NZ794000A - System and Method for Finding Similar Documents Based on Semantic Factual Similarity - Google Patents
System and Method for Finding Similar Documents Based on Semantic Factual SimilarityInfo
- Publication number
- NZ794000A NZ794000A NZ794000A NZ79400017A NZ794000A NZ 794000 A NZ794000 A NZ 794000A NZ 794000 A NZ794000 A NZ 794000A NZ 79400017 A NZ79400017 A NZ 79400017A NZ 794000 A NZ794000 A NZ 794000A
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Abstract
The present disclosure is directed towards systems and methods for finding documents that are similar to a reference text. The inventive systems and methods examine a set of collected documents to determine the facts present in those documents by, for example, extracting triplets and expanding them. A user’s input reference text is similarly examined to extract and expand triplets therein and the facts identified with respect to the reference text are used as a basis to find documents having similar facts. The present disclosure is also related to systems and methods for mining facts from documents relating to a primary source such as a piece of legislation and using the mined facts to improve the results of subsequent searches. A user’s input reference text is similarly examined to extract and expand triplets therein and the facts identified with respect to the reference text are used as a basis to find documents having similar facts. The present disclosure is also related to systems and methods for mining facts from documents relating to a primary source such as a piece of legislation and using the mined facts to improve the results of subsequent searches.
Description
The present disclosure is ed towards systems and methods for finding nts that
are similar to a reference text. The inventive systems and methods examine a set of collected
documents to determine the facts present in those documents by, for example, extracting triplets
and expanding them. A user’s input reference text is rly examined to extract and expand
triplets therein and the facts identified with respect to the reference text are used as a basis to
find documents having similar facts. The present disclosure is also related to systems and methods
for mining facts from documents relating to a primary source such as a piece of legislation and
using the mined facts to improve the results of subsequent searches.
NZ 794000
SYSTEM AND METHOD FOR FINDING SIMILAR DOCUMENTS
BASED ON SEMANTIC L SIMILARITY
This application for letters patent disclosure nt describes inventive aspects
that include s novel innovations (hereinafter osure”) and contains material that is
subject to copyright, mask work, and/or other intellectual property protection. The respective
owners of such ectual property have no objection to the facsimile reproduction of the
disclosure by anyone as it s in published Patent Office file/records, but otherwise
reserve all rights.
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of and priority to U.S. Provisional Application
No. 62/426,727, filed November 28, 2016, and U.S. Provisional Application No. 62/550,839,
filed August 28, 2017, which are both hereby incorporated by reference in their entireties.
BACKGROUND
The present innovations generally address tools finding documents that are
similar to a reference. Previously, in order to find documents of interest, researchers were
required to carefully craft search strategies for obtaining the information sought. In many
cases, substantial skill and experience on the part of the researcher were needed in order to
craft a search that would successfully and efficiently obtain the information sought. For
example, a researcher’s experience with information classification systems and even foreknowledge
of a document’s exact contents were sometimes ed in order to find some
documents.
At a basic level, one previous approach for finding nts provided a word
search in which a user can search for all documents containing a certain word or . The
results may be filtered or otherwise restricted (e.g., by date, author, county of origin, etc.) to
yield a result set. More ed searches were possible using Boolean and other operators,
but still these searches required skill and/or advanced dge of the documents sought in
order to be successful.
[0005] Other us approaches took the basic word search a step further by
performing an initial analysis of documents available for ing to identify a relative
importance of words or topics relating to the documents. For example, documents ed
into a research collection or library may be analyzed to produce a vector space model for
each document representing the relative importance of various index terms that are related to
the document. A particular example is the term frequency-inverse document frequency
model df”). Subsequent word searches produce results based on the predetermined
ance of search terms within result documents. In other examples, conceptual topics are
identified in documents (manually and/or through the use of computer software) and searches
may be performed on the previously identified topics or the topics may be browsed.
However, there still remains a need for a system and method for finding
documents based on ic similarity n the documents. The new tools for finding
documents in this manner presented herein improve access to such documents, make
searching for documents that are similar to a reference quicker, more efficient, less prone to
error and yield a more comprehensive, yet more precisely targeted result set of nts
than was previously possible.
In order to develop a reader's understanding of the innovations, disclosures have
been compiled into a single description to illustrate and clarify how aspects of these
innovations operate ndently, interoperate as between individual innovations, and/or
cooperate collectively. The application goes on to further describe the interrelations and
synergies as between the various innovations; all of which is to further compliance with 35
U.S.C. §112.
BRIEF SUMMARY
The present invention es a system and method for finding and retrieving
documents that are similar to a reference, and in particular where the similarity is determined
based at least in part on the semantic similarity of facts present in both.
In one aspect, a method for finding nts comprises ingesting at least two
library documents by extracting and indexing library triples therefrom, receiving a nce
text string, extracting at least one reference triple from the reference text string, fying
one or more library triples similar to the at least one reference triple, and returning a list of
one or more result library documents based on the fied library triples.
In some implementations, the method further comprises expanding the library
triples based on a semantic corpus to obtain ed y triples and indexing the
expanded y triples while maintaining a record of the library document from which the
library triples used to obtain them were extracted, wherein the identifying step includes
identifying one or more expanded y triples similar to the at least one reference triple
and the list of one or more result library documents returned by the returning step is based on
the identified library triples and expanded library triples.
In other implementations, the method further comprises expanding the at least one
reference triple based on a semantic corpus to obtain at least one expanded nce triple,
wherein the identifying step includes identifying one or more library triples similar to the at
least one ed reference triple.
In other implementations, the expanding step includes forming multi-word tokens
as components of a library triple based on a ic corpus.
[0013] In other implementations, the expanding step includes forming word tokens
as components of a reference triple based on a semantic corpus.
In other implementations, the returned list is ranked based on a similarity between
the fied library triples in each listed library document and the one or more reference
triples.
[0015] In other implementations, the method further comprises scoring library
documents from which fied library triples were extracted based on an aggregation of
similarity scores between each identified library triple and its corresponding nce triple.
In other implementations, the list that is returned includes only library documents
having a similarity score above a predefined threshold.
[0017] In other implementations, the listed y documents are ranked according to
their similarity scores.
In other implementations, the method further comprises ing a second
reference text string after returning the list, extracting at least one second reference triple
from the second reference text string, identifying one or more library s similar to the at
least one second reference triple, and returning an updated list of one or more result library
reference nts based on the library triples identified with respect to both the first
reference triples and second reference triples.
In another aspect, a method for mining facts from a body of documents,
comprises ingesting two or more library nts by extracting and indexing y triples
therefrom that relate to a primary source, grouping similar triples into one or more fact
groups, ingesting a later document after the two or more y documents by extracting
later triples therefrom that relate to a primary source, and grouping the later triples into the
one or more fact groups based on a similarity between the later triples and the library triples
previously comprising the one or more fact .
[0020] In some implementations, the method further comprises receiving a reference text
string, extracting at least one reference triple from the reference text string, expanding the at
least one reference triple based on the one or more fact groups to obtain at least one
expanded reference , identifying one or more library triples similar to the at least one
expanded reference , and returning a list of one or more result library documents based
on the identified library triples.
In other implementations, the method further comprises receiving a reference text
string, extracting at least one reference triple from the reference text string, ing the at
least one reference triple based on the one or more fact groups to obtain at least one
expanded nce triple, identifying one or more y triples similar to the at least one
expanded reference triple, and returning a list of one or more primary sources based on the
identified library triples.
In another , a method for finding documents relating to a primary source
comprises ingesting two or more library nts by extracting and indexing library s
therefrom that relate to a primary source, receiving a nce text string, extracting at least
one reference triple from the reference text string, identifying one or more library triples
similar to the at least one reference triple, and ing a list of one or more primary sources
based on the identified library triples.
In another aspect, a measure of rity between two documents based on a
combination of one or more of the semantic similarity between the different components of
the facts that are ted from each document, the sequence of the facts in both documents
and how much they agree on, the ic similarity between sentences in both documents,
other metadata that describe the documents such as their topics and references to other
documents and/or authorities, and/or the weights of each of these factors, determined by the
user, to reflect their significance, which results in ing the overall similarity score of a
given document.
In some implementations, the method further comprises optimizing the search
process to avoid computing the similarity to each document in the document collection by
indexing the semantically expanded facts from the document collection and scoring and/or
ranking the results from the index lookups to compute an overall relevance score for each
document and present the results ordered accordingly.
In another aspect, a new search workflow is implemented as a browser extension
allowing for seamless integration of the search functionality without leaving the current
document context. Search results may be yed in the browser ion window to
overlay the current context without disrupting it.
[0026] In another , a new interactive search workflow where users enter facts or
statements line by line and the results view is updated automatically in real-time to show the
documents that are most relevant to the current list of statements.
In another aspect, a system and method for mining facts that are extracted from a
collection of legal documents comprises extracting and mining facts from nts that
cite a particular law, grouping similar facts into fact groups according to their semantic
similarity and treating a fact group as a single item in the mining process, and utilizing the
overall frequency of mentions of a fact in the whole corpus to avoid generating generally
popular facts as relevant.
In another aspect, a new method for semantically expanding terms in search
queries is guided by the dataset generated as described above to restrict and guide the
expansion only to semantically r terms that are related to the same legislation, and
hence, have similar legal implications. For example, search queries comprise mainly of facts
to be searched, facts in a search query are matched against the dataset to find most relevant
laws, retrieving the matched tual fact groups to use for expansion, and the terms of a
fact are expanded utilizing other facts in the matched conceptual fact groups that: (a) mention
the same law; (b) are most relevant to the fact in the search query; and (c) are most relevant
to the target law.
In some implementations, the method further comprises extracting facts from the
search query text and using them to query the dataset to find relevant laws and the retrieved
laws are ranked according to aggregating the score of their relevance to the facts in the
search query.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings illustrate various miting, example, innovative
aspects in accordance with the t descriptions:
Fig. 1 is a tic diagram illustrating the high-level architecture of how one
ment of an exemplary system may be implemented;
Fig. 2 is a flow chart that shows an exemplary embodiment of preprocessing
which may run offline;
[0033] Fig. 3 is a flow chart that shows an exemplary embodiment of a fact extraction
process or module such as those depicted in Figs. 1 and 2;
Fig. 4 is a flow chart that bes in more detail the process of expanding facts
semantically;
Fig. 5 shows a block diagram illustrating embodiments of a Factual Similarity
System controller according to an exemplary embodiment.
