U.S. patent application number 17/667149 was filed with the patent office on 2022-05-26 for systems and methods for facilitating data object extraction from unstructured documents.
The applicant listed for this patent is Palantir Technologies Inc.. Invention is credited to John Doyle, Brandon Marc-Aurele.
Application Number | 20220164521 17/667149 |
Document ID | / |
Family ID | 1000006124866 |
Filed Date | 2022-05-26 |
United States Patent
Application |
20220164521 |
Kind Code |
A1 |
Marc-Aurele; Brandon ; et
al. |
May 26, 2022 |
SYSTEMS AND METHODS FOR FACILITATING DATA OBJECT EXTRACTION FROM
UNSTRUCTURED DOCUMENTS
Abstract
Systems and methods are provided for facilitating data object
extraction from unstructured documents. Unstructured documents may
include data in an unorganized format, such as raw text. The system
may use natural language processing to determine characteristics of
the terms used in the unstructured document. The system may prompt
a user to select terms from the document corresponding in
characteristics to properties of a data object being generated. The
user may select terms from the document and the system may generate
a data object according to the selected terms.
Inventors: |
Marc-Aurele; Brandon;
(Arlington, VA) ; Doyle; John; (Washington,
DC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Palantir Technologies Inc. |
Palo Alto |
CA |
US |
|
|
Family ID: |
1000006124866 |
Appl. No.: |
17/667149 |
Filed: |
February 8, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16799788 |
Feb 24, 2020 |
11244102 |
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17667149 |
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15633239 |
Jun 26, 2017 |
10572576 |
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16799788 |
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62482457 |
Apr 6, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/117 20200101;
G06F 40/169 20200101; G06F 40/279 20200101; G06F 40/295
20200101 |
International
Class: |
G06F 40/117 20060101
G06F040/117; G06F 40/169 20060101 G06F040/169; G06F 40/279 20060101
G06F040/279; G06F 40/295 20060101 G06F040/295 |
Claims
1. A system for extracting object data from an unstructured
document, the system comprising: one or more processors and a
memory storing instructions that, when executed by the one or more
processors, cause the system to: retrieve an unstructured document,
the unstructured document comprising a plurality of terms, each
term including at least one word, wherein the unstructured document
is converted to a particular ontology; assign a term classification
to the plurality of terms of the unstructured document via natural
language processing; obtain a tagging template, of tagging
templates for the unstructured document, the tagging template
comprising tagging elements, each tagging element having an element
classification, the tagging template being obtained based on the
particular ontology, the tagging elements of the tagging template
corresponding to one or more properties of the particular ontology;
receive, from a user via an interface, a plurality of selected
terms corresponding to the plurality of tagging elements; generate,
from the unstructured document based on the plurality of selected
terms corresponding to the plurality of tagging elements, a data
object organized according to the particular ontology to extract
organized object-based data from the unstructured document; link
the first data object to a second data object based on the
particular ontology and the selected terms; ingest the first data
object or the second data object into an object-based data analysis
platform; and analyze the first data object or the second data
object using an object-based data analysis platform.
2. The system of claim 1, wherein the instructions further cause
the system to: receive, from the user, a selection of one or more
media components corresponding to the tagging elements; and the
generating of the data object is based on the selection of the one
or more media components.
3. The system of claim 1, wherein the linking of the first data
object to the second data object indicates a whether a link between
the first data object and the second data object comprises a
symmetric link based on whether the first data object or the second
data object comprises a parent or a child object.
4. The system of claim 3, wherein, in response to the linking of
the first data object to the second data object being asymmetric,
the linking indicates a directionality of the link between the
first data object and the second data object.
5. The system of claim 1, wherein the particular ontology comprises
a dynamic ontology in which associations between types of data
objects are updated.
6. The system of claim 1, wherein the instructions further cause
the system to: store an unstructured document in a first portion of
an electronic storage; and store tagging templates in a second
portion of the electronic storage.
7. The system of claim 6, wherein the instructions further cause
the system: store the data object in a third portion of the
electronic storage.
8. The system of claim 7, wherein the first portion, the second
portion, or the third portion of the electronic storage is remote
from the system.
9. The system of claim 1, wherein the instructions further cause
the system to: receive, from the user, a text input indicating one
or more relationships among the tagging elements; and the linking
of the first data object to the second data object is based on the
text input.
10. The system of claim 1, wherein the object comprises temporal
and geospatial data and is derived from a multimedia file.
11. A method for extracting object data from an unstructured
document, the method being performed on a computer system having
one or more physical processors programmed with computer program
instructions that, when executed by the one or more physical
processors, cause the computer system to perform the method, the
method comprising: retrieving an unstructured document, the
unstructured document comprising a plurality of terms, each term
including at least one word, wherein the unstructured document is
converted to a particular ontology; assigning a term classification
to the plurality of terms of the unstructured document via natural
language processing; obtaining a tagging template, of tagging
templates for the unstructured document, the tagging template
comprising tagging elements, each tagging element having an element
classification, the tagging template being obtained based on the
particular ontology, the tagging elements of the tagging template
corresponding to one or more properties of the particular ontology;
receiving, from a user via an interface, a plurality of selected
terms corresponding to the plurality of tagging elements;
generating, from the unstructured document based on the plurality
of selected terms corresponding to the plurality of tagging
elements, a data object organized according to the particular
ontology to extract organized object-based data from the
unstructured document; linking the first data object to a second
data object based on the particular ontology and the selected
terms; ingesting the first data object or the second data object
into an object-based data analysis platform; and analyzing the
first data object or the second data object using an object-based
data analysis platform.
12. The method of claim 11, further comprising: receiving, from the
user, a selection of one or more media components corresponding to
the tagging elements; and the generating of the data object is
based on the selection of the one or more media components.
13. The method of claim 11, wherein the linking of the first data
object to the second data object indicates a whether a link between
the first data object and the second data object comprises a
symmetric link based on whether the first data object or the second
data object comprises a parent or a child object.
14. The method of claim 13, wherein, in response to the linking of
the first data object to the second data object being asymmetric,
the linking indicates a directionality of the link between the
first data object and the second data object.
15. The method of claim 11, wherein the particular ontology
comprises a dynamic ontology in which associations between types of
data objects are updated.
16. The method of claim 11, further comprising: storing an
unstructured document in a first portion of an electronic storage;
and storing tagging templates in a second portion of the electronic
storage.
17. The method of claim 16, further comprising: storing the data
object in a third portion of the electronic storage.
18. The method of claim 17, wherein the first portion, the second
portion, or the third portion of the electronic storage is remote
from the system.
19. The method of claim 11, further comprising: receiving, from the
user, a text input indicating one or more relationships among the
tagging elements; and the linking of the first data object to the
second data object is based on the text input.
