U.S. patent application number 10/803040 was filed with the patent office on 2005-09-22 for context driven topologies.
Invention is credited to MacPherson, Deborah L..
Application Number | 20050209983 10/803040 |
Document ID | / |
Family ID | 34987550 |
Filed Date | 2005-09-22 |
United States Patent
Application |
20050209983 |
Kind Code |
A1 |
MacPherson, Deborah L. |
September 22, 2005 |
Context driven topologies
Abstract
The invention uses mathematical patterns, aesthetics, varying
views, and a new system of scale, pacing and edges similar to
walking in nature to draw the geometry of knowledge as it changes
over time. These drawings have no straight lines, only arcs. There
are no corners, only transitions and rotations in specific places
on irregular high-dimensional waveforms threading their way through
time. Each pattern and each memory form is a unique continuous
whole perceived as objects in spaces where both the object and
space around it have meaning. When data and data relationships
preserved in Context Driven Topologies are interpreted in the
future, each whole is broken into components, reinterpreted,
recreated, fixed into a new pattern and memory form and
reintroduced into the stream. Each component in every topology
carries a history of its priority and placement. Very efficient,
accurate searches recognize continuous wholes using these
histories. Shared context draws data and data arrangements together
deep in the background to "gravitate" and "snap" relative
proportions, measurements and historical relationships into groups.
The creation of new patterns, new memory forms, and the shared
memory space will simplify and streamline these geometries over
time which will improve the quality of dynamic shared data stores.
The intention is to change the communication mode between people
and machines and to develop more precise records over longer
periods of time.
Inventors: |
MacPherson, Deborah L.;
(Vienna, VA) |
Correspondence
Address: |
DEBORAH L. MACPHERSON
512 BEULAH ROAD N.E.
VIENNA
VA
22180
US
|
Family ID: |
34987550 |
Appl. No.: |
10/803040 |
Filed: |
March 18, 2004 |
Current U.S.
Class: |
1/1 ;
707/999.001 |
Current CPC
Class: |
G06T 11/206 20130101;
G06N 5/02 20130101 |
Class at
Publication: |
707/001 |
International
Class: |
G06F 017/30 |
Claims
1. These claims, and the specifications and drawings before, define
the invention as a new human computer interaction process comprised
of the following steps and procedures: new techniques to organize
and use data histories (3.34) to place data in context (A1) (B.32)
(B3.7) [FIG. 1 to 10] (1.1) (1.23) (2.3) (3.1) (33) (3.6 and 3.7)
(3.10) (3.12 and 3.13) (3.18) (3.20) (3.37) (6.8) (7.2) (7.8 to
7.12) (7.28) (7.31) (7.33) (7.41) (8.2 and 8.3) (8.18) (9.2) (9.4)
(9.11) (10.2), which provides a new form for data arrangements (A1)
(B1.2) (B1.5) (B3.2) (B3.7) (D1) [FIG. 2 to 10] (1.12) (1.24) (2.1)
(2.7) (3.3 and 3.4) (3.9) (3.12) (4.5 and 4.6) (4.14) (7.2) (7.4)
(7.14), and a new format for data descriptions (B3.7) (2.2) (2.8)
(3.20) (3.24) used in shared dynamic time dependent complex data
collections (B1.4) (1.9) (3.7) (6.7) (8.5) (9.8) (9.17). The
invention is used to draw the geometry of knowledge as it changes
over time (A1) [FIG. 3]. The pace and record of these changes is
represented by mathematical configurations, or "knots of
information". When the space around these knots changes, so does
the interpretation of the information itself (1.2), likewise, when
the interpretation changes the patterned "space around" will be
changed. Mapping this back and forth process [FIGS. 6, 7 and 8]
over time [FIGS. 2, 3 and 4] is one way the invention is used to
interpret, manage and selectively preserve records of human
knowledge. Data and data collections are mapped, organized,
searched and interpreted using sets of "knowledge patterns" also
called "filters" and "templates" (B1.5) [FIG. 10] (2.3) (3.35)
(7.40). A second "opposite" and "related" set of "display patterns"
(C1) (3.21) (3.23) (3.27) (7.1 to 7.50) (8.3) (9.1 and 9.2) (9.5)
(9.10) (9.21) are used to subsequently transform and simplify each
data arrangement even further to be displayed through an evolving
automatic language of light and sound (7.5) (9.2) (10.2), textures
[FIG. 7] (1.23), colors (7.28 and 7.29) (7.39) (7.43) (7.48) and
forms (A1) (B1.5) (B3.2 and B3.3) (C1) (D1) [FIG. 6] [FIG. 10]
(1.24) (2.1) (3.4) (3.9) (3.11) (3.13) (3.20) (7.2) (7.38 and 7.39)
(7.43) (9.6) (9.13 and 9.14) that continually update and evolve
into new generations of knowledge and display patterns. People's
knowledge (A1) (B1 to B3) (C) [FIG. 1 to 10] (7.1 to 7.49),
awareness, abilities to perceive, measure and question meaning in
data and data arrangements is used to change and develop these
mathematical patterns over time. The invention applies mathematical
topology, algebra and new pattern generation and recognition
techniques to digital information context to see how ideas and
concurrent or conflicting views (claim 4) become entangled, can be
separated from their background, recognized differently from
different points of view, interrelated, and influenced over time
(1.1). The invention is used to discuss new versus old ideas, draw
new conclusions (B1.1 and B1.2) (B3.2) (7.1) (7.30) (7.47) (8.16),
create new mathematical relationships and new conceptual
associations (1.4) perceived and used in the following states: as
scale free configurations connecting and placing data components in
data arrangements (B1.2) (1D) [FIGS. 6,7,8 and 10](1.2) (3.18)
(3.28) (6.6 and 6.7) (7.12) (7.18) (7.33 and 7.34) (7.36) (7.39 and
7.40) (8.3) (8.14) (8.18) (8.20) (9.4 and 9.5) (9.15); as symbols
that map the history of hierarchy placements within each
component's mathematical description (B1.2) (B3.4) (D1) [FIG. 10]
(2.1 and 2.2) (3.7) (3.10 to 3.14) (3.18) (3.20) (3.23 to 3.26)
(3.31 to 3.33) (3.37 and 3.38) (4.4) (4.9) (4.17) (7.1) (7.32)
(735) (7.39 and 7.40) (8.3) (8.18) (9.3) (10.2); and as
multidimensional waveforms used to distribute, streamline and
consolidate these patterns and forms over time (A1) (B1.2) (B1.5)
[FIG. 10] (D1) (1.24) (3.11) (3.26) (4.1 to 4.18) (7.4 and 7.5)
(7.39) (9.3). Context Driven Topologies remain mathematically the
same and recognizable regardless of whether they are being used in
the configuration, symbol or waveform state. Context Driven
Topologies in the symbol state (Section 3) are used to trace (B1.4)
(B3.2) [FIG. 6] (1.4) (3.7) (3.10) (3.12) (3.26) (5.1) (7.14)
histories of previous context and associations originating deep in
the background (A1) (1.5) (7.32) to gently "push" (7.1) (7.26)
(9.21), precisely align (B1.4) (1D) [FIG. 8] (1.23) (33) (3.7)
(3.31) (4.17) (9.5) (9.7) (10.2) and lock the relative proportion
(A1) (B1) (B3) [FIGS. 2, 6 and 7] (3.8) (3.27) (3.36) (4.14) (53)
(6.7) (7.15) (7.34) (7.36) of data and data arrangements into
groups. Context Driven Topologies form a new kind of data
collection composed of a new kind of objects and spaces used to map
and understand complex data and data collections in both smaller
groups (A1) (B1.2) (B1.5) [FIG. 8] (D1) (1.4) (1.23) (2.3) (2.4 and
2.5) (2.7) (2.9 to 2.12) (3.2) (3.11) (3.15 to 3.17) (3.22) (3.28)
(4.11 and 4.12) (5.4) (6.3) (6.7 and 6.8) (7.1) (7.6 and 7.7)
(7.17) (731 to 7.33) (8.2 and 8.3) (8.6) (8.11 and 8.12) (8.19)
(9.6) (9.11) and larger overalls (B3.6) (1.10 and 1.11) (23) (7.25
and 7.26) (7.28) (7.38) (9.1) (9.12) than are currently available.
Current data relationships, network topologies and data stores
(even dynamic data stores) are typically in even arrangements with
equal, practically interchangeable components geared for machine
processing rather than the fluid, variable human imagination and
investigation process. This is claimed by the inventor to be caused
by an overdependence on electrical pulses. The inventions
mathematical memory patterns are more suited to continuous
patterned waveforms, similar to existing radio or cell phone
technology, rather than electrical pulses. The invention is
intended be independent of electricity and electrical pulses (1.24)
(Section 4) (claim 2). Existing technology does not allow data or
data relationships to vary, characterize over time, or appear as
one whole (A1) [FIG. 3 to 5] 91.3) (3.5) (3.10) (4.3) (4.6) (5.7)
(7.28) (7.32 and 7.33) (7.39) (8.3) (8.11) 8.24) (9.6). The
invention measures changes in mathematical patterns constructed for
temporal reasons where aesthetics (A1) (B3.5) [FIG. 6] (7.50)
(8.13) (9.1), proportion (B1.4) (B3.6)(7.38) (9.5) (10.6), "pace"
or flow (B3.2) [FIG. 2] (1.21) (3.11) (3.26) (4.12) (Section 6)
(7.3) (7.5) (8.3), proximity [FIG. 6] (3.10) (6.2) (6.3) (7.7)
(7.12) and density (1.24) become typical, comparable measurements.
Context Driven Topologies reside in a boundless abstract cloud,
also called a "stateless space" [FIG. 1] (3.27) (4.7 to 4.9) (4.14)
(4.18) 63) (6.9) (7.45) (8.1) (8.3) (8.11) (8.18) (9.1 and 9.2)
accessible to any number of users. Mathematically perfect copies
(9.4) are handed down from generation to generation. The intention
of these claims, drawings, specifications and patent is to protect
the core principles of the inventor's idea, the inventor's
techniques, processes and steps disclosed, and to have greater
control over ways the invention and its intended purpose is
implemented in the future through the following steps (C) [FIG. 6]
(1.25) (2.13) (3.38) (4.18) (6.10) (7.49) (8.26) (9.22) (10.1 to
10.14).