Fig. 6 is a flow chart that shows an online or real-time phase in which the present
system and method can be used to find documents that are r to a particular reference
document or snippet of text;
Figs. 7-10 are screenshots rating exemplary ations of the present
system and method;
Fig. 11 is a schematic diagram illustrating an exemplary overview of a process
that generates a target dataset;
Fig. 12 is a flow chart that illustrates an exemplary extraction process ing
to an exemplary embodiment;
[0040] Fig. 13 is a flow chart that illustrates an exemplary flow of a fact extraction
Fig. 14 is a flow chart that depicts an exemplary process of expanding facts
semantically;
Fig. 15 is a flow chart that illustrates an exemplary fact mining process according
to an exemplary embodiment;
Fig. 16 is a flow chart that illustrates an exemplary process of semantically
expanding fact terms; and
Fig. 17 is a flow chart that illustrates an exemplary application utilizing a
legislation-related fact t to find relevant laws and statutes that apply to an input fact
scenario.
ED DESCRIPTION
Embodiments of systems and methods for finding r documents based on
ic factual similarity are described herein. While s of the described systems and
methods can be ented in any number of different configurations, the embodiments are
described in the context of the ing exemplary configurations. The ptions and
s of well-known components and structures are omitted for simplicity of the
description, but would be readily familiar to those having ordinary skill in the art.
The description and figures merely illustrate exemplary embodiments of the
inventive systems and methods. It will thus be appreciated that those skilled in the art will be
able to devise various arrangements that, although not explicitly bed or shown herein,
embody the principles of the present subject matter. rmore, all examples recited herein
are intended to be for illustrative purposes only to aid the reader in understanding the
principles of the present subject matter and the concepts contributed by the inventors to
furthering the art, and are to be construed as being without limitation to such specifically
recited examples and conditions. Moreover, all statements herein reciting principles, aspects,
and ments of the present subject , as well as specific examples thereof, are
intended to encompass all equivalents thereof.
In general, the systems and methods described herein may relate to improvements
to aspects of using computers to find similar documents based on semantic factual similarity.
These improvements not only improve the functioning of how such a computer (or any
number of computers employed in a search for similar documents) is able to operate to serve
the user’s research goals, but also es the accuracy, efficiency and usefulness of the
search results that are returned to the searcher. The inventive search tools described herein
generally are configured to receive a reference text from a user and to compare the reference
text to the text of cataloged documents to find similar documents to the reference text. The
comparison may be accomplished by, for example, extracting, expanding and indexing facts
from documents to be catalogued and comparing these against facts extracted and expanded
from the reference texts input by users.
[0048] The tools described herein are particularly suited to legal documents and research
and are generally discussed in that context, however it will be appreciated that many other
types of documents, research and chers will benefit from the inventive tools disclosed
and d herein.
One of the goals of legal research is to find precedents. In common law, judges
use precedents such as past ons to guide their t decisions. Lawyers also use
precedents to t their arguments or build case strategies, among other tasks.
Finding legal precedents is one example of an application of the systems and
methods described herein in which a goal is to find nt cases with r facts to a
present ion. In an exemplary process, the semantic factual similarity measure described
herein is used as a tool to enable legal researchers to find precedents.
Fig. 1 is a tic diagram illustrating the evel architecture of how one
embodiment of an ary system may be implemented. It shows the different system
components and the operations that may be done in the preprocessing phase (offline) and at
runtime (online). Of course, various tasks may also be performed at any time or
continuously. For example, new documents 102 may be ingested at the same time or after a
user enters a reference text 104 through their browser extension 106 in online operation. In
one example, a search operation is exposed via a web service 108 that can be accessed and
interacted with remotely, e.g., through a r extension. For example, a browser
extension may be configured to serve as a remote web client that performs HTTP GET/POST
operations to a REST web service that is hosted and provided by a server.
Fig. 2 shows an exemplary embodiment of preprocessing which may run e.
The goal of this process is to build 202 an index 204 on the semantically expanded 400 facts
that are ted 300 from ingested documents 206.
Fig. 3 shows an exemplary embodiment of a fact extraction process or module
300 such as those depicted in Figs. 1 and 2. The extraction module may be configured to
receive an input text 302, clean it (e.g., to remove tags and headers) 304 and split it into
sentences 306. In one example, full case documents may be retrieved from Westlaw (a legal
research service). In this example, cleaning and cessing may include isolating the body
of a case from the document. Each sentence may then be sent to a triple extraction process or
module 308, which may be ured to analyze the structure of the sentence (e.g., attach
part-of-speech tags) and produce c triples in the format subject-predicate-object based
on the structure of the sentence. The extracted sentences and triples (“facts”) may then be
stored in a se 310 for later is. The database may retain a record of the
provenance or source (e.g., a source document or a location within a source document) of
each sentence and triple for later analysis.
Fig. 4 describes in more detail the process of expanding facts semantically. This
segment of the process is intended to ensure that the semantics of the facts are captured
regardless of how they are expressed in the text. The semantic expansion module 400
expands the extracted facts.
[0055] The ic expansion s 400 that takes the extracted sentences and s
as input 402 and tokenizes 404 the text of their components (e.g., of the subject, predicate, or
object) into le-word tokens whenever valid. The multi-word tokenization 404
determines the permissible combination of words to preserve the original meaning because
the meaning of each separate word might be ent from the meaning of the multi-word
combination. This is done by looking up candidate multi-word combinations in a domainspecific
semantic corpus, ontology, dictionary or thesaurus 406. An example of such an
external semantic corpus 406 may be built by analyzing large text collections or other
(domain-specific) ontologies that are manually curated to control the expansion of tokens.
Each component of the extracted triples and sentences (subjects, predicates,
objects, and multi-word tokens) are then expanded 408 using the same or different domainspecific
corpus 410 to produce synonyms, hypernyms and other similar words (expanded
tokens) 412. These expanded facts and sentences may then be indexed to allow search and
analytics on this ed data.
In an online or ime phase, shown generally in Fig. 6, the present system and
method can be used to find documents that are similar to a particular reference document or
snippet of text. Given the input reference text 602, the fact extraction 300 produces a set of
triples present in the reference text as described in Fig. 3 which are fed to the semantic
expansion s 400 to find related terms, just as with the ingested documents as described
above with reference to Fig.4. The expanded facts 412 are then used to search 604 in the pre-
built index 606, and the results of the search may then be aggregated to filter, rank and score
608 the retrieved nts and then the results 610 are returned accordingly.
Figs. 7-10 illustrate an exemplary application of the t system and method.
In Fig. 7, a user may select a phrase of interest 702 from a reference document (“Air France
jet that overran the runway and caught fire at Pearson ational Airport”) and be
presented with a list of result documents 704 that are similar to the selected text, the
similarity being determined by a comparison of the extracted and expanded facts from the
nce text and the potentially relevant, previously ingested documents. The search may
be integrated into a browser extension to allow for seamless integration with a user’s
research workflow without interrupting the current context. For example, a user may
highlight the text of st in their browser window and click on a browser extension icon
706 to cause a similar result nts to be displayed in an extension window 708 ranked
by their relevance.
In the example shown in Fig. 7, the selected text 702 may be processed to extract
the following triples:
t Predicate Object
Air France jet overrun runway
Air France jet catch fire
Air France jet catch fire at Pearson International
In an exemplary expansion process, the tokens in the extracted triples may be
normalized to their base forms using stemming and lemmatization techniques (e.g., “caught”
is d to “catch”). The tokens of each component of the triples are then expanded
semantically using the same corpus that was used in the offline process. Taking the second
triple as an example, the triple object “fire” is expanded to [“ignite”, ”, “explosion”,
“gunfire”, “machine_gun”, … ] and the predicate “catch” is expanded to [“capture”, “find”,
“chase”, “bait”, “arrest”, “stop”, … ]. These terms are grouped according to their relation
to the original tokens.
[0061] Given the extracted triples and sentences and their expanded tokens, the next step
is the semantic similarity calculation. The expanded s are used to query the pre-built
index to find other similar triples in the index. Different fields of a triple and its expansions
are used in multiple queries with different weights, which weights may be customizable by
the user or may be adaptively set based on current or prior use of the particular user or of a
group of (or all) users. The retrieved triples may be ed according to which fields
matched and how similar they are. Again, the weighting may be customizable by the user or
may be adaptively set based on current or prior use of the particular user or of a group of (or
all) users. The results are then aggregated and may be ranked according to multiple factors
including their relevance scores and weights of the matched fields. This cumulative relevance
score may be used to rank the retrieved case documents.
In one particular non-limiting example, the triples extracted and/or expanded from
the reference text (reference triples) are compared to indexed triples that were previously
extracted and/or expanded from the cataloged library of potential result documents (result
triples) and a similarity score is tabulated between pairs of similar triples. For example,
reference triple A may be determined to be 30% similar to result triple Y and 80% similar to
result triple Z. Next, all result documents containing result triple Y or Z are identified and a
similarity score for each result document is calculated based on the presence and/or
prevalence of result triples Y and/or Z in the result documents. If more than one reference
triple is extracted and/or expanded from the reference text, result nts are again
identified and scored in a like n for each reference triple and nt similarity
scores may be aggregated for all reference triples. The aggregated document similarity scores
may be used to rank and/or filter the result documents returned to the user.
[0063] User-settable weights for the similarity scoring include but are not limited to the
semantic rity between the different components of the facts that are extracted from a
reference and a library document, the ce of the facts in a reference and a library
document and how much they agree on, the semantic similarity between sentences in a
reference and a y document, as well as other metadata that describe the reference or the
library document such as their topics and references to other nts and/or authorities.
As shown in Fig. 7, the retrieved result documents may be displayed by the
browser ion as a list ordered according to their relevance scores. The user can expand a
particular nt listing 710 to show the reasoning for the inclusion of this nt in
the results, i.e., explain what makes the document similar to the ed text by highlighting
the similar sentences 712 that contain related facts.
Fig. 8 shows a list of result documents. As discussed above, users are provided
with the functionality to expand a nt item to explain why it is deemed similar to the
highlighted reference text. Matching sentences from both the ed reference text 702 and
the result nt 712 may be highlighted in ent colors.
For example, Fig. 8 shows that the highlighted sentence “Air France jet overran
the runway and caught fire at n ational Airport” 702 is similar to the two
sentences “On August 2, 2005 an Air France flight landed in a severe thunderstorm at
Toronto's Pearson International Airport.” and “It overshot the runway, pitched into a ravine,
and burst into flames.” 712 The first sentence is related to the fact that the case discusses an
Air France flight that landed in Toronto Pearson International Airport, while the second
sentence is related to the fact that the aircraft overran the runway and was consumed by fire.
The second sentence depicts how the semantic similarity aspect of the presented invention
captures the similarity between “caught fire” and “burst into flames”. The two phrases
describe a similar concept even though they are expressed in different ways.
In another exemplary input method, shown lly in Fig. 9, a researcher may
ctively and dynamically enter or remove nce text 902 while result documents are
concurrently fied and displayed in an adjacent result window 904. For e, as a
researcher enters the facts of a case (or a potential case to be litigated) line by line, the
system shows a list of similar cases that are updated as the researcher enters more details.
Each piece of added information (e.g., word, triplet, sentence, line) may be used to issue new
search queries to refine the search results and re-rank the retrieved cases to better match the
new input. Similarly, if reference text is removed, the search and ranking may be redone at
any al during or after removal to s the results on the remainder of the reference
text.
