U.S. patent application number 13/898361 was filed with the patent office on 2013-10-17 for computer-implemented system and method for conducting a document search via metaprints.
The applicant listed for this patent is MetaJure, Inc.. Invention is credited to Dennis R. Adler, Richard J. Corbett, Kevin J. Harrang, Martin F. Smith.
Application Number | 20130275420 13/898361 |
Document ID | / |
Family ID | 48365426 |
Filed Date | 2013-10-17 |
United States Patent
Application |
20130275420 |
Kind Code |
A1 |
Adler; Dennis R. ; et
al. |
October 17, 2013 |
Computer-Implemented System And Method For Conducting A Document
Search Via Metaprints
Abstract
A computer-implemented system and method for conducting a
document search based on metaprints is provided. A plurality of
metaprints comprising metadata keywords and associated frequencies
of occurrence of the metadata keywords within a plurality of
documents are maintained. A search query is received and executed
against the metaprints. The metadata keywords that match the query
are identified and the metaprints associated with the matched
metadata keywords are obtained. A further search query is generated
based on the obtained metaprints and is applied to a set of
documents. Those documents that match the frequencies of occurrence
associated with the obtained metaprints are identified.
Inventors: |
Adler; Dennis R.; (Seattle,
WA) ; Smith; Martin F.; (Bainbridge Island, WA)
; Corbett; Richard J.; (Seattle, WA) ; Harrang;
Kevin J.; (Kirkland, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MetaJure, Inc. |
Seattle |
WA |
US |
|
|
Family ID: |
48365426 |
Appl. No.: |
13/898361 |
Filed: |
May 20, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13019959 |
Feb 2, 2011 |
8447758 |
|
|
13898361 |
|
|
|
|
61301162 |
Feb 3, 2010 |
|
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Current U.S.
Class: |
707/723 ;
707/769 |
Current CPC
Class: |
G06F 16/24578 20190101;
G06F 16/9032 20190101 |
Class at
Publication: |
707/723 ;
707/769 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer-implemented system for conducting a document search
via metaprints, comprising: a plurality of metaprints comprising
metadata keywords and associated frequencies of occurrence of the
metadata keywords within a plurality of documents; a search module
to receive a search query, to execute the query against the
metaprints, and to identify the metadata keywords that match the
query; a matching module to obtain the metaprints associated with
the matched metadata keywords; a metaprint query module to generate
a further search query based on the obtained metaprints; and a
further search module to apply the further search query to a set of
documents and to identify those documents that match the
frequencies of occurrence associated with the obtained
metaprints.
2. A system according to claim 1, further comprising: a result
module to provide the search results as a link to cached versions
of the result documents.
3. A system according to claim 2, further comprising: a ranking
module to rank the search results based on one of automatically
ranking the documents and receiving rankings from a user.
4. A system according to claim 3, wherein the automatic ranking is
based on user click-through rates.
5. A system according to claim 1, further comprising: a term search
module to concurrently apply the search query to the set of
documents and identifying those documents that match the search
query as different results of the search query.
6. A system according to claim 1, wherein the word frequencies
comprise inverse word frequencies.
7. A system according to claim 1, further comprising: a merge
module to combine one or more of the search results in a single
document.
8. A system according to claim 1, further comprising: a synonym
module to associate one or more synonyms with at least one of the
metadata keywords.
9. A system according to claim 1, further comprising: a filter
module to filter the metadata keywords prior to executing the
search query against the metaprints based on a predictive value of
each such metadata keyword to identify documents.
10. A system according to claim 1, further comprising: a frequency
determination module to determine the occurrence frequencies
associated with each of the metadata keywords based on one of
previously generated word frequencies and an on-demand request to
determine the occurrence frequencies.
11. A computer-implemented method for conducting a document search
via metaprints, comprising: maintaining a plurality of metaprints
comprising metadata keywords and associated frequencies of
occurrence of the metadata keywords within a plurality of
documents; receiving a search query and executing the query against
the metaprints; identifying the metadata keywords that match the
query; obtaining the metaprints associated with the matched
metadata keywords; generating a further search query based on the
obtained metaprints; and applying the further search query to a set
of documents and identifying those documents that match the
frequencies of occurrence associated with the obtained
metaprints.
