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Claims  |
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What is claimed is:
1. A research system for computerized searching of textual objects, wherein
the textual objects are stored in a database, comprising:
a computer processor for processing commands and manipulating the textual
objects stored in the database;
a means, coupled to the computer processor, for entering the commands to be
processed by the computer processor;
a means for indexing the textual objects using the computer processor and
the entered commands comprising:
a means for creating vectors representing the textual objects wherein the
vectors are created using non-semantical relationships that exist among or
between the textual objects;
a means for searching the indexed textual objects using the vectors to
obtain a pool of textual objects comprising a means for vector searching
of the indexed textual objects using the vectors;
a graphical user interface means for converting the pool of textual objects
into a graphical view comprising:
a means for forming a box to graphically represent one or more of the
textual objects in the pool; and
a display, operably coupled to the graphical user interface means, for
showing the graphical view including any of the boxes formed.
2. The research system of claim 1 wherein the means for searching the
indexed textual objects further comprises:
means for receiving processed commands from the computer processor which
identify a pool of textual objects;
means for locating textual objects similar to those textual objects in the
pool; and
means for ranking the importance of the textual objects in the pool.
3. The research system of claim 1 wherein the means for searching the
indexed textual objects further comprises:
means for creating a vector representing a paradigm textual object.
4. The research system of claim 1 wherein the graphical user interface
means further comprises:
means for selecting one of the boxes;
means for displaying further information on the selected box;
zoom-in means for enlarging the size of a portion of the graphical view;
and
zoom-out means for decreasing the size of a portion of the graphical view.
5. The research system of claim 1 wherein the textual objects contain core
words, the means for entering commands comprises a keyboard, and wherein
the means for indexing further comprises:
a means for generating a boolean word index of the core words found in the
textual objects; and
wherein the means for searching uses both pattern vectors and the boolean
word index and further comprises:
a means for performing boolean word searches of the textual objects using
the boolean word index.
6. The research system of claim 1 wherein a textual object may be selected
using the means for entering the commands and wherein the means for
searching the indexed textual objects further comprises:
means for receiving the identity of a selected textual object;
cases-after means for identifying textual objects that refer to the
selected textual object; and
cases-in means for identifying textual objects to which the selected
textual object refers.
7. The research system of claim 1 wherein a pool of textual objects may be
selected using the means for entering the commands and wherein the means
for searching the indexed textual objects further comprises:
means for receiving the identity of the selected pool of textual objects;
pool-similarity means for identifying a similar pool of textual objects
wherein the objects in the similar pool are similar to the objects in the
selected pool; and
pool-importance means to identify an important pool of textual objects
wherein the objects in the important pool are important in relation to the
objects in the selected pool.
8. The research system of claim 1 wherein the means for creating vectors
representing the textual objects further comprises:
extractor means for creating an initial numerical representation of each
textual object wherein the initial numerical representations are based on
direct non-semantical relationships between the textual objects;
patterner means for analyzing the initial numerical representations of each
textual object to find relationships that exist between or among the
textual objects comprising:
means for calculating a pattern vector representation for each textual
object.
9. The research system of claim 8 wherein the means for creating vectors
representing the textual objects further comprises:
weaver means for generating proximity vectors based upon the pattern vector
representations for each object.
10. The research system of claim 9 wherein the weaver means for generating
proximity vectors comprises:
a means for calculating euclidean distances between pattern vectors.
11. The research system of claim 9 wherein the means for creating vectors
further comprises:
means for calculating similarity vectors representing the similarity
between textual objects using the proximity vectors.