2. Because of the steps and processes throughout these drawings,
specifications and claim 1, Context Driven Topologies will
initially be "powered" by use, similar to passing stories and songs
across generations or propagating information across the internet
(B1.5) (B2.2) (D1) (1.21) (3.19) (8.17) (8.24) therefore, the
invention and the purpose of the invention, is independent of
electricity (1.24) (claim 1). I further claim the inventions
mathematical patterns, processes and uses for long term data
curation and digital preservation (9.1 to 9.22) will also allow
this organized and preserved knowledge to be independent of
unstable media (1.1 to 1.25) (claim 2) and changing natural and
machine languages (3.32). The intended life span of the knowledge
and display patterns (claim 1) interpreted and managed using the
invented process is no less than 1,000 years (7.12). It is
critically important to know this claim, steps and processes
include the human decision, evaluation and review process over time
to selectively delete data and data arrangements that are not
cohesive (2.10) (3.9) (5.6) (7.28), valuable (B3.7) [FIG. 5] (1.15)
(2.6) (4.14) (7.2) (7.16) (7.19 and 7.20) (7.24 and 7.25) (7.33)
(8.13) (10.10), true (B2.2) (1.6 and 1.7) (2.4 to 2.6) (7.23)
(7.47) (8.3), interesting (1.5) (1.18) (7.3) (7.23) (8.13) (8.24)
(9.9), attached to or sharing significant histories (A1) [FIG. 6]
(1.5) (1.21) (3.13) (3.25) (3.33) (4.18) (6.6) (7.11) (7.16) (7.32)
(7.45) (83) (8.7) (8.9) (8.11) (9.15) (claim 1) with other data and
data arrangements. Non-relevant, non-valuable, potentially
misleading, out of date and incorrect information is removed from
dynamic shared data stores through a shared continuous discussion
and interpretation forum that uses a shared memory (8.1 to 8.26)
area within the stateless space (claim 1). These actions and this
process will streamline (1.7) complex data collections,
automatically organize shared data stores (1.7) (9.1) and make
complex collections easier for people to look through. I claim
existing machine protocols and languages (3.32), unstable media
(D1) [FIG. 6] (1.15 and 1.16) (2.5) (8.18) (9.2) and the parade of
machines currently accepted as an unfortunate, but irreconcilable,
part of the information age (1.25) is unnecessarily divisive and
detrimental to long term digital preservation and international
research and communications across cultures and domains (1.1 to
1.25). I further claim the year 2004 is the dawn of a new connected
age (10.14) with incredible potential (1.22) where communications
should not be hampered by electricity (1.24) (8.17 and 8.18),
media, changing machines (7.3) (7.12) (8.3) and different natural
and machine languages (3.32). The kind of data and data
arrangements understood through Context Driven Topologies involve
imagination (B3.2) (1.13) (1.24 and 1.25) (2.10) (4.17) (7.27)
(7.30) (9.2) (9.6), visualization (B3.2) [FIG. 6] (1.21) (3.2)
(3.23) (7.5) (7.8) (7.27) (7.44) (10.1) (10.8), and patterns that
constructed in a place (7.8) where natural language is no longer
useful, media is immaterial, and machine languages may be able to
be changed to understand the expressions, reasons and
investigations captured by the invention over time through the
following steps (1.25) (2.13) (3.38) (4.18) (6.10) (7.49) (8.26)
(9.22) (10.1 to 10.14) (claim 10).
3. Because of the steps and processes in claims 1 and 2, I claim
the invention will typically transform (D1) (1.21) (3.21) (4.1 and
4.2) (4.4) (4.11) (4.17 and 4.18) (5.4 and 5.5) (7.1) (9.5) (9.21)
(10.1) (claims 1 and 2) and present knowledge and knowledge objects
differently than it was originally captured and recorded. The
invention is a consistent method (B3.2) (B3.6) (C1 to C7) (D1)
(1.6) (2.9 and 2.10) (6.9) (7.12) (7.30) (7.44) (8.3) (9.4) (10.2)
(10.11) (claims 1 and 2) for an unlimited (7.37) (8.14), changing
(B1.4) (B1.5) (B3.2) [FIG. 6] (1.12) (1.20) (3.9) (4.1) (7.37)
(7.40) (8.3) (9.2) (9.15) series of users, media and machines to
automatically (D1) [FIG. 6] [FIG. 8] (1.4) (1.17) (2.1) (3.21)
(4.13) (5.6) (7.7) (7.14) (7.26) (8.3) 98.12) (8.17) (9.4 and 9.5)
(claims 1 and 2) and always defer to higher quality (A1) (D1) (C7)
[FIG. 6] (1.6) (1.11) (1.18) (3.27 and 3.28) (7.5) (7.37) (8.11)
(9.4) (10.13), denser (3.2) (7.26), more original (B3.7) [FIG. 6]
(1.19) (1.22) (2.3) (2.6 and 2.7) (2.10) (3.12) (3.24) (4.6) 4.12)
(5.6) (7.9 and 7.10)) (7.37) (7.40) (7.42) (8.3) (8.18) (9.4) (9.8)
(claim 3), authentic (2.2) (8.3) (8.24) (9.2) (9.14) (10.1)
original information held in a placeholder position (2.6) (2.10)
(3.14) (3.26) (7.21) (10.8) accessed through the steps indicated in
(1.25) (2.13) (3.38) (4.18) (6.10) (7.49) (8.26) (9.22) (10.1 to
10.14). This claim includes priority addressing (6.1 to 6.10) (7.7)
(10.1) and mapping to master recordings (10.4); high resolution
still and moving imagery (7.5); partially interpreted (B2.2) (B3.2)
[FIG. 6] (1.23) (3.1) (7.18) or raw results (2.6) (3.24) (10.13);
current locations (9.13) (10.1) (10.6) (10.12) of genuine events,
objects and living beings; purely mathematical relationships and
other ideas that can be represented, described, associated and
derived with machines using the invented processes (1.25) (2.13)
(3.38) (4.18) (6.10) (7.49) (8.26) (9.22) (10.1 to 10.14) to
evaluate, maintain and preserve dynamic complex data collections
over longer periods of time than a person, research group, entire
field of study, or machine's lifetime. Definition: The word
"Machine" as it is used throughout these claims and specifications
is intended to mean a computer with a life expectancy of five to
ten years--including an operating system or platform (ex. Mac or
PC) that may be incompatible with other systems or platforms,
various shared and specialized software with a life expectancy of
one to three years, and an internet connection equal to current DSL
or Broadband. The word "Machines" as it is used in these
specifications is intended to mean advanced networks of machines
that change and improve over one person, research group, or entire
field of study's lifetime.
4. I claim the invention will eliminate redundant (1.4) (3.12)
(3.31) (4.15) (10.1), out of date, misleading and incorrect data
and data arrangement from dynamic shared data stores by isolating
and identifying non-original copies and non-meaningful variations
within datasets using user defined similarity measures, also
described throughout these specfications as "the same" (B3.2) (D1)
[FIG. 6] [FIG. 10] (1.4) (1.9) (2.3) (2.12) (3.3) (3.10) (3.12)
(3.16) (3.18) (3.20) (3.30 and 3.31) (4.6) (4.18) (5.1 to 5.7) (7.1
and 7.2) (7.40) (8.2 and 8.3) (8.20) (9.4) (9.11) (10.1) (10.6)
(10.14) to automatically mask, eliminate and conceal excess
information using these related patterns to map back and forth
[FIG. 8] (3.10) (3.21) (7.13) (7.22) (7.26) (7.30) (8.3) (8.22)
(9.15) (10.6) until the redundant, misleading or incorrect
information, ideas and techniques (9.1 to 9.22) are exposed and
removed in both the users current data arrangement and across more
levels over longer periods of time (1.1 to 4.18) and (7.1 to 7.50).
These templates, also called the "knowledge and display patterns"
(7.1 to 7.4) (claim 1), act as known "opposite" or "rotated"
topologies to expose and combat specifically redundant, false or
misleading information (1.7) (1.15) (2.5) (7.18) as defined by
people who understand and use this information by realistically
accommodating concurrent and conflicting interpretations (1)1)
(1.7) (2.5) (7.30) (10.4) and getting these data descriptions and
data components to influence and eventually cancel each other over
time. I claim that people who create and interpret complex data and
data arrangements understand this knowledge and these knowledge
objects the most clearly and therefore should be the ones who
decide and define which data and data arrangements are interesting,
correct, unique and worth preserving for further contemplation
using new knowledge and new machines in the future. These steps and
processes are also referred to throughout these specifications as
"streamlining" (1.4) (1.7) (10.6). The invention will cause data
and data relationships to characterize (B3.2) (3.25), automatically
become more organized, cluster (B3.2) (3.25) (5.3) (7.12) in
dynamic shared data stores and generally become more authenticated
as it is evaluated from more points of view over longer periods of
time. For readers familiar with problems of redundant, misleading,
out of date or incorrect information, the implications of this
claim are obvious.
5. Because of the steps, processes and applications outlined in
(claims 1 to 4), the invention has a real world value (B3.7) (1.25)
(10.1 to 10.14) by clarifying the roles of human creative and
conceptual abilities versus the computational skills of machines as
summarized in (1.25) (2.13) (338) (4.18) (6.10) (7.49) (8.26)
(9.22). The invention will help us (1.23) (7.18) (9.4) (9.11)
(9.21), as individuals and a global society to decide (2.8) (3.1)
(6.6) (7.10) (7.35 and 7.36) (7.41) (7.44) (8.6) which data and
data arrangements are important, accurate and worth keeping (3.12)
(8.6) (8.20) (claim 4). New and conceptual associations are made by
people and advanced networks of machines over time using Context
Driven Topologies and the virtual "bridges" constructed following
the steps in (A1) (B1 to B3) (C1) (D1) [FIG. 1 to FIG. 10] (1.1)
(1.5) (1.10 and 1.11) (1.19 to 1.23) (2.2 and 23) (2.7) (3.5) (3.7)
(3.11 and 3.12) (3.19 to 3.22) (3.24) (3.26) (3.28 o 3.31) (3.35)
94.14) (6.6 to 6.8) (7.1) (7.3 and 7.4) (7.9 and 7.10) (7.14 and
7.15) (7.18) (7.22) (7.26 and 7.27) (7.30 and 7.31) (7.33) (7.38
and 7.39) (7.49 and 7.50) (8.3 and 8.4) (8.9) (8.12) (8.20) (8.23
and 8.24) (9.1 and 9.2) (9.5 to (0.8) (9.11) (9.13) (9.15 and 9.16)
(10.5 and 10.6) (10.14) and (claims 1 to 4) These new bridges and
the affect of the concurrent and conflicting viewpoints in (claim
4) lead to a portrait of new ideas and changes to historical
comprehension over time so people using the invention can also use
these historical ideas and changes to decipher, comprehend, unravel
and solve new kinds of problems. The primary use for the invention
today is to organize and interpret museum and library digitization
(1.6) (10.1); data generated by automated scientific experiments
(1.6) (10.4) (10.7 and 10.8); security (8.3) (9.14) (10.6) (10.12);
and to promote a clearer (8.9), more meaningful understanding of
each other, our environment, the natural world around us (10.14),
American (2.5), global and future societies (B3.5), and to stay
current with the status of our individual and shared knowledge
(4.10) (4.14) (5.7) (7.21) (7.27) (7.30) (9.2)(claim 4).
