U.S. patent application number 13/740446 was filed with the patent office on 2013-05-23 for mind map with data feed linkage and social network interaction.
The applicant listed for this patent is Jan Mirus. Invention is credited to Jan Mirus.
Application Number | 20130132339 13/740446 |
Document ID | / |
Family ID | 44912653 |
Filed Date | 2013-05-23 |
United States Patent
Application |
20130132339 |
Kind Code |
A1 |
Mirus; Jan |
May 23, 2013 |
Mind Map with Data Feed Linkage and Social Network Interaction
Abstract
Embodiments of the disclosed technology comprise a method of
augmenting a mind map of a plurality of objects based on at least
one data feed. The method comprises providing an interface which
contains visual representations of objects and associates semantic
data with these objects. The interface allows for a user to access
data from a data feed, and it analyzes these data in order to
identify additional objects which may be semantically related to
the object. A visual representation of the additional object is
then augmented with a connector to the original object. Information
about the relationships of the objects may be certified
automatically or manually.
Inventors: |
Mirus; Jan; (Munchen,
DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Mirus; Jan |
Munchen |
|
DE |
|
|
Family ID: |
44912653 |
Appl. No.: |
13/740446 |
Filed: |
January 14, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12779140 |
May 13, 2010 |
8380716 |
|
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13740446 |
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Current U.S.
Class: |
707/610 ;
707/736; 707/737 |
Current CPC
Class: |
G06F 16/954 20190101;
G06F 16/285 20190101; G06F 16/27 20190101; G06Q 10/10 20130101;
G06F 16/284 20190101 |
Class at
Publication: |
707/610 ;
707/736; 707/737 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1) A method of augmenting a mind map based on at least one data
feed accessed via a network node, where the method comprises: a)
generating and exhibiting a visual representation of an object; b)
associating semantic data with said object, wherein said semantic
data describes a characteristic or feature of said object; c)
receiving from a user identification of at least one said data feed
to be accessed via said network node; d) providing a control that
is operable to access data from said at least one data feed
including said data feed identified by said user via said network
node; e) analyzing output of said at least one data feed as said
output becomes available and identifying, in real-time, at least
one additional object which is semantically related to said object;
and f) augmenting said visual representation of said object with a
connector from said object to a visual representation of said
additional object, said visual representation of said additional
object providing a visual indication of a source of said additional
object.
2) The method of claim 1, further comprising ft steps of:
disambiguating said semantic data corresponding to said object by
determining third party data to be related to said object; and
following said disambiguating, updating said visual representation
of said object to visually indicate at least one of said semantic
data having been disambiguated and a source of said third party
data used for said disambiguating.
3) The method of claim 2, wherein said step of disambiguating data
comprises using an object found in another mind map as a data
source for disambiguating said semantic data.
4) (canceled)
5) (canceled)
6) The method of claim 1, wherein a visual representation of said
connector comprises a visual representation of at least one of
interdependency between said object and said additional object and
of information pertaining to a type of an association of which it
is representative.
7) The method of claim 6, wherein said type of association between
said object and said additional object is automatically assigned
using said semantic data, wherein said semantic data describes a
characteristic or feature of said object, and wherein said visual
representation of said connector includes a visual indication of a
source of said semantic data.
8) (canceled)
9) (canceled)
10) (canceled)
11) The method of claim 1, further comprising a step of adding
semantic information corresponding to said object by classifying
said object as a member of a class, wherein said class has
associated semantic information, and providing a visual indication
of said class in said visual representation of said object.
12) The method of claim 11, wherein said classifying comprises
classifying said object as a member of a class comprising data
representative of time.
13) The method of claim 11, wherein said classifying comprises
classifying said object as a member of a class comprising data
associated with individual social networking profiles of people,
and data from said social networking platform is imported.
14) The method of claim 11, wherein said classifying comprises
classifying said object as a member of a class comprising data
representative of geographic locations.
15) (canceled)
16) The method of claim 1, wherein said data feed identified by
said user comprises data associated with at least one person
imported from a third party social networking platform, wherein
said at least one person is selected from a group consisting of
said user, contacts of said user, and all users of said social
networking platform.
17) The method of claim 16, further comprising: updating said
social networking platform data feed identified by said user by
importing new data as it becomes available from said third party
social networking platform; and automatically synchronizing said
mind map with said updated social networking platform data feed by
mapping said new data to said mind map by creating a new object
with a corresponding new visual representation thereof as said new
object becomes available in said social networking platform data
feed, said new visual representation including a visual indication
of a source of said new object.
18) The method of claim 17, further comprising: associating
semantic data with said new object and providing a visual
indication of said semantic data associated with said new object in
said new visual representation; and augmenting said new visual
representation of said new object with a new connector from said
new object to a pre-existing visual representation of a preexisting
object, said new connector including a visual indication of at
least one of a type of association and an interdependency between
said new object and said pre-existing object.
19) The method of claim 17, further comprising, by way of
interacting with said visual representation of a said object, a
response to data received from said social networking platform data
feed selected by said user and instructing said social networking
platform to augment said data feed selected by said user with data
provided during said interacting.