12. A method according to claim 11, further comprising: providing
the search results as a link to cached versions of the result
documents.
13. A method according to claim 12, further comprising: ranking the
search results, comprising at lest one of: automatically ranking
the documents; and receiving rankings from a user.
14. A method according to claim 13, wherein the automatic ranking
is based on user click-through rates.
15. A method according to claim 11, further comprising:
concurrently applying the search query to the set of documents and
identifying those documents that match the search query as
different results of the search query.
16. A method according to claim 11, wherein the word frequencies
comprise inverse word frequencies.
17. A method according to claim 11, further comprising: combining
one or more of the search results in a single document.
18. A method according to claim 11, further comprising: associating
one or more synonyms with at least one of the metadata
keywords.
19. A method according to claim 11, further comprising: filtering
the metadata keywords prior to executing the search query against
the metaprints based on a predictive value of each such metadata
keyword to identify documents.
20. A method according to claim 11, further comprising: determining
the occurrence frequencies associated with each of the metadata
keywords based on one of previously generated word frequencies and
an on-demand request to determine the occurrence frequencies.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This non-provisional patent application is a continuation of
U.S. patent application, Ser. No. 13/019,959, filed Feb. 2, 2011,
pending, which claims priority to U.S. Provisional Patent
Application, Ser. No. 61/301,162, filed Feb. 3, 2010, the
disclosures of which are incorporated by reference.
FIELD
[0002] This application relates in general to information retrieval
and, in particular, to a computer-implemented system and method for
conducting a document search via metaprints.
BACKGROUND
[0003] Electronic data management, particularly in large enterprise
computing environments, is increasingly complicated. For instance,
a decreasing cost of electronic storage space in combination with
regulatory and legal obligations to retain data has led to
exponential growth in data accumulated throughout organizations.
Data is often stored in many sites, including local, remote, and
centralized databases. Additionally, data is frequently stored on
different systems, by different methods, and in multiple
formats.
[0004] For example, a typical corporate legal department has a
large wealth of knowledge contained in stored data, such as
documents, databases, and email, which can be leveraged to aid
attorneys in preparing new work product. Further, an emphasis on
cost-consciousness drives a desire for increased efficiencies in
the amount of time spent on legal matters. The volume and dispersed
nature of the data makes tracking, searching, and reutilization of
such data difficult.
[0005] Currently, corporations use different data management tools
to address their various needs. For instance, content management,
electronic mail, accounting, and deadline tracking are handled by
different solutions. Unfortunately, the need for multiple solutions
leads to data segregated into many different information silos,
each with their own storage formats. Locating and searching content
in each silo can require unique user login requirements and
individualized search methodologies that return standalone,
segregated, and customized search results.
[0006] Conventional content management and search tools have proven
inadequate for providing efficient detection of related documents.
For example, BA-Insight LLC, a Delaware limited liability company,
conducts post-processing of search query results of documents.
Documents matching a user search query are first identified. The
identified documents are then grouped based on shared metadata
information, such as author or date, and returned to the user.
However, documents that may be relevant to the user's query, but
lack the search query terms, are not considered.
[0007] Thus, there remains a need for a system and method for
increasing the efficiency of document search by identifying content
similarity across documents.
SUMMARY
[0008] Words and word frequencies contained in a particular type of
a document can serve as a "metaprint" of that type of document.
Document metaprints allow a user to search for and identify
documents of interest that may not precisely match the user's
search query.
[0009] One embodiment provides a computer-implemented system and
method for conducting a document search via metaprints. A plurality
of metaprints comprising metadata keywords and associated
frequencies of occurrence of metadata keywords within a plurality
of documents are maintained. A search query is received and
executed against the metaprints. The metadata keywords that match
the query are identified and the metaprints associated with the
matched metadata keywords are obtained. A further search query is
generated based on the obtained metaprints and is applied to a set
of documents. Those documents that match the frequencies of
occurrence associated with the obtained metaprints are
identified.
[0010] Still other embodiments of the present invention will become
readily apparent to those skilled in the art from the following
detailed description, wherein are described embodiments by way of
illustrating the best mode contemplated for carrying out the
invention. As will be realized, the invention is capable of other
and different embodiments and its several details are capable of
modifications in various obvious respects, all without departing
from the spirit and the scope of the present invention.