Fig. 10 shows an updated view of Fig. 9 as the user adds another sentence. The
list of relevant documents 904 is automatically updated to match the new input, which is
reflected in the similarity between the input text and the levant case. This usage allows
for an exploratory and interactive approach of finding relevant documents.
[0069] In another embodiment, the present system and method may be d for
particular use in a context involving a set of core documents and a set of subordinate
documents that relate to and cite the core documents. One such context is present in the legal
field, ing legislative documents such as laws, codes, etc. (core documents) that are
interpreted, applied, argued over, and cited by subordinate documents such as case decisions,
legal briefs, secondary sources, etc. dinate nts). By examining the facts in the
subordinate documents citing the core documents, a map may be built and exploited between
facts (derived from the subordinate documents) and particular ns of the core documents
(e.g., a particular statute).
For example, the present disclosure provides a new system and method for mining
facts from a collection of legal documents to find sets of semantically similar facts that are
most relevant to laws. Facts may be mined pivoted around citations to ent laws and
legislations that are cited in the same legal document in which the facts appear. The present
system and method may be configured to produce a dataset that maps each law to a list of
facts that are sorted according to their relevance to the law and their ncy of mentions in
the cases that cite it.
It is one objective of the t disclosure to use the generated dataset in guiding
the query expansion when searching for documents in a corpus of legal documents. The
dataset is used to restrict and guide the semantic expansion of fact terms to other terms that
are semantically similar to the original terms and are related to the same legislation, i.e., have
similar legal implications.
It is another objective of the present disclosure to utilize the generated dataset to
search for the laws that are most relevant to a specific case based on the facts that are
extracted from the case and querying the generated dataset.
The mining process may be configured to produce a dataset that contains laws and
a set of facts most nt to the laws. This method is focused on the legal domain where
legal documents cite related laws, i.e., the fact mining operation is pivoted around the laws
that are cited across a collection of legal documents. The end goal is to use this dataset to
control and guide the semantic ion of the facts that appear in a search query to other
terms that are both semantically similar and follow the same laws, and accordingly have the
same legal implications. This produces a legislation-aware semantic expansion as opposed to
the general purpose semantic expansion that relies on the linguistic semantics of a term.
Two exemplary applications are described where the generated dataset can be
utilized. However, these e applications do not ass all possible ations of
this technology, but are used as a reference for describing the content of the generated dataset
and how it can power downstream applications.
There are two main types of sources of legal documents: primary s and
secondary sources. y sources include statements of the law, such as court decisions,
statutes, and legislative bills. Secondary sources are materials that interpret a legislation or a
statute, explain or discuss legal issues, or analyze the laws. Examples of secondary s
are law reviews, legal news, books about law, encyclopedias, and legal memoranda. They
provide extensive citations to primary sources and give summaries and conclusions about
different legal issues.
Laws and statutes describe the legislation relating to a particular subject matter
and they are reted and applied by courts and judges as they rule in particular factual
ios. The text of a ation itself states some rules that should be followed or should
not be broken. When a legal document (e.g., a case decision or a ndum) cites a
statute, it is because there is a legal issue that is relevant to the rules of the cited statute. The
documents that cite a specific legislation y contain facts that are related to that
legislation.
[0077] The present invention may be configured to t and mine facts from the legal
documents that cite legislations in order to find facts that appear frequently in these
documents and use this as an identifier of a set of legislation-related facts that are relevant to
a particular legislation.
Fig. 11 is a schematic diagram illustrating an exemplary overview of the process
that generates a target dataset (i.e., Legislation-Related Facts 1102). From a high-level, the
process is divided into extraction 1104 and fact mining 1106. The legal database 1108
contains a tion of legal nts of different types (e.g., legal memoranda,
encyclopedias, and cases) and is also used to store the citations between documents. The
facts database 1110 stores the facts that are extracted from the documents, and the facts are
also indexed in a facts index 1112. Figs. 16 and 17 explain how downstream applications
utilize this dataset.
The fact mining process may be configured to run in an e phase to generate
the target dataset of legislations and relevant facts. Of course, as bed with reference to
the embodiment of Fig. 1, such “offline” processes may be conducted at any time, including
during and after a user invokes the system to begin a search.
The extraction s runs on the ingested legal documents that are stored in the
Legal DB 1108. The goal of the extraction process that is depicted in Fig. 12 is two-fold:
identifying citations of laws in the documents and ting facts from the text of the
documents.
[0081] The citation extraction process 1202 fies mentions of laws, statutes, and
legislations in general. For example, the system may be configured to employ one or more
Natural Language sing tools that combine expert-defined rules with machine learning
techniques to detect mentions of laws (citations) in the text. Optionally, there is a humanbased
post-processing phase that is done by experienced content s to verify the
correctness of the extracted content and generate high quality data. Facts may also be
extracted 1300 as described below with reference to Fig. 13 and the extraction results may be
populated in the database. The extracted facts may be semantically expanded 1400 as
described below with reference to Fig. 14 and the semantically expanded facts may be
indexed 1204 in an inverted index 1112 to enable efficient search.
Fig. 13 describes an exemplary flow of a fact extraction process 1300. The text
body 1302 of a document is extracted, pre-processed, and cleaned 1304 (e.g., to remove tags
and headers) in preparation for extraction. The text is split into sentences 1306. Using a triple
extraction module 1308, facts in the form of triples are ted from sentences, where each
ce can produce multiples triples. The triples are in the format (subject, predicate,
object). These triples are stored in a database 1110 for further analysis and to in the
provenance of facts.
To further explain the output of the fact extraction process, consider the following
snippets of text that are retrieved from multiple legal documents including court ons
and legal memoranda. Shown below is a sample output of the fact extraction results and later
refer to the extracted triples to explain the mining process. Each table contains the sed
t of text and the triples (subject, predicate, object) that were extracted from it. The left
column includes an ID of the snippet and IDs of the extracted s to refer to them later.
S1 “The plaintiff was a ger on the motorcycle driven by her husband,
the defendant, when the motorcycle collided with a deer.”
t1 plaintiff be passenger
t2 plaintiff be a passenger on motorcycle
t3 motorcycle drive by she husband
t4 motorcycle collide with deer
S2 “There was no traffic in the area when the vehicle hit the moose”
t5 there be no traffic
t6 there be no traffic in the area
t7 vehicle hit moose
S3 “The truck admittedly struck a deer”
t8 truck strike deer
S4 “The left front corner of the truck struck the deer,
propelling it towards the west shoulder.”
t9 truck have left front corner
t10 truck strike deer
t11 left front corner of the truck strike deer
The tokens in the extracted triples may be normalized to their base forms using
stemming and lemmatization techniques (e.g., “struck” is changed to “strike”).
The semantic expansion module expands the extracted s. Fig. 14 describes in
more detail an ary process 1400 of expanding facts 1402 ically. The multiword
tokenization 1404 determines the correct combination of words to preserve their
meaning because the meaning of each separate word might be different from the meaning of
the multi-word combination. This may be done by looking up ate multi-word
combinations in a domain-specific semantic corpus, ontology, dictionary or thesaurus 1406.
Such an external semantic corpus may be built by analyzing large text collections or other
n-specific) ontologies that are ly curated to control the expansion of tokens.
Each component of the extracted triples and sentences (subjects, predicates, objects, and
multi-word tokens) may then be expanded 1408 using the same or different domain-specific
corpus 1410 to produce synonyms, hypernyms and other r words (expanded tokens)
1412. These expanded facts and sentences are then indexed to allow search and ics on
this data.
After preprocessing all nts to identify citations of legislation or other
primary sources, extract fact triples and, index facts, the mining process may be applied to
the extracted and indexed data. The fact mining module may be configured to implement
nt itemset mining algorithms, for example where a database transaction that contains
items corresponds to a legal document that contains facts and the items correspond to
extracted facts. However, the goal is to group semantically similar facts together as a single
item called a fact group. Therefore, one may choose not to rely on mere equality between
facts. Instead of calculating the frequency of equal (identical) facts, one may calculate the
support of a fact group. This requires constructing fact groups that contain semantically
similar facts.
In order to mine facts that are related to a particular legislation, simple scoping
1502 and filtering 1504 processes may be d first to identify facts that were extracted
from the legal documents that cite the particular legislation. This limits the set of facts to
those nt to a user’s current line of inquiry. In the example discussed herein and with
respect to the s, it is assumed that all extracted facts are relevant for the mining
process.
[0088] The process of fact mining (shown generally in Fig. 15) may include grouping
facts 1506 into groups that contain semantically r facts. Comparing facts to one another
may not scale. Therefore, a facts index 1508 may be used to find facts that are most similar
1510 to a particular fact. As a part of the fact grouping process 1506, the input facts to be
grouped may be scanned. For each fact, a check may be conducted to determine if there is a
fact group that is already constructed and contains that fact. If no matching groups are found,
a search may be conducted of the facts index to find the most semantically similar facts
based on the terms in the al fact and the semantically expanded and indexed terms in
the facts index 1508. A fact group may then be ucted from the returned results for all
the facts that have a relevance score that is above a user-defined threshold. It is possible that
this grouping mechanism may produce redundant groups, in which case ant groups
that have substantially common facts may be merged.
Continuing on the present example, each extracted fact from t1 to t11 may be
examined to search for the most relevant facts, constructing a fact group from the retrieved
results, unless the fact is already used in one of the pre-constructed fact groups. For example,
using t1 and t4 as queries, the following two fact groups FG1 and FG2 may constructed:
FG1 plaintiff be passenger
plaintiff be a ger on motorcycle
FG2 motorcycle collide with deer
e hit moose
truck strike deer
The next step is computing the support 1512 for each fact group. The original
facts may be scanned again, and the support (frequency of mentions) of all the fact groups
that the current fact belongs to maybe incremented again. In the given example, the support
for FG1 is 2 since it will be matched by {t1, t2}, and the support for FG2 is 5 since it be
matched by {t4, t7, t8, t10, t11}. Therefore, FG2 has the highest frequency among the
ucted fact groups.
The generated dataset (legislation-related facts) 1514 can be used to support
multiple applications. One target application is performing a legislation-aware semantic
expansion. A user might run a search query that ns facts, and the goal is to find cases
that have r facts. A part of the s is to semantically expand the facts in order to
match more relevant cases. However, when expanding facts, the expansion must be aware of
the legislation. Instead of using general-purpose ontologies to find semantically similar
terms, the ation-related facts dataset may be used.