20. The method of claim 11, wherein the object comprises temporal
and geospatial data and is derived from a multimedia file.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
patent application Ser. No. 16/799,788, filed Feb. 24, 2020, which
is a continuation of U.S. patent application Ser. No. 15/633,239,
filed Jun. 26, 2017, now U.S. Pat. No. 10,572,576, which claims the
benefit under 35 U.S.C. .sctn. 119(e) of the U.S. Provisional
Application Ser. No. 62/482,457, filed Apr. 6, 2017, the contents
of which are hereby incorporated by reference in their
entirety.
TECHNICAL FIELD
[0002] This disclosure relates to approaches for facilitating data
object extraction from unstructured documents.
BACKGROUND
[0003] Under some approaches, a platform for analyzing various data
may be deployed. The data-analysis platform may support an
object-based data modeling framework. Data provided in an
unstructured format, such as a free-form text-based document,
cannot be analyzed via object-based data modeling platforms without
having some form of organization applied. Document tagging may
assist in classifying an unstructured document into an object
format, but conventional approaches have significant drawbacks.
Automated metadata tagging is typically very noisy and requires
significant manual review to produce useful results. Manual tagging
is laborious and frequently error-filled. Users may not fully grasp
the data object structure underlying the tagging scheme. Users may
struggle with the interface mechanisms of tagging. Users may be
unable to satisfy complex object ontologies through manual
tagging.
[0004] These and other drawbacks exist with some data management
systems.
SUMMARY
[0005] A claimed solution rooted in computer technology overcomes
problems specifically arising in the realm of computer technology.
In various implementations, a computing system is configured to
provide tools that facilitate tagging of unstructured documents for
the creation of structured data objects suitable for analysis via
an object-based data modeling framework. The system may provide a
user with a structured tagging scheme for a class of documents and
prompt the user to select a matching term from the document for
each element of the structured tagging scheme. To facilitate the
tagging, the system may user natural language processing analysis
of the document to highlight terms from the document that may match
the element. After tagging is complete, the system may transform
the document into a data object based on the user tagging. The
resulting computer system, thus, facilitates the creation of data
objects from unstructured documents via user tagging.
[0006] The system may be used for the creation of objects within an
object based data ontology from unstructured data. The data object
structure may serve as a template to be applied to the unstructured
data in a controlled fashion. Data objects may include multiple
object properties or data fields. The object properties may be
defined by the types of data that they support, e.g., names,
addresses, e-mail addresses, dollar amounts, etc. To assist a user
in properly tagging an unstructured document based on a data object
structure template for data object creation, the system may perform
natural language processing on the unstructured document. The
system may then prompt the user to select phrases, terms, words, or
other portions of the document that match each object property of
the data object to be created, highlighting potential matches in
the structured document. For example, to assist a user in defining
a payment record object comprising a payee, a payor, an amount
paid, and a date of payment, the system may first highlight all
potential payees (e.g., proper names and/or company names) from the
unstructured document and prompt the user to select the payee. The
system may continue this process by, in turn, highlighting all
potential payors, listed dollar amounts, and dates in the
unstructured document while prompting the user to select from
amongst these to populate the other object properties of the data
object. After completing a data object, the system may present the
user with another unstructured document to create another data
object.
[0007] In some implementations, a system for extracting object data
from an unstructured document is provided. The system may include
one or more processors and a memory storing instructions. When
executed by the one or more processors, the instructions may cause
the system to receive an unstructured document comprising a
plurality of terms, each term including at least one word, assign a
term classification to the plurality of terms of the document via
natural language processing, obtain a tagging template for the
document, the tagging template including a plurality of tagging
elements, each tagging element having an element classification,
receive, from a user via an interface, a plurality of selected
terms corresponding to the plurality of tagging elements, wherein
the term classifications of the selected terms matches the element
classification of the corresponding tagging elements, and generate
a data object from the unstructured document based on the plurality
of selected terms corresponding to the plurality of tagging
elements.
[0008] In some implementations, a method for extracting object data
from an unstructured document is provided. The method may be
performed on a computer system having one or more physical
processors programmed with computer program instructions that, when
executed by the one or more physical processors, cause the computer
system to perform the method. The method may include receiving, by
the computer system, an unstructured document comprising a
plurality of terms, each term including at least one word,
assigning, by the computer system, a term classification to the
plurality of terms of the document via natural language processing,
obtaining, by the computer system, a tagging template for the
document, the tagging template including a plurality of tagging
elements, each tagging element having an element classification,
receiving, by the computer system, from a user via an interface, a
plurality of selected terms corresponding to the plurality of
tagging elements, wherein the term classifications of the selected
terms matches the element classification of the corresponding
tagging elements; and generating, by the computer system a data
object from the unstructured document based on the plurality of
selected terms corresponding to the plurality of tagging
elements.
[0009] These and other objects, features, and characteristics of
the system and/or method disclosed herein, as well as the methods
of operation and functions of the related elements of structure and
the combination of parts and economies of manufacture, will become
more apparent upon consideration of the following description and
the appended claims with reference to the accompanying drawings,
all of which form a part of this specification, wherein like
reference numerals designate corresponding parts in the various
figures. It is to be expressly understood, however, that the
drawings are for the purpose of illustration and description only
and are not intended as a definition of the limits of the
invention. As used in the specification and in the claims, the
singular form of "a", "an", and "the" include plural referents
unless the context clearly dictates otherwise.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Certain features of various embodiments of the present
technology are set forth with particularity in the appended claims.
A better understanding of the features and advantages of the
technology will be obtained by reference to the following detailed
description that sets forth illustrative embodiments, in which the
principles of the technology are utilized, and the accompanying
drawings of which
[0011] FIG. 1 depicts an object based data structure.
[0012] FIG. 2 depicts a user interface for facilitating the
creation of data objects from unstructured data.
[0013] FIG. 3 depicts a system for data analysis assistance.
[0014] FIG. 4 depicts a process flow chart of a method for
performing data analysis, according to some implementations.
[0015] FIG. 5 depicts a block diagram of an example computer system
in which any of the embodiments described herein may be
implemented.
DETAILED DESCRIPTION
[0016] The technology described herein relates to systems and
methods for assisting a user in generating or extracting data
objects from unstructured data via prompted tagging. Object and
table based data structures may provide powerful tools for
generating insights about data and links between data. Unstructured
data, however, cannot benefit from such tools unless it is
organized. Unstructured data includes data that has not been
organized by a formal ontology or schema and may include, but is
not limited to, raw text, notes, filled forms, and others. In some
implementations, the technology may facilitate the generation or
extraction of data objects from unstructured data. The technology
described herein provides systems and methods for facilitating the
transformation of unstructured data into a structured, tabular, or
object based structure through a tagging process. The technology
described herein further provides systems and methods for
facilitating the application of a data ontology or schema to
unstructured data to facilitate further use of the data.