Accordingly, the drawings and detailed description are to be
regarded as illustrative in nature and not as restrictive.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1 is a block diagram showing a system for identifying
documents matching a document metaprint, in accordance with one
embodiment.
[0012] FIG. 2 is a is flow diagram showing a method for identifying
documents matching a document metaprint, in accordance with one
embodiment.
[0013] FIG. 3 is a process flow diagram showing, by way of example,
a method for generating a metadata search index for use in the
method of FIG. 2.
[0014] FIG. 4 is a block diagram showing, by way of example,
metadata types for use in the method of FIG. 2.
[0015] FIG. 5 is a block diagram showing tool types for metaprint
queries.
DETAILED DESCRIPTION
[0016] Previously-created and electronically-stored documents
contain valuable knowledge and can be leveraged for increased time
and cost efficiencies in preparing similar new documents. For
example, a typical software licensing agreement, although specific
to the particular parties involved, may have many similarities in
types of clauses and language used when compared to other software
license agreements. The words and word frequencies contained in a
type of a document can serve as a "metaprint" of that type of
document. The metaprint can then be used to identify similar
documents. FIG. 1 is a block diagram showing a system 100 for
identifying documents matching a document metaprint, in accordance
with one embodiment. By way of illustration, the system 100
operates in a distributed computing environment, which includes a
plurality of heterogeneous systems and metadata sources.
Henceforth, a single source of metadata will be referenced as a
"document," although metadata sources can include other forms of
non-document data. Documents include all forms and types of
structured and unstructured data including electronic message
stores, word processing documents, electronic mail, Web pages, and
graphical or multimedia data. Documents can also include subparts
of larger documents, for example, chapters or paragraphs of a book
or clauses or sections of contract or other legal document.
Notwithstanding, the documents could be in the form of structurally
organized data, such as stored in spreadsheets or databases.
Although discussed in reference to documents, the system and
methods disclosed herein can apply to any source of words that can
be associated with one or more pieces of metadata, including
username, IP address, or data repository location.
[0017] A search server 101 is coupled to a storage device 102,
which stores a corpus of documents 103 and associated metadata 104
in the form of structured or unstructured data, a search database
105 for maintaining a forward index 106 and search index, or
inverted index, 107 of the documents, and a metaprint database 108
for storing document metaprints, a metaprint forward index 109, and
a metadata search index 110. The forward index 106 stores a list of
terms, or words, for each document 103 along with the frequency and
location of each word in the document, and the search index 107
stores a list of documents 103 that contain a particular word. The
metaprint forward index 109 stores a list of metadata and word
frequencies for each document 103. The metaprint forward index 109
is resorted and merged, using specialized machine learning
techniques, to create the metadata search index 110, which stores
word frequencies for each metadata keyword, stored by metadata
keyword and merged across all documents.
[0018] The search server 101 is coupled to an intranetwork 121 and
executes one or more software modules for automated document
management, processing, indexing, and analysis, as discussed
herein. The modules can be implemented as a computer program or
procedure written as source code in a conventional programming
language and presented for execution by a CPU as object or byte
code, as is known in the art. Alternatively, the modules could also
be implemented in hardware, either as integrated circuitry or
burned into read-only memory components. Other types of modules and
module functions are possible.
[0019] The various implementations of the source code and object
and byte codes can be held on a computer-readable storage medium,
such as a floppy disk, hard drive, digital video disk (DVD), random
access memory (RAM), read-only memory (ROM) and similar storage
mediums, or embodied on a transmission medium in a carrier wave. In
a further embodiment, the search server 101 can be accessed via an
internetwork 122. The search server 101 can include other
components, such as such as a input/output ports, network
interfaces, and non-volatile storage.
[0020] A user interacts with the system through a user computer 124
that can be located locally on the intranetwork 121 or remotely
through the internetwork 122. The user inputs a search query of one
or more query terms through the user computer 124, which is then
received on, and executed by, the search server 101, as further
described below beginning with reference to FIG. 2. Search results
are returned for display, or other output, on the client computer
124, including a link, such as hyperlink, to access the documents
returned. Other modes of presentation are possible. In a further
embodiment, documents matching only a subset of the query terms are
obtained and presented to the user.