6. I claim the steps and processes enumerated summarized and
enumerated in (1.25) (2.13) (3.38) (4.18) (6.10) (7.49) (8.26)
(9.12) and (claims 1 to 5) will show users of the invention new
kinds of objects that exhibit new kinds of associations, expressed
through a new kind of mathematics (B2.2) (B3.1) (D1) [FIG. 6] [FIG.
10] (2.1) (3.31) (6.9) (7.12) (734) (8.24) (9.11) (10.7 and 10.8)
(10.14), a new language of sounds and images (7.1 to 7.50) and
other techniques. I claim the way that data and data arrangements
are configured, described, identified, derived and extracted from
dynamic shared data stores [FIG. 1] [FIG. 2] is dependent on the
users knowledge, the era which they live in, the machines and
networks they are using and they way each user or group of users is
looking at this data and data arrangements [FIG. 6] (1.6) (1.20)
(3.2) (4.12) (4.15) (5.3) (5.6) (7.19) (7.23) (7.27) (7.30) (7.38)
(9.7) (10.6). The invention is not an abstract idea or mere
arrangement of data, because of the invention, we will understand
more about fluidity, shapes, objects and spaces [FIG. 5] (9.13), we
will also understand more, and be forced into new ways to draw,
different elements becoming mixed or separated (10.8). By comparing
shapes, objects, spaces, arrangements, sequences, theories and
ideas we do not understand (3.11) (claim 5) with ideas and
knowledge we do understand, the invention will allow users to draw
some parallels and achieve clarification (3.15) (6.9) (8.5) and
increased understanding that is currently not possible without the
invention. I further claim that because of this increased
understanding, Context Driven Topologies generated by the invention
and perpetuated through people's investigations will become like
objects (3.12) people will form attachments to (B3.7) (2.8) and
begin to prefer certain patterns and forms over others which will
affect human perception (B3.5) [FIG. 10] (3.31), aesthetics (7.23)
(7.34) (7.50), and performance requirements for our media and
machines particularly as enumerated in (Sections 6 to 10) and
(claim 10 of 10) below.
7. I claim that because of the better organization, better
descriptions and more realistic annotation system disclosed
throughout these specifications and claims 1 to 6 above, the
invention is a better, more continuous (A1) (1.24) (3.2) (3.4)
(3.34) (4.1 to 4.3) (4.16) (7.28) (7.40) (7.49) (8.13) (9.3) (9.6),
fluid form (D1) (1.24) (10.7 and 10.8) (claim 6) of metadata (B3.2)
(2.3) (2.9) (7.17) (7.27) (10.2) and mapping comprised of the steps
summarized in (C) [FIG. 6] (1.25) (2.13) (338) (4.18) (6.10) (7.49)
(8.26) (9.22) (10.1 to 10.14). I specifically claim that current
metadata methods rely too heavily on text without providing
mechanisms for translation (B2.2) (B3.4) [FIG. 6] (5.6) (10.1)
(10.2), cultural interpretation (1.20) (7.25) (9.1), or change and
variation in word meaning (B3.4) (10.2) over time. I claim the
invention is a more reliable (1.18) (2.5) (6.9) (7.18) (7.20) (9.2)
(9.14), accurate (A1) (1.18) (2.2) (2.4) (2.6) (3.12) (4.14) (5.1)
(8.3) (8.18) (9.1) (9.9) (claim 5) and subtle [FIG. 6] (10.2)
method to communicate (B3.4) [FIG. 7] (2.1) (3.13) (9.2) (9.11) at
concrete and abstract (B1.4) (B3.5) (C7) (D1) (3.5) (3.8) (3.19)
(4.18) (7.28) (7.38) (7.44) (8.4) (9.10) levels which will enable
our shared designs, mathematics, studies, investigations, stories
and curiosities to advance and be expressed in ways we could not
have imagined before (claim 8).
8. I claim the invention will give machines something to measure
that is closer to the way people think, imagine and work. These
measurements are comprised of the techniques, process and steps
specified in (B3.7) (C1 to C7) [FIG. 6] (1.1) (1.19) (1.23) (2.2)
(2.6) (2.9 and 2.10) (3.6) (3.13) (3.15) (3.27) (6.9) (7.1) (7.8)
(7.16) (7.21) (7.26 and 7.27) (7.30 and 7.31) (7.44) (8.3) (8.23
and 8.24) (9.2) (9.4) (9.11) (9.15) (10.4) (10.6)
9. All of the claims, specifications, drawing, descriptions and
steps are interdependent and related. The purpose of these claims,
specifications, drawings, descriptions and steps is to particularly
point out and distinctly claim the invention as it compares to
other existing and future inventions (B1 to B3), and to protect the
right to develop the inventions future technologies (claims 1 to
10). Each of these claims is directly related to mathematical
operation steps of a process as disclosed in (A1) (B1.4) (B1.5)
(B2.2) B3.1) (C1) (D1) [FIG. 3] [FIG. 6] [FIG. 7] [FIG. 8] [FIG.
10] (1.1) (1.4 and 1.5) (1.17) (1.20 and 1.21) (1.24) (2.1 to 2.3)
(2.13) (3.2) (3.7) (3.10 and 3.11) (3.18) (3.20) (3.26) (3.31)
(3.34) (4.5) (4.9) (4.11 to 4.13) (4.15) (4.18) (6.2) (6.4) (6.9)
(7.1) (7.3) (7.12) (7.15 to 7.18) (7.25 and 7.26) (7.31) (7.33 and
7.34) (7.39) (7.42 to 7.44) (7.47 to 7.49) (8.2 and 8.3) (8.18)
(8.24) (9.1 to 93) (9.6) (9.11 and 9.12) (9.20 and 9.21) (10.7 and
10.8) (10.10 and 10.11) (10.14). These written descriptions, claims
and drawings are intended by the inventor to be an enabling and
complete disclosure to protect this idea and process both in the
United States and Internationally (C1 to C7) [FIG. 6]. The
practical applications (10.1 to 10.14) (claims 1 to 10) of the
computer-related invention disclosed are statutory subject matters.
The invention, specifications, drawings, descriptions and steps
claimed herein are intended by the inventor to be fully consistent
with the Freeman-Walter-Abele test; statutory subject matters under
Section 101 of the Patent Act; and current understanding of United
States and International laws including 35 U.S.C. 101; 35 U.S.C.
102; 35 U.S.C. 103; 35 U.S.C. 112 including the 2.sup.nd and
6.sup.th paragraphs; 35 U.S.C. 154 including section (d)
Provisional Rights as applicable; and is intended by the inventor
to be in compliance with every statutory requirement and criteria
including any binding precedents of the United States Supreme
Court, the U.S. Federal Circuit; the Federal Circuit's predecessor
courts; and international laws or courts not listed. The ideas,
processes, and specific future technologies disclosed throughout
these specifications and claims were conceived of (B2) and belong
exclusively to the inventor (C).
10. I claim the invention is a better form of search, organization
and identification for data, data arrangements, advanced networks
of machines and for people. I claim the invention will be useful to
investigate, create, and manipulate new and old ideas and map
knowledge and historical comprehension over time across cultures
and domains, and not only claim the practical applications
indicated in (claims 1 to 9) and (10.1 to 10.4), but also claim
that the invention in its current embodiment will prompt, inspire
and enable additional techniques and future technologies to
distribute, implement and expand the invention's usefulness through
additional practical applications as indicated below in (APPENDIX
A). Tools, systems, and methods that may be claimed to have been
prompted by the invention, its implementation and usefulness follow
a mathematical and perceptual process summarized in (1.25) (2.13)
(338) (4.18) (6.10) (7.49) (8.26) (9.22) that includes but is not
limited to: measurement, evaluation, testing, authentication,
calibration, analysis, interpretation, exploration, vision,
generation, conversion, translation, transformation, logic,
purification, error and consistency detection, tuning,
classification, registry, identification, recognition, composition,
consolidation, masking, similarity measures, redundancy
elimination, error detection and correction, visualization, design,
imaging, modeling, simulation, drawing, recording, processing,
compression, decompression, distribution, cryptography, navigation,
communications, transmission, signaling, preservation, and other
research, educational, entertainment or business products and
practices that use techniques discovered using the invention. As
indicated in Section (C) and [FIG. 6], future techniques and
technologies associated with the invention will be developed: by
the inventor; with formal research partners; and in cooperation
with other inventors and/or their research partners identified by
searching patents and existing inventions related to the future
technology that has been prompted, necessitated or inspired by the
invention. Especially because the forms and patterns generated,
perpetuated and interpreted through the invention reside in a
stateless, constantly updating space without electricity or a
capturing media--it is possible existing and new inventions in the
enumerated classes (including subclasses which are not listed)
below originally served a different purpose, or the existing
subject matters and inventions within these classifications were
conceived of and made for reasons that may initially seem
unrelated, but in fact, are related because the invention will give
us new ways to understand, new ways to look, measure, connect,
break apart, demonstrate and control data and data arrangements
using virtual forms and patterns that people may not have found
ways to control using `real` patterns, forms, languages and
processes.
Description
FIELD OF THE INVENTION
[0001] Definition: The word "Machine" as it is used throughout
these specifications is intended to mean a computer with a life
expectancy of five to ten years--including an operating system or
platform (ex. Mac or PC) that may be incompatible with other
systems or platforms, various shared and specialized software with
a life expectancy of one to three years, and an internet connection
equal to current DSL or Broadband. The word "Machines" as it is
used in these specifications is intended to mean advanced networks
of machines that change and improve over one person, research
group, or entire field of study's lifetime.
[0002] The invention specifically relates to search, time dependent
data compilation and user controlled display methods. The invention
will clarify the roles of human conceptual and creative abilities
versus the computational skills of machines and corresponds to the
fields of Artificial Intelligence (AI); Knowledge Management (KM);
Human Computer Interaction (HCI); Coded Data Generation, Processing
and Conversion; Horology; Acoustic and Image Analysis; Measuring
and Testing; and Dynamic Information Storage and Retrieval. The
invention will lead to the ultimate compression and feature
extraction algorithm.