12. A legal research system for computerized searching of textual objects
containing core words, wherein the textual objects are stored in a
database, comprising:
a computer processor for processing commands and manipulating the textual
objects stored in the database;
a keyboard means, coupled to the computer processor, for entering the
commands to be processed by the computer processor;
a means for indexing the textual objects using the computer processor and
the entered commands comprising;
a means for creating vectors representing the textual objects; and
a means for generating a boolean word index of the core words found in the
textual objects;
a means for searching the indexed textual objects using the vectors and the
boolean word index to obtain a pool of textual objects comprising;
a means for performing boolean word searches using the boolean word index:
and
a means for vector searching of the indexed textual objects using
the vectors, comprising:
means for receiving processed commands from the computer processor which
identify a selected textual object;
cases-after means for identifying textual objects that refer to the
selected textual object;
cases-in means for identifying textual object to which the selected textual
object refers; and
similarity means for identifying textual objects which have similar
characteristics to the selected textual object;
a graphical user interface means for converting the pool of textual objects
into a graphical view comprising:
a means for forming a box to graphically represent one or more of the
textual objects in the pool; and
a display, operably coupled to the graphical user interface means, for
showing the graphical view including any of the boxes formed.
13. A system for proximity indexing a plurality of data comprising:
storage means, connected to the grouping means, for storing a plurality of
data in a database;
a computer processor for manipulating the plurality of data;
means for enabling the computer processor to access the plurality of data
stored in the database;
extractor means for creating a numerical representation of each accessed
datum;
patterner means for analyzing the numerical representation of the plurality
of data for patterns comprising:
means for a calculating a pattern representation for each datum based upon
that datums relationship to every other datum; and
means for weighing the significance of the pattern representation;
weaver means for generating an index on the proximity of each datum to
every other datum comprising:
a means for determining the Euclidian distance between two pattern
representations; and
memory for storing the index on the proximity of each datum to every other
datum.
14. The system of claim 13 wherein each of the purality of data is a
non-textual object and wherein the extractor means further comprises:
means for numerically representing with a vector the non-textual objects;
and
means for clustering non-textual objects having similar characteristics.
15. The system of claim 13 wherein the extractor means further comprises:
means for generating a reference number for each of the plurality of data;
means for determining which of the plurality of data refer to any other of
the plurality of data; and
means for creating a core word index.
16. The system of claim 13 wherein the patterner means further comprises:
means for analyzing the numerical representation against a plurality of
empirically defined patterns, wherein certain of the patterns are more
important than others; and
wherein the means for weighing further comprises means for heavily weighing
certain patterns.
17. The system of claim 13 wherein the weaver means further comprises:
means for making a similarity determination based upon the Euclidian
distances calculated.
18. The system of claim 13 wherein the plurality of data are received in a
digital signal, the system further comprising:
means for receiving the digital signals;
means, connected to the receiving means, for interpreting the digital
signals into data;
means, connected to the interpreting means and storage means, for grouping
the plurality of data into a database format;
means, connected to the memory, for converting the index into a
transmission signal; and
means, connected to the converting means, for transmitting the transmission
signal representing the index.
19. A system for computerized searching of an index which catalogs a
database of objects comprising:
key means for entering search commands;
a processor, connected to the key means, for processing the search
commands;
means to retrieve the index utilizing the processor;
multiple search means to analyze the index and identify a pool of one or
more of the objects based upon a processed search command comprising:
means for interpreting a processed search command as a selection of an
object;
means for identifying a pool of objects that have a relation to the
selected object;
means for generating a paradigm object; and
means for defining a pool of objects that have non-semantical
characteristics similar to the paradigm object; and
a display for viewing the objects in a pool.
20. The system of claim 19 further comprising:
means for creating an alphanumeric list of names of the objects in a pool
for display.
21. A graphical user interface to display a pool of identified objects
stored in a database comprising:
means for receiving the identity of objects to be displayed;
means for collecting data indicating a first relationship between objects
in the pool and data indicating a second relationship between objects in
the pool;
means for determining a coordinate X/Y location for each identified object
in the pool based upon the data indicating a first and second relationship
comprising:
means for comparing the data indicating the first relationship for
determining an X coordinate for each object; and
means for comparing the data indicating the second relationship for
determining a Y coordinate for each object;
means for generating a first window with an X axis and Y axis;
means for creating a box for each identified object;
means for placing the box for each identified object in the correct X/Y
position in the first window;
means for displaying the first window with one or more boxes; and
means to select a displayed box and obtain further information about the
object represented by the displayed box.