[0021] The search server 101 operates on documents 103 and metadata
104, which can be retrieved from the storage 102, as well as a
plurality of local and remote sources. The local sources include
documents 125 maintained in a storage device 126 coupled to a local
server 127 and documents 129 maintained in a storage device 130
coupled to a local user computer 131. The local server 127 and user
computers 124, 131 are interconnected to the search server 101 over
the intranetwork 121. In addition, the search server 101 can
identify and retrieve documents via a search application, or
spiders, from remote sources over the internetwork 122, including
the Internet, through a gateway 123 interfaced to the intranetwork
121. The remote sources include documents 132 maintained in a
storage device 133 coupled to a remote server 134 and documents 135
maintained in a storage device 136 coupled to a remote user
computer 137. Other document sources, either local or remote, are
possible.
[0022] In a further embodiment, the storage 102 maintains a cached
copy 103 of retrieved documents 125, 129, 132, 135. The cached
copies 103, including metadata 104, in the storage 102 can retain
the original formatting of the documents or have the original
formatting removed and stored in a normalized form. The cache 103
can also include a pointer, such as a hyperlink or file path, to
the original document and source. The storage 102 is updated
periodically to reflect any changes to the documents 103, 125, 129,
132, 135, such as new documents, deleted documents, or otherwise
altered documents.
[0023] Document metaprints allow a user to search and identify
documents of interest that may not otherwise match the user's
search query. FIG. 2 is a process flow diagram showing a method 200
for identifying documents matching a document metaprint, in
accordance with one embodiment. A search query comprising one or
more search terms is received from a user (step 201). The search
query is applied against the metadata search index 110 (step 202)
and metadata keywords matching the search query terms are
identified (step 203). In a further embodiment, various
linguistics, such as word stemming, synonym expansion, and spelling
corrections can be applied to the search query. The metadata search
index 110 contains word frequencies for each metadata keyword
merged across all cached documents 103 stored by metadata keyword,
which is called a metaprint, as further described below with
reference to FIG. 3. In a further embodiment, the word frequencies
can be filtered to contain only the words with a frequency above a
threshold value or to exclude common "stop" words such as "the" and
"a".
[0024] The metaprint associated with each of the matched metadata
keywords is retrieved (step 204) and used to generate a metaprint
search query (step 205). The metaprint search query is applied to
the search index (step 206) to identify documents that contain
similar word frequencies, or metaprint, but may not match the
metadata keywords either because the identified documents contain
different metadata keywords or are not tagged with any metadata.
Additionally, documents that may match a subset of a search query
and that would be ranked lower in the search results can be ranked
higher when using the metaprint search query. The identified
documents are then returned and displayed to the user as search
results (step 207). The search results can be presented as
pointers, for example, a list of hyperlinks, such as universal
resource locators, to the documents. The pointers can be to the
cached documents in the storage 102, to the original location of
the documents, or both. Users can then manually tag or rank the
search results or automated ranking can be conducted by, for
example, taking user click-through rates into account, which can be
used to improve the machine learning used to generate the
metaprints, as discussed further below with reference to FIG.
3.
[0025] In a further embodiment, a traditional search (not shown) is
carried out substantially in parallel to the metaprint search. The
search query is applied against the search index 117 and documents
containing the search terms, or a subset of the terms, are returned
to the user as search results.
[0026] In a still further embodiment, automated or user interactive
controls are available to adjust what search results are obtained
and displayed. Search results that match only the metadata
keywords, only the metaprint query, only the search query terms, or
permutations on combinations of the above possibilities can be
displayed.
[0027] In a yet further embodiment, metaprints and metaprint
queries can be leveraged to aid user identification and analysis of
documents, as further discussed below with reference to FIG. 5.
[0028] Metaprints are used identify documents of a particular type.
FIG. 3 is a process flow diagram showing, by way of example, a
method 300 for generating a metadata search index for use in the
method of FIG. 2. A metadata database is generated (step 301) and
stores a list of metadata keywords 104 for each document 103. Any
security information or access controls associated with the
document can also be captured and stored as part of the metadata
database or search index. In a further embodiment, each metadata
keyword can be associated with one or more synonyms. The synonyms
can be pre-generated or added manually by a user. The frequency of
words in each document is determined (step 302). The word
frequencies can be generated on-demand or previously generated,
such as in the forward index 109, and then accessed. The metadata
and word frequencies of each document are combined on a per
document basis to generate the metaprint forward index 109 (step
303) that stores a document metaprint for each metadata keyword.