[0017] Implementations may involve the organization of unstructured
data into object based data structures defined by a data ontology
and/or into tabular based data structures defined by a data
schema.
[0018] FIG. 1 depicts an object based data structure 100. Object
based data structure 100 is centered around data objects 101. Each
data object 101 may include several components, including one or
more object properties 102, one or more data notes 103, one or more
media components 104, and one or more data links 105. The origin of
data stored in data object 101 may be stored in a data source
record 106 that indicates a data source 107 of the stored data.
[0019] The object model is the framework for how data is stored.
The object model is further defined by an ontology, defining the
types of data and how they are stored in a given system. The
ontology may be dynamic, updated to match evolving needs of the
system and analysts. The ontology may define types of data objects
101, object properties 102, and data links 105. The ontology may
further define which data types may be associated with each other.
Each data type may have a URI (uniform resource identifier) that
identifies it.
[0020] Object types define the kinds of things that may be
represented in the system, and provide structure for data objects
101. Object types may be derived from, for example, entity types,
event types, document types, and multimedia types. Event and
document types may have temporal and geospatial data directly
included within the data object 101 itself. An object type may
define the number and composition of properties 102, notes 103, and
media components 104 of a data object 101. The object type may
further define what other types of objects that data links 105 may
permit association with. For example, an entity object type may
define a data object 101 used to store data about a person, and may
include data properties 102 for storing name, address, occupation,
e-mail address, phone number, etc. Data links 105 of an entity
object 101 may permit the entity object 101 to be linked to other
entity objects (e.g., friends or business associates), linked to
event objects (e.g., events attended or invited to), linked to
document objects (e.g., authored), etc. In implementations of the
system, a user may define object types to be applied by the system
to unstructured data.
[0021] Property types may define the type and behavior of input
data. Property types may define the structure of the data stored in
an object property 102. The property type may define one or more
data fields, the type of data associated with the field, as well as
tools that may operate on the data fields. Property types may be
simple, including a single data field, and/or may be composite,
including multiple data fields. For example, an e-mail property
type may define an e-mail object property. For example, the e-mail
address john@acmeinc.com may be stored in an e-mail object property
as follows: URI: com.property.Email, Base Type: Composite, with
these components: EMAIL_USERNAME with the value "john,"
EMAIL_DOMAIN with the value "acmeinc.com." Further, the e-mail
property type may define tools for parsing and concatenating the
username and the domain, depending on what is required.
[0022] Link types may define the types of data links 105 that can
exist between two objects 101. Links may be symmetric or
asymmetric. All links may have one object that is considered the
"parent" object, and the other that is the "child." In the case of
symmetric links, e.g., "Spouse Of," which the parent and child
objects are not contextually important. In the case of asymmetric
links, like "Manager Of/Managed By," the parent and child may
reflects the direction of the link.
[0023] Thus, the ontology of the object based data system may
define the way in which data is organized in the object based data
system. The ontology defines the types of objects that may be
stored and the components of the defined data objects 101 as well
as the manner in which the defined data objects may link to one
another via data links 105.
[0024] FIG. 2 depicts a user interface for facilitating the
creation of data objects from unstructured data. A user may operate
the interface via a personal user device, such as a laptop, tablet,
smartphone, or other computing device. In some implementations, a
remote server may provide the data object tagging tasks to the user
device. Tasks may be transmitted to the user device individually,
one by one, as the user completes each previous task. Tasks may
also be transmitted to the user device in groups, wherein a user
device receives multiple tasks at once to be completed before
additional tasks are sent. In some implementations, data object
tagging tasks may be generated by the user device itself, based
either on remotely or locally stored data. In some implementations,
data object tagging tasks may be provided as part of a
crowd-sourcing project. In a crowd-sourcing project the same task
may be provided to more than one user to improve the accuracy of
tagging. In such an example, the system may extract a data object
according to all of the user responses.
[0025] User device 200 may provide a data object tagging task to a
user via user interface 250. User interface 250 may be configured
to provide prompts to a user to assist in a task of tagging
unstructured data for data object generation. A tagging task may be
provided to a user for the generation or extraction of a created
data object 201, including at least one or more object properties
202, one or more data notes 203, one or more media components 204,
and one or more data links 205. Unstructured data 212, for example,
free form text describing a cash transaction, may be displayed to a
user. The structure of the created data object 201 may provide a
template for the tagging task and may correspond to the description
provided by the unstructured data 212. For example, a created data
object 201 corresponding to unstructured data 212 describing a cash
transaction may include object properties 202 for a payor, a payee,
a dollar amount, and a transaction date.
[0026] The task provided to the user may include a tagging
structure 206 including one or more tagging elements 210 and a
narrative structure. Each tagging element 210 may include an
element classification corresponding to an object property type to
which it corresponds. For example, a tagging element 210
corresponding to the payor object property 202 may include an
element classification that matches the object property type of the
payor object property 202, i.e., a person's or institution's name.
The narrative structure 206 may include information about
relationships between object properties 202 to which tagging
elements 210 correspond. For example, a narrative structure
representing the relationship between a payor object A and a payee
object B may be "A paid B." Narrative structure may further include
information, such as hints, prompts, and questions, to describe
attributes or characteristics of object properties 210. For
example, prompt 214 may be included within the narrative structure
of a tagging structure 206.
[0027] The system may perform natural language processing on the
unstructured data 212. A natural language processing module in
operation on the system may perform content detection on the text
of unstructured data 212 and/or associated terms. As user herein,
"terms" refers to words, groups of words, and/or phrases that may
appear in the text of the unstructured data. Content detection may
be used to recognize the grammar, structure, and content of the
unstructured data 212. Content detection may be performed to
determine the nature of the terms used in the text of unstructured
data 212. For example, the natural language processing module may
identify terms for their role as parts of speech (nouns,
adjectives, etc.), role in sentences (subject, direct object, verb,
etc.), and role in the overall content of the unstructured data 212
(e.g., whether the term plays a major or minor role).
[0028] The system may analyze the natural language processing
results of the unstructured data 212 in accordance with tagging
structure 206 to identify one or more potential term
classifications of the terms used in the unstructured data 212. For
example, natural language processing may determine that a term is
the name of a person. When processed in accordance with tagging
structure 206, the person names may be interpreted as potentially
corresponding to object properties 202 that may accept a person's
name, e.g., the payor and payee fields in the cash transaction
example. Each term used in the unstructured data 212 may receive
one or more potential term classifications according to the tagging
structure 206 and natural language processing results. More than
one potential term classifications may be applied to each term
because some of the tagging elements 210 of tagging structure 206
may accept the same type of term. For example, the payor and payee
fields of a cash transaction may both accept names of people or
institutions.