20) A method of providing a visual representation of a plurality of
objects where the method comprises: a) providing an interface
operable to generate a visual representation of a first
thoughtbubble, wherein said first thoughtbubble has associated
semantic data, which semantic data describes a characteristic or
feature of said first thoughtbubble, wherein said visual
representation of said first thoughtbubble includes a visual
indication of a source of said first thoughtbubble; b) generating a
visual representation of a second thoughtbubble, wherein said
second thoughtbubble has associated semantic data, wherein said
visual representation of said second thoughtbubble includes a
visual indication of a source of said second thoughtbubble; c)
providing an interface operable to generate a visual representation
of a connector between said first thoughtbubble and said second
thoughtbubble, d) generating the connector wherein said connector
has associated semantic data, said connector having a visual
representation including at least one of a visual indication of a
source of said semantic data associated with said connector and a
visual indication of information pertaining to a type of
association represented by said connector; e) updating said
semantic data associated with one or both of said thoughtbubbles
based on said semantic data associated with said connector; f)
updating said semantic data associated with said first
thoughtbubble based on said semantic data associated with said
second thoughtbubble; g) providing a user interface for user
identification of at least one data feed and operable means for
accessing data from said at least one data feed identified by said
user by way of a network node; h) analyzing a result provided from
said at least one data feed identified by said user as said result
becomes available from said at least one data feed, in order to
identify information that is semantically related to each of said
thoughtbubbles; and i) augmenting said visual representation of
said connector with a visual indicator that is representative of
interdependency of said first and second thoughtbubbles with
respect to each other.
21) The method of claim 20, wherein said at least one data feed
identified by said user is a social networking data feed and said
social networking content is augmented based on an addition of a
connector or content within a thoughtbubble in a said visual
representation.
22) The method of claim 1, wherein said step of identifying
additional objects further comprises identifying semantic data
associated with a said additional object based on semantic data
associated with an object from a separate data source; wherein said
separate data source is a mind map of another user.
23) (canceled)
24) A method of augmenting a mind map based on at least one data
feed accessed via a network node, where the method comprises: a)
providing a user interface for user addition of a thoughtbubble to
a mind map; and b) automatically augmenting said mind map to
include a second thoughtbubble based on said thoughtbubble added by
said user, wherein said second thoughtbubble is created in
real-time based on data obtained from at least one user selected
data feed; and wherein a choice is presented to said user for
selecting said at least one user selected data feed to comprise a
social network and data associated with at least some of said
user's pre-selected friends on said social network by way of an
interface of said social network.
25) The method of claim 6, wherein said visual representation of at
least one of interdependency between said object and said
additional object and of information pertaining to a type of
association comprises at least one of a connector color, a
connector line type, a connector weight, and a connector line
boldness.
26) The method of claim 1, wherein said associating semantic data
comprises, in a graphic user interface, receiving from a user an
indication of at least one type of semantic data to be associated
with said object.
27) The method of claim 1, also comprising a step of, prior to said
associating semantic data, allowing a user to disambiguate an
identity of said object in order to allow said associating to
accurately associate said semantic data with said object.
Description
FIELD OF THE DISCLOSED TECHNOLOGY
[0001] The disclosed technology relates generally to mind maps.
More specifically, the disclosed technology relates to semantic
communications between mind maps and third party data sources and
networks.
BACKGROUND OF THE DISCLOSED TECHNOLOGY
[0002] Social networking and other networked tools have become an
increasingly important part of professional and personal lives. As
online tools have grown in popularity, these tools have allowed web
users to generate access and link more and richer data sources.
With this growth, these tools have grown in their potential to
provide insight into relationships between humans and concepts. A
problem that is presented by these increasingly rich webs of
information is their complexity.
[0003] Among the traditional tools utilized in organizing access to
information networks are search engines. Common electronic
information "search engines" and search technology providers
include: AOL, Northern Light, Yahoo, Google, AlltheWeb, Infoseek,
Teoma, AltaVista, AskJeeves, HotBot, Inktomi, LookSmart, Lycos,
FAST, Overture, About.com, Roadrunner, various Microsoft search
engines, FindWhat.com, E-spotting, Search.com, and A9.com.
Typically, these search engines provide textual, "key word," word
and/or text based searching as human interface options for access
to associated electronic information. Incorporation of "Boolean"
connector (such as "and," "or," "not," etc.) functionality and/or
"wild characters" (such as * or $) have proven beneficial. It is
common for web browsers such as Mozilla Firefox, Google Chrome,
Apple Safari, and so forth to incorporate an interface, or,
interfaces, to one or more of these search engines.
[0004] As the numbers and types of information in searchable
networks have expanded, they have become increasingly difficult for
the web users who have created them to understand. This problem has
become exacerbated through the explosion of online information
associated with Web 2.0 technologies. Web 2.0 is a catch-all term
for a group of technologies which include web applications that
facilitate interactive information sharing, interoperability,
user-centered design and collaboration on the Internet. Examples of
Web 2.0 include web-based communities, hosted services, web
applications, social-networking sites, video-sharing sites, wikis,
and blogs. A Web 2.0 site allows its users to interact with other
users or to change website content, in contrast to non-interactive
websites where users are limited to the passive viewing of
information that is provided to them.
[0005] Individuals, community members, employees, shareholders and
customers are all impacted by the development of these complex
online networks. Locating, accessing and utilizing all of this
electronic information sometimes presents a daunting task. This is
especially true with regard to non-structured electronic
information that is arranged in the ad hoc webs generated as web
user's link to each other's information. Unlike the almost
universally utilized Dewey decimal system for locating items in
physical libraries, a diverse array of "standards" has been
introduced for accessing electronic information.
[0006] These networks can be built by web users interacting with
digital networks that transmit and receive many different media and
messages. When uploaded, these media and messages can become linked
with the web user's online profiles and interests. As different web
users link to other's profiles and data, rich networks are created.
Typically, the information in these networks is simply listed in
chronological order. Trying to follow a page of updates by hundreds
of "friends" results in a disorganized and never-ending list of
content with no real structure in which to make sense of all the
data. Still further, organizing and distilling this information is
a difficult and unsolved task in the art.