[0003] The invention is a human computer interaction process using
individual and collaborative human cognitive abilities, memories,
aesthetics, preferences, knowledge, and conceptual integration
skills to arrange, index and record data relationships using
advanced networks of machines. Relationships among data and data
arrangements are measured by machines and perceived by people as:
evolving configurations of data in groups over time; scalable
character-like symbols that refer and place each component within
each configuration; and multidimensional hierarchical waveforms
composed of light, sound and other machine derived data display
techniques to distribute and compare overall data arrangements and
characteristics before the data itself is retrieved from the
original collection.
[0004] Context Driven Topologies are continually invented and
reinvented through their use. Precisely matching versions may not
be observed in real life or in real machines, however, overall
relationships captured by the topologies are commonly understood
without special training or programming.
[0005] The invention creates a level of abstraction and
simplification for the search, comparison and analysis of complex,
evolving data collections. These changing records are virtual, time
dependent and measured for comparison, presence, location, traces
and signs using non-linear dynamics, knot theory topology, algebra,
Fourier analysis and other mathematical techniques. The most
appropriate mathematical measurements vary by purpose and may
include frequency, proportion, density, distance, relative degree
of rotation, similarities and variations in alignment or intensity
and other specific techniques contained in the "knowledge
patterns".
[0006] Supplemental technical specifications for the future
technologies claimed herein (claims 1 to 10), partially disclosed
throughout these specifications, and prototyped through an upcoming
project (C) [FIG. 6] include, but are not limited to:
[0007] Mathematical templates/patterns for masking and redundancy
elimination; special focusing, fine tuning, resolution, intensity,
color, texture, phase and polarization techniques; controls (e.g.
switching, gating or modulating) to modify and adjust the direction
and orientation of light, sound and other derived data waveforms
arriving from independent and concurrent sources. These sources
vary in number, physical location and era of time, therefore, are
always fixed relatively to the origins of each query and
transmission. Variations due to this relativity are corrected,
streamlined or otherwise made consistent for particular uses
through the use of the mathematical patterns themselves. Each
pattern is constructed for a different reason, uses its own
measures, has its own similarities and will therefore encounter and
reconcile each variation its own way.
[0008] Context Driven Topologies are constructed to show data and
data relationships as they are periodically recorded, as they
change over time, as they are interpreted with different knowledge,
and as they are interpreted from different points of view. Each
topology can be demodulated to reflect these views and changes
through a process using techniques similar to harmony and discord,
or blending and contrast, to break information into smaller groups
and components. Likewise, new groups are created to simplify,
remove, consolidate, blend or merge components, smaller groups and
topologies to be perceived as one new component, group or Context
Driven Topology.
[0009] New pattern constructions and modulation techniques may be
initiated by an individual, a society or research group, one
computational machine or network of machines (D1)(9.13).
[0010] The mathematical patterns and/or their modulations may be
transferred locally or globally using the methods disclosed to
expand or compress the space these patterns and forms are perceived
to be in by changing the frequency of light, sound and other
encoded logic elements as they are processed and displayed by
devices and systems specially controlled by individual or networked
users to investigate and interpret data and data relationships for
specific reasons.
[0011] The invention is used to obtain and interpret records using
waves that in some cases are other than optical waves. The
invention is a dynamic, shared memory (Section 8) using image and
other specific data arrangements as records.
[0012] Context Driven Topologies are broadcast to be distributed in
the waveform state, similar to existing radio or cell phone
technology and initially `powered` simply by being propagated
through use, similar to language, songs, stories and information on
the internet. Special compilers, broadcasting, retrieval and
presentation equipment will be developed in the future. See
paragraph (1.24) regarding electrical pulses.
BACKGROUND OF THE INVENTION
[0013] The inventor is an independent curator who organizes museum
content and collections by selecting, categorizing, numbering,
indexing, describing and presenting objects in meaningful
hierarchies to tell cultural, scientific and historical stories
through physically designed spaces, objects, voices, projection
geometries and immersive environments that simulate a feeling of
`being there`.
[0014] The invention was prompted in 2001 while the inventor was
researching thermodynamics for Shanghai Scienceland in China.
Influences include a series of readings in physics, mathematics,
new physics, quantum mechanics, chemistry, biology, light and
optics, acoustics, philosophy; and a continuous dialogue discussing
the merits of various learning interactives listed in Chinese and
legible only by their numbers, then the design of these same
interactives (by the inventor) using as little natural language as
possible to avoid the intricacies of multiple translations. This
was followed by the International Spy Museum which included
concepts of encryption, encoding, revealing/concealing, and piecing
together a puzzle from the "partially seen" and "partially true".
This project lacked one clear direction or voice, the content
included constantly updating artifacts, stories, architectures,
spaces, environments, programs and scopes of work documented by the
inventor through matrices, specifications, photographs and CAD
drawings. Each of these influences were added together in the
inventors own imagination to lead to the invention in its current
embodiment.
PRIOR ART
[0015] Mathematics: the invention is a new application of Graph
Theory; Knot Theory Topology; Algebra, Group Theory, Combinatorics,
Fourier Analysis, and various interrelationships between these
fields and other pure or applied prior art that is most clearly
expressed and understood through mathematics.
[0016] Artificial Intelligence (AI), Knowledge Management (KM),
Human Computer Interaction (HCI): the invention particularly
addresses subject matters related to mapping; complex indexing of
events, objects and agents; parallel processing; data mining and
privacy; user directed interface; hierarchical structures; sequence
and flow in comparison processes; new forms of node representation
and topologies; visualization and simulation; a new system and
theory of computational linguistics and process grammars;
mechanisms for shared memory; machine learning and training;
design; scalable data and networks; automatic updating; compression
and decompression; techniques for data curation, interpretation and
preservation; pattern, shape, motif and object generation,
identification and recognition; text, visual, audio and other
machine derived representations of encoded information;
unsupervised clustering; techniques for the interpretation of
partially described data and data relationships; illustrative
embodiments; containers, wrappers and boundaries; parsing; traces;
new abilities for machines to generalize, associate and categorize;
selection methods; rules; heuristics; priority registry and
addressing; periodicities; thresholds; infinite variables;
redundancy and masking; custom consistency and similarity measures;
error and irregularity detection; new types of I/O devices,
methodologies and purposes; an improved process for metadata,
determining order, partial order and concepts of matching; machine
implementation and simulation of human intelligence, decision
making, and conceptual integration; the directed use of language,
memory, imagery, sounds and encoding for specific purposes. The
invention gives machines "something to measure" that is closer to
our imagination, cultures, changing interpretations, and historical
comprehension. Context Driven Topologies are used to compile,
generate and present results a new way. They are a better form of
metadata that easily scales and a marked departure from tree
structures, or other standard data arrangements, because these
topologies provide a new way for information to characterize,
organize and identify itself in context over time.
[0017] Physics, Quantum Mechanics, Astronomy, Chemistry, Biology
and other Sciences: the need to measure; our quest to discover,
diagnose, explore, and evaluate; logic; problem solving and
accuracy; fundamental relationships; simplicity and complexity;
elegance; the desire for robust, rigorous, precise investigations
based on solid foundations with the intention of leading to
significant, new proofs and conclusions; and, our basic human
relationship with time, nature and understanding of forms and
processes.
[0018] Cognitive Science, Ontological Engineering and Semiotics:
symbols; language; translation; word meaning; history; schemas,
foundations and rationale; metaphor and representation; our need to
communicate across cultures and generations; our need to share
information, record and discuss.
[0019] Art and Music: aesthetics; composition; clarity;
simplification; abstraction; layering; similarities and patterns,
returning to the same; unique variations and interpretations;
reflections of cultures; questions about conventions and our
societies; perception; awareness; preference; and the need to
express.
[0020] Architecture and Design: drawing methods, perspective and
rendering for discussion versus schedules, plans, elevations,
details, sections and overalls for building; careful attention to
proportion, and relationships between adjacent spaces; lighting and
acoustics; material properties including durability, compatibility,
texture and color.
[0021] Statement on Prior Art versus the Invention: Similar
patented subject matters identify or create information object
types, properties, subsets of properties, data characteristics and
arrange information units into ordered sequences or relationships,
however outside of Classes 706 and 707, very few subjects even
nominally address what the information itself means, why it was
generated, the reality that some information is more important or
lasting than other information, and how this influences peoples
interpretation of these graphs, patterns, objects, properties and
characteristics. Nor does most prior art allow for these properties
and characteristics to evolve, be influenced, and recorded over
time. Generally, prior art is based on a delicate balance between
the ways data relationships are described and derived but does not
allow these data descriptions or derivations to vary by preference
or specific quality assurances, and how these preferences and
assurances affect the value of data. Very generally, it is the
inventor's belief that objects and experiences, such as artworks
and scientific studies, reflect or attempt to capture what is
genuine and the process of curation, interpretation, and
preservation of data generated to represent these objects and
experiences needs to aim for a virtual connection that is as direct
as possible between maker and viewer, nature and observer. The
invention is intended for information that has had, at least at one
time, a profound attachment to the original user(s). The invention
is a tool to let these attachments become more obvious and is based
on the belief that a more thorough understanding of context will
not only ensure more meaningful and direct connections in the
future, but that use of the invention will dramatically increase
our abilities to consolidate and manage shared long term data
resources of higher quality and value, which is also barely, if
ever, addressed in similar patents and subject matters reviewed in
prior art.
C. Future Plans for the Invention/General Notes
[0022] The inventor has organized a project in collaboration with
individual theorists, mathematicians, artists, engineers, and other
inventors to: look carefully at the reasons and purposes for the
invention from a variety of view points (A2); to generate a
representative dataset [FIG. 6]; to develop an enhanced prototype
that is a mathematical, visual and audio model and new conceptual
framework; to create and define the first set of knowledge
patterns, display patterns, memory forms, measurable arcs and to
further demonstrate and clarify the techniques described herein
using a sampler set of ideas that reference these individual's
methods of constructing ideas, and the ways these ideas are
manifest through art, science, engineering and language. See the
detailed description of [FIG. 6] for an explanation of this
process.
[0023] This particular project, which may or may not be in
collaboration with an established US research partner such as a
public or private university, museum, research institute, or
information technology company, will be called "Digitizing the
Non-Digital", "Visualization of Context Driven Topologies"
"Inside/Outside" or other name as determined by the inventor.
Research efforts for the development of technology based on the
ideas and processes disclosed that are concealed behind other names
or purposes may be an infringement upon the inventor's claims and
subject to legal action.