[0029] The system may be configured to receive from the user,
selections of terms corresponding to the tagging elements 210 of
tagging structure 206. The system may assist the user in selecting
the most appropriate terms for each tagging element 210. The system
may highlight or otherwise emphasize or indicate a tagging element
210 field for which a corresponding term is sought. The system may
provide the user with a prompt 214 providing information about the
term being sought as a corresponding match to the highlighted
tagging element 210. Prompt 214 may include a question, e.g., "Who
is the payee?," as shown in FIG. 2. Prompt 214 may also include
hints, single words, descriptions, and any other information useful
in helping a user select an appropriate term to correspond to the
tagging element. The system may further provide suggested terms 211
from the unstructured data 212 as being potential matches for
tagging elements 210. Suggested terms 211 may be highlighted,
bolded, italicized, or otherwise emphasized or indicated within the
body of the unstructured document 212. Suggested terms 211 may be
selected according to attributes shared with tagging elements 210
for which they are suggested to conform. The system may receive,
from the user, a selection of one or more terms from the
unstructured data 212 to correspond to the prompted tagging element
210. It is not required that the selected term be chosen from the
suggested terms 211.
[0030] For example, in the cash transaction example, the system may
prompt the user to successively choose terms from unstructured
document 212 that correspond to tagging elements 210 based on the
tagging structure 206 "[PERSON/INSTITUTION] paid [AMOUNT] to
[PERSON/INSTITUTION] on [DATE]," as illustrated in FIG. 2. Prompt
214 may ask the user questions to assist in selecting the
appropriate term, such as "Who is the payor?", "How much was
paid?", "Who is the payee?", and "When was payment made?" In turn,
the system may provide suggested terms 211 that correspond to the
object property type required by tagging element 210. After a user
has made a selection for each of the tagging elements 210, the
tagging task may be completed by the system by generating created
data object 201 from the user's selections, including at least one
or more object properties 202, one or more data notes 203, and one
or more media components 204 corresponding to the tagging elements
210.
[0031] In some implementations, the system may further prompt the
user in the creation of one or more data links 205. For example,
the system may present the user with a plurality of records and
request that the user select the record corresponding to the just
selected payee (or any other tagging element 210 of tagging
structure 206.) The system may then generate a link 205 between the
newly created data object 201 and the record selected by the
user.
[0032] Tagging structure 206, tagging elements 210, created data
object 201, and unstructured data 212 are discussed with respect to
FIG. 2 in the context of a specific example. It is understood that
the technology is not limited to the presented example, and that
tasks generated by the system and presented to a user may be used
to extract objects from unstructured data of many alternative
forms.
[0033] FIG. 3 depicts a system 300 for facilitating the transform
and visualization of tabular based data. In one implementation,
system 300 may include a computer system 310, a user device 340, a
template storage module 350, an unstructured data storage module
351, and a created data object module 352, in communication via
network 302, and/or other components. Data modules 350, 351, and
352 are illustrated in FIG. 1 as separate from computer system 310
and user device 340. In some implementations, data modules 350,
351, 352 may be stored on the computer system 310, user device 340,
or at a remote location.
[0034] Template storage module 350 may be a computer memory
configured to store data. Template storage module 350 may store
tagging templates 206 for use by system 300 in facilitating tagging
of and object generation from unstructured data. Unstructured data
storage module 351 may be a computer memory configured to store
data. Unstructured data storage module 351 may store unstructured
data 212 from which the system 300 may extract organized data
objects. Created data object module 352 may be a computer memory
configured to store data. Created data object module 352 may store
created data objects 201 generated by tagging operations of system
300.
[0035] Computer system 310 may be configured as a server (e.g.,
having one or more server blades, processors, etc.), a personal
computer (e.g., a desktop computer, a laptop computer, etc.), a
smartphone, a tablet computing device, and/or other device that can
be programmed to receive tabular data or object based data, provide
services for the manipulation of the data, and provide services for
transformation and display of the data.
[0036] Computer system 310 may include one or more processors 332
(also interchangeably referred to herein as processors 332,
processor(s) 332, or processor 332 for convenience), one or more
storage devices 334, and/or other components. Processors 332 may be
programmed by one or more computer program instructions stored on
storage device 334. For example, processors 332 may be programmed
by database access module 312, natural language processing module
314, template creation module 316, tagging module 318, object
creation module 322, and/or other instructions that program
computer system 310 to perform various operations, each of which
are described in greater detail herein. As used herein, for
convenience, the various instruction modules, systems, and engines
will be described as performing an operation, when, in fact, the
various instructions program the processors 332 (and therefore
computer system 310) to perform the operation. Further details and
features of a computer system 310 configured for implementing
features of the described technology may be understood with respect
to computer system 500 as illustrated in FIG. 5.
[0037] User device 340 may be configured as a server (e.g., having
one or more server blades, processors, etc.), a personal computer
(e.g., a desktop computer, a laptop computer, etc.), a smartphone,
a tablet computing device, and/or other device that can be
programmed to receive tabular data or object based data, provide
services for the manipulation of the data, and provide services for
transformation and display of the data.
[0038] User device 340 may include one or more processors 342 (also
interchangeably referred to herein as processors 342, processor(s)
342, or processor 342 for convenience), one or more storage devices
344, and/or other components. Processors 342 may be programmed by
one or more computer program instructions. For example, processors
342 may be programmed by interface module 324, and/or other
instructions that program user device 340 to perform various
operations, each of which are described in greater detail herein.
As used herein, for convenience, the various instruction modules
will be described as performing an operation, when, in fact, the
various instructions program the processors 342 (and therefore user
device 340) to perform the operation. User device 340 may further
be programmed with database access module 312, natural language
processing module 314, template creation module 316, tagging module
318, object creation module 322, as described with respect to
computer system 310.
[0039] Various aspects of the transform facilitation system may
operate on computer system 310 and/or on user device 340. That is,
the various modules described herein may each operate on one or
both of computer system 310 and/or user device 340.
[0040] Database access module 312, may be a software module
operating on computer system 310 and/or user device 340. Database
access module 312 may be configured to provide system access to
data sources 350, 351, 352. Database access module 512 may be
configured to read and write to data source 350, 351, 352, as well
as carry out searches, queries, and any other database
functionality required by computer system 310 and/or user device
340. Database access module 312 may access data objects 201,
unstructured data 211, tagging structures 206, and any other data
stored in memory.
[0041] Natural language processing module 314 may be a software
module operating on computer system 310 and/or user device 340.