[0007] Accordingly, there exists the need for an improved
electronic information organization and access system that
facilitates quick and efficient information gathering and
understanding for networks of data created by concepts, pages and
data sources over the Internet.
SUMMARY OF THE DISCLOSED TECHNOLOGY
[0008] The disclosed technology described herein addresses a need
unfulfilled in the prior art by providing an electronic information
visualization and access method that facilitates quick and
efficient visualization of networks created by concepts, pages and
data sources over the Internet.
[0009] Accordingly, it is an object of the disclosed technology to
provide an improved electronic visualization and access method
which will encourage visualization and analysis of the complex
relationships created by Web 2.0 tools such as online
encyclopedias, online question and answer services (e.g. "Yahoo
Answers"), and social networking platforms (e.g. Facebook and
LinkedIn).
[0010] It is also an object of the disclosed technology to provide
an improved electronic visualization and access system which is
automatically updatable or constantly or persistently updated
(defined as at least as often as new data becomes available), in
order to reflect changes made to the data sources which feed data
to a visualization system of the disclosed technology.
[0011] It is a further object of the disclosed technology to
provide an improved electronic visualization and access system
which uses semantic data to verify data feed information and to
develop visualizations of rich data types that include web pages
and other sources that are constantly changing.
[0012] Therefore, provided herein is a method for mapping semantic
data using an electronic information visualization and access
method that facilitates quick and efficient visualization of
networks created by concepts, pages and data sources over the
Internet.
[0013] In an embodiment of the disclosed technology, a method of
augmenting a mind map based on at least one data feed accessed via
a network node follows the foregoing steps. First, a visual
representation of an object is generated and exhibited. An
"object," as used in this disclosure, refers to a concept, idea,
goal, person, location, or (data) file. Next, semantic data are
associated with the object. A control is then provided that is
operable to access data from a data feed via a network node
(defined as a point on a electronic network). The data feed is one
in which is provided, generally by a third party, over a network
cloud and comprising, in some embodiments, an updating stream of
data. This stream of data is appendable by a user of the disclosed
technology or by a plethora of third parties. Examples include a
social networking profile with comments and the like, or an article
with links to others. A "control," as used in this disclosure,
refers to a component of the interface which allows a user to
initiate some action manually, typically by the click of a mouse
button or keystroke. An output of the data feed is then analyzed,
in order to identify additional objects which may be semantically
related to the original object. The visual representation of the
original object is then augmented with a connector which connects
the original object and the additional object.
[0014] In an embodiment of a method of the disclosed technology,
the step of identifying additional objects may also prompt a user
with a related object imported from another mind map. The source of
that related object may be signified by visual indicia which
accompanies the visual representation of the related object.
[0015] In one embodiment of a method of the disclosed technology,
an additional step of certifying (also known as "disambiguating")
the semantic data which corresponds to the object. The
certification may be carried out automatically, using a data
source, or manually by a user. In another embodiment, the
certification step may be performed by using an object found in
another mind map as a data source for certifying the semantic data.
Some visual indicia may be included with the visual representation
of the object in order to signify whether the corresponding
semantic data has been certified and which certification data
source was use. The certification data source may include, but is
not limited to, a hypertext markup language (HTML) document, a
global navigation satellite system receiver, or a storage
medium.
[0016] In another embodiment, a similar icon may be used to signify
the type of association one object forms with another. In other
words, it may contain information which classifies the type of
interrelation between the two objects that are linked. A further
embodiment of a method of the disclosed technology automatically
assigns a type of association to the connector using semantic data.
An alternative embodiment allows a type of association to be
assigned manually by a user.
[0017] In yet another embodiment of a method of the disclosed
technology, an additional step of arranging the plurality of
objects in a hierarchical fashion based on the interdependency of
the original object with at least one additional object. This
embodiment may further comprise the step of centralizing an object
which possesses a greater number of connectors. Thus creating a
hierarchical or graph/rhizome structure where the object with the
most associated objects is centered, or the connectors are
emboldened based on the number of connections.
[0018] In yet a further embodiment of a method of the disclosed
technology, an additional step of adding semantic information
corresponding to the object by way of classifying the object as a
member of a class is disclosed. The class also has semantic
information associated with it. In one embodiment of the disclosed
technology, the class contains or comprises data associated with
individual people that is imported from a social networking
platform. In an alternative embodiment of the disclosed technology,
the class contains or comprises information regarding geographic
locations.
[0019] In another embodiment of a method of the disclosed
technology, the connector contains information indicative of the
type of association of which it is representative.
[0020] In yet another embodiment, the data feed comprises or
contains information associated with a person imported from a third
party social networking platform. In this embodiment, a method of
the disclosed technology involves one or a plurality of the
following additional steps. The social networking platform data may
be used to update a visual representation of interconnected or
semantically linked data by importing new data as it becomes
available from the third party social networking platform. Then,
the new data are automatically mapped to the interface by creating
a new object with a corresponding new visual representation
thereof. New semantic data and a new visual representation will
then be associated with the new object. Furthermore, a new
connector will be provided to link the new object with a
pre-existing object.
[0021] Another method of providing a visual representation of a
plurality of objects follows the foregoing steps. First, an
interface is provided that is operable to generate a visual
representation of a first thoughtbubble. A "thoughtbubble," as used
in this disclosure, is a visual representation of an object or
concept which contains semantic data associated with the object.