[0092] An exemplary process of semantically expanding fact terms is described generally
in Fig. 16. It starts by extracting 1602 facts 1604 from the search query (input text) 1606,
which are used as queries 1608 to a dataset of legislation-related facts 1610. The goal is to
retrieve fact groups 1612 to which the search query facts (input facts) 1604 belong. Then, the
facts comparison and expansion module 1614 may be configured to compare the input facts
1604 with the matched fact groups 1612 in order to produce other facts 1616 that are
semantically similar. The facts comparison and expansion module 1614 compares the
components of the input fact (subject, predicate, object) 1604 against the ents of each
fact in the matched fact groups 1612. After finding most similar facts (or identical facts if
available), the module 1614 finds other facts from the same fact groups and expands each
component separately, producing other similar facts 1616.
As an example, assume that the search query is “Plaintiff’s car struck a moose on
the y”. One triple that is extracted from this query is (Plaintiff’s car, strike, moose).
When matched t the fact groups in a legislation-related fact dataset, FG2 is retrieved as
the most relevant Fact Group. The Facts ison and Expansion module compares the
query triple to other triples within FG2, and expands “car” to [“car”, “vehicle”, “truck”,
“motorcycle”] and s “moose” to [“moose”, “deer”]. These form the terms in the new
search queries that will be used instead of the terms in the original search query. This
restriction of expanded terms based on the legislation-related fact t has a significant
legal implications since “moose” and “deer” are considered wildlife and do not have owners,
as opposed to “cow” or “horse” which have other legal implications. A general-purpose
semantic expansion tool cannot make this distinction.
Another application that utilizes a ation-related fact dataset is finding
relevant laws and statutes that apply to an input fact scenario. Fig. 17 depicts the high-level
flowchart of this s. Given an input text 1702, the fact extraction module 1704 extracts
facts from the text. The facts are used as queries 1706 to the legislation-related facts database
1708 in order to find the most relevant fact groups. The resulting fact groups from using each
fact as a query are aggregated in order to find laws that are holistically most relevant to the
set of extracted facts 1710. This application is useful for legal researchers who need to know
which laws are most relevant to a particular factual scenario and use these laws and statutes
to support their arguments.
Following up on the same example query discussed above, the extracted triple
(Plaintiff’s car, strike, moose) s FG2, which has a high support among the cases that
discuss hitting a wildlife animal on the highway. These cases usually cite the Highway
Traffic Act, RSNL 1990, c H-3 that is related to driving under the speed limit.
An Exemplary System
i. Factual Similarity System Controller
Fig. 5 shows a block diagram illustrating ments of a Factual Similarity
System controller. In this embodiment, the Factual Similarity System controller 501 may
serve to aggregate, process, store, search, serve, identify, instruct, generate, match, and/or
facilitate interactions with a computer, and/or other related data.
Typically, users, which may be people and/or other s, may engage
information technology systems (e.g., ers) to facilitate information processing. In
turn, computers employ processors to process information; such sors 503 may be
ed to as central processing units (CPU). One form of processor is referred to as a
microprocessor. CPUs use communicative ts to pass binary encoded signals acting as
instructions to enable various operations. These instructions may be ional and/or data
instructions containing and/or referencing other ctions and data in various processor
accessible and operable areas of memory 529 (e.g., registers, cache , random access
memory, etc.). Such communicative instructions may be stored and/or transmitted in batches
(e.g., batches of instructions) as programs and/or data components to facilitate desired
operations. These stored instruction codes, e.g., programs, may engage the CPU circuit
components and other motherboard and/or system components to perform desired operations.
One type of m is a computer operating system, which, may be executed by CPU on a
computer; the operating system enables and facilitates users to access and operate computer
information logy and ces. Some resources that may be employed in information
technology systems e: input and output mechanisms through which data may pass into
and out of a computer; memory storage into which data may be saved; and processors by
which information may be processed. These information technology systems may be used to
t data for later retrieval, analysis, and manipulation, which may be facilitated through a
database program. These information technology systems provide interfaces that allow users
to access and operate various system components.
In one embodiment, the Factual Similarity System controller 501 may be
connected to and/or communicate with entities such as, but not limited to: one or more users
from user input devices 511; peripheral devices 512; an optional cryptographic processor
device 528; and/or a communications network 513.
ks are commonly t to comprise the interconnection and
interoperation of clients, servers, and intermediary nodes in a graph topology. It should be
noted that the term “server” as used throughout this application refers generally to a
computer, other device, m, or ation thereof that processes and responds to the
requests of remote users across a communications network. Servers serve their information to
requesting “clients.” The term “client” as used herein refers generally to a computer,
program, other device, user and/or combination thereof that is capable of processing and
making requests and ing and processing any responses from servers across a
communications network. A computer, other , program, or combination thereof that
facilitates, processes information and requests, and/or furthers the passage of information
from a source user to a destination user is ly referred to as a “node.” Networks are
generally thought to facilitate the transfer of information from source points to destinations.
A node specifically tasked with furthering the passage of information from a source to a
destination is ly called a “router.” There are many forms of networks such as Local
Area Networks (LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks
(WLANs), etc. For e, the Internet is generally ed as being an interconnection of
a multitude of networks whereby remote clients and s may access and interoperate with
one another.
[0100] The l Similarity System controller 501 may be based on computer systems
that may comprise, but are not d to, components such as: a computer systemization 502
connected to memory 529.
ii. Computer Systemization
A computer systemization 502 may comprise a clock 530, central processing unit
(“CPU(s)” and/or “processor(s)” (these terms are used interchangeable throughout the
disclosure unless noted to the contrary)) 503, a memory 529 (e.g., a read only memory
(ROM) 506, a random access memory (RAM) 505, etc.), and/or an interface bus 507, and
most frequently, although not necessarily, are all onnected and/or communicating
through a system bus 504 on one or more (mother)board(s) 502 having conductive and/or
otherwise ortive circuit pathways through which instructions (e.g., binary encoded
s) may travel to effectuate communications, operations, storage, etc. The computer
systemization may be connected to a power source 586; e.g., optionally the power source
may be internal. Optionally, a cryptographic processor 526 and/or transceivers (e.g., ICs) 574
may be ted to the system bus. In another embodiment, the cryptographic processor
and/or transceivers may be connected as either internal and/or external peripheral devices
512 via the interface bus I/O. In turn, the transceivers may be connected to antenna(s) 575,
thereby effectuating wireless transmission and reception of s ication and/or
sensor protocols; for example the antenna(s) may connect to: a Texas Instruments WiLink
WL1283 eiver chip (e.g., providing 802.11n, Bluetooth 3.0, FM, global positioning
system (GPS) (thereby allowing l Similarity System controller to determine its
location)); Broadcom BCM4329FKUBG transceiver chip (e.g., providing n, Bluetooth
2.1 + EDR, FM, etc.); a Broadcom BCM4750IUB8 receiver chip (e.g., GPS); an Infineon
Technologies X-Gold 618-PMB9800 (e.g., providing 2G/3G HSDPA/HSUPA
communications); and/or the like. The system clock typically has a l oscillator and
tes a base signal through the computer systemization’s circuit pathways. The clock is
typically coupled to the system bus and s clock multipliers that will increase or
decrease the base operating frequency for other components interconnected in the computer
systemization. The clock and various components in a computer systemization drive signals
ing information throughout the system. Such transmission and reception of
instructions embodying information throughout a computer ization may be commonly
referred to as communications. These icative instructions may further be
transmitted, received, and the cause of return and/or reply communications beyond the
instant computer systemization to: communications networks, input devices, other computer
systemizations, peripheral devices, and/or the like. It should be understood that in alternative
embodiments, any of the above components may be connected ly to one another,
connected to the CPU, and/or organized in numerous variations employed as exemplified by
various computer systems.
The CPU comprises at least one high-speed data processor adequate to execute
program components for executing user and/or system-generated requests. Often, the
sors themselves will incorporate various specialized processing units, such as, but not
limited to: integrated system (bus) llers, memory management control units, ng
point units, and even specialized processing sub-units like cs processing units, digital
signal processing units, and/or the like. Additionally, processors may include internal fast
access addressable memory, and be capable of mapping and addressing memory 529 beyond
the processor itself; internal memory may include, but is not limited to: fast registers, various
levels of cache memory (e.g., level 1, 2, 3, etc.), RAM, etc. The processor may access this
memory h the use of a memory s space that is ible via instruction address,
which the processor can construct and decode allowing it to access a circuit path to a specific
memory address space having a memory state. The CPU may be a microprocessor such as:
AMD’s Athlon, Duron and/or Opteron; ARM’s application, embedded and secure
processors; IBM and/or Motorola’s DragonBall and PowerPC; IBM’s and Sony’s Cell
processor; Intel’s Celeron, Core (2) Duo, Itanium, Pentium, Xeon, and/or XScale; and/or the
like sor(s). The CPU interacts with memory through instruction passing through
tive and/or transportive conduits (e.g., (printed) electronic and/or optic circuits) to
e stored instructions (i.e., program code) according to tional data processing
techniques. Such instruction passing tates communication within the Factual Similarity
System controller and beyond h s interfaces. Should processing requirements
dictate a greater amount speed and/or capacity, distributed processors (e.g., Distributed
Factual Similarity System), mainframe, multi-core, parallel, and/or super-computer
architectures may similarly be employed. Alternatively, should deployment requirements
dictate greater portability, smaller Personal Digital Assistants (PDAs) may be employed.
Depending on the ular implementation, features of the Factual Similarity
System may be achieved by implementing a microcontroller such as CAST’s R8051XC2
microcontroller; Intel’s MCS 51 (i.e., 8051 microcontroller); and/or the like. Also, to
ent certain features of the Factual rity System, some feature implementations
may rely on embedded components, such as: Application-Specific Integrated Circuit
("ASIC"), Digital Signal Processing ("DSP"), Field Programmable Gate Array ("FPGA"),
and/or the like embedded technology. For e, any of the Factual Similarity System
component collection (distributed or otherwise) and/or features may be implemented via the
microprocessor and/or via embedded components; e.g., via ASIC, coprocessor, DSP, FPGA,
and/or the like. Alternately, some implementations of the Factual Similarity System may be
implemented with embedded components that are configured and used to achieve a variety of
features or signal processing.
Depending on the particular implementation, the embedded components may
include software solutions, hardware solutions, and/or some combination of both
hardware/software ons. For example, Factual Similarity System features discussed
herein may be achieved through implementing FPGAs, which are a semiconductor devices
containing programmable logic ents called "logic blocks", and programmable
interconnects, such as the high performance FPGA Virtex series and/or the low cost Spartan
series manufactured by . Logic blocks and interconnects can be programmed by the
customer or designer, after the FPGA is manufactured, to implement any of the Factual
Similarity System es. A hierarchy of programmable interconnects allow logic blocks to
be interconnected as needed by the Factual Similarity System designer/administrator,
somewhat like a one-chip programmable breadboard. An FPGA's logic blocks can be
programmed to perform the operation of basic logic gates such as AND, and XOR, or more
complex combinational operators such as decoders or mathematical operations. In most
FPGAs, the logic blocks also include memory elements, which may be t flip-flops or
more complete blocks of memory. In some circumstances, the Factual rity System may
be developed on r FPGAs and then migrated into a fixed version that more resembles
ASIC implementations. Alternate or coordinating implementations may migrate Factual
Similarity System ller features to a final ASIC instead of or in addition to FPGAs.