[0024] This collaborative project will be proposed to US Federal,
private and international agencies along with research partners
identified by the inventor as legitimate collaborators in these
proposals. No other persons, institutions or companies have
legitimate past, present or future claims to these ideas and
processes, nor the right to develop the specific technologies
disclosed. Detailed knowledge of these ideas and processes that are
independent from the protection of these patent specifications
would naturally be based on confidential, proprietary conversations
with the inventor for planning purposes to assess the
qualifications of these persons or institutions in relation to the
invention and the inventor's plans for its implementation and
distribution.
[0025] Genuine interest in the ideas, processes and technologies
disclosed should be directed to the inventor so that more diverse
knowledge, the use of shared or more advanced machines, generally
more people thinking, and other time saving partnerships may be
arranged through a formal, equitable partnership agreement.
[0026] Future research partners may be from the United States or
foreign countries, these arrangements will be specifically
indicated in future patent applications. The protection provided by
this patent or future patents is intended to be in complete
concordance between all US and International patent/IP systems.
[0027] Sharing or assigning future patents for future technologies
specifically listed herein, inspired by and/or built upon the
invention's core principles is at the sole discretion of the
inventor and within all applicable US and International laws as
determined by the USPTO, IPO and others, the inventor, and her
attorney.
[0028] The invention will be introduced and partially implemented
with a variety of US and international individuals and institutions
to assess the invention's compatibility with specifically varying
resources. It will be proposed that the work of this project be
presented and discussed at US and international art museums,
research institutes, conferences, universities and other places and
events in an effort to: disseminate the ideas and methodology of
the invention; gather feedback from a variety of cultures; form
lasting partnerships with these individuals and institutions to use
the invention on larger, broader and more specific collections of
complex and abstract information. The more widely the invention is
used, the more useful it will be. The purpose of the project to
establish quality controls and a firm foundation for future
technologies so use of the invention is not confusing, geared to
one domain or culture over another, related too strongly to natural
language, or current machine processing, indexing, computation and
display methods.
D. BREIF SUMMARY OF THE INVENTION
[0029] A simple, evolving interactive method of mathematical
abstraction and conceptual illustration for complex data curation,
interpretation and preservation. The invention is a measurement
system independent of electricity, media, natural and machine
languages for networks of people and computational machines to
capture the infinite ways ideas are constructed, and to record
relationships between ideas for further contemplation. Virtual
representations of concurrent and conflicting data relationships,
called Context Driven Topologies, scale in resolution and
compactness to be perceived in three states: fluid configurations
of information components connected using the multidimensional
topologies [FIG. 10A]; compressed symbols, similar to music
annotations or character writing but limitless, to fit inside each
components mathematical description [FIG. 10B] indicating each
component location within each hierarchy a special, self-referring
way [FIG. 9]; and multidimensional waveforms [FIG. 10C]
continuously being simplified, streamlined and consolidated in an
abstract, boundless, stateless cloud [FIG. 1] [FIG. 2]. Retrieved
waveforms are broadcast as object-like periodicities to be
interpreted. Even though the topologies are perceived differently
in each state, they remain mathematically the same regardless of
their use. Over time, relationships between configuration, symbol
and waveform uses will generate patterns used to identify and
create new data relationships. These relationships may be initiated
by either people or machines (B1.5c). Input and output easily map
between text, mathematics, imagery, sounds and other means because
each means of communication is described and derived using the same
mathematical system. Context Driven Topologies are passed like
stories from one generation to the next, transformed as interpreted
then returned slightly modified. These cycles leave indelible
marks, data without these marks or significant connections does not
persist. Redundant data and data relationships have precisely
matching topologies that automatically align to mask and cancel
each other, defining similarity measures is a decision process
between users and machines. "Better" topologies are ideal forms,
compactly identified, and automatically preserved over time through
evaluation and use.
E. DRAWINGS
[0030] FIGS. 1-10
BREIF DESCRIPTION OF THE DRAWINGS
[0031] FIG. 1: The boundless abstract data cloud in a stateless
space.
[0032] FIG. 2: Users provide machines with related examples,
patterns and measurements begin to emerge.
[0033] FIG. 3: Users begin to construct a hierarchy, create new
information and make decisions.
[0034] FIG. 4: Conclusions are drawn and a Context Driven Topology
becomes fixed into its own pattern.
[0035] FIG. 5: Ideas traveling in different directions overlapping
in time, three separate background histories.
[0036] FIG. 6: The process of the upcoming project and enhanced
prototype.
[0037] FIG. 7: Components appearing to change scale to new levels
by modifying their description boundary.
[0038] FIG. 8: Rotating, aligning and scaling data components and
groups until they fit into a new arrangement.
[0039] FIG. 9: The self-referring relationship between a Context
Driven Topology and a component location.
[0040] FIG. 10: A Context Driven Topology in the three states:
configuration, symbol, waveform.
[0041] Views within the Drawings are indicated by a numbered gray
circle that corresponds to a (bold number in brackets) in the
detailed descriptions. References to paragraphs and sections within
the text are italic.
DETAILED DESCRIPTION OF THE DRAWINGS
[0042] FIG. 1: People and machines confront a boundless
disorganized abstract cloud which cannot be drawn because any
drawing of any view is inseparable from an interpretation of this
content.
[0043] FIG. 2: People begin to specify or create data components
and organize data relationships by providing advanced networks of
machines with related examples using words, images, sounds,
drawings, dimensions, flow sequences, hierarchical structures and
any other relevant description. Immediately, well over 99% of the
abstract cloud goes `dark` or cannot be perceived. Knowledge
patterns and measurements begin to emerge. Information that has
been related in the past is "drawn" together to become initially
arranged, or placed (3.18) (7.2) (8.3) (9.4 to 9.6), and
temporarily "locked" into a relative proportion or automatic
sequence.
[0044] FIG. 3: People use this initial "group" and begin to add new
ideas, take away what is not important, rearrange, re-prioritize
and construct a new data relationship. The view that is perceived
is naturally the users, even if it is a research group sharing the
same view. The lines in this drawing represent a stream of ideas
over time (2), the ellipses and bounded areas (1) represent an idea
coming together on its own. Machine measurements are continuously
updating (3). Machines never see "views" such as foreground and
background; they continuously process and reprocess the changing
groups of mathematical descriptions (data components, data groups,
data relationships, hierarchy) as if it was one whole group.
Mathematical descriptions and relationships come and go as the user
arranges their information, begins to make decisions and prepares
to draw conclusions. Machine generated suggestions of simpler
arrangements may be used to refine the new data and data
arrangement at the discretion of the user. Below, and FIGS. 4 and
5, illustrates an example of three users: curator; scientist; and
detective; an assumed point of view for machines is also given.
[0045] the curator decides to focus on an era, style or media and
begins to define a group of art and artists.
[0046] the scientist specifies a hidden markov model will be used,
a certain range will be measured.
[0047] the detective begins to look around and talk to people to
gather rumors and facts about the crime.
[0048] machines display the ideas and information people are
working through and begins to calculate and streamline (1.7) the
mathematical data descriptions and algorithms into smaller groups
and begins to establish an internal order. Immeasurably vast
quantities of unrelated data and data relationships from both deep
in the background and practically adjacent are not recognized and
therefore eliminated from the current dynamic calculation and
group.
[0049] FIG. 4: This drawing shows a close up view of a Context
Driven Topology that is almost complete. Each evolving arrangement
is a unique, high dimensional, measurable structure and form
constructed of various information components placed into a
hierarchy with varying emphasis, proportions and adjacencies (6.1
to 6.10). Histories are simplified and aligned more precisely using
machines. Clues about the content are simplified and indicated
abstractly using colors, textures and other features of the
automatic language (7.1 to 7.50). When the user determines this
arrangement is complete, the next step is to fix the topology into
a permanent pattern that includes the patterned space around it;
then send it into the shared memory area (Section 8) of the
stateless space for discussion. These outside interpretations are
able to change the histories and clues about this content which
therefore influences the "appearance" of these objects, spaces and
patterns in the future. The "original idea" is left untouched in
placeholder position (2.6) [FIG. 6] beyond the reach or influence
of outside interpretations. Each virtual object gradually begins to
add new layers of interpretation and meaning that surround each
original arrangement as it was detailed and put together by the
author.
[0050] the curator oversees the installation of the exhibition,
publishes written conclusions and reads criticisms.
[0051] the scientist publishes a paper, justifies their conclusions
and faces challenges from their peers.
[0052] the detective arrests the perpetrator, it is the right
person, the facts are presented in court and documented.
[0053] machines map the final arrangement and final context, or
placement, of the data components. All of the techniques are
separated and consolidated as required (9.4) (9.11). What is not
there is just as important, if not more important, that what is
there.
[0054] FIG. 5: A closer inspection of the process to establish any
one point of connection in any one topology reveals a more detailed
decision process and history of background arrangements for each
user.
[0055] FIG. 5A--the curator reviewed hundreds of potential pieces
in person, on the internet, and in photographs. Initial research,
early conclusions and writings began to push the exhibition in new
directions, certain preferred pieces are found to be unavailable
during the time they are needed, characteristics of these pieces
may be referred to in the conclusions, but these pieces cannot be
presented with the exhibition.
[0056] FIG. 5B--the scientist discovers a bothersome variable, a
decision needs to be made about what to focus on, different aspects
of the dataset are compared, different comparisons are compared,
variations of the same techniques are tested until the simplest,
most reliable techniques are selected and run.
[0057] FIG. 5C--the detective intellectually sorts through and
compares the rumors and facts that have been gathered, they compare
details of this case with similar cases they remember from the
past, and information they and their associates gather from their
own databases/analysis systems, and the databases/analysis systems
their agency shares with other crime fighting agencies at city,
state, federal and international levels.
[0058] the machine is continually updating the current priorities,
value and placement, scanning the shared memory space and other
remote sources to update as required. As the person's conclusions
and decisions begin to be more defined, the machine is continuously
consolidating these sets and subsets of encoded elements,
algorithms, techniques and functions into a more mathematically
compact whole. Various options for components, previous
arrangements and abandoned techniques that are not part of the
final set are completely eliminated from this record.
[0059] FIG. 6: The process of generating and interpreting a
representative dataset through an upcoming project described in (C)
will be undertaken in this manner:
[0060] FIG. 6A--A group of theorist/mathematicians individually
present a visual and mathematical talk to a group of artists
describing their work, the mathematics they use, and the images
they create (for example visualizations, graphs and diagrams).