The thoughtbubble may be representative of a concept, idea, goal,
person, location, or file. This first thoughtbubble has semantic
data associated with it. Next, a visual representation of a second
thoughtbubble is generated. The second thoughtbubble also has
semantic data associated with it. Subsequently, an interface is
provided that is operable to generate a connector between the first
thoughtbubble and the second thoughtbubble. A connector is then
generated and contains associated semantic data as well. Next, the
semantic data associated with one or both of the thoughtbubbles is
updated to reflect the semantic data associated with the connector.
Now, the semantic data associated with the first thoughtbubble is
updated based on the semantic data associated with the second
thoughtbubble. An operable means for accessing data from a data
feed is then provided. The data feed produces a result which is
then analyzed for information that is semantically related to each
of the thoughtbubbles. Finally, a visual representation is
augmented for each of the thoughtbubbles. It contains or comprises
a visual indicator which is representative of the interdependency
of the thoughtbubbles with respect to each other.
[0022] Still further, in accordance with any of the embodiments
described above, by way of interacting with said visual
representation of an object, a response to data received from a
social networking platform data feed, or new data to be injected
into a social networking platform, may be created through the
visual representation provided in embodiments of the disclosed
technology. That is, a thoughtbubble or visualization may be
interacted with to instruct a social networking platform to augment
the data feed or data on the social network with data provided
during the interaction with the thoughtbubble or visualization.
[0023] In accordance with these and other objects which will become
apparent hereinafter, the disclosed technology will now be
described with particular reference to the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] FIG. 1 is an example of a mind map visualization
representation of an interface of a method of the disclosed
technology.
[0025] FIG. 2 shows the mind map visualization representation of
FIG. 1 with the addition of connectors.
[0026] FIG. 3 shows the mind map visualization representation of
FIG. 2 with certain objects becoming centralized, based on the
number of connections.
[0027] FIG. 4 shows the mind map visualization representation of
FIG. 2 becoming merged with another mind map.
[0028] FIG. 5 shows an alternative embodiment of the mind map
visualization representations of FIG. 4.
[0029] FIG. 6 is another example of a mind map visualization
representation showing objects based on data associated with an
individual which is imported from a third-party social networking
platform.
[0030] FIG. 7 is yet another example of a mind map visualization
representation in which some objects include a visual
representation identifying a classification of semantic
content.
[0031] FIG. 8 is yet another example of a mind map visualization
representation in which the connections include a visual
representation identifying the type of association of which they
are indicative.
[0032] FIG. 9 shows an alternative embodiment of a method of the
disclosed technology in which data is certified using an additional
step of prompting a user.
[0033] FIG. 10 shows a flow chart of a method of carrying out
embodiments of the disclosed technology.
[0034] FIG. 11 shows a flow chart of a method of carrying out
embodiments of the disclosed technology relative to certification
and updating of semantic data.
[0035] FIG. 12 shows an alternative embodiment of a method of the
disclosed technology in which a related object is proposed to a
user from another user's mind map.
[0036] FIG. 13 shows an alternative embodiment of a method of the
disclosed technology in which data is certified using data
associated with another user's mind map.
[0037] FIG. 14 shows a high-level block diagram of a device that
may be used to carry out a method of the disclosed technology
[0038] A better understanding of the disclosed technology will be
obtained from the following detailed description of the preferred
embodiments taken in conjunction with the drawings and the attached
claims.
DETAILED DESCRIPTION OF EMBODIMENTS OF THE DISCLOSED TECHNOLOGY
[0039] Embodiments of the disclosed technology provide a method of
augmenting a mind map of a plurality of objects based on at least
one data feed. The method comprises or consists of providing an
interface which contains visual representations of objects and
associates semantic data with these objects. The interface allows
for a user to access data from a data feed, and it analyzes these
data in order to identify additional objects which may be
semantically related to the object. A visual representation of the
additional object is then augmented with a connector to the
original object. Information about the relationships of the objects
may be corroborated automatically or manually
[0040] With reference now to the drawings, a method is shown for
augmenting a mind map based on at least one data feed using an
interface and associated semantic data.
[0041] FIG. 1 is an example of a mind map visualization
representation of an interface of a method of the disclosed
technology. A "mind map" is a tool that allows users to create
visual representations of relationships between simple data
structures, such as files, objects, concepts, or text segments. A
mind map is a diagram used to represent words, ideas, tasks, or
other items linked to and arranged around a central key word or
idea. The elements of a given mind map are arranged intuitively
according to the importance of the concepts, and are classified
into groupings, branches, or areas, with the goal of representing
semantic or other connections between portions of information. The
method begins with the identification of an object. An "object," as
used in this disclosure, refers to a concept, idea, goal, person,
location, or file. A visual representation 100 of the object is
generated and exhibited within an interface. The interface may be
any electronic information visualization or display including on a
computer monitor, LCD screen, series of hard copy printouts in ink,
and the like. The interface may further have input/output
capabilities, such as by using a keyboard, mouse, or the like. FIG.
12 shows a high level interface and is described in further detail
below.
[0042] In the example shown in FIG. 1, the visual representation
100 is representative of the object "Panamericana road trip." In
this particular example, the object is an idea, or could possibly
also be referred to as a concept or goal. Referring back to FIG. 1,
additional visual representations 110 of objects are also generated
and exhibited. Element number 110 points to the visual
representation "prep work," but in this specification element,
number 110 collectively refers to all of the additional
representations (i.e., "adventure," "people I know," etc. . . . ).
In this example, these objects are in some way associated with the
original object, "Panamericana road trip." However, this does not
necessarily have to be the case. Any concept, idea, goal, person,
location, or file may be the basis of a visual representation of an
object. As will become apparent with reference to the subsequent
figures, the less important objects will eventually become more
de-centralized with regard to the other objects.