The metaprint forward index 109 is resorted and merged to create
the metadata search index 110 (step 304) that stores word
frequencies for each metadata keyword merged across all documents,
stored by metadata keyword.
[0029] The metadata search index 110 is generated from the
metaprint forward index 109 using machine learning techniques
applied to the document corpus 102. The metaprint generated for
each metadata keyword utilizes multiple document numbers and types
that share one or more of the same, or synonymous, metadata
keywords, resulting in a more efficient and broadly applicable
approach than using only one document and that document's
associated word frequency.
[0030] All or a subset of metadata keywords and their associated
word frequencies can be used to generate the metadata search index
110. Some metadata keywords may have more predictive value than
others in identifying documents. One or more metadata keywords can
be removed, or filtered, from inclusion in the metadata search
index 110 based on automated or user-input mechanisms. In some
instances, for example, metadata regarding date of creation of a
document may be of lower interest when looking for examples of a
particular document type than metadata regarding the title of the
document. Thus, the less predictive metadata can be filtered from
the metadata search index 110. Other filtering methods are
possible.
[0031] In a further embodiment, the word frequencies associated
with the metadata keywords can be adjusted, or otherwise
manipulated, prior to generating the metadata search index 110. For
example, only words with a frequency above a threshold value would
be included as part of the metaprint. In a further embodiment,
common "stop" words such as "the" and "a" are excluded from the
metaprint. Additionally, use of a simple average or weighed average
of words instead of word frequencies is possible. In a further
embodiment, inverse word frequencies instead of word frequencies
can be used. Other word frequency adjustments are possible.
[0032] In a further embodiment, the machine learning used to
generate metaprints incorporates implicit or direct user feedback
to improve the machine learning. For example, implicit user
feedback can include taking user click-through rates into account.
Direct user feedback allows users to manually tag or rank the
search results. Other types of user feedback are possible.
[0033] Metadata associated with documents is utilized to generate
metaprints of different types of documents. FIG. 4 is a block
diagram showing, by way of example, metadata types 400 for use in
the method of FIG. 2. Types of metadata include title metadata 401,
author metadata 402, tag metadata 403, location metadata 404,
status metadata 405, section heading metadata 406, definitions
metadata 407, and security metadata 408. Other types of metadata
are possible.
[0034] Title metadata 401 can include the file name given a
document or can be identified within the content of a document.
Author metadata 402 includes the creator of a document and any
other individuals who have otherwise edited the document. Tag
metadata 403 includes any information with which the document has
been tagged. A user can manually tag an entire document or subset
of the document, by, for example, highlighting a paragraph and
adding one or more tags. Location metadata 404 includes the file
path of the document. Location metadata 404 can also include other
location information, such as IP address where the document is
located. Status metadata 405 includes whether the document is a
draft or a final version. Status can also include whether a
document was filed in court, for example a court pleading, or the
document was a signed contract.
[0035] Section heading metadata 406 and definition metadata 407 are
identified in the content of the document. Section heading metadata
406, for example, can include section heading of contract
provisions. Definition metadata 407 can include defined terms in an
agreement. Security metadata 408 includes information regarding
access control to a document. For example, if a particular user
does not have access to a specific document, the specific document
can be removed from the search results shown to the particular
user.
[0036] Metaprints and metaprint queries can be used in a variety of
ways to aid in discovery, analysis, and generation of additional
information regarding documents for the user. FIG. 5 is a block
diagram showing tool types 500 for metaprint queries. Types of
tools include document assembly 501, document scan 502, document
history 503, alert 504, trend analysis 505, optimization 506,
manage 507, standards 508, outside knowledge 509, and retention
510. Other types of tools are possible.
[0037] Document assembly 501 allows a user to generate new
documents from search results. A user generates search results, as
discussed above with reference to FIG. 2 and then can interact with
the results, for example drag and drop through the mouse, into a
new document. Multiple search results from the same or different
searches can be combined into the same document. The results in the
new document can then be assembled, edited, or rearranged as
needed. In one embodiment, the new document is automatically
formatted in a preferred document format, such as a contract or
other legal document. In a further embodiment, the new selected
search results are initially placed in an outline format that then
can be assembled into a document.