Natural language processing module 314 may include programming
instructions that cause the host computer system to perform natural
language processing operations, including context recognition on
unstructured textual data. In system 300, natural language
processing module 314 may perform natural language processing on
the terms of unstructured data 212. Natural language processing may
determine characteristics of the terms used in unstructured data
212, both individually (e.g., parts of speech) and contextually
(e.g., grammatical role in a sentence.) Natural language processing
module 314 may assign term classifications to one or more of all
the terms of the unstructured data 212. Identified characteristics
of terms from unstructured data 212 may be used by other aspects of
system 300 to assist with object generation and extraction.
[0042] Template creation module 316 may be a software module
operating on computer system 310 and/or user device 340. Template
creation module 316 may include programming instructions that cause
the host computer system to receive template creation instructions
from a user and create a tagging template 206. A tagging template
206 may be created to facilitate the creation, generation, and/or
extraction of a specific data object 201. Thus, tagging template
206 may include one or more tagging elements 210, each associated
with an object property of a data object 201. Template creation
module 316 may generate the appropriate tagging elements 210 for a
tagging template 206 according to the properties, fields,
attributes, and characteristics of a data object 201. Template
creation module 316 may further receive from a user a narrative
structure describing the relationship between the object properties
202 of a data object 201.
[0043] Tagging module 318 may be a software module in operation on
computer system 310 and/or user device 340. Tagging module 318 may
include programming instructions that cause the host computer
system to receive tagging instructions from a user and apply the
instructions to the terms of unstructured data 212. Tagging module
318 may receive, via interface module 324, one or more selected
terms of the unstructured data 212 indicated as corresponding to
one or more tagging elements 210. Term classifications of the
selected terms may match the element classifications of their
corresponding tagging elements 210.
[0044] Object creation module 322 may be a software module
operating on computer system 310 and/or user device 340. Object
creation module 322 may include programming instructions that cause
the host computer system to create or generate a data object 201
from the unstructured data 212 according to the selected terms
corresponding to the tagging elements 210 of the tagging template
206. As discussed above, the tagging elements 210 of tagging
template 206 may each correspond to an object property 202 of a
data object 201. Accordingly, the selected terms received from the
user, which each correspond to a tagging element 210 of the tagging
template 206, may also correspond to the object properties 202 of a
data object 201. Object creation module 322 may generate a data
object 201 based on the selected terms corresponding to the tagging
elements 201. Thus, a data object 201 may be generated from
unstructured data 212.
[0045] Interface module 324 may be a software module operating on
computer system 310 and/or user device 340. Interface module 324
may include programming instructions that cause the host computer
system to provide a computer display interface to a user and to
receive input from the user. Interface module 324 may generate and
provide interface 250 to a user via a computer display. Interface
module 324 may be configured to present to the user, via a
generated interface, unstructured data 212, tagging template 206,
and prompt 214. Interface module 324 may be configured to highlight
suggested terms 211 according to a comparison between element
classifications of tagging elements 210 and term classifications of
the suggested terms 211. Interface module 324 may be configured to
guide a user through the successive selection of selected terms
corresponding to tagging elements 210 by providing prompt 214 and
highlighting suggested terms 211. Interface module 324 may be
configured to receive user input identifying selected terms.
Interface module 324 may be configured to transmit information
indicative of the user inputs about selected terms to tagging
module 320 for creation of data object 201.
[0046] Although illustrated in FIG. 3 as a single component,
computer system 310 and user device 340 may each include a
plurality of individual components (e.g., computer devices) each
programmed with at least some of the functions described herein. In
this manner, some components of computer system 310 and/or user
device 340 may perform some functions while other components may
perform other functions, as would be appreciated. The one or more
processors 332, 342 may each include one or more physical
processors that are programmed by computer program instructions.
The various instructions described herein are exemplary only. Other
configurations and numbers of instructions may be used, so long as
the processor(s) 332, 342 are programmed to perform the functions
described herein.
[0047] Furthermore, it should be appreciated that although the
various instructions are illustrated in FIG. 3 as being co-located
within a single processing unit, in implementations in which
processor(s) 332, 342 includes multiple processing units, one or
more instructions may be executed remotely from the other
instructions.
[0048] Additionally, the modular software breakdown as illustrated
in FIG. 3 is prepared for illustrative purposes only. The various
instructions described with respect to specific software modules
may be implemented by alternative software modules configured in
different arrangements and with alternative function sets.
[0049] The description of the functionality provided by the
different instructions described herein is for illustrative
purposes, and is not intended to be limiting, as any of
instructions may provide more or less functionality than is
described. For example, one or more of the instructions may be
eliminated, and some or all of its functionality may be provided by
other ones of the instructions. As another example, processor(s)
332, 342 may be programmed by one or more additional instructions
that may perform some or all of the functionality attributed herein
to one of the instructions.
[0050] The various instructions described herein may be stored in a
storage device 334, 344 which may comprise random access memory
(RAM), read only memory (ROM), and/or other memory. The storage
device may store the computer program instructions (e.g., the
aforementioned instructions) to be executed by processor 332, 342
as well as data that may be manipulated by processor 332, 342. The
storage device may comprise floppy disks, hard disks, optical
disks, tapes, or other storage media for storing
computer-executable instructions and/or data.
[0051] The various components illustrated in FIG. 3 may be coupled
to at least one other component via a network 302, which may
include any one or more of, for instance, the Internet, an
intranet, a PAN (Personal Area Network), a LAN (Local Area
Network), a WAN (Wide Area Network), a SAN (Storage Area Network),
a MAN (Metropolitan Area Network), a wireless network, a cellular
communications network, a Public Switched Telephone Network, and/or
other network. In FIG. 3, as well as in other drawing Figures,
different numbers of entities than those depicted may be used.
Furthermore, according to various implementations, the components
described herein may be implemented in hardware and/or software
that configure hardware.
[0052] FIG. 4 depicts a process flow chart of a method 400 for
extracting data objects from unstructured data. The various
processing operations and/or data flows depicted in FIG. 4 (and in
the other drawing figures) are described in greater detail herein.
The described operations may be accomplished using some or all of
the system components described in detail above and, in some
implementations, various operations may be performed in different
sequences and various operations may be omitted. Additional
operations may be performed along with some or all of the
operations shown in the depicted flow diagrams. One or more
operations may be performed simultaneously. Accordingly, the
operations as illustrated (and described in greater detail below)
are exemplary by nature and, as such, should not be viewed as
limiting.
[0053] In an operation 402, method 400 may include obtaining
unstructured data 212. Unstructured data may be obtained, for
example, from unstructured data storage module 351, by database
access module 312. Obtained unstructured data 212 may be used for
the generation of data objects 201 based on the data. Obtaining
unstructured data 212 may include obtaining a single unstructured
document for the generation of a single corresponding data object
201 and/or may include obtaining many unstructured documents for
the generation of a corresponding number of data objects 201.