[0043] Referring still to FIG. 1, each of the objects will have
semantic content data associated with them. "Semantic content," as
used in this disclosure, generally refers to data or a concept
associated with an object which describes a characteristic or
feature of the object itself. Semantic data can be textual content
representative of an idea, a goal, an association with another
object, a source of information, or textual description of an
object. Semantic data may also be an image, audio, video, document,
feed, or any other file type which may be stored on a local or
remote drive. Semantic data in the form of a source of information
may be a rich text field or concept, such as data from Wikipedia,
online mapping tools, online social networks (e.g. Facebook), or
any other web-based information resource or web-link thereof.
[0044] Semantic content data, in embodiments of the disclosed
technology, is assigned to an object by a user via the interface,
or programmatically through a data feed or some other data source.
A "data feed" involves the transfer of data from one or more data
sources via a network node and a communication channel. A data feed
may be an RSS data feed ("Really Simple Syndication", as is known
in the art), a data feed on a social network associated with a
person, group (e.g. people who are fans of eggs with multiple yolks
or in the tautology club), or application (e.g. a "Farmville" or
other game). A data feed may include, a database, the contents of a
hard drive, a web page with dynamic content, a web service, a
satellite information service such a global positioning system
("GPS"), a wireless data source, or any other source. Some examples
of a web-based data feed may include a feed of Wikipedia articles
or updates thereto, social networking platforms (e.g. Facebook or
LinkedIn) and messages to a user or responses to a comment, link or
the like, online maps (e.g. Google Maps or other geographic
location data associated with a class or object) and so forth.
[0045] Such data feeds may be associated automatically or manually
with an object represented in the mind map. For example, referring
still to FIG. 1, the visual representation of the object entitled
"Guatemala" may programmatically associate encyclopedic data from a
data source such as an online encyclopedic reference (e.g.
Wikipedia, available at the time of this writing), whereby an
encyclopedia entry regarding the country of Guatemala will be
automatically linked to the visual representation of the object.
The launching of a document, such as a HyperText Markup Language
("HTML") document in the form of an encyclopedia entry is done by
way of a user-initiated "control" which will be explained further
in the detailed description with reference to FIG. 6. In the
instance in which an object refers to a person, a third party
social networking platform may comprise one or more of the data
feeds and data sources associated with that object. For example,
referring still to FIG. 1, the visual representation entitled
"Claudia" may programmatically assign semantic data associated with
a person named "Claudia" from a third party social networking
platform (e.g. a "friend" or 1.sup.st degree connection on a social
networking platform. The type of data imported and assigned to the
object may include an image of Claudia contained in a social
networking profile, or other online profile for Claudia, a text
document containing Claudia's resume, and/or text containing her
contact information, background, interests, etc. The type of
semantic data to be imported depends on a user's preferences and is
configured by the user. For instance, an employment/occupational
mind map may only include work-related and contact information for
a given person or be filtered in another manner, as desired.
[0046] The semantic content data associated with various objects
is, in embodiments of the disclosed technology, continuously or
persistently (as available or at regular time intervals, such as 15
or 30 seconds) updated, so long as the data sources and data feeds
are accessible. In this way, the mind web is always up-to-date and
contains real-time data. For example, a person may change a
hypertext markup language document associated with him/her on
his/her social networking profile to reflect changes to his/her
occupational status. If this profile is a data feed assigned to an
object representative of that person in a mind web, the changed
information will sync to the object automatically in the
visualization thereof.
[0047] With reference now to FIG. 2, connectors 200 or links
between the various visual representations of objects are shown.
The visual representations 100 and 110 of objects can have
associated connectors 200 that link them to other objects, data
feeds or data sources. When visual representations 100 and 110 of
objects are displayed, their associated connectors 200 can also be
displayed in order to represent a set of linked items as a web. The
connectors 200 are augmented manually by the user or may be
augmented automatically, using semantic data associated with each
of the objects.
[0048] The connectors 200 likewise may contain associated semantic
data themselves. Connectors 200 can have associated weights that
indicate the strength of the connection between two or more items.
For example, a connector's weight or importance may be represented
by a thicker line, a line of a certain color, or a line of a
certain style (i.e., dotted). Moreover, a connector can also
possess a visual trait which is indicative of the nature of
interrelation between objects of which it is representative.
Examples of the types of associations may include "is the cause
for," "follows after," "influences," "helps with," and "is
alternative to."
[0049] Another feature of the objects and connectors of the method
of the disclosed technology is the ability of the interface to
certify user-entered semantic data using one or more data sources.
An object which possesses associated certified content is
disambiguated by determining which third party content should be
related, and how it is related. The data sources which certify
semantic data are comparable to those which are used as automatic
data feeds, i.e., from an online encyclopedia or reference site,
social network, etc. An example of certification of data could be
the instance in which a user manually enters contact information
about an individual into a visual representation of an object
representing that individual. The interface may be configured to
certify such information, using a third party social networking
database. Upon certifying the contact information on the
individual's social networking profile, the visual representation
of the object may contain an icon containing, for example,
Facebook.com's trademarked white "F" within a blue square. Such an
icon indicates to a user that information contained within a given
object has been certified using Facebook.com as a data source.
Other examples include certifying locations using an online map as
a data source. Still further, once this is accomplished, related
data may be added which is pulled down from the data source. That
is, when adding and certifying "Guatemala" or any other object to a
mind map and semantic data or another object included "rivers",
then rivers within Guatemala, by way of example, may be added
appropriately to the mind map and connected, as will be discussed
further below.