22. The graphical user interface of claim 21 further comprising:
means for displaying a second window stacked on top of the first window;
and
means for moving the second window on the display.
23. The graphical user interface of claim 21, wherein the first
relationship is importance and the second relationship is similarity,
further comprising:
means to zoom in on a particular portion of the first window; and
means to zoom out to view a greater proportion of the first window.
24. The graphical user interface of claim 21 further comprising:
means for requesting a database search comprising:
an active display box;
a mouse for entry of commands by a user based on the display; and
means for converting the mouse entered commands into a database search
request.
25. The graphical user interface of claim 21,
wherein the means for displaying further comprises a color monitor;
wherein the means for creating a box further comprises means to color the
box;
wherein the means for generating a first window further comprises a means
to generate a light colored background and dark lines representing a
coordinate grid.
26. A non-semantical method for numerically representing objects in a
computer database and for computerized searching of the numerically
represented objects in the database, wherein direct and indirect
relationships exist between objects in the database, comprising:
marking objects in the database so that each marked object may be
individually identified by a computerized search;
creating a first numerical representation for each identified object in the
database based upon the object's direct relationship with other objects in
the database;
storing the first numerical representations for use in computerized
searching;
analyzing the first numerical representations for indirect relationships
existing between or among objects in the database;
generating a second numerical representation of each object based on the
analysis of the first numerical representation;
storing the second numerical representation for use in computerized
searching; and
searching the objects in the database using a computer and the stored
second numerical representations, wherein the search identifies one or
more of the objects in the database.
27. The non-semantical method of claim 26, wherein the objects in the
database include words, and semantic indexing techniques are used in
combination with the non-semantical method, the method further comprising
the step of creating and storing a boolean word index for the words of the
objects in the database.
28. The non-semantical method of claim 26, wherein
the first and second numerical representations are vectors that are
arranged in first and second matrices;
the direct relationships are express references from a one object to
another object in the database;
the objects in the database are assigned chronological data; and
wherein the step of searching comprises the steps of
matrix searching of the second matrices; and
examining the chronological data.
29. The non-semantical method of claim 26 wherein the step of analyzing the
first numerical representation further comprises:
examining the first numerical representation for patterns which indicate
the indirect relationships.
30. The non-semantical method of claim 29, given that object A occurs
before object B and object c occurs before object A, and wherein the step
of creating a first numerical representation comprises examining for the
direct relationship B cites A and wherein the step of examining for
patterns further comprises the step of examining for the following
pattern:
A cites c, and B cites c.
31. The non-semantical method of claim 29, wherein a, b, c, A, d, e, f, B,
g, h, and i are objects in the database and given that;
a, b, and c occur before A;
A occurs before d, e, and f, which occur before B; and
B occurs before g, h, and i;
and wherein the step of examining for patterns further comprises the step
of examining for one or more of the following patterns:
(i) g cites A, and g cites B;
(ii) B cites f, and f cites A;
(iii) B cites f, f cites e, and e cites A;
(iv) B cites f, f cites e, e cites d, and d cites A;
(v) g cites A, h cites B, g cites a, and h cites a;
(vi) i cites B, i cites f (or g), and f (or g) cites A;
(vii) i cites g, i cites A, and g cites B;
(viii) i cites g (or d), i cites h, g (or d) cites A, and h cites B;
(ix) i cites a, i cites B, and A cites a;
(x) i cites A, i cites e, B cites e;
(xi) g cites A, g cites a, A cites a, h cites B, and h cites a;
(xii) A cites a, B cites d, i cites a, and i cites d;
(xiii) i cites B, i cites d, A cites a, and d cites a;
(xiv) A cites b, B cites d (or c), and d (or c) cites b;
(xv) A cites b, B cites d, b cites a, and d cites a;
(xvi) A cites a, B cites b, d (or c) cites a, and d (or c) cites b.
32. The non-semantical method of claim 26, wherein the step of analyzing
further comprises the step of weighing, wherein some indirect
relationships are weighed more heavily than other indirect relationships.