[0054] In an operation 404, the unstructured data 212 may be
processed via natural language processing. Natural language
processing module 314 may operate to classify the terms of
unstructured data 212. Unstructured data 212 may be stored in
unstructured data storage module 351 with information about the
term classifications for later access. In some implementations,
unstructured data 212 and associated classified terms may be
transmitted to interface module 324 for display to a user via a
computer display.
[0055] In an operation 406, a tagging template 206 may be obtained.
Tagging template 206 may be obtained by database access module 312
from template data storage module 350. The obtained tagging
template 206 may include one or more tagging elements 210 and a
narrative structure indicating a relationship between the tagging
elements 210.
[0056] In an operation 408, method 400 may include identifying
suggested terms 211 in unstructured data 212. Identified suggested
terms 211 may include a term classification (as determined by
natural language processing) that matches an element classification
of the tagging element 210 for which it is suggested as a
match.
[0057] In an operation 410, method 400 may include receiving one or
more selected terms corresponding to tagging elements 210 and
according to user input. Interface module 324 may be provided to
facilitate input from a user designating terms to correspond to the
tagging elements 210 of tagging template 206 as selected terms. The
selected terms may be transmitted by interface module 324 to
tagging module 318.
[0058] In an operation 412, method 400 may include generating or
creating a data object from the unstructured data 212 and according
the one or more selected terms corresponding to tagging elements
210 of tagging template 206. Each of the selected terms may
correspond to a tagging element 210, which may in turn correspond
to a data object property 202 of a created data object 201. The
generated data object 201 may thus be generated from the
unstructured data 212 in an organized fashion. The generated data
object 201 may be associated with the unstructured data 212 such
that the source of the object properties 202 data object 201 may
remain known. In some implementations, unstructured data 212 may be
included in data object 201.
[0059] Thus, object extraction method 400 may provide a method for
extracting organized object based data from an unstructured data
set. A user's assistance may be facilitated during the data
extraction process. The user may provide input by selecting terms
from the unstructured data set that match the object properties 202
of the data object 201 being created. The system may assist the
user in selecting the correct terms by highlighting or otherwise
emphasizing terms in the unstructured data 212 that may match the
object properties 202.
[0060] FIG. 5 depicts a block diagram of an example computer system
500 in which any of the embodiments described herein may be
implemented. The computer system 500 includes a bus 502 or other
communication mechanism for communicating information, one or more
hardware processors 504 coupled with bus 502 for processing
information. Hardware processor(s) 504 may be, for example, one or
more general purpose microprocessors.
[0061] The computer system 500 also includes a main memory 506,
such as a random access memory (RAM), cache and/or other dynamic
storage devices, coupled to bus 502 for storing information and
instructions to be executed by processor 504. Main memory 506 also
may be used for storing temporary variables or other intermediate
information during execution of instructions to be executed by
processor 504. Such instructions, when stored in storage media
accessible to processor 504, render computer system 500 into a
special-purpose machine that is customized to perform the
operations specified in the instructions.
[0062] The computer system 500 further includes a read only memory
(ROM) 508 or other static storage device coupled to bus 502 for
storing static information and instructions for processor 504. A
storage device 510, such as a magnetic disk, optical disk, or USB
thumb drive (Flash drive), etc., is provided and coupled to bus 502
for storing information and instructions.
[0063] The computer system 500 may be coupled via bus 502 to a
display 512, such as a cathode ray tube (CRT) or LCD display (or
touch screen), for displaying information to a computer user. An
input device 514, including alphanumeric and other keys, is coupled
to bus 502 for communicating information and command selections to
processor 504. Another type of user input device is cursor control
516, such as a mouse, a trackball, or cursor direction keys for
communicating direction information and command selections to
processor 504 and for controlling cursor movement on display 512.
This input device typically has two degrees of freedom in two axes,
a first axis (e.g., x) and a second axis (e.g., y), that allows the
device to specify positions in a plane. In some embodiments, the
same direction information and command selections as cursor control
may be implemented via receiving touches on a touch screen without
a cursor.
[0064] The computing system 500 may include a user interface module
to implement a GUI that may be stored in a mass storage device as
executable software codes that are executed by the computing
device(s). This and other modules may include, by way of example,
components, such as software components, object-oriented software
components, class components and task components, processes,
functions, attributes, procedures, subroutines, segments of program
code, drivers, firmware, microcode, circuitry, data, databases,
data structures, tables, arrays, and variables.
[0065] In general, the word "module," as used herein, refers to
logic embodied in hardware or firmware, or to a collection of
software instructions, possibly having entry and exit points,
written in a programming language, such as, for example, Java, C or
C++. A software module may be compiled and linked into an
executable program, installed in a dynamic link library, or may be
written in an interpreted programming language such as, for
example, BASIC, Perl, or Python. It will be appreciated that
software modules may be callable from other modules or from
themselves, and/or may be invoked in response to detected events or
interrupts. Software modules configured for execution on computing
devices may be provided on a computer readable medium, such as a
compact disc, digital video disc, flash drive, magnetic disc, or
any other tangible medium, or as a digital download (and may be
originally stored in a compressed or installable format that
requires installation, decompression or decryption prior to
execution). Such software code may be stored, partially or fully,
on a memory device of the executing computing device, for execution
by the computing device. Software instructions may be embedded in
firmware, such as an EPROM. It will be further appreciated that
hardware modules may be comprised of connected logic units, such as
gates and flip-flops, and/or may be comprised of programmable
units, such as programmable gate arrays or processors. The modules
or computing device functionality described herein are preferably
implemented as software modules, but may be represented in hardware
or firmware. Generally, the modules described herein refer to
logical modules that may be combined with other modules or divided
into sub- modules despite their physical organization or
storage.
[0066] The computer system 500 may implement the techniques
described herein using customized hard-wired logic, one or more
ASICs or FPGAs, firmware and/or program logic which in combination
with the computer system causes or programs computer system 500 to
be a special-purpose machine. According to one embodiment, the
techniques herein are performed by computer system 500 in response
to processor(s) 504 executing one or more sequences of one or more
instructions contained in main memory 506. Such instructions may be
read into main memory 506 from another storage medium, such as
storage device 510. Execution of the sequences of instructions
contained in main memory 506 causes processor(s) 504 to perform the
process steps described herein. In alternative embodiments,
hard-wired circuitry may be used in place of or in combination with
software instructions.
[0067] The term "non-transitory media," and similar terms, as used
herein refers to any media that store data and/or instructions that
cause a machine to operate in a specific fashion. Such non-
transitory media may comprise non-volatile media and/or volatile
media. Non-volatile media includes, for example, optical or
magnetic disks, such as storage device 510. Volatile media includes
dynamic memory, such as main memory 506. Common forms of
non-transitory media include, for example, a floppy disk, a
flexible disk, hard disk, solid state drive, magnetic tape, or any
other magnetic data storage medium, a CD-ROM, any other optical
data storage medium, any physical medium with patterns of holes, a
RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip
or cartridge, and networked versions of the same.