[0050] FIG. 3 shows the mind map visualization representation of
FIG. 2 with certain objects becoming centralized, based on the
number of connections. Referring now to FIG. 3, the ability of the
interface to automatically augment connections becomes apparent. A
step of a method of the disclosed technology involves analyzing the
output of a data feed and identifying additional objects which are
semantically related to the original object. In FIG. 3, the visual
representation of the object labeled "Claudia" may contain semantic
information imported from a third party social networking database
about an individual named "Claudia." This information may include
geographic information pertaining to where "Claudia" resides. In
this example, semantic data associated with the individual
"Claudia" gathers that she has some connection to Bogota. Likewise,
the visual representation of the object labeled "Colombia" may
contain semantic information imported from a web-based
encyclopedia. This semantic information is likely to include a list
of all the major cities in Colombia. All of the data contained in
the mind map is then analyzed, and it is identified that the
objects labeled "Claudia" and "Colombia" are semantically related
(namely that "Claudia" has some connection to Bogota, which is the
capital city of the country of Colombia). Thereafter, a connector
is automatically augmented between the visual representations of
"Claudia" and "Colombia." In this instance, the connector may
exhibit a characteristic which serves to indicate that the nature
of interrelation is geographic.
[0051] The example shown in FIG. 3 also makes apparent another
feature of an embodiment of the method of the disclosed technology.
This feature is a step of the method in which the "link weight" of
a certain object lends credence to the size and location of its
respective visual representation. "Link weight," as used in this
specification, refers to the number of connectors a given object
possesses. In other words, an object with a greater number of
connections to other objects has a greater link weight and thus
becomes more centralized within the mind map interface. Referring
back to the example shown in FIG. 3, the object labeled "Colombia"
now possesses additional connections to those originally manually
assigned by a user, as shown in FIG. 2. The new connections are
represented by the highlighted joints 330 in FIG. 3. Because of the
greater link weight of the object labeled "Colombia," the visual
representation of the object has grown in size and has nucleated
towards the center of the mind web as a whole. Conversely, those
objects which possess a smaller number of connections become less
centralized, moving towards the outskirts of the mind map.
[0052] FIG. 4 shows the mind map visualization representation of
FIG. 2 becoming merged with another mind map. Embodiments of the
method of the disclosed technology may encompass multiple mind maps
containing multiple nuclei. Mind maps which contain elements in
common with other mind maps may overlap with one another. In the
example exhibited in FIG. 4, the mind map of FIG. 2 is shown to
have the object reflecting the idea of "longing" in common with an
additional mind map centralized around an object also containing
the idea of "longing."
[0053] FIG. 5 shows an alternative embodiment of the mind map
visualization representations of FIG. 4. In this example, the
objects in common between the two distinct mind webs merge. The
visual representation of the object reflecting the idea of
"longing" becomes the centralized nucleus of another mind web. Thus
a single mind web is formed in which the objects labeled
"Panamericana road trip" and "Longing" become the most centralized
themes.
[0054] FIG. 6 is another example of a mind map visualization
representation showing objects based on data associated with an
individual which is imported from a third-party social networking
platform. This example is centralized around an object which
references a "Latin American dinner party." In this example certain
objects within the mind map form associations with data associated
with an individual person. The visual representation of the object
"caipirinha creme" is semantically related to data associated with
a contact imported from the third party social networking platform.
As shown, the object 600 associated with a contact "Melissa," an
individual, is visually distinct from those objects which represent
locations, ideas, concepts, foods, etc. The visual representation
of the individual not only contains an image of the contact which
it represents, it also contains an icon with a trademarked logo of
the social network for which it connects. This connection may be
generated in one of a few ways. It may be manually inputted by a
user to connect to the "caipirinha creme" object, it may be
inserted based on seeking out social networking data feeds and
finding content on the social network (or other) feeds relevant to
objects on the mind map, or "Melissa" (or others from social
network or other feeds) may be linked to any food item, or an item
within a class. This will be described in greater detail with
reference to later figures.
[0055] In one embodiment of the disclosed technology, this icon may
serve to indicate the source of that object, as a trademark
indicates a source of goods. Alternatively, in another embodiment
of the method of the disclosed technology, the logo may also serve
to indicate the type of semantic data associated with the content,
e.g. "Melissa" having been certified using Facebook as a data
source has social networking content. In this particular example of
a mind map according to a method of the disclosed technology, a
user is using a mind map in order to plan a Latin American Dinner
Party. The user lists the various courses, and corresponding foods
and drinks associated with these courses. The interface is
configured to then automatically analyze a data feed from a data
source, which in this case is the third party social networking
platform, Facebook. The data feed may be, for instance, relegated
to a user's particular "friends" on Facebook, or a particular
subset of those friends classified by comments, pictures, location,
college, network, age, etc which is relevant to the party and thus,
send invites to them via the social network, add their data feeds
or a subset thereof (such as a conversation to them related to this
event) to the mind map, or both.
[0056] A data feed associated with a third party social networking
data source such as a social network is imported, in embodiments of
the disclosed technology, using hypertext markup language (herein
"HTML") or an extensible markup language (XML) extension thereof.
An application programming interface (herein "API"), such as those
provided by a social networking platform, is generally used to
interact with the third party content and update content of the
mind map (visualization) or the social network. In the particular
case of Facebook, for example, Facebook Markup Language (herein
"FBML") is used. An API entitled XFBML, known in the art, is used
in order to allow other programs to interact and exchange
information with the Facebook database. Referring back to FIG. 6,
the interface of the disclosed technology uses the XFBML API in
order to import and translate data to the mind map. Other data
sources provide similar API's in order to allow compatibility with
other software programs such as the interface of the disclosed
technology.