Depending on the implementation all of the aforementioned ed components and
microprocessors may be ered the “CPU” and/or “processor” for the Factual Similarity
System.
iii. Power Source
The power source 586 may be of any standard form for powering small electronic
circuit board devices such as the following power cells: alkaline, lithium e, lithium
ion, lithium polymer, nickel cadmium, solar cells, and/or the like. Other types of AC or DC
power sources may be used as well. In the case of solar cells, in one embodiment, the case
provides an aperture through which the solar cell may e photonic energy. The power
cell 586 is connected to at least one of the interconnected subsequent components of the
Factual Similarity System thereby providing an electric current to all subsequent
components. In one e, the power source 586 is connected to the system bus
ent 504. In an alternative embodiment, an e power source 586 is provided
through a connection across the I/O 508 interface. For example, a USB and/or IEEE 1394
connection carries both data and power across the connection and is therefore a le
source of power.
iv. Interface Adapters
Interface bus(ses) 507 may accept, connect, and/or communicate to a number of
interface adapters, tionally although not necessarily in the form of adapter cards, such
as but not limited to: input output interfaces (I/O) 508, storage interfaces 509, network
interfaces 510, and/or the like. Optionally, cryptographic processor interfaces 527 similarly
may be connected to the interface bus. The interface bus provides for the communications of
interface adapters with one another as well as with other components of the er
systemization. Interface adapters are adapted for a compatible interface bus. Interface
adapters conventionally connect to the interface bus via a slot architecture. Conventional slot
architectures may be ed, such as, but not limited to: Accelerated Graphics Port
(AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel
Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI
Express, Personal Computer Memory Card International Association (PCMCIA), and/or the
like.
Storage interfaces 509 may accept, communicate, and/or connect to a number of
storage devices such as, but not limited to: storage s 514, ble disc devices,
and/or the like. Storage interfaces may employ connection ols such as, but not limited
to: (Ultra) (Serial) ed Technology Attachment (Packet Interface) ((Ultra) (Serial)
ATA(PI)), (Enhanced) Integrated Drive Electronics E), Institute of Electrical and
Electronics Engineers (IEEE) 1394, fiber channel, Small Computer Systems Interface
(SCSI), Universal Serial Bus (USB), and/or the like.
Network interfaces 510 may accept, communicate, and/or connect to a
communications network 513. Through a communications network 513, the Factual
Similarity System ller is accessible through remote clients 533b (e.g., computers with
web browsers) by users 533a. Network interfaces may employ connection ols such as,
but not limited to: direct connect, Ethernet (thick, thin, twisted pair 10/100/1000 Base T,
and/or the like), Token Ring, wireless connection such as IEEE 802.11a-x, and/or the like.
Should processing requirements dictate a greater amount speed and/or capacity, distributed
network controllers (e.g., Distributed Factual rity System), ectures may similarly
be employed to pool, load balance, and/or otherwise increase the communicative bandwidth
ed by the Factual Similarity System controller. A communications network may be any
one and/or the combination of the following: a direct interconnection; the Internet; a Local
Area Network (LAN); a Metropolitan Area Network (MAN); an Operating Missions as
Nodes on the Internet (OMNI); a secured custom connection; a Wide Area Network (WAN);
a wireless network (e.g., employing protocols such as, but not limited to a Wireless
Application Protocol (WAP), I-mode, and/or the like); and/or the like. A network interface
may be regarded as a specialized form of an input output ace. r, multiple k
interfaces 510 may be used to engage with various communications network types 513. For
example, multiple network aces may be employed to allow for the communication over
broadcast, multicast, and/or unicast networks.
Input Output interfaces (I/O) 508 may accept, communicate, and/or connect to
user input s 511, peripheral devices 512, graphic processor devices 528, and/or
the like. I/O may employ connection protocols such as, but not limited to: audio: analog,
digital, monaural, RCA, stereo, and/or the like; data: Apple Desktop Bus (ADB), IEEE
1394a-b, serial, sal serial bus (USB); infrared; joystick; keyboard; midi; optical; PC
AT; PS/2; parallel; radio; video interface: Apple Desktop Connector (ADC), BNC, coaxial,
component, composite, digital, Digital Visual Interface (DVI), high-definition multimedia
interface (HDMI), RCA, RF antennae, o, VGA, and/or the like; wireless transceivers:
802.11a/b/g/n/x; Bluetooth; cellular (e.g., code division multiple access (CDMA), high speed
packet access (HSPA(+)), high-speed downlink packet access (HSDPA), global system for
mobile communications (GSM), long term evolution (LTE), WiMax, etc.); and/or the like.
One typical output device may include a video display, which typically comprises a e
Ray Tube (CRT) or Liquid Crystal Display (LCD) based monitor with an interface (e.g., DVI
try and cable) that accepts signals from a video interface, may be used. The video
interface composites information generated by a computer systemization and generates video
signals based on the composited information in a video memory frame. Another output
device is a television set, which accepts signals from a video interface. Typically, the video
interface es the composited video information through a video connection interface
that accepts a video display interface (e.g., an RCA composite video connector accepting an
RCA composite video cable; a DVI connector accepting a DVI display cable, etc.).
User input devices 511 often are a type of peripheral device 512 (see below) and
may e: card readers, dongles, finger print readers, gloves, graphics s, joysticks,
keyboards, microphones, mouse (mice), remote controls, retina readers, touch screens (e.g.,
capacitive, resistive, etc.), trackballs, trackpads, sensors (e.g., accelerometers, ambient light,
GPS, gyroscopes, ity, etc.), styluses, and/or the like.
eral devices 512 may be connected and/or communicate to I/O and/or other
facilities of the like such as network aces, storage aces, directly to the ace
bus, system bus, the CPU, and/or the like. Peripheral devices may be external, internal and/or
part of the Factual Similarity System controller. Peripheral devices may include: antenna,
audio devices (e.g., line-in, line-out, microphone input, speakers, etc.), cameras (e.g., still,
video, , etc.), dongles (e.g., for copy protection, ensuring secure transactions with a
digital ure, and/or the like), external processors (for added capabilities; e.g., crypto
devices 528), force-feedback devices (e.g., vibrating motors), k interfaces, printers,
scanners, storage s, transceivers (e.g., cellular, GPS, etc.), video devices (e.g., goggles,
monitors, etc.), video sources, visors, and/or the like. eral devices often include types
of input devices (e.g., cameras).
It should be noted that although user input devices and peripheral devices may be
employed, the Factual Similarity System controller may be embodied as an embedded,
dedicated, and/or monitor-less (i.e., headless) device, wherein access would be provided over
a network interface connection.
Cryptographic units such as, but not limited to, microcontrollers, processors 526,
interfaces 527, and/or devices 528 may be attached, and/or communicate with the Factual
Similarity System controller. A MC68HC16 microcontroller, manufactured by Motorola Inc.,
may be used for and/or within cryptographic units. The MC68HC16 microcontroller utilizes
a 16-bit multiply-and-accumulate instruction in the 16 MHz uration and requires less
than one second to perform a 512-bit RSA private key operation. Cryptographic units support
the authentication of communications from interacting agents, as well as allowing for
anonymous transactions. Cryptographic units may also be configured as part of the CPU.
Equivalent ontrollers and/or sors may also be used. Other commercially
available specialized graphic processors include: Broadcom’s CryptoNetX and other
Security Processors; nCipher’s nShield; SafeNet’s Luna PCI (e.g., 7100) ; Semaphore
Communications’ 40 MHz Roadrunner 184; Sun’s Cryptographic Accelerators (e.g.,
Accelerator 6000 PCIe Board, rator 500 Daughtercard); Via Nano Processor (e.g.,
L2100, L2200, U2400) line, which is e of performing 500+ MB/s of cryptographic
ctions; VLSI Technology’s 33 MHz 6868; and/or the like.
v. Memory
[0114] Generally, any mechanization and/or embodiment allowing a processor to affect
the e and/or retrieval of information is regarded as memory 529. However, memory is
a fungible technology and resource, thus, any number of memory embodiments may be
employed in lieu of or in concert with one another. It is to be understood that the Factual
Similarity System controller and/or a computer systemization may employ various forms of
memory 529. For example, a computer systemization may be configured wherein the
operation of on-chip CPU memory (e.g., registers), RAM, ROM, and any other storage
devices are provided by a paper punch tape or paper punch card mechanism; however, such
an embodiment would result in an extremely slow rate of ion. In a typical
configuration, memory 529 will include ROM 506, RAM 505, and a storage device 514. A
storage device 514 may be any conventional computer system storage. Storage s may
include a drum; a (fixed and/or ble) magnetic disk drive; a magneto-optical drive; an
optical drive (i.e., y, CD M/Recordable (R)/ReWritable (RW), DVD R/RW,
HD DVD R/RW etc.); an array of devices (e.g., Redundant Array of Independent Disks
(RAID)); solid state memory devices (USB memory, solid state drives (SSD), etc.); other
sor-readable storage mediums; and/or other s of the like. Thus, a computer
systemization generally requires and makes use of memory.
vi. Component Collection
The memory 529 may n a tion of program and/or database
components and/or data such as, but not limited to: operating system component(s) 515
(operating system); information server component(s) 516 mation server); user interface
ent(s) 517 (user interface); Web browser component(s) 518 (Web browser);
database(s) 519; mail server component(s) 521; mail client component(s) 522; cryptographic
server component(s) 520 (cryptographic server); the Factual Similarity System component(s)
535; the fact extraction component 541; the t expansion component 542, the web
e component 543; the browser extension component 544; the semantic similarity
calculation component 545; the ranking component 546; the index searching component 547
and/or the like (i.e., collectively a component collection). These components may be stored
and accessed from the storage devices and/or from storage devices accessible through an
interface bus. gh non-conventional program components such as those in the
component collection, typically, are stored in a local storage device 514, they may also be
loaded and/or stored in memory such as: peripheral devices, RAM, remote storage facilities
through a communications network, ROM, various forms of memory, and/or the like. Also,
while the components are described separately herein, it will be understood that they may be
combined and/or subdivided in any compatible manner.
vii. Operating System
The operating system component 515 is an executable m component
facilitating the operation of the Factual Similarity System ller. Typically, the ing
system facilitates access of I/O, network aces, peripheral s, storage devices,
and/or the like. The operating system may be a highly fault tolerant, scalable, and secure
system such as: Apple Macintosh OS X (Server); AT&T Plan 9; Be OS; Unix and Unix-like
system distributions (such as AT&T’s UNIX; Berkley Software Distribution (BSD)
ions such as FreeBSD, NetBSD, OpenBSD, and/or the like; Linux distributions such as
Red Hat, Ubuntu, and/or the like); and/or the like operating systems. However, more limited
and/or less secure operating systems also may be employed such as Apple osh OS,
IBM OS/2, Microsoft DOS, Microsoft Windows
/8/7/2003/2000/98/95/3.1/CE/Millennium/NT/Vista/XP (Server), Palm OS, and/or the like.