33. The non-semantical method of claim 26, wherein the step of analyzing
the first numerical representations for indirect relationships further
comprises:
creating an interim vector representing each object; and wherein the step
of generating a second numerical representation uses coefficients of
similarity and further comprises:
calculating euclidean distances between interim vector representations of
each object;
creating proximity vectors representing the objects using the calculated
euclidean distances; and
using the proximity vectors and using coefficients of similarity to
calculate the second numerical representations.
34. The non-semantical method of claim 26, wherein objects in the database
may be divided into subsets and wherein the marking step includes the step
of marking subsets of objects in the database and wherein relationships
exist between or among subsets of objects in the database.
35. The non-semantical method of claim 34 wherein the objects are textual
objects with paragraphs and the subsets are the paragraphs of the textual
objects, the method further comprising the steps of:
creating a subset numerical representation for each subset based upon the
relationships between or among subsets;
analyzing the subset numerical representations;
clustering the subsets into sections based upon the subset analysis; and
generating a section numerical representation for each section,
wherein the section numerical representations are available for searching.
36. The non-semantical method of claim 26, wherein the step of searching
the objects comprises the steps of:
selecting an object;
using the second numerical representation to search for objects similar to
the selected object.
37. The non-semantical method of claim 26, wherein the step of searching
includes the step of graphically displaying one or more of the identified
objects.
38. The non-semantical method of claim 26, wherein the step of searching
comprises the step of identifying a paradigm object.
39. The non-semantical method of claim 26, wherein the step of searching
the objects comprises the steps of:
selecting a pool of objects;
pool-similarity searching to identify a similar pool of textual objects,
similar in relation to the objects in marked pool; and
pool-importance searching to identify an important pool of textual objects,
important in relation to the objects in the selected pool.
40. The non-semantical method of claim 26, the step of searching comprising
the steps of:
identifying a paradigm pool of objects; and
searching for relationships between the objects and the paradigm pool of
objects;
wherein the searched for relationship is pool importance or pool
similarity.
41. A method for the non-semantical indexing of objects stored in a
computer database, the method for use in searching the database for the
objects, comprising the steps of:
extracting, comprising the steps of:
labeling objects with a first numerical representation; and
generating a second numerical representation for each object based on each
object's references to other objects;
patterning, comprising the step of creating a third numerical
representation for each object using the second numerical representations,
wherein the third numerical representation for each object is determined
from an examination of the second numerical representations for
occurrences of patterns that define indirect relations between or among
objects;
weaving, comprising the steps of:
calculating a fourth numerical representation for each object based on the
euclidean distances between the third numerical representations; and
determining a fifth numerical representation for each object by processing
the fourth numerical representations through similarity processing; and
storing the fifth numerical representations in the computer database as the
index for use in searching for objects in the database.
42. The method of claim 41 wherein the first through fifth numerical
representations are vector representations and further comprises the step
of clustering objects having similar characteristics.
43. The method of claim 42, wherein the objects are paragraphs and the step
of clustering objects comprises the step of clustering adjacent paragraphs
that have similar characteristics.
44. The method of claim 41 wherein the step of creating the third numerical
representations further comprises the steps of:
analyzing the second numerical representation against a plurality of
empirically defined patterns, wherein certain patterns are more important
than others; and
weighing the analyzed second numerical representations according to the
importance of the patterns.
45. A method for searching indexed objects, wherein the index is stored,
comprising the steps of:
entering search commands;
processing the search commands with a processor;
retrieving the stored index using the processor;
analyzing the index to identify a pool of objects, comprising the steps of:
interpreting the processed searched commands as a selection of an object;
identifying a group of objects that have a relationship to the selected
object, wherein the step of identifying comprises the steps of:
identifying objects that are referred to by the selected object; and
identifying objects that refer to the selected object
quantifying the relationship of the selected object to each object in the
group of objects; and
ranking the objects in the group of objects in accordance to the quantified
relationship to the selected object; and
presenting one or more objects from the group of objects in ranked order.