[0057] FIG. 7 is yet another example of a mind map visualization
representation in which some objects include a visual
representation identifying a classification of semantic content. As
described to in the above paragraphs, different objects will
possess different classes of semantic content. For instance, an
object associated with an individual may contain semantic content
in the form of text-based contact information and an image of the
person, whereas an object associated with a geographic location may
contain a map of the location and text-based content with an
address or longitude/latitude coordinates.
[0058] Referring back to FIG. 7, the classification (also referred
to as, "the class" or "classes" of semantic data refers to a type
of object, i.e., website, document, contact information, type of
source (e.g. "social network"), actual source (e.g. pictures from
friends profile on a specific social network), accompanies the
visual representation of various objects. For example, the visual
representation of an object entitled "cash flow forecast" possesses
two different classification icons 700. One icon 700 comprises a
circle with the text "xls" written within. This indicates to a user
that this object, amongst other data, contains associated semantic
content in the form of a spreadsheet document and is in the class
of spreadsheet documents. Another icon 700 contained in the visual
representation of the object referring to "cash flow forecast"
comprises a circle containing a "Wikipedia" logo. This serves to
indicate that this particular object also possesses semantic data
in the form of an encyclopedic entry associated with it, or other
data which is defined in the class for which this object belongs.
As discussed previously, such semantic data may be manually
assigned by a user or synchronized automatically by the
interface.
[0059] Other types of classifications of semantic content shown in
FIG. 7 include PowerPoint Slideshow ("pps"), websites ("www"), text
documents ("doc"), emails (and feeds thereof), graphic file
("png"), and data associated with individuals imported from social
networking platforms. The types of classifications that may be used
are essentially limitless, given the vast expanse of file types and
data sources associated with the Internet and computers. Moreover,
visual indicia of associated semantic content may take any form,
such as, but not limited to, icons accompanying a visual
representation, images which are representative of the
corresponding class of semantic content, and different-sized shaped
and colored visual representations. A class of data, or an object
associated with classified data may comprise data indicative of
geographic location (place) or time. In this manner, any added
object can be linked semantically with a location on a map or a
time in which it occurred. The time may be the current time, the
time the object was added or the like. The place or geographic
location may also be obtained by way of the use of a satellite
navigation system receiver, such as a GPS (global positioning
system) or satellite navigation system.
[0060] FIG. 8 is yet another example of a mind map visualization
representation in which the connections include a visual
representation identifying the type of link of which they are
indicative. As previously alluded to, the connectors 800, likewise
may contain associated semantic data themselves. The connectors 800
may have associated weights that indicate the strength of the
connection between two or more objects. For example, a connector's
800 weight or importance may be represented by a thicker line, a
line of a certain color, or a line of a certain style (i.e.,
dotted). Moreover, a connector 800 can also possess a visual trait
which is indicative of the nature of interrelation between those
objects of which it is representative. Referring still to FIG. 8,
the connectors 800 may also contain an actual text describing the
nature of the connection, a number indicating the weight of the
connection, and an arrow which indicates the direction of the
association. Examples of the types of associations may include "is
the cause for," "follows after," "influences," "helps with," and
"is alternative to."
[0061] FIG. 9 shows an alternative embodiment of a method of the
disclosed technology in which data are certified using an
additional step of prompting a user. The method may include an
intermediary step of a certification of semantic data which
involves a user's input to confirm the actual substance of a given
visual representation of an object. In this example, a user has
created a visual representation of an object labeled "FDA." Using a
certification data source (in this case an editable encyclopedia),
the interface prompts the user with a list of full names to which
the acronym "FDA" may be referring. In this particular example, the
user prompt is in the form of a pop-up window in the mind map
interface. The user is then able to select the name which best
describes his or her intended meaning of "FDA." Upon certification,
the interface will assign associated semantic data with the
particular object. In this case, the semantic data includes
information from an encyclopedia entry. Thus, the logo icon for the
encyclopedia is used to signify the classification of the
associated semantic data which accompanies the object.
[0062] FIG. 10 depicts a flow chart of a method of carrying out
embodiments of the disclosed technology. The method begins with the
identification of an object in step 1000. As previously stated
above, an "object," as used in this disclosure, refers to a
concept, idea, goal, person, location, or file. In step 1010, a
visual representation of the object is generated and exhibited
within an interface. The next step involves associating semantic
data with the object. Either in step 1020, a user manually
associating semantic data with the object (the user may confirm
data associated with the visual representation of an object and/or
assign which data sources to use as data feeds for the given
object) or, alternatively, step 1025 is carried out whereby an
interface is automatically configured to associate semantic data,
using one or more data sources, to such a data source via a network
note. Semantic data can be textual content representative of an
idea, a goal, an association, a source of information, or textual
description of an object. Semantic data may be an image, audio,
video, document, or any other file type which may be stored on a
local or remote drive. Semantic data in the form of a source of
information may be a rich text field or concept, such any other
web-based information resource or web-link thereof.
[0063] Next, the method proceeds with step 1030 in which data about
the object is accessed from one or more data feeds. That is, for
example, a FBXML or RSS data feed is read and relevant data is
parsed or filtered and added to a mind map. In step 1040, the
accessed data are then analyzed and additional related objects are
identified, such as additional links, friends, or data which is
relevant to already existing or newly added visualizations of
objects. In step 1050, a visual representation is created for each
of one or more of the additional objects. That is, for example,
when adding "Germany" to a list of countries, the Rhine river is
then added to a list of rivers to visit with channels for boat
passage on another part of the mind map where such activities are
being planned. Finally, in step 1060, a connector is augmented
(added) between the original or existing object or objects and each
of one or more of the additional semantically related objects.