An operating system may communicate to and/or with other components in a component
collection, including itself, and/or the like. Most frequently, the operating system
communicates with other program components, user interfaces, and/or the like. For example,
the operating system may contain, communicate, generate, obtain, and/or provide program
component, , user, and/or data communications, requests, and/or responses. The
operating system, once ed by the CPU, may enable the ction with
communications networks, data, I/O, peripheral devices, program components, memory, user
input devices, and/or the like. The operating system may provide communications protocols
that allow the Factual Similarity System controller to communicate with other entities
through a ications network 513. Various ication protocols may be used by
the Factual rity System controller as a subcarrier ort mechanism for interaction,
such as, but not limited to: multicast, TCP/IP, UDP, unicast, and/or the like.
viii. Information Server
An information server component 516 is a stored program component that is
executed by a CPU. The information server may be a conventional Internet ation
server such as, but not limited to Apache Software Foundation’s , Microsoft’s
Internet Information Server, and/or the like. The information server may allow for the
ion of program components through facilities such as Active Server Page (ASP),
ActiveX, (ANSI) (Objective-) C (++), C# and/or .NET, Common Gateway Interface (CGI)
scripts, dynamic (D) hypertext markup language (HTML), FLASH, Java, JavaScript,
Practical Extraction Report Language , Hypertext ocessor (PHP), pipes,
Python, wireless application protocol (WAP), WebObjects, and/or the like. The information
server may support secure communications protocols such as, but not limited to, File
er Protocol (FTP); HyperText Transfer Protocol (HTTP); Secure Hypertext Transfer
Protocol ), Secure Socket Layer (SSL), messaging protocols (e.g., America Online
(AOL) Instant Messenger (AIM), Application Exchange (APEX), ICQ, Internet Relay Chat
(IRC), Microsoft Network (MSN) Messenger Service, Presence and Instant Messaging
Protocol (PRIM), Internet Engineering Task Force’s (IETF’s) Session Initiation Protocol
(SIP), SIP for Instant Messaging and Presence Leveraging Extensions (SIMPLE), open
XML-based Extensible Messaging and Presence Protocol (XMPP) (i.e., Jabber or Open
Mobile Alliance’s (OMA’s) Instant Messaging and Presence Service ), Yahoo!
Instant Messenger Service, and/or the like. The ation server provides results in the
form of Web pages to Web browsers, and allows for the manipulated generation of the Web
pages through ction with other program components. After a Domain Name System
(DNS) resolution portion of an HTTP request is resolved to a particular information server,
the information server resolves requests for information at specified locations on the Factual
rity System controller based on the remainder of the HTTP request. For example, a
request such as http://123.124.125.126/myInformation.html might have the IP portion of the
request “123.124.125.126” resolved by a DNS server to an information server at that IP
address; that information server might in turn further parse the http request for the
“/myInformation.html” portion of the request and resolve it to a on in memory
containing the information “myInformation.html.” Additionally, other information serving
protocols may be employed across various ports, e.g., FTP communications across port 21,
and/or the like. An information server may communicate to and/or with other ents in
a component collection, including itself, and/or facilities of the like. Most frequently, the
information server communicates with the Factual Similarity System databases 519,
ing systems, other program components, user interfaces, Web browsers, and/or the
like.
Access to the Factual Similarity System se may be achieved through a
number of database bridge mechanisms such as through scripting languages as enumerated
below (e.g., CGI) and h application communication channels as ated
below (e.g., CORBA, WebObjects, etc.). Any data ts through a Web browser are
parsed through the bridge mechanism into appropriate grammars as required by the Factual
Similarity System. In one embodiment, the information server would e a Web form
accessible by a Web browser. Entries made into supplied fields in the Web form are tagged
as having been entered into the particular fields, and parsed as such. The entered terms are
then passed along with the field tags, which act to instruct the parser to generate queries
directed to appropriate tables and/or fields. In one embodiment, the parser may generate
queries in rd SQL by instantiating a search string with the proper join/select ds
based on the tagged text entries, wherein the resulting command is provided over the bridge
mechanism to the Factual Similarity System as a query. Upon ting query results from
the query, the results are passed over the bridge mechanism, and may be parsed for
formatting and generation of a new results Web page by the bridge mechanism. Such a new
results Web page is then provided to the information server, which may supply it to the
requesting Web browser.
Also, an information server may n, communicate, generate, obtain, and/or
provide program component, system, user, and/or data communications, requests, and/or
responses.
ix. User Interface
Computer interfaces in some respects are similar to automobile operation
aces. Automobile operation interface elements such as steering , gearshifts, and
speedometers facilitate the access, operation, and display of automobile resources, and status.
Computer interaction interface elements such as check boxes, cursors, menus, scrollers, and
windows (collectively and commonly referred to as widgets) similarly facilitate the access,
capabilities, operation, and display of data and computer hardware and ing system
resources, and status. Operation interfaces are commonly called user aces. Graphical
user interfaces (GUIs) such as the Apple Macintosh Operating System’s Aqua, IBM’s OS/2,
Microsoft’s Windows 2000/2003/3.1/95/98/CE/Millennium/NT/XP/Vista/7 (i.e., Aero),
Unix’s X-Windows (e.g., which may include additional Unix graphic interface libraries and
layers such as K Desktop Environment (KDE), mythTV and GNU Network Object Model
Environment (GNOME)), web interface libraries (e.g., ActiveX, AJAX, (D)HTML, FLASH,
Java, JavaScript, etc. interface libraries such as, but not limited to, Dojo, jQuery(UI),
MooTools, Prototype, script.aculo.us, SWFObject, Yahoo! User ace, any of which may
be used and) provide a baseline and means of ing and displaying information
graphically to users.
A user interface component 517 is a stored program ent that is executed
by a CPU. The user interface may be a conventional graphic user interface as provided by,
with, and/or atop operating systems and/or operating environments such as already discussed.
The user ace may allow for the display, execution, interaction, manipulation, and/or
operation of program components and/or system facilities through textual and/or graphical
facilities. The user interface provides a facility through which users may affect, interact,
and/or operate a computer system. A user interface may communicate to and/or with other
components in a component tion, including itself, and/or facilities of the like. Most
frequently, the user interface communicates with operating s, other program
ents, and/or the like. The user interface may contain, communicate, generate, obtain,
and/or provide program component, , user, and/or data communications, requests,
and/or responses.
x. Web Browser
A Web browser component 518 is a stored program component that is ed
by a CPU. The Web browser may be a conventional hypertext g application such as
Microsoft Internet Explorer or Netscape Navigator. Secure Web browsing may be supplied
with 128bit (or r) encryption by way of HTTPS, SSL, and/or the like. Web browsers
allowing for the execution of program components through facilities such as ActiveX,
AJAX, (D)HTML, FLASH, Java, ript, web browser plug-in APIs (e.g., Firefox, Safari
Plug-in, and/or the like APIs), and/or the like. Web browsers and like information access
tools may be integrated into PDAs, cellular telephones, and/or other mobile devices. A Web
browser may communicate to and/or with other ents in a component collection,
including , and/or facilities of the like. Most frequently, the Web r communicates
with information servers, operating systems, integrated program components (e.g., plug-ins),
and/or the like; e.g., it may contain, communicate, generate, obtain, and/or provide program
ent, system, user, and/or data communications, requests, and/or responses. Also, in
place of a Web browser and information server, a combined application may be developed to
perform similar operations of both. The combined application would similarly affect the
obtaining and the provision of information to users, user agents, and/or the like from the
Factual Similarity System enabled nodes. The combined application may be nugatory on
systems employing standard Web browsers.
xi. Mail Server
[0123] A mail server component 521 is a stored program component that is executed by a
CPU 503. The mail server may be a conventional et mail server such as, but not limited
to sendmail, Microsoft Exchange, and/or the like. The mail server may allow for the
execution of program components h facilities such as ASP, X, (ANSI)
(Objective-) C (++), C# and/or .NET, CGI scripts, Java, JavaScript, PERL, PHP, pipes,
Python, WebObjects, and/or the like. The mail server may support ications protocols
such as, but not limited to: Internet message access protocol (IMAP), ing Application
Programming Interface (MAPI)/Microsoft Exchange, post office protocol (POP3), simple
mail transfer protocol (SMTP), and/or the like. The mail server can route, forward, and
process ng and outgoing mail messages that have been sent, relayed and/or otherwise
traversing through and/or to the Factual Similarity System. Mail may also take the form of
messages sent from one Factual Similarity System user to another that is not in the form of
traditional email but is more akin to direct ing or the like conventionally enabled by
social networks.
Access to the Factual Similarity System mail may be achieved through a number
of APIs offered by the dual Web server components and/or the operating system.
Also, a mail server may contain, communicate, generate, obtain, and/or provide
program component, system, user, and/or data communications, requests, information, and/or
xii. Mail Client
[0126] A mail client component 522 is a stored program component that is ed by a
CPU 503. The mail client may be a tional mail viewing application such as Apple
Mail, Microsoft age, Microsoft Outlook, Microsoft Outlook Express, Mozilla,
Thunderbird, and/or the like. Mail clients may support a number of transfer protocols, such
as: IMAP, Microsoft Exchange, POP3, SMTP, and/or the like. A mail client may
icate to and/or with other components in a component collection, ing itself,
and/or facilities of the like. Most frequently, the mail client communicates with mail servers,
operating systems, other mail clients, and/or the like; e.g., it may n, communicate,
te, obtain, and/or provide program component, system, user, and/or data
communications, requests, information, and/or ses. Generally, the mail client provides
a ty to compose and it electronic mail messages.