[0068] Non-transitory media is distinct from but may be used in
conjunction with transmission media. Transmission media
participates in transferring information between non-transitory
media. For example, transmission media includes coaxial cables,
copper wire and fiber optics, including the wires that comprise bus
502. Transmission media can also take the form of acoustic or light
waves, such as those generated during radio-wave and infra-red data
communications.
[0069] Various forms of media may be involved in carrying one or
more sequences of one or more instructions to processor 504 for
execution. For example, the instructions may initially be carried
on a magnetic disk or solid state drive of a remote computer. The
remote computer can load the instructions into its dynamic memory
and send the instructions over a telephone line using a modem. A
modem local to computer system 500 can receive the data on the
telephone line and use an infra-red transmitter to convert the data
to an infra-red signal. An infra-red detector can receive the data
carried in the infra-red signal and appropriate circuitry can place
the data on bus 502. Bus 502 carries the data to main memory 506,
from which processor 504 retrieves and executes the instructions.
The instructions received by main memory 506 may retrieves and
executes the instructions. The instructions received by main memory
506 may optionally be stored on storage device 510 either before or
after execution by processor 504.
[0070] The computer system 500 also includes a communication
interface 518 coupled to bus 502. Communication interface 518
provides a two-way data communication coupling to one or more
network links that are connected to one or more local networks. For
example, communication interface 518 may be an integrated services
digital network (ISDN) card, cable modem, satellite modem, or a
modem to provide a data communication connection to a corresponding
type of telephone line. As another example, communication interface
518 may be a local area network (LAN) card to provide a data
communication connection to a compatible LAN (or WAN component to
communicated with a WAN). Wireless links may also be implemented.
In any such implementation, communication interface 518 sends and
receives electrical, electromagnetic or optical signals that carry
digital data streams representing various types of information.
[0071] A network link typically provides data communication through
one or more networks to other data devices. For example, a network
link may provide a connection through local network to a host
computer or to data equipment operated by an Internet Service
Provider (ISP). The ISP in turn provides data communication
services through the world wide packet data communication network
now commonly referred to as the "Internet". Local network and
Internet both use electrical, electromagnetic or optical signals
that carry digital data streams. The signals through the various
networks and the signals on network link and through communication
interface 518, which carry the digital data to and from computer
system 500, are example forms of transmission media.
[0072] The computer system 500 can send messages and receive data,
including program code, through the network(s), network link and
communication interface 518. In the Internet example, a server
might transmit a requested code for an application program through
the Internet, the ISP, the local network and the communication
interface 518.
[0073] The received code may be executed by processor 504 as it is
received, and/or stored in storage device 510, or other
non-volatile storage for later execution.
[0074] Each of the processes, methods, and algorithms described in
the preceding sections may be embodied in, and fully or partially
automated by, code modules executed by one or more computer systems
or computer processors comprising computer hardware. The processes
and algorithms may be implemented partially or wholly in
application-specific circuitry.
[0075] The various features and processes described above may be
used independently of one another, or may be combined in various
ways. All possible combinations and sub-combinations are intended
to fall within the scope of this disclosure. In addition, certain
method or process blocks may be omitted in some implementations.
The methods and processes described herein are also not limited to
any particular sequence, and the blocks or states relating thereto
can be performed in other sequences that are appropriate. For
example, described blocks or states may be performed in an order
other than that specifically disclosed, or multiple blocks or
states may be combined in a single block or state. The example
blocks or states may be performed in serial, in parallel, or in
some other manner. Blocks or states may be added to or removed from
the disclosed example embodiments. The example systems and
components described herein may be configured differently than
described. For example, elements may be added to, removed from, or
rearranged compared to the disclosed example embodiments.
[0076] Conditional language, such as, among others, "can," "could,"
"might," or "may," unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
steps. Thus, such conditional language is not generally intended to
imply that features, elements and/or steps are in any way required
for one or more embodiments or that one or more embodiments
necessarily include logic for deciding, with or without user input
or prompting, whether these features, elements and/or steps are
included or are to be performed in any particular embodiment.
[0077] Any process descriptions, elements, or blocks in the flow
diagrams described herein and/or depicted in the attached figures
should be understood as potentially representing modules, segments,
or portions of code which include one or more executable
instructions for implementing specific logical functions or steps
in the process. Alternate implementations are included within the
scope of the embodiments described herein in which elements or
functions may be deleted, executed out of order from that shown or
discussed, including substantially concurrently or in reverse
order, depending on the functionality involved, as would be
understood by those skilled in the art.
[0078] It should be emphasized that many variations and
modifications may be made to the above-described embodiments, the
elements of which are to be understood as being among other
acceptable examples. All such modifications and variations are
intended to be included herein within the scope of this disclosure.
The foregoing description details certain embodiments. It will be
appreciated, however, that no matter how detailed the foregoing
appears in text, the invention can be practiced in many ways. As is
also stated above, it should be noted that the use of particular
terminology when describing certain features or aspects of the
invention should not be taken to imply that the terminology is
being re-defined herein to be restricted to including any specific
characteristics of the features or aspects of the invention with
which that terminology is associated. The scope of the invention
should therefore be construed in accordance with the appended
claims and any equivalents thereof.
Engines, Components, and Logic
[0079] Certain embodiments are described herein as including logic
or a number of components, engines, or mechanisms. Engines may
constitute either software engines (e.g., code embodied on a
machine-readable medium) or hardware engines. A "hardware engine"
is a tangible unit capable of performing certain operations and may
be configured or arranged in a certain physical manner. In various
example embodiments, one or more computer systems (e.g., a
standalone computer system, a client computer system, or a server
computer system) or one or more hardware engines of a computer
system (e.g., a processor or a group of processors) may be
configured by software (e.g., an application or application
portion) as a hardware engine that operates to perform certain
operations as described herein.
[0080] In some embodiments, a hardware engine may be implemented
mechanically, electronically, or any suitable combination thereof.
For example, a hardware engine may include dedicated circuitry or
logic that is permanently configured to perform certain operations.
For example, a hardware engine may be a special-purpose processor,
such as a Field-Programmable Gate Array (FPGA) or an Application
Specific Integrated Circuit (ASIC). A hardware engine may also
include programmable logic or circuitry that is temporarily
configured by software to perform certain operations. For example,
a hardware engine may include software executed by a
general-purpose processor or other programmable processor. Once
configured by such software, hardware engines become specific
machines (or specific components of a machine) uniquely tailored to
perform the configured functions and are no longer general-purpose
processors. It will be appreciated that the decision to implement a
hardware engine mechanically, in dedicated and permanently
configured circuitry, or in temporarily configured circuitry (e.g.,
configured by software) may be driven by cost and time
considerations.