[0064] FIG. 11 depicts a flow chart of a method of carrying out
embodiments of the disclosed technology relative to certification
and updating of semantic data. In step 1100, this embodiment of the
disclosed technology begins with semantic data being associated
with the object. As discussed in detail in this specification, the
semantic data may be assigned manually or automatically. Similarly,
the certification of semantic data may be automatic, manual, or
semi-automatic. In step 1110, the user certifies the semantic data
manually. In this instance, the user confirms the accuracy of the
semantic data with the click of a mouse button or stroke of the
keyboard. Alternatively, in step 1120, the semantic data is
certified automatically using one or more data feeds. The data
feeds use data sources in order to certify data. Such certification
data sources may be, for example, an online social networking
platform (a data feed updatable by any friend of the operator of
the feed or member of a group pertaining to the feed) to certify
personal information, or an online application such as a map to
certify geographical information. Step 1130 is yet another
alternative mode of certification of semantic data which involves
partial or semi-automatic certification. In this mode, a user is
prompted with a list of possible associative semantic references in
step 1140. The list may be, for example, a search result generated
by a data source such as a search for patents on the U.S. Patent
Office database, using a keyword or classification search. In step
1150, the user selects the proper reference from the list, thereby
certifying the semantic data associated with the given object. Step
1160 begins the process of synchronizing semantic data in order to
keep it up-to-date. In this embodiment, the certification source is
synchronized to the object and is used as a data feed. Step 1170
proceeds as long as there is access to the sources of the data
feed. If a data source is a webpage, this would mean a connection
to the Internet or local copy of the data is required. If a data
source is a local file, access to a local hard drive or networked
drive masquerading as a local drive must be enabled. In step 1180,
data is imported to the interface from a data feed. Finally in step
1190, the objects and any associated semantic data are revised in
order to reflect any changed or updated information.
[0065] FIG. 12 shows an alternative embodiment of a method of the
disclosed technology in which a related object is proposed to a
user from another user's mind map. In this embodiment, a user is
prompted with a visual representation of a related object 1210
which is imported from another user's mind map. The system proposes
the related object based on semantic data associated with an object
1200 originally found in the user's mind map. The connector 1220 is
shown as being dotted and having a question mark in order to
indicated to the user that the addition of the related object to
the user's mind map is contingent upon the user accepting the
proposed related object. Referring still to FIG. 12, the visual
representation of the related object 1210 further comprises, in
embodiments of the disclosed technology, a visual or other
indicator 1215 which indicates its source. The source may be the
author of another mind map or connection on a social network. In
the example shown in FIG. 12, the source is another user's mind
map, thus depicted by an icon with the user's name and picture.
[0066] FIG. 13 shows an alternative embodiment of a method of the
disclosed technology in which data is certified using data
associated with another user's mind map. This embodiment builds on
the embodiment shown with respect to FIG. 12. In this embodiment,
after generating a visual representation of an object, associated
semantic data may be certified using data from a common object
found in another user's mind map. In the example provided in FIG.
13, user A generates a visual representation of an object "NYC"
1300. The system automatically recognizes that a similar object
1310 is contained in another user's mind map (User B). This object
1310, in User's B's mind map, already has certified semantic data
1315 associated with it. Being that User A and User B may be
acquaintances; chances are that they may have the same interests
with regard to the acronym "NYC". Thus, User A may elect to certify
and associate semantic data in his or her mind map using the
semantic data found in User's B's mind map. Also shown in FIG. 13
is a window 1320 exhibiting a plurality of data sources which may
be associated with and used to certify the object "NYC". As
indicated by the solid arrow, User B has already chosen which
semantic data sources to associate with "NYC" amongst those
contained in the window. Likewise, User A may choose to associate
and certify semantic data from one or more of the data sources
listed in the window 1320. Alternatively, User A may choose to use
the same data sources as User B to associate and certify the object
representing "NYC".
[0067] FIG. 14 shows a high-level block diagram of a device that
may be used to carry out the disclosed technology. Device 1400
comprises a processor 1450 that controls the overall operation of
the computer by executing the device's program instructions which
define such operation. The device's program instructions may be
stored in a storage device 1420 (e.g., magnetic disk, database) and
loaded into memory 1430 when execution of the console's program
instructions is desired. Thus, the device's operation will be
defined by the device's program instructions stored in memory 1430
and/or storage 1420, and the console will be controlled by
processor 1450 executing the console's program instructions. A
device 1400 also includes one or a plurality of input network
interfaces for communicating with other devices via a network
(e.g., the internet). The device 1400 further includes an
electrical input interface for receiving electrical current. A
device 1400 also includes one or more output network interfaces for
communicating with other devices, such as routers, switches, hubs,
and other network nodes. Device also includes input/output
representing devices which allow for user interaction with a
computer (e.g., display, keyboard, mouse, speakers, buttons, etc.).
One skilled in the art will recognize that an implementation of an
actual device will contain other components as well, and that FIG.
12 is a high level representation of some of the components of such
a device for illustrative purposes. It should also be understood by
one skilled in the art that the method and devices depicted in
FIGS. 1 through 13 may be implemented on a device such as is shown
in FIG. 14.
[0068] While the disclosed technology has been taught with specific
reference to the above embodiments, a person having ordinary skill
in the art will recognize that changes can be made in form and
detail without departing from the spirit and the scope of the
disclosed technology. The described embodiments are to be
considered in all respects only as illustrative and not
restrictive. All changes that come within the meaning and range of
equivalency of the claims are to be embraced within their scope.
Combinations of any of the methods, systems, and devices described
hereinabove are also contemplated and within the scope of the
disclosed technology.
* * * * *