xiii. Cryptographic Server
A graphic server component 520 is a stored program component that is
executed by a CPU 503, cryptographic processor 526, cryptographic processor interface 527,
cryptographic processor device 528, and/or the like. Cryptographic processor interfaces will
allow for expedition of encryption and/or decryption requests by the cryptographic
component; however, the graphic component, alternatively, may run on a conventional
CPU. The cryptographic component allows for the encryption and/or decryption of provided
data. The cryptographic component allows for both symmetric and asymmetric (e.g., Pretty
Good Protection (PGP)) encryption and/or decryption. The cryptographic component may
employ cryptographic ques such as, but not limited to: digital certificates (e.g., X.509
authentication ork), digital signatures, dual signatures, enveloping, password access
protection, public key management, and/or the like. The cryptographic ent will
facilitate numerous (encryption and/or decryption) security protocols such as, but not limited
to: checksum, Data Encryption Standard (DES), Elliptical Curve Encryption (ECC),
International Data Encryption Algorithm (IDEA), Message Digest 5 (MD5, which is a one
way hash operation), passwords, Rivest Cipher (RC5), Rijndael, RSA (which is an et
encryption and authentication system that uses an algorithm developed in 1977 by Ron
Rivest, Adi Shamir, and Leonard Adleman), Secure Hash thm (SHA), Secure Socket
Layer (SSL), Secure Hypertext Transfer Protocol (HTTPS), and/or the like. Employing such
encryption security protocols, the l Similarity System may encrypt all incoming and/or
outgoing communications and may serve as node within a virtual private network (VPN)
with a wider communications network. The cryptographic component facilitates the process
of “security authorization” whereby access to a ce is inhibited by a security protocol
wherein the cryptographic component effects authorized access to the secured resource. In
addition, the cryptographic component may e unique identifiers of t, e.g.,
employing and MD5 hash to obtain a unique signature for a digital audio file. A
cryptographic component may communicate to and/or with other ents in a
component collection, including itself, and/or facilities of the like. The cryptographic
component supports encryption schemes allowing for the secure transmission of information
across a communications network to enable the Factual Similarity System component to
engage in secure transactions if so desired. The cryptographic component facilitates the
secure accessing of resources on the Factual Similarity System and facilitates the access of
d resources on remote systems; i.e., it may act as a client and/or server of secured
resources. Most frequently, the cryptographic component communicates with information
servers, operating systems, other program ents, and/or the like. The cryptographic
component may contain, communicate, generate, obtain, and/or provide program component,
system, user, and/or data communications, requests, and/or responses.
xiv. The Factual Similarity System Databases
The Factual Similarity System databases component 519 may be embodied in one
se and its stored data, may be embodied in two or more ct databases and their
stored data, or may be partially or wholly embodied in an unstructured . For the
es of simplicity of description, discussion of the Factual Similarity System databases
component 519 herein may refer to such component in the singular tense, however this is not
to be considered as limiting the Factual rity System databases to an embodiment in
which they reside in a single database. The database is a stored program component, which is
executed by the CPU; the stored program component portion configuring the CPU to process
the stored data. The database may be a conventional, fault tolerant, relational, scalable,
secure se such as Oracle or . Relational ses are an extension of a flat file.
Relational ses consist of a series of related tables. The tables are interconnected via a
key field. Use of the key field allows the combination of the tables by indexing against the
key field; i.e., the key fields act as dimensional pivot points for combining information from
various tables. Relationships generally identify links maintained between tables by matching
primary keys. Primary keys represent fields that uniquely fy the rows of a table in a
relational database. More precisely, they ly identify rows of a table on the “one” side
of a one-to-many relationship.
Alternatively, the Factual rity System database may be implemented using
various standard data-structures, such as an array, hash, (linked) list, struct, structured text
file (e.g., XML), table, and/or the like. Such data-structures may be stored in memory and/or
in (structured) files. In another alternative, an object-oriented database may be used, such as
Frontier, ObjectStore, Poet, Zope, and/or the like. Object databases can include a number of
object collections that are grouped and/or linked together by common attributes; they may be
related to other object collections by some common attributes. Object-oriented databases
perform similarly to relational databases with the exception that objects are not just pieces of
data but may have other types of capabilities encapsulated within a given object. If the
Factual Similarity System database is implemented as a data-structure, the use of the Factual
Similarity System se 519 may be integrated into another component such as the
Factual Similarity System component 535. Also, the database may be implemented as a mix
of data structures, objects, and relational structures. Databases may be consolidated and/or
distributed in ess variations h standard data processing techniques. Portions of
databases, e.g., tables, may be exported and/or imported and thus ralized and/or
integrated.
In one embodiment, the database component 519 may e several included
databases or tables 519a-f, examples of which are described above.
In one embodiment, the Factual Similarity System database 519 may interact with
other database systems. For example, employing a distributed database system, queries and
data access by a search Factual Similarity System component may treat the combination of
the Factual Similarity System databases 519, an ated data security layer database as a
single database entity.
In one embodiment, user programs may n s user interface primitives,
which may serve to update the Factual Similarity System. Also, various accounts may require
custom database tables depending upon the environments and the types of clients the Factual
Similarity System may need to serve. It should be noted that any unique fields may be
designated as a key field throughout. In an ative embodiment, these tables have been
decentralized into their own databases and their respective database controllers (i.e.,
individual database controllers for each of the above tables). Employing standard data
processing techniques, one may further distribute the databases over several computer
systemizations and/or storage devices. Similarly, urations of the decentralized database
controllers may be varied by consolidating and/or distributing the various database
components 519a-f. The Factual Similarity System may be configured to keep track of
various settings, inputs, and ters via database controllers.
The Factual Similarity System se may communicate to and/or with other
ents in a component tion, including itself, and/or facilities of the like. Most
frequently, the Factual Similarity System se communicates with the l Similarity
System component, other program components, and/or the like. The database may contain,
retain, and provide information regarding other nodes and data.
xv. The Factual rity Systems
The Factual Similarity System component 535 is a stored program component
that is executed by a CPU. In one embodiment, the Factual Similarity System component
incorporates any and/or all combinations of the aspects of the Factual Similarity System that
was discussed in the previous figures. As such, the Factual Similarity System affects
ing, obtaining and the provision of information, services, transactions, and/or the like
across various communications networks. The features and embodiments of the Factual
Similarity System discussed herein increase network efficiency by reducing data transfer
requirements the use of more efficient data structures and mechanisms for their transfer and
storage. As a consequence, more data may be transferred in less time, and latencies with
regard to transactions, are also reduced. In many cases, such reduction in storage, er
time, bandwidth ements, latencies, etc., will reduce the capacity and structural
infrastructure requirements to support the Factual Similarity System’s features and facilities,
and in many cases reduce the costs, energy consumption/requirements, and extend the life of
Factual Similarity System’s underlying infrastructure; this has the added benefit of making
the Factual Similarity System more reliable. rly, many of the features and mechanisms
are designed to be easier for users to use and access, thereby ning the audience that
may enjoy/employ and exploit the feature sets of the Factual Similarity System; such ease of
use also helps to increase the reliability of the Factual Similarity System. In addition, the
e sets include ened ty as noted via the Cryptographic components 520, 526,
528 and throughout, making access to the features and data more reliable and secure.
The Factual Similarity System component enabling access of information
between nodes may be developed by employing standard development tools and ges
such as, but not limited to: Apache components, Assembly, X, binary executables,
(ANSI) tive-) C (++), C# and/or .NET, database rs, CGI scripts, Java,
JavaScript, mapping tools, procedural and object oriented development tools, PERL, PHP,
Python, shell s, SQL commands, web application server extensions, web development
environments and libraries (e.g., Microsoft’s ActiveX; Adobe AIR, FLEX & FLASH; AJAX;
(D)HTML; Dojo, Java; JavaScript; jQuery(UI); MooTools; Prototype; script.aculo.us;
Simple Object Access Protocol (SOAP); SWFObject; Yahoo! User Interface; and/or the
like), WebObjects, and/or the like. In one embodiment, the Factual Similarity System server
employs a cryptographic server to encrypt and decrypt communications. The Factual
rity System component may communicate to and/or with other components in a
component collection, including itself, and/or facilities of the like. Most frequently, the
Factual Similarity System component communicates with the Factual Similarity System
se, operating systems, other program components, and/or the like. The Factual
Similarity System may contain, communicate, generate, , and/or e program
component, system, user, and/or data communications, requests, and/or responses.
xvi. Distributed Factual Similarity Systems
The structure and/or operation of any of the Factual Similarity System node
controller components may be combined, consolidated, and/or distributed in any number of
ways to facilitate development and/or deployment. Similarly, the component collection may
be combined in any number of ways to facilitate deployment and/or development. To
accomplish this, one may ate the components into a common code base or in a facility
that can dynamically load the components on demand in an integrated fashion.
The component collection may be consolidated and/or distributed in countless
ions through standard data processing and/or development techniques. Multiple
instances of any one of the m components in the program ent collection may
be instantiated on a single node, and/or across numerous nodes to improve performance
through alancing and/or data-processing techniques. Furthermore, single ces may
also be distributed across multiple controllers and/or storage devices; e.g., databases. All
program component instances and controllers working in concert may do so through standard
data processing communication techniques.
[0138] The uration of the Factual Similarity System controller will depend on the
context of system deployment. Factors such as, but not limited to, the budget, capacity,
location, and/or use of the ying hardware resources may affect deployment
ements and configuration. Regardless of if the configuration s in more
consolidated and/or integrated program components, results in a more distributed series of
program components, and/or results in some combination between a consolidated and
distributed configuration, data may be communicated, obtained, and/or provided. ces
of components idated into a common code base from the program component
collection may communicate, obtain, and/or provide data. This may be accomplished through
intra-application data sing communication techniques such as, but not limited to: data
referencing (e.g., pointers), internal messaging, object instance variable communication,
shared memory space, le passing, and/or the like.
If component collection components are discrete, separate, and/or external to one
another, then communicating, obtaining, and/or providing data with and/or to other
component components may be accomplished h inter-application data processing
communication techniques such as, but not limited to: Application Program Interfaces (API)
information passage; (distributed) Component Object Model ((D)COM), (Distributed) Object
Linking and Embedding ((D)OLE), and/or the like), Common Object Request Broker
Architecture (CORBA), Jini local and remote ation program interfaces, JavaScript
Object Notation (JSON), Remote Method Invocation (RMI), SOAP, process pipes, shared
files, and/or the like. Messages sent between discrete ent components for inter-
application communication or within memory spaces of a singular component for intraapplication
communication may be tated through the creation and parsing of a grammar.
A grammar may be developed by using development tools such as lex, yacc, XML, and/or
the like, which allow for grammar generation and parsing capabilities, which in turn may
form the basis of communication messages within and between ents.
For example, a grammar may be ed to recognize the tokens of an HTTP
post command, e.g.:
w3c -post http://... Value1
where Value1 is discerned as being a parameter because “http://” is part of the r
, and what follows is considered part of the post value. Similarly, with such a
grammar, a variable “Value1” may be ed into an “http://” post command and then sent.
The grammar syntax itself may be presented as structured data that is interpreted and/or
otherwise used to generate the parsing mechanism (e.g., a syntax description text file as
processed by lex, yacc, etc.). Also, once the parsing mechanism is generated and/or
instantiated, it itself may process and/or parse structured data such as, but not limited to:
character (e.g., tab) delineated text, HTML, structured text streams, XML, and/or the like
ured data. In another embodiment, application data processing protocols
themselves may have integrated and/or readily available parsers (e.g., JSON, SOAP, and/or
like parsers) that may be employed to parse (e.g., ications) data. Further, the parsing
grammar may be used beyond message parsing, but may also be used to parse: databases,
data collections, data stores, structured data, and/or the like. Again, the desired configuration
will depend upon the context, environment, and requirements of system deployment.