Aesthetics and relationships between the theorists' talks are
interpreted differently by each artist (1). Their unique
interpretations are manifest through an art object, performance,
media or layered, digital system (2). The collaboration overall
will generate a series of technical papers and other writings
across several domains (3), one complete publication or book (4), a
changing internet presence (5), two art exhibitions (6), and
sets/series of discussions (7)--a limitless series of panel
discussions, a structured series of lectures, and simple questions
from kids. All of these words will be translated into an
`occurrence` model (8) of ten natural languages to supplement the
mathematical and aesthetic connections. Each participant will
produce at least one paper (9) describing and illustrating the
connections they see among this group of ideas. Both of the
exhibitions and all of the discussion topics will be modeled as a
set of ideas that have originated at the same time through the same
series of theorist talks (10).
[0061] FIG. 6B--The two exhibits and discussion forums are divided
and progress at their own pace in their own locations.
"Constructions" is an exhibition of painting, sculpture and
performance in one place at one time, the scale of each piece is
known to be correct in relation to the viewer, the proportions,
materials, lighting and appearance are known to be as intended by
the artist. This exhibition is methodically relocated to ten
different cultures that speak the ten different natural languages
used in the model. The artworks and performances displayed in
person through the Constructions exhibit are digitally documented
and recorded to represent the object or event `placeholder` concept
disclosed in paragraphs (2.6) (2.10) (3.14) (3.26) (7.21) (10.8)
and (claim 3 of 10). During the time Constructions is predictably
moving in time from one location to another, each artwork within
this exhibition `unfolds` `expands` or has `variations of the same`
as the tour progresses (11). All of these objects, actions, events
and sequences are digitally mapped and recorded.
[0062] A second exhibit, which is unnamed at this writing, is
simultaneously and spontaneously shown in a number of locations
around the globe (12). Many of the reference points tied deep into
the Constructions exhibit are lost, the scale varies according to
presentation mode, device and environment. Individual viewers or
venues may have controls, but the original creator of the material
does not. Over time each of the original artworks and systems in
the second exhibit are able to expand to be presented according to
the artist, engineer or inventors original intent through
non-constrained, non-remote finely adjusted presentation means. An
interactive shared system will be designed to present a series of
screens in a circle. The viewer can walk into a mini-theater in the
center of this circle where high resolution images of the artwork
in both exhibitions and the theorist's examples are projected all
around the viewer field of vision. Subtle sounds, language
variations and audio translations of the other materials are
gradually introduced to coincide with the imagery. Eventually, the
mini-theaters in the centers of these interactive screens become a
`placeholder` for high quality, remote, simultaneous presentation.
Periodically, the interactive screens and mini-theater in the
second exhibit coordinate to show one set of work created by one
participant. Scale will be indicated for paintings and sculptures
using an icon image of the artist themselves in a corner of the
screen, this will be adjusted to 1:1 scale in the theater but
cannot be controlled on various screens, therefore the proportions
of the artist to the art will be fixed. See (Section 10) for useful
examples of fixed proportions. Since the performers are people and
so are the viewers, scale is not corrected regardless of the
presentation mode.
[0063] The theorists' visualizations, graphs and other examples are
generally not able to be shown in a scale with direct relationship
to the viewer using an icon that can work across ALL of the
theorist's examples. Documenting, and getting the shared system to
understand or automatically associate scale relationships with
information depending on the way it is presented is an important
focus for the prototype (13).
[0064] The structure of the model is based on connections between
the participant's ideas, observations and conclusions. In effect,
each project participant is assigned "a dimension". When it is too
confusing to draw, the three roles (theorist/mathematician, artist,
and "integrator") will always be able to be narrowed to three
dimensions, even if the relationship that attempting to be drawn
has an unequal number of participants in any one role or one role
is missing (14). A series of connections between the participants
using these dimensions--whether arbitrarily observed, clearly
stated from the participants own view, or partially shown through
the participants work--will be modeled using Context Driven
Topologies to visualize and mathematically capture these
connections using a configuration of arcs, mathematical patterns,
audio and/or visual forms that change over time as disclosed
throughout these specifications including (A1) and (Sections 7 to
10).
[0065] Regardless of the genuine proximity or distance of
simultaneous exhibitions, the Constructions exhibit location on the
round sphere of the earth during the time it is displayed in this
one known place on a predictable path, is always recorded to be the
center of each unique arrangement (15). The second exhibition
typically has one version in the same city as Constructions, may
have one version stay in the same place and evolve over the course
of the project, but generally, the second exhibitions path is
unpredictable because it may be prompted and displayed in any
number of locations for any length of time. These paths gives
another set of "things to draw and measure" using the new system.
Because the schedules, duration and locations of the two
exhibitions rarely coincide, these paths and the arcs that may be
drawn to show where each exhibition is in relation to the other one
at any point in time will begin to form an evolving history that
can be measured, over time, using these arcs. These movements will
also help to "place" each of the participants "dimensions" against
a structured background. The participants, the artwork, the
theorists examples and the museum architectures can all be compared
to the "objects and spaces over time" in these specifications.
[0066] FIG. 6C--At the end of this process, all of the artwork,
theorists' examples, digital systems and other representations are
brought together with all of the project participants in one large
space at the same time. Each of these people, their individual
points of view and ideas, an example of how they work both in `real
life` and as `digitally represented`--becomes one unit, at one
time, in one place that is captured into one Context Driven
Topology. Each previous exhibition, discussion, paper, artwork,
visualization, mathematical relationship and other references are
indicated as smaller, self referring, expandable topologies leading
to the final arrangement that is arbitrarily declared to be
complete at the last exhibition. The histories and paths of each
participant, object, event, and point of view are traceable through
the histories in the last Context Driven Topology, which will have
evolved and been fine tuned over the course of the project, and
"made to work" with ten different natural languages, mathematics,
individual aesthetics, and ten different cultural understandings.
Therefore, the upcoming project generates a representative dataset
to test the invention in real life, using real ideas, real art,
real science, real mathematics, real cultures and real systems.
[0067] The purpose for the invention is initially realized through
this project. One use is a better way to present scientific
visualization and art museum content online. In the last
exhibition, the art in Constructions and the art that can vary in
scale and reference in the second exhibit will--switch places--to
be shown both in person and on a screen. The relationship between
these modes is captured mathematically and manipulated through an
automatic audio and visual language that IS the invention. Figuring
out this switch between presentation modes, looking at this
question of scale, getting the placeholder position to persist in
the digital mode, and mapping between this one defined set of
ideas, examples and a "sampler set" of work is an enhanced
prototype using a representative dataset. This working model,
initially introduced THROUGH this project, will be formally
presented to US and international museums, libraries, and
scientific research communities. This invention and documented
framework will be presented as a better way to capture ideas, map
between ideas, and correspond with the way ideas are manifest
through art, science and other systems.
[0068] FIG. 7: As indicated throughout these specifications and as
will be demonstrated (C) [FIG. 6], when previously disassociated
data components, relationships and topologies scale to be compared,
they:
[0069] FIG. 7A--only appear to `change size` (3.16) (3.18) (4.1)
(7.31) (9.11) to be recognizable in a field with what the user is
defining as the "same" or similar components and relationships
(claim 4 of 10). When data and data relationships are known to have
a relative scale, this proportion becomes locked until the units
being compared are dissolved from this topology or otherwise set
free from the group. Data components, groups and topologies change
between hierarchical levels by compressing and expanding their
mathematical descriptions. The state of compactness or expansion of
the descriptions affects the texture [FIG. 4] of visual components
(1.23) (3.18) (3.33) (7.29) (7.36) (7.38) (7.42) (7.47) (8.7)
(8.11) (claim 1 of 10).
[0070] FIG. 7B--the invention allows data components, arrangements
and other relationships that are not typically able to be
associated, to become virtually associated (10.6) by rotating,
aligning and scaling data components and groups until they fit into
the new arrangement (3.31) (7.3) (7.9) (7.42) (7.47).
[0071] FIG. 8: When components that originate at any scale, era,
level or configuration are assembled into a new group, these
arrangements and priorities are tracked using the Context Driven
Topologies system.
[0072] FIG. 8A--If a person or research group chooses to compare
only one certain kind of temporal or mathematically assigned
connection reflected by a certain type of arc, radius and rotation;
the topology overall twists, transforms, scales, and moves as
necessary until the radius are aligned, compressed, expanded and
virtually organized in high dimensions that do not change the
overall mathematical identity. After the alignment and scaling
procedure, usually working back and forth between the details and
the overall (3.11) (3.21) (7.13) (7.22) (7.26) (7.30) (8.3) (8.22)
(9.15) (10.6) (claims 1 and 4 of 10) eventually the user knows
exactly which particular data components and pre-existing groups
(6.8) need to be extracted and analyzed.
[0073] FIG. 8B--Context Driven Topologies are mathematical patterns
composed of a series of vector arcs, without straight lines,
corners, or pixels at any time. When there is a "tight connection"
between two or more ideas, it is assigned a short arc, "looser
connections" have longer arcs, the radius and rotation also varies
according to connection type (A1) (Section 4) (Section 7) (Section
9). The arcs, their current scale or compactness, radius and
orientation in each individual arrangement are what is measured. By
default, the arcs become arranged into a seashell pattern with the
tighter arcs and therefore more connected objects and ideas toward
the top and the looser connections automatically falling into the
background. The viewer can always control how much information they
see at any time (Section 7) (Section 9). In some cases, the tight
connections are too obvious and a user prefers to go deeper to make
new connections, when this happens even if the components are not
changed, the invention is used to reprioritize and structure or
otherwise "flip the shape" to make a new topology showing a new set
of tight to loose connections.
[0074] FIG. 9: Using a sketch of the content of these
specifications and a hand-drawn non-correct approximation of
reference arcs between the paragraphs and claims, the
self-referring relationship between a Context Driven Topology [FIG.
10A] and a component [FIG. 9] location is shown on three
levels.
[0075] FIG. 10: Context Driven Topologies are high-dimensional and
dynamic, they cannot be drawn as fixed two dimensional lines,
nevertheless, the topologies in their three states of use are
illustrated as:
[0076] FIG. 10A--A Context Driven Topology as an underlying
structure connecting a group of arranged data.
[0077] Step 1: Using the content of these specifications again the
pages, sections, and paragraphs are shown.
[0078] Step 2: Because page breaks are not relevant, this
unintended grouping is removed and simplified.
[0079] Step 3: Context and references between paragraphs are
indicated by symbolic hand drawn (incorrect) arcs.
[0080] Step 4: Typically the arcs are arranged to correspond to the
temporal or mathematically assigned degree of connection [FIG. 8],
however, this particular topology (made by assigning arcs to the
references within this document) show a set of linear connections
(the pages and paragraphs in this order). In the future, this
topology can be use to show this set of content arranged in this
same order pages 1 to 117, or by priority from tighter to looser
connections, or the density of the most connections (in this case
the claims and 8.3).