[0081] Accordingly, the phrase "hardware engine" should be
understood to encompass a tangible entity, be that an entity that
is physically constructed, permanently configured (e.g.,
hardwired), or temporarily configured (e.g., programmed) to operate
in a certain manner or to perform certain operations described
herein. As used herein, "hardware-implemented engine" refers to a
hardware engine. Considering embodiments in which hardware engines
are temporarily configured (e.g., programmed), each of the hardware
engines need not be configured or instantiated at any one instance
in time. For example, where a hardware engine comprises a
general-purpose processor configured by software to become a
special-purpose processor, the general-purpose processor may be
configured as respectively different special-purpose processors
(e.g., comprising different hardware engines) at different times.
Software accordingly configures a particular processor or
processors, for example, to constitute a particular hardware engine
at one instance of time and to constitute a different hardware
engine at a different instance of time.
[0082] Hardware engines can provide information to, and receive
information from, other hardware engines. Accordingly, the
described hardware engines may be regarded as being communicatively
coupled. Where multiple hardware engines exist contemporaneously,
communications may be achieved through signal transmission (e.g.,
over appropriate circuits and buses) between or among two or more
of the hardware engines. In embodiments in which multiple hardware
engines are configured or instantiated at different times,
communications between such hardware engines may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware engines have access. For
example, one hardware engine may perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware engine may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware engines may also initiate communications
with input or output devices, and can operate on a resource (e.g.,
a collection of information).
[0083] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented engines that operate to perform one or more
operations or functions described herein. As used herein,
"processor- implemented engine" refers to a hardware engine
implemented using one or more processors.
[0084] Similarly, the methods described herein may be at least
partially processor- implemented, with a particular processor or
processors being an example of hardware. For example, at least some
of the operations of a method may be performed by one or more
processors or processor-implemented engines. Moreover, the one or
more processors may also operate to support performance of the
relevant operations in a "cloud computing" environment or as a
"software as a service" (SaaS). For example, at least some of the
operations may be performed by a group of computers (as examples of
machines including processors), with these operations being
accessible via a network (e.g., the Internet) and via one or more
appropriate interfaces (e.g., an Application Program Interface
(API)).
[0085] The performance of certain of the operations may be
distributed among the processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the processors or processor-implemented engines may be
located in a single geographic location (e.g., within a home
environment, an office environment, or a server farm). In other
example embodiments, the processors or processor-implemented
engines may be distributed across a number of geographic
locations.
Language
[0086] Throughout this specification, plural instances may
implement components, operations, or structures described as a
single instance. Although individual operations of one or more
methods are illustrated and described as separate operations, one
or more of the individual operations may be performed concurrently,
and nothing requires that the operations be performed in the order
illustrated. Structures and functionality presented as separate
components in example configurations may be implemented as a
combined structure or component. Similarly, structures and
functionality presented as a single component may be implemented as
separate components. These and other variations, modifications,
additions, and improvements fall within the scope of the subject
matter herein.
[0087] Although an overview of the subject matter has been
described with reference to specific example embodiments, various
modifications and changes may be made to these embodiments without
departing from the broader scope of embodiments of the present
disclosure. Such embodiments of the subject matter may be referred
to herein, individually or collectively, by the term "invention"
merely for convenience and without intending to voluntarily limit
the scope of this application to any single disclosure or concept
if more than one is, in fact, disclosed.
[0088] The embodiments illustrated herein are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed. Other embodiments may be used and derived
therefrom, such that structural and logical substitutions and
changes may be made without departing from the scope of this
disclosure. The Detailed Description, therefore, is not to be taken
in a limiting sense, and the scope of various embodiments is
defined only by the appended claims, along with the full range of
equivalents to which such claims are entitled.
[0089] It will be appreciated that an "engine," "system," "data
store," and/or "database" may comprise software, hardware,
firmware, and/or circuitry. In one example, one or more software
programs comprising instructions capable of being executable by a
processor may perform one or more of the functions of the engines,
data stores, databases, or systems described herein. In another
example, circuitry may perform the same or similar functions.
Alternative embodiments may comprise more, less, or functionally
equivalent engines, systems, data stores, or databases, and still
be within the scope of present embodiments. For example, the
functionality of the various systems, engines, data stores, and/or
databases may be combined or divided differently.
[0090] "Open source" software is defined herein to be source code
that allows distribution as source code as well as compiled form,
with a well-publicized and indexed means of obtaining the source,
optionally with a license that allows modifications and derived
works.
[0091] The data stores described herein may be any suitable
structure (e.g., an active database, a relational database, a
self-referential database, a table, a matrix, an array, a flat
file, a documented-oriented storage system, a non-relational No-SQL
system, and the like), and may be cloud-based or otherwise.
[0092] As used herein, the term "or" may be construed in either an
inclusive or exclusive sense. Moreover, plural instances may be
provided for resources, operations, or structures described herein
as a single instance. Additionally, boundaries between various
resources, operations, engines, engines, and data stores are
somewhat arbitrary, and particular operations are illustrated in a
context of specific illustrative configurations. Other allocations
of functionality are envisioned and may fall within a scope of
various embodiments of the present disclosure. In general,
structures and functionality presented as separate resources in the
example configurations may be implemented as a combined structure
or resource. Similarly, structures and functionality presented as a
single resource may be implemented as separate resources. These and
other variations, modifications, additions, and improvements fall
within a scope of embodiments of the present disclosure as
represented by the appended claims. The specification and drawings
are, accordingly, to be regarded in an illustrative rather than a
restrictive sense.
[0093] Conditional language, such as, among others, "can," "could,"
"might," or "may," unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
steps. Thus, such conditional language is not generally intended to
imply that features, elements and/or steps are in any way required
for one or more embodiments or that one or more embodiments
necessarily include logic for deciding, with or without user input
or prompting, whether these features, elements and/or steps are
included or are to be performed in any particular embodiment.
[0094] Although the invention has been described in detail for the
purpose of illustration based on what is currently considered to be
the most practical and preferred implementations, it is to be
understood that such detail is solely for that purpose and that the
invention is not limited to the disclosed implementations, but, on
the contrary, is intended to cover modifications and equivalent
arrangements that are within the spirit and scope of the appended
claims. For example, it is to be understood that the present
invention contemplates that, to the extent possible, one or more
features of any embodiment can be combined with one or more
features of any other embodiment.
[0095] Other implementations, uses and advantages of the invention
will be apparent to those skilled in the art from consideration of
the specification and practice of the invention disclosed herein.
The specification should be considered exemplary only, and the
scope of the invention is accordingly intended to be limited only
by the following claims.
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