For example, in some implementations, the Factual rity System controller
may be executing a PHP script implementing a Secure Sockets Layer (“SSL”) socket server
via the information server, which listens to incoming communications on a server port to
which a client may send data, e.g., data encoded in JSON format. Upon identifying an
incoming communication, the PHP script may read the incoming message from the client
device, parse the received JSON-encoded text data to extract information from the JSON-
encoded text data into PHP script variables, and store the data (e.g., client fying
information, etc.) and/or extracted ation in a relational se accessible using the
Structured Query Language (“SQL”). An exemplary listing, written substantially in the form
of PHP/SQL commands, to accept JSON-encoded input data from a client device via a SSL
connection, parse the data to extract variables, and store the data to a database, is provided
below:
<?PHP
header('Content-Type: text/plain');
// set ip address and port to listen to for incoming data
$address = ‘192.168.0.100’;
$port = 255;
// create a server-side SSL socket, listen for/accept incoming communication
$sock = socket_create(AF_INET, SOCK_STREAM, 0);
socket_bind($sock, $address, $port) or die(‘Could not bind to address’);
socket_listen($sock);
$client = socket_accept($sock);
// read input data from client device in 1024 byte blocks until end of message
do {
$input = “”;
$input = socket_read($client, 1024);
$data .= $input;
} while($input != “”);
// parse data to extract variables
$obj = json_decode($data, true);
// store input data in a database
mysql_connect("201.408.185.132",$DBserver,$password); // access database server
select("CLIENT_DB.SQL"); // select se to append
mysql_query(“INSERT INTO UserTable (transmission)
VALUES ($data)”); // add data to UserTable table in a CLIENT database
mysql_close("CLIENT_DB.SQL"); // close connection to database
?>
Also, the following resources may be used to e example embodiments
regarding SOAP parser implementation:
http://www.xav.com/perl/site/lib/SOAP/Parser.html
http://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm
.IBMDI.doc/referenceguide295.htm
and other parser implementations:
http://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm
.doc/referenceguide259.htm
all of which are hereby sly incorporated by reference.
A. sion
FIGS. 1 through 23 are tual illustrations allowing for an explanation of the
present disclosure. It should be understood that various s of the embodiments of the
present disclosure could be implemented in hardware, firmware, software, or combinations
thereof. In such embodiments, the various components and/or steps would be implemented in
hardware, firmware, and/or software to perform the functions of the present disclosure. That
is, the same piece of hardware, firmware, or module of software could m one or more
of the illustrated blocks (e.g., ents or steps).
In software implementations, computer software (e.g., programs or other
instructions) and/or data is stored on a machine readable medium as part of a computer
m product, and is loaded into a computer system or other device or machine via a
removable storage drive, hard drive, or communications interface. Computer programs (also
called computer control logic or computer readable program code) are stored in a main
and/or secondary memory, and executed by one or more processors (controllers, or the like)
to cause the one or more processors to perform the functions of the disclosure as bed
. In this document, the terms “machine readable medium,” “computer program
medium” and “computer usable ” are used to generally refer to media such as a
random access memory (RAM); a read only memory (ROM); a removable storage unit (e.g.,
a magnetic or optical disc, flash memory device, or the like); a hard disk; or the like.
[0145] Notably, the figures and examples above are not meant to limit the scope of the
present disclosure to a single embodiment, as other embodiments are possible by way of
interchange of some or all of the described or illustrated elements. Moreover, where certain
ts of the present disclosure can be partially or fully implemented using known
components, only those portions of such known components that are necessary for an
understanding of the present disclosure are bed, and detailed descriptions of other
portions of such known components are omitted so as not to obscure the disclosure. In the
present specification, an embodiment g a singular component should not necessarily
be limited to other embodiments including a plurality of the same component, and vice-versa,
unless explicitly stated otherwise herein. Moreover, the applicants do not intend for any term
in the specification or claims to be ascribed an uncommon or special meaning unless
explicitly set forth as such. r, the present sure encompasses present and future
known equivalents to the known components ed to herein by way of illustration.
The foregoing description of the ic embodiments so fully reveals the general
nature of the disclosure that others can, by applying knowledge within the skill of the
relevant art(s), readily modify and/or adapt for various applications such specific
embodiments, without undue mentation, without departing from the general t of
the t disclosure. Such adaptations and modifications are therefore intended to be within
the meaning and range of equivalents of the disclosed embodiments, based on the teaching
and guidance presented herein. It is to be understood that the phraseology or terminology
herein is for the purpose of description and not of limitation, such that the terminology or
ology of the present specification is to be reted by the d n in light of
the teachings and guidance presented herein, in combination with the knowledge of one
skilled in the relevant art(s).
In order to address various issues and advance the art, the entirety of this
application for LEGAL FACTUAL SIMILARITY SYSTEM (including the Cover Page,
Title, Headings, Cross-Reference to Related Application, Background, Brief Summary, Brief
Description of the Drawings, Detailed Description, Claims, Figures, and otherwise) shows,
by way of illustration, various embodiments in which the claimed innovations may be
practiced. The advantages and features of the application are of a representative sample of
embodiments only, and are not exhaustive and/or exclusive. They are presented only to assist
in understanding and teach the claimed principles. It should be understood that they are not
representative of all claimed innovations. As such, certain aspects of the sure have not
been discussed herein. That alternate ments may not have been presented for a
specific portion of the innovations or that further undescribed alternate embodiments may be
available for a portion is not to be considered a disclaimer of those alternate embodiments. It
will be appreciated that many of those undescribed embodiments incorporate the same
principles of the innovations and others are equivalent. Thus, it is to be tood that other
embodiments may be utilized and functional, logical, operational, zational, structural
and/or topological modifications may be made without departing from the scope and/or spirit
of the disclosure. As such, all es and/or embodiments are deemed to be non-limiting
hout this disclosure. Also, no nce should be drawn regarding those embodiments
discussed herein relative to those not sed herein other than it is as such for purposes of
ng space and repetition. For instance, it is to be understood that the logical and/or
topological structure of any combination of any program components (a component
collection), other components and/or any present feature sets as described in the figures
and/or throughout are not limited to a fixed operating order and/or arrangement, but rather,
any disclosed order is exemplary and all equivalents, regardless of order, are contemplated
by the disclosure. Furthermore, it is to be understood that such features are not limited to
serial execution, but rather, any number of threads, processes, services, servers, and/or the
like that may e asynchronously, concurrently, in parallel, simultaneously,
synchronously, and/or the like are contemplated by the disclosure. As such, some of these
features may be mutually contradictory, in that they cannot be simultaneously present in a
single embodiment. Similarly, some features are applicable to one aspect of the innovations,
and inapplicable to others. In addition, the disclosure includes other innovations not presently
claimed. Applicant reserves all rights in those presently med innovations ing the
right to claim such innovations, file additional applications, continuations, continuations in
part, divisions, and/or the like thereof. As such, it should be understood that advantages,
ments, examples, onal, features, logical, ional, organizational, ural,
topological, and/or other aspects of the disclosure are not to be considered limitations on the
disclosure as d by the claims or limitations on equivalents to the claims. It is to be
understood that, depending on the particular needs and/or characteristics of an individual
and/or enterprise user, se configuration and/or relational model, data type, data
transmission and/or network framework, syntax structure, and/or the like, various
embodiments may be implemented that enable a great deal of flexibility and customization.
For example, aspects may be adapted for video, audio or any other content. While various
embodiments and discussions have included reference to applications in the legal industry, it
is to be understood that the embodiments described herein may be readily configured and/or
customized for a wide variety of other applications and/or implementations.
Claims (14)
1. A method for finding documents, comprising: ingesting at least two library documents by extracting and indexing y triples 5 therefrom; receiving a reference text string; extracting at least one reference triple from the reference text string; identifying one or more library triples r to the at least one reference triple; and returning a list of one or more result library documents based on the identified library 10 triples.
2. The method of claim 1, further comprising: expanding the library triples based on a semantic corpus to obtain expanded library triples; and indexing the expanded y triples while maintaining a record of the library document 15 from which the library triples used to obtain them were extracted, wherein the identifying step includes identifying one or more expanded library triples similar to the at least one reference triple and the list of one or more result library documents returned by the returning step is based on the identified library triples and expanded library triples. 20
3. The method of claim 1, further comprising: expanding the at least one nce triple based on a semantic corpus to obtain at least one expanded nce triple, wherein the identifying step includes identifying one or more library triples similar to the at least one expanded reference triple. 25
4. The method of claim 2, wherein the expanding step includes forming multi-word tokens as components of a y triple based on a semantic corpus.
5. The method of claim 3, wherein the ing step includes g multi-word tokens as components of a reference triple based on a semantic corpus.
6. The method of claim 1, wherein the returned list is ranked based on a rity n 30 the identified library s in each listed library document and the one or more reference triples.
7. The method of claim 1, further comprising scoring library documents from which identified library triples were extracted based on an aggregation of rity scores n each identified library triple and its corresponding reference triple.
8. The method of claim 7, wherein the list that is returned includes only library documents 5 having a similarity score above a predefined threshold.
9. The method of claim 7, n the listed library documents are ranked according to their similarity scores.
10. The method of claim 1, further comprising: receiving a second reference text string after returning the list; 10 extracting at least one second reference triple from the second reference text string; identifying one or more library triples similar to the at least one second reference triple; returning an updated list of one or more result library nce documents based on the library triples identified with respect to both the first reference triples and second reference 15 triples.
11. A method for mining facts from a body of nts, comprising: ingesting two or more library nts by extracting and indexing library triples therefrom that relate to a primary source; grouping similar s into one or more fact groups; 20 ingesting a later document after the two or more library documents by extracting later triples therefrom that relate to a primary source; and grouping the later triples into the one or more fact groups based on a similarity between the later triples and the library triples previously comprising the one or more fact groups.
12. The method of claim 11, further comprising: 25 receiving a reference text string; extracting at least one reference triple from the reference text string; expanding the at least one reference triple based on the one or more fact groups to obtain at least one expanded reference triple; fying one or more library triples r to the at least one expanded nce 30 triple; and returning a list of one or more result library documents based on the identified library triples.
13. The method of claim 11, further comprising: ing a reference text ; extracting at least one reference triple from the reference text string; expanding the at least one nce triple based on the one or more fact groups to obtain 5 at least one expanded reference triple; fying one or more library triples similar to the at least one expanded reference triple; and returning a list of one or more primary sources based on the identified library triples.
14. A method for g documents relating to a primary source, comprising: 10 ingesting two or more library documents by extracting and indexing library triples therefrom that relate to a primary source; receiving a reference text string; ting at least one reference triple from the reference text string; identifying one or more library triples similar to the at least one reference triple; and 15 returning a list of one or more primary sources based on the identified library triples.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US62/426,727 | 2016-11-28 | ||
US62/550,839 | 2017-08-28 |
Publications (1)
Publication Number | Publication Date |
---|---|
NZ794000A true NZ794000A (en) | 2022-11-25 |
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