[0081] FIG. 10B--A Context Driven Topology `compressed` into a
symbol inside descriptions, used as a map
[0082] Step 1: An area of concentration, in this case paragraph
8.2, is identified and isolated.
[0083] Step 2: Placement within the hierarchy is indicated by a
dot.
[0084] Step 3: The topology itself, including a self-referring
[FIG. 9] emphasis on this placement is embedded into the
mathematical (in this case just the paragraph number 8.2) as part
of this component's description. In the future, this history of
this component will always show how it has been placed in the
context of this overall document. If this exact component, all of
the words in 8.2 described by the number 8.2 are used in a
different document or assigned a different context in this
document, this will create another, linked, topology. Streamlining
these links over time, and providing machines with "something to
measure" that is able to reflect and compare histories of temporal
connections (claims 1 and 8 of 10) is one purpose for the
invention
[0085] FIG. 10C--A Context Driven Topology expanding into a
multidimensional waveform then put into the stream to being
simplified overall and compared with other topologies.
[0086] In each case, the Context Driven Topology is mathematically
the same in any state to machines at all times regardless of how it
is being used. For example, topologies used in a library will
typically stay in the symbol phase, a theorist will typically
rearrange the structures, an analyst will compare waveforms and see
where pathways contained within the history of symbols and
configurations leads. Patterns generated by comparing these
topology behaviors and uses is recognized by either people or
machines for different reasons, people may be interested in how
often information has been used, machines may be able to compile
and consolidate the topologies in groups we may not have put
together or broken apart yet. People will not know what machines
are consolidating until we look for it this particular way,
otherwise it is a structure, a symbol, or a waveform we are using
for our own knowledge, investigations and expressions.
G. DETAILED DISCLOSURE AND SPECIFIC EMBODIMENTS OF THE
INVENTION
[0087] Sections
[0088] 1. Context Driven Topology
[0089] 2. Concept Boundaries and the Annotation Process
[0090] 3. Symbolic Characters and their Function
[0091] 4. Evolving Mathematical Knowledge Patterns Converted into
Multidimensional Wave Forms
[0092] 5. Metaphors
[0093] 6. Monitoring, Controlling, and Influencing Information
Placement and Proximity over Time
[0094] 7. Use of the Automatic Evolving Audio and Visual Language
and Display Patterns
[0095] 8. Shared Memory
[0096] 9. Data Curation and Digital Preservation
[0097] 10. Specific Embodiments and Applications
Context Driven Topology
[0098] When mathematical topologists consider knots and
entanglements, they usually imagine a knot by also imagining the
space around it. If neither changes, then the knot will persist.
The invention is applying mathematical topology, algebra and new
pattern generation and recognition techniques to digital
information context by putting knowledge and ideas into a stream to
see how they become entangled, can be separated from their
background, recognized from different points of view, interrelated,
and influenced over time.
[0099] Context Driven Topologies are evolving records of data
interpretations between people and machines. They are
configurations, or knots, of information that people understand
together, the space around them is everything else which we have no
capacity to interpret or understand without computational machines.
If the patterned space around a knot of information changes, so
will the interpretation of the information.
[0100] Context Driven Topologies are an information `whole`
constructed of any number of components.
[0101] As introduced in (B1.5) (B3.2), comparing or consolidating
groups of Context Driven Topologies that share components
automatically overlaps, masks and deletes duplicative components to
eliminate redundancy (3.12) (3.31) (4.15) (10.1) (claim 4 of 10)
(claim 10 of 10). Measurements to specify what makes components
"the same" is adjusted by each user through the use of mathematical
description patterns (Section 2) and a history of previous
associations (2.12) (3.7) (3.10) (7.2) (7.16) (claim 1 of 10).
[0102] The process of consolidating and comparing Context Driven
Topologies through the use of mathematical knowledge patterns and
their shared histories will cause components, and topologies that
share components, to be drawn towards each other from common
histories deep in the background. This `movement` or `relocation`
towards adjacency is prompted by machines processing the
mathematical description patterns (Section 2) into more simplified
sets that are easier to calculate. There is usually a reason why
information has been associated before. Associations made visible
using the invention may exist over longer time scales than an
individual or research group exists, which could lead to new
insights and interesting discoveries.
[0103] The invention is a process and methodology to begin looking
through large scale museum and library digitization projects,
automated scientific experiments, specialized databases, internet
accessible publishing and other complex shared information. It is
also an automatic system to improve the quality of data in dynamic
shared data stores (1.11). It will train the shared information and
memory space to prefer threads of knowledge that have been
thoroughly reviewed and discussed to give these data arrangements a
greater chance of persisting because they might be true, regardless
of the fact neither people nor our current machines may be capable
of fully understanding these arrangements yet.
[0104] People accept so many ideas as true today and false
tomorrow. Along the way, we have always retained too many
misleading misconceptions, too many copies of ideas that were not
proven to be useful. Simple truths can become lost in a labyrinth
of inaccessible, disorganized record keeping styles and priorities.
As introduced in (A1) (B1.5), the invention makes direct use of
concurrent and conflicting priorities and varying interpretations
of complex shared information to gradually streamline [FIG. 3]
(3.11) (3.25) (4.6) (4.18) (6.9) (7.8) (7.18) (7.23) (7.37) (8.1)
(8.6) (8.7) (8.18) (8.22) (9.17) (claim 2 of 10) and delete
specifically incorrect shared information a very precise way
determined by the people who use and understand this
information.
[0105] Most ideas and processes change in small increments or
through events that are never recorded; the invention is needed to
help fill in these gaps.
[0106] The pace which ideas and processes change within the same
complex evolving data arrangements and data collections directly
corresponds with the pace each idea or process changes. This is
necessary because different domains and cultures accept and reject
ideas and processes at different rates, therefore, any dynamic data
collection that crosses domains and cultures must have mechanisms
to allow the pace of each individual change to be reflected at the
rate which each domain or culture is satisfied with the change.
[0107] The invention enables people to see ideas, processes and
changes more clearly because each one can be evaluated apart from
their background at any point in time. This will provide new ways
for people to understand overall patterns, trends,
interrelationships, failures and successes that led to each
incremental change. Measuring these changes, which people do
evaluate and understand both incrementally and overall, will lead
to future understandings people were unable to evaluate or
understand in the past.
[0108] The invention will help weed out low quality or incorrect
shared data, and provides tools to fill in the blanks, gaps and
consolidate new overall pictures that people did not have reasons,
or the means, to interpret before (2.7).
[0109] Both people and current shared data stores preserve far too
much information, such as bank transactions, that by its very
nature is either temporary or continually changing and could be
eliminated from long term data stores by assigning these kinds of
data and data relationships an automatic lifespan. It is also true
that some ideas simply have their time and need to gradually fade
away (9.8).
[0110] Unexpected commonalities are beginning to emerge between
unrelated fields at all levels. Context Driven Topologies are a
mechanism to build virtual bridges of understanding between
cultures and domains at deep levels to pose and investigate
unexpected questions that may not be developed between knowledge
systems and machines at the time when they make sense in a person
or research group's imagination.
[0111] The invention is intended to be used for published, open
source information, such as scientific journals and art criticism
that is meant to be shared and challenged. Using the invention with
information that is not as open to interpretation or may require
only certain components and relationships to be shared, is
specified in (Sections 7, 8, and 9).
[0112] Irrelevant, outdated, misleading or incorrect information
not only distracts all searches and research efforts; but
coinciding with the exact time most people are overwhelmed by
unreal quantities of readily available and closely related data,
all people are rapidly losing valuable information generated by
expensive studies and told through unique voices we will not hear
in the future if this information resides on unstable media created
through systems and devices that no longer function and will never
be repaired or reinstated. Currently, there is no unified language
free forum to discuss information's relevance, "up-to-dateness", or
correctness. There is no shared storage space to maintain together,
no reason to cling to obsolete programs, devices or media;
therefore, most current information is permanently lost.
[0113] At 200 years, books have a longer life than current digital
information. At 100 years Daguerreotype type photographs survive
yet early photographs that were claimed to be permanent because
they were printed on paper, are degrading faster than people can
scan them. What will we do with all the images that are scanned?
What resolution are these scanned images? Will people be able to
read them on systems and media in ten years? Who is choosing which
ones to scan while the others degrade? There is a hope of promise
that shared information, such as images, is being captured in an
encoded digital form that, theoretically, should be able to
transfer across generations independent of a media. The first step
to implementing the invention is already underway, the question is,
what use is this code representing these images that represent our
experience if there are far more images than can be understood by
one person or even a large scale study involving hundreds of people
over a decade? Machines do not "need" this knowledge like people
do.
[0114] The mathematical patterns in Context Driven Topologies also
encapsulate, consolidate and automatically update specific program
functions required to read the specific group of data components
and data arrangements captured in each topology (9.4) (9.12) [FIG.
4]. Long term digital preservation requires both the data itself,
and the means to interpret it, be united. It is the inventor's
position that the forced separation between hardware and software
to enable the rapid, staged development of information technology
is a great disservice to long and short term digital
preservation.
[0115] The purpose of the invention is to establish a consistent,
reliable way to organize, identify and retrieve information that is
relevant or interesting for specific purposes without the user,
research group or data itself becoming lost in an unmapped sea. It
will never be possible to accurately identify specific purposes or
uses that current information may have in the future. People simply
need to digitize, organize, and store as much high quality
information as possible in a very precise, measurable manner so
that the more specific a future query is, the more relevant,
interesting and accurate the retrieved information will be. A
preservation effort of this type is partially to understand the
past, but more to participate in the future.
[0116] The relationship between `things` and `information about
things` has gradually slipped out of control over the past 150
years. For example, what do the specifications for the first,
original patent of a gasoline powered engine look like compared to
the new 2004 engines? Who would wish to compare these? If real life
worked as neatly as one single patent that had a tight and
immediate connection to every 2004 model as it is coming off the
belt at the factory, it would be easy to compare the original idea
with a series of current variations. In reality, life and progress
are much messier and disconnected. It is staggering how many
classifications of inventions are patented in the United States
alone, internal combustion engines are assigned their own class,
there may actually be fewer 2004 engines. The invention answers a
need for the relationship between `things` and `information about
things` to become more realistic and efficient.