46. A method for searching indexed objects, wherein the index is stored,
chronological information is associated with each object in the group, and
a paradigm object may be identified, comprising the steps of:
entering search commands;
processing the search commands with a processor;
retrieving the stored index using the processor;
analyzing the index to identify a pool of objects, comprising the steps of:
interpreting the processed searched commands as a selection of an object;
identifying a group of objects that have a relationship to the selected
object;
quantifying the relationship of the selected object to each object in the
group of objects;
ranking the objects in the group of objects in accordance to the quantified
relationship to the selected object;
chronologically ordering the objects in the group to form a pool of
objects;
ordering the objects in the pool by rank based upon their relationship to a
paradigm object; and
presenting one or more objects from the pool of objects in ranked order.
47. A method for graphically displaying and interfacing with a pool of
identified objects stored in a database using information indicating
relationships, comprising the steps of:
receiving the identity of objects in the pool;
collecting information indicating a first relationship among objects in the
pool;
gathering information indicating a second relationship among objects in the
pool;
determining a coordinate X/Y position for each identified object in the
pool based upon the information indicating a first and second relationship
comprising the steps of:
comparing the information indicating the first relationship for determining
an X coordinate for each identified object; and
comparing the information indicating the second relationship for
determining a Y coordinate for each identified object;
generating a first window with an X axis and Y axis, wherein the X and Y
axis are able to accommodate the X and Y coordinate for each object;
creating a graphical box for each identified object, the box having sides
and a bottom;
placing a side and the bottom of the graphical box for each identified
object in the correct X/Y axis position in the first window;
labeling the placed box;
displaying the first window with one or more labeled boxes; and
selecting a displayed box to obtain further information about the
identified object represented by the displayed box.
48. The method of claim 47 further comprising further comprising the steps
of:
generating a second window stacked on top of the first window; and
moving the second window on the display.
49. The method of claim 47, wherein the first relationship is importance
and the second relationship is similarity, and wherein the step of
displaying comprises the steps of:
zooming in on a particular portion of the displayed first window; and
zooming out to view a different proportion of the displayed first window.
50. The method of claim 47 wherein an active display box is used, further
comprising the steps of:
requesting a database search, comprising the steps of:
displaying the active display box;
entering commands from a user by operation of a mouse on the active display
box; and
converting the entered commands into a database search request.
51. A system for computerized searching of an index which catalogs a
database of objects comprising:
key means for entering search commands;
a processor, connected to the key means, for processing the search
commands;
means to retrieve the index utilizing the processor;
multiple search means to analyze the index and identify a pool of one or
more of the objects based upon a processed search command comprising:
means for interpreting a processed search command as a selection of an
object;
means for identifying a pool of objects that have a relation to the
selected object, wherein the means for identifying a pool of objects
further comprises:
means for identifying objects that are referred to by the selected object;
means for identifying objects that refer to the selected object; and
means for identifying objects that have a similar characteristic to the
selected object;
means for generating a paradigm object; and
means for defining a pool of objects that have characteristics similar to
the paradigm object; and
a display for viewing the objects in a pool.
52. A system for computerized searching of an index which catalogs a
database of objects comprising:
key means for entering search commands;
a processor, connected to the key means, for processing the search
commands;
means to retrieve the index utilizing the processor;
multiple search means to analyze the index and identify a pool of one or
more of the objects based upon a processed search command comprising:
means for interpreting a processed search command as a selection of an
object;
means for identifying a pool of objects that have a relation to the
selected object;
means for generating a paradigm object; and
means for defining a pool of objects that have characteristics similar to
the paradigm object;
means for chronologically ordering the objects in a pool;
means for rank ordering the objects in a pool based upon their relationship
to the selected object;
means for rank ordering the objects in a pool based upon their relationship
to the paradigm object; and
a display for viewing the objects in a pool. |
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Claims |
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Description |
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TECHNICAL FIELD
This invention pertains to computerized research tools. More particularly,
it relates to computerized research on stored databases. Specifically, the
invention indexes data, searches data, and graphically displays search
results with a user interface.
