
footnotes more question those after case for moreover questioned
though again cases from most rely to against cf general much rev
told ago ch good must right too all chicago had my rights toward
already citation has near rule towards also citations have
nevertheless rules trial although cite he no ruling under am cited
held not said up among civ her now say upon an co here nv says us
and code him of section use another could his on see very any court
how once set was app courts I one shall way appeal defendant if
only she we appellant defendants ill or should well appellants did
in other so were appellate district including others some what
appellee do into otherwise stat when appellees doc is our state
where appropriate does it ours statute whether appropriately done
its out still which are down itself over subdivision while argue
each judge own subsection who argued end judgment par such wholly
as enough just penal supra whom at erred last per th whose away
error later petition than will be even law petitioned that with
became ever laws petitioner the within because evidence less
petitioners their without been existing like plaintiff theirs would
before fact many plaintiffs them yet both facts may pp there you
your
[0186] Modifications and variations of the above-described
embodiments of the present invention are possible, as appreciated
by those skilled in the art in light of the above teachings. For
example, the particular programming language used, the hardware
platform on which the inventions are executed, the medium on which
the executable code is recorded, the particular method of
generating a word list, the particular method of scoring sentences,
the particular method of selecting the reasons for citing based on
scores, the particular method of calculating or enhancing any of
the various scores used in the methods, the particular values of
parameters and criteria used during execution of the methods, and
the like, may be varied by those skilled in the art while still
remaining within the scope of the invention. It is therefore to be
understood that, within the scope of the appended claims and their
equivalents, the invention may be practiced otherwise than as
specifically described.
* * * * *