[0117] Currently, the dialogue between people and machines is
either encoding that has little meaning to people, or descriptions
based on natural language, or key words, that have too many
associations and cultural interpretations to be a precise system of
measurement for machines (7.22). One current problem the invention
specifically addresses, and our new connected age needs to address
from many directions, is the fact that people need to be able to
describe what they are looking for to machines in more meaningful,
measurable ways. The inventions automatic language (Section 7) and
system of mathematical patterns evolves specifically to accommodate
changing natural and machine languages a measurable way.
[0118] The invention is a new form of measurement that allows for
new measurements and more precise descriptions over time.
Currently, people cannot search images, visualizations, high
dimensional graphs and other mathematical, visual or audio
materials except through key words or meaningless measurements,
such as 78% cyan. People today do not have a way to describe the
data they seek in machine based language that corresponds to human
based reasons for the query. To navigate and interpret the vast
seas of data that are currently unmapped, a user needs to be able
to get to the essence of why each particular data arrangement is
unique. The invention's processes (3.27) allow users to search and
access complex histories, thoughts, images, studies,
visualizations, drawings, flow, transformations, cultural objects,
stories, expressions and purely mathematical relationships using
the actual measurements, related images, graphs etc, themselves as
a search and recognition technique.
[0119] The invention is intended for information that has been
generated for a reason. This data may be measured for one reason
when it is originated and different reasons in the future. For
example, if an astronomer captures a 999 dimension data array
originating from a particular point at a particular time to detect
a particular relationship, in fact, this astronomer may have
captured something else that even the most brilliant observer is
unable to recognize yet or know how to describe using current
knowledge and machines. The important and new measurements
potentially hidden in this data array will naturally be described
and derived differently by scientists in the future because they
may be measuring the array for different reasons. However, the
reasons the future scientist may even aware of new relationships in
the existing array are often directly related to the reasoning and
questions posed by the original scientist. A teenager downloading
pop music is not likely to stumble across this information because
they do not know how to describe these measurements precisely
enough, and float through the shared information space at a
different level.
[0120] An important purpose of the invention is to give machines
something to measure that reflects human reasoning (3.6). The way
people think, describe, partially describe, remember, recognize,
identify and derive associations between information so that
machines can help us to identify and create new associations we may
not be capable of recognizing on our own. The invention is a new
way to show machines related examples and similar versions that
explain why certain groups of information, contexts, time periods,
and relationships are more important than others. Currently, most
data components `feel` interchangeable and equal, there is no depth
or texture. A machine or network can capture which information is
used more often but has no means to measure, compare or understand
why except pure statistics. The invention illustrates these reasons
through alignment (9.8), density (3.17) (4.2) (4.11) (5.3) (7.26)
(7.28) (7.36) (8.3), texture (1.23) (3.18) (3.33) (7.29) (7.36)
(7.38) (7.42) (7.47) (8.7) (8.11), color (7.28) (7.29) (7.42)
(7.47), intensity (B1.5) (7.7) (7.28) (7.47) and other disclosed
techniques that machines are already capable of measuring (claim 1
of 10).
[0121] Current data relationships, network topologies and data
stores (even dynamic data stores) are typically in even
arrangements with equal, practically interchangeable components
geared for machine processing rather than the fluid, variable human
imagination and investigation process. This is believed by the
inventor to be caused by an overdependence on electrical pulses.
The inventions mathematical memory patterns are more suited to
continuous patterned waveforms, similar to existing radio or cell
phone technology, rather than electrical pulses which are more
suited to current machine languages and equal information packets
or components. These waveforms (Section 4) are intended be
independent of electricity and electrical pulses.
[0122] The form of the long-term dialogue between people and
machines needs to change soon or our reasons for generating this
deluge of data that keeps growing exponentially, every minute, in
both complexity and quantity, will not be as useful as it could be.
Current machines also have no sense of deadlines which have an
enormous affect on most users. The invention will change this
dialogue by clarifying the roles of human imagination, conceptual
leaps, decision making skills and real world concerns versus the
role of machine computation and advanced network communication
across domains, cultures, platforms and languages using software,
programs and systems spelled out in (claims 1 to 10), using
techniques partially disclosed herein (B1.5) and developed further
through an upcoming project (C) and [FIG. 6].
Concept Boundaries and the Annotation Process
[0123] People are able to communicate with each other, and
machines, concisely and clearly using symbols. Symbols become
associated with ideas very easily. Mathematics and symbols do not
speak one natural language over another, their arrangements and
sequences can be precisely recreated, and both people and machines
can learn to understand them. Creating symbolic mathematical forms
and patterns to illustrate data characteristics; map data
relationships, understanding and knowledge over time; and
`automatically` preserving these symbols as pathways for future
interpretation is the heart of the invention.
[0124] Information at any level is easiest to identify, work with,
and derive meaning from when it is concisely and accurately
described. Even with all of the current and confusing protocols,
data descriptions still vary tremendously in different fields
especially for new ideas. The invention's annotation system is
mathematically based and enhanced by a series of character-like
symbol generation and recognition processes that will eventually
streamline (1.7) and authenticate interrelated data descriptions
over time. As these data descriptions become more intertwined,
streamlined and authentic so will information dynamically retrieved
and stored knowledge.
[0125] The relationship between the inventions overall process and
the annotation system is more similar to music than any natural or
machine language and involves the following steps:
[0126] To assign an identity to a group of data it is first
described through an intellectually assigned alphanumeric code. For
example, the USPTO uses patent numbers that are further
contextualized into classes and subclasses. Many existing numbering
systems, such as patent classifications, evolved from systems that
were established before computers. Over time these existing systems
have needed to expand and separate in certain areas to keep each
field narrow and precise. For example 345/440 Graph Generating
versus 345/440.1 Real Time Wave Form Display versus 345/440.2 Bar
Graph. It may seem convoluted to people that do not regularly use
the system, but the majority of numbering systems that have evolved
gradually over time generally do reflect a certain logic or have
meaning to the people that use them. Therefore, the first number
for any group of information at any level is assigned by the person
or institution responsible for this information's interpretation,
maintenance and organization. Other examples in addition to patent
classifications include: museum object numbers, scientific reports,
Dewey decimal system etc. Generally even today, if a user can
identify the person or organization who either created or is
responsible for the information they seek, provide a specific
number within their particular classification or numbering system,
the information is easily found in its entirety, and usually also
linked to related information.
[0127] If an established numbering system does not exist for a
newly created data group, it is assigned a code based on numerals
to avoid the potential confusion of accidentally producing
words.
[0128] As information begins to be associated with other
information, new layers of encoded descriptions begin to supplement
the original number. The example below is a museum object number
for a painting. This, partly fictitious, description is for a
painting by Franz Klein entitled "Delaware Gap". This numerical
code, which eventually becomes part of the mathematical
description, could begin as:
[0129] 123 4567 89 the Smithsonian Institution
[0130] 42 the Hirshhom Museum and Sculpture Garden
[0131] 66.2751 the original object number, in a unique system
established by the museum themselves
[0132] Of course, none of these numbers describe anything about the
painting itself.
[0133] There may be a URL address to see an image on the web, which
in this case is:
http://hirshhorn.si.edu/collection/Artist=Franz+Kline&Title-
=Delaware+Gap
[0134] There is more information specific to this painting:
[0135] American, born Wilkes-Barre, Pennsylvania 1910-1962
[0136] Oil on canvas
[0137] 781/4.times.1061/8 in. 198.6.times.269.5 cm.
[0138] Gift of the Joseph H. Hirshhorn Foundation, 1966
[0139] Any user can infer from this description that this painting
is taller than most people and very wide even if they are not able
to be in a space next to it. An internet user can see it is black
and white. A reader can understand the artist is American, remember
something about the country's mood at that time, or do the math and
learn that the painting was accessioned after the artists death.
The group of facts in the description begins to tell the story
behind this object, however, each description searched individually
is too broad to ever lead to this particular object quickly as an
individual query out of context.
[0140] Assigned codes for the painting described above could read
as: American, 5789034; born 1910, B1910; died 1962, D1962;
Wilkes-Barre Pennsylvania, 18701 which happens to be a zip code;
oil on canvas, 1524.5693; 78.25.times.106.125 in.,
198.6.times.269.5 cm.; Gift of the Joseph H. Hirshhom Foundation,
99508; accessioned 1966, A1966. There are some areas where the
alphanumeric codes actually correspond to the information they
abbreviate, but more often they do not, especially when viewed
together in a string:
[0141] 123 4567
89/42/66.2751/http://Hirshhorn.si.edu/collection/Artist=Fr-
anz+Kline&Title=Delaware+Gap/5789034/B910/D1962/18701/1524.5693/78.25.time-
s.106.125 in/99508/A1966
[0142] People understand long descriptions that are not words, much
easier if the components stack:
[0143] 123 4567 89
[0144] 42
[0145] 66.2751
[0146]
http://Hirshhorn.si.edu/collection/Artist=Franz+Kline&Title=Delawar-
e+Gap
[0147] 5789034
[0148] B1910
[0149] D1962
[0150] 18701
[0151] 1524.5693
[0152] 78.25.times.106.125 in
[0153] 99508
[0154] A1966
[0155] But there is no room for these kinds of stacks in records
that people prefer to have condensed. There may be a break between
pages or strings which leads to partial descriptions and potential
confusion. A priority is implied by information being on top or in
the beginning of the description (Section 6). If the user does not
know the order of the categories, they may infer the wrong ideas or
background--did the artist live in Anchorage Alaska 99508? No, he
lived in Wilkes-Barre Pennsylvania 18701, the painting was a Gift
of the Joseph H. Hirshhorn Foundation 99508. Therefore, context
driven descriptions cannot be in any specific order (8.3) any more
than they can use one mandated numbering system.
[0156] Codes to describe groups of information in more detail are
also initially assigned user by user, institution by institution,
numbering system by numbering system. Today new systems are being
implemented, such as the Dublin Core Metadata Initiative
http://dublincore.org/, to establish standards across institutions
and their individual description or numbering systems. As
introduced in (B3.2) (2.11), the invention takes the descriptions
of digital information a step further (7.17) (7.27) (10.2) (claim 7
of 10). As shared groups of information are associated [FIG. 7]
[FIG. 8] (1.5) (2.1) (2.3c) (2.5) (3.12) (3.14) (7.10) (7.11)
(7.14) (7.15) (claim 10 of 10) and evolve together, common
descriptions will eventually consolidate (B1.5) (B3.7) (D1) [FIG.
4] [FIG. 10] (1.11) (1.17) (3.11) (3.25) (7.21) (7.34) (7.34)
(7.43) (8.18) (9.12) (10.6) (claim 1 of 10) as machines compile
endless variations of similar descriptions together at the same