[0038] Document scan 502 scans documents to identifying portions,
such as sections of a document, that are or are not preferred by
the user. For example, a metaprint of a type of clause or a
particular version of a clause in a contract can be selected. The
selected metaprint is then used to search the document for a match.
Whether a matching clause is found in the document is then
displayed to the user. For example, a corporate legal department
can select one or more metaprints of clauses that the department
does not want to see in any contracts, such as an arbitration
clause, or clauses that are wanted, such as a limitation on
liability. When an inside or outside counsel is preparing to review
a contract for the company, the document scan 502 identifies
clauses that are historically objected to by the company or
identifies clauses the company requires or prefers to have in
contracts that are currently missing. Similarly, when a contract is
routed for approval, the tool checks the contract for required or
prohibited metaprints or variations of those metaprints .
[0039] Document history 503 analyzes a document and identifies any
similar documents and any associated information about the
identified documents. For example, a legal department may want to
find out whether they have had a contract or legal case similar to
a current contract or case in the past. Based on the current
document, such as a contract or court pleading, similar past
documents can be identified. Additionally, the number of similar
documents, which party or parties were involved, which attorneys
handled the previous matters, the costs involved, timeline for
resolution of the matter, outcome, and any other relevant
information can be identified from the associated metadata.
[0040] Alert 504 analyzes metaprints of a document, such as
relevant contractual or other language, that has been adjudicated
in a recent court decision and then searches the company's
databases for the relevancy of that decision to the company's
documents. For example, a contractual clause can be ruled as
unenforceable in a court decision. The metaprint of the clause is
used to search the document database of the company to identify any
documents that contain a similar clause.
[0041] Trend analysis 505 provides a user with trend details of
created documents. For example, a managing attorney can review what
documents are created the most, whether the type and number of
documents vary based on time of year, and which kind of documents
have been created with a particular client or customer of the
company.
[0042] Optimization 506 analyzes a selected process, such as
negotiation and execution of a standard contract, for possible
areas of improvement or optimization. For example if, during
drafting and negotiation of a contract, one of the clauses is
always eventually removed or otherwise edited prior to
finalization, the change can be identified. Additionally, any fall
back clause used instead of the original clause can be identified.
In the future, drafters can start with the fall back clause to
potentially speed up the negotiation process.
[0043] The manage 507 tool tracks what documents users are
associated with, such as drafting, editing, or reviewing within a
specified timeframe. Additionally, which user may have the most
knowledge about a particular topic or client can be determined from
the user metadata associated with a particular metaprint.
[0044] The standards 508 tool can be used to analyze documents
across a variety of corporations or other entities and identify the
documents that are the most broadly used. The most used documents
can be then used to develop adoption of standard documents, such as
contract clauses and terms. The more users work with standard
provisions, the faster transactions can become. This offers a
bottom-up approach to developing standards as actual documents used
by corporations are used in their development. For example, the top
two definitions of "source code" can be identified. Once
determined, the definitions can be used to play a role in naming
and getting standards created around the definitions.
[0045] The outside knowledge 509 tool allows a user to leverage
relevant knowledge from sources outside of the user's corporate
environment. For example, publishers of relevant legal knowledge,
such as textbooks, law firm newsletters, and law review articles,
can tag, or otherwise associate, their material with relevant
metaprints which can then be accessed by users. A publisher or
author can tag an article, such as on intellectual property
indemnity, with one or more metaprints for intellectual property
indemnity clauses. When a user searches for that type of clause in
the system 100, the results from the outside resources are made
available to the user in addition to the results from within the
corporate environment. The results from outside sources can be
displayed separately from, or integrated with, the inside
results.
[0046] Retention 510 utilizes a metaprint of a document that is
scheduled for deletion from the company's records pursuant to the
company's document retention policy and searches for all copies of
the document, or similar documents, within the corporate network.
The tool then confirms that all copies have been deleted, or
alternatively, identifies where copies of the document reside on
the databases or local drives and notifies the user.
[0047] While the invention has been particularly shown and
described as referenced to the embodiments thereof, those skilled
in the art will understand that the foregoing and other changes in
form and detail may be made therein without departing from the
spirit and scope.
* * * * *