BACKGROUND
Our society is in the information age. Computers maintaining databases of
information have become an everyday part of our lives. The ability to
efficiently perform computer research has become increasingly more
important. The area in our society in which this is most evident is the
legal profession. A major problem in the legal profession today is the
great deal of time spent performing legal research. Many aspects of legal
research are tedious and time consuming. Therefore, performing legal
research detracts from the amount of time the attorney is able to spend on
tasks that actually require him to utilize his legal judgment and
reasoning. Recent efforts in the art of computer research have been aimed
at reducing the time required to accomplish legal research. Current
computer search programs use a text-by-text analysis procedure (Boolean
Search) to scan a database and retrieve items from a database. The
attorney must input a string of text, and the computer evaluates this
string of text. Then the computer retrieves items from the database that
match the string of text. The two most popular systems for computerized
searching of data used in the legal profession are Westlaw.TM., a service
sold by West Publishing Company, 50 W. Kellogg Blvd., P.O. Box 64526, St.
Paul, Minn. 55164-0526, and Lexis.TM., a service sold by Mead Data
Central, P.O. Box 933, Dayton, Ohio 45401.
However, Boolean searches of textual material are not very efficient.
Boolean searches only retrieve exactly what the computer interprets the
attorney to have requested. If the attorney does not phrase his or her
request in the exact manner in which the database represents the textual
object, the Boolean search will not retrieve the desired textual object.
For example, if the attorney desires to retrieve cases in which a judge
decided the issue before the jury could decide it, the attorney may enter
"Summary Judgment" as his textual string. However, such a request will not
retrieve cases that were decided by the judge under a motion to dismiss.
Therefore, the researcher may effectively be denied access to significant
cases, statutes, laws or other textual objects that may be crucial to the
project on which the attorney is working. A second problem encountered
with Boolean searches is that the search retrieves a significant amount of
irrelevant textual objects. (It should be noted that in the context of
legal research, a textual object could be any type of written legal
material such as a judicial opinion, a statute, a treatise, a law review
article, etc. The term textual object is used to stress the fact that the
present invention applies to all types of databases, and not just legal
research databases). The only requirement that a textual object must
satisfy in order to be selected by a Boolean search program is that part
of the textual object match the particular request of the researcher. For
example, if the researcher desires to recover all cases that relate to a
Fourth Amendment issue, the researcher may input "search and seizure" as
his textual string. However, the computer will retrieve every case that
happens to mention "search and seizure" one time, even if the case has
nothing to do with a Fourth Amendment issue. Since the researcher cannot
possibly know all of the groupings of text within all the textual objects
in the database, the researcher is unable to phrase his request to only
retrieve the textual objects that are relevant.
Aside from the inefficiency of Boolean searches, the present systems for
computerized searching of data are inadequate to serve the needs of a
researcher for several other reasons. Even if one assumes that all the
textual objects retrieved from a Boolean search are relevant, the listing
of the textual objects as done by Westlaw.TM. or Lexis.TM. does not convey
some important and necessary information to the researcher. The researcher
does not know which textual objects are the most significant (i.e., which
textual object is referred to the most by another textual object) or which
textual objects are considered essential precedent (i.e., which textual
objects describe legal doctrines).
In addition, both Westlaw.TM. and Lexis.TM. have a Shepardizing.TM. feature
that enables the researcher to view a list of textual objects that mention
a particular textual object. The shepardizing feature does not indicate
how many times a listed textual object mentions the particular textual
object. Although the shepardizing feature uses letter codes to indicate
the importance of a listed textual object (e.g. an "f" beside a listed
textual object indicates that the legal rule contained in particular
textual object was followed in the listed textual object), data on whether
a listed textual object followed the rule of a particular textual object
is entered manually by employees of Shepard's.TM./McGraw Hill, Inc., Div.
of McGraw-Hill Book Co., 420 N. Cascade Ave., Colorado Springs, Colo.
80901, toll free 1-800-525-2474. Therefore, such process is subjective and
is prone to error.
Another legal research system that is available is the Westlaw.TM. key
number system. The Westlaw.TM. key number system has a problem similar to
the shepardizing feature on the Lexis.TM. and Westlaw.TM. systems. West
key numbers are groups of textual objects organized by topic. The West key
numbers enable a researcher to search for textual objects on a
computerized system via the key numbers. However, the employees of
West.TM. manually determine which cases should be categorized under which
key number. Therefore, such a numbering process is subjective and is prone
to error. Furthermore, many people in the legal profession have criticized
the West key number system because the system is very slow to recognize
new topic areas, very rigid and very difficult to keep up to date. In
addition, the West.TM. key number system, like Boolean searches, produces
pools of cases that are over-inclusive or under-inclusive.
The video displays of both the West.TM. and Lexis.TM. systems are difficult
to use. The simple text displays of these systems do not provide a
researcher with all the information that is available in the database.
Computerized research tools for legal opinions and related documents are
probably the most sophisticated computer research tools available and
therefore form the background for this invention. However, the same or
similar computer research tools are used in many other areas. For example,
computer research tools are used for locating prior art for a patent
application. The same problems of inefficiency discussed above exist for
computer research tools in many areas of our society.
What is needed is a system for computerized searching of data that is
faster than the available systems of research.
What is needed is a system for computerized searching of data that enables
attorneys to research in a manner in which they are familiar.
What is needed is a computerized research tool that will reorganize,
re-index or reformat the data into a more efficient format for searching.
What is needed are more sophisticated methods to search data.
What is needed is a system for computerized searching of data that will
significantly reduce the number of irrelevant textual objects it
retrieves.
What is needed is a user friendly computerized research tool.
What is needed is a visual user interface which can convey information to a
user conveniently.
What is needed is a system for computerized searching of data that easily
enables the attorney himself to classify the textual object according to
his or her own judgment.
What is needed is a system for computerized searching of data that provides
a visual representation of "lead" textual objects and "lines" of textual
objects, permitting a broad overview of the shape of the relevant legal
"landscape."
What is needed is a system for computerized searching of data that provides
an easily-grasped picture or map of vast amounts of discrete information,
permitting researchers (whether in law or other databases) to "zero in" on
the most relevant material.
What is needed is a system for computer searching of data that provides a
high degree of virtual orientation and tracking, the vital sense of where
one has been and where one is going, and that prevents researchers from
becoming confused while assimilating a large amount of research materials.
Accordingly, there is an unanswered need for a user friendly computerized
research tool. There is a need for "intelligent" research technology that
emulates human methods of research. There is a need in the marketplace for
a more efficient and intelligent computerized research tool.
The present invention is designed to address these needs.
SUMMARY OF THE INVENTION
This invention is a system for computerized searching of data.
Specifically, the present invention significantly aids a researcher in
performing computerized research on a database. The invention simplifies
the research task by improving upon methods of searching for data
including textual objects and by implementing a user interface that
significantly enhances the presentation of the data. Simplifying such
research reduces the amount of human time that must be allocated to
research.
The invention begins with an existing database and indexes the data by
creating a numerical representation of the data. This indexing technique
called proximity indexing generates a quick-reference of the relations,
patterns, and similarity found among the data in the database. Using this
proximity index, an efficient search for pools of data having a particular
relation, pattern or characteristic can be effectuated. This relationship
can then be graphically displayed.
There are three main components to the invention; a data indexing
applications program, a Computer Search Program for Data Represented by
Matrices ("CSPDM"), and a user interface. Various indexing application
programs, CSPDMs, and user interface programs can be used in combination
to achieve the desired results. The data indexing program indexes data
into a more useful format. The CSPDM provides efficient computer search
methods. The preferred CSPDM includes multiple search subroutines. The
user interface provides a user friendly method of interacting with the
indexing and CSPDM programs. The preferred user interface program allows
for easy entry of commands and visual display of data via a graphical user
interface.
The method which the invention uses to index textual objects in a database
is called Proximity Indexing. Proximity Indexing is a method of preparing
data in a database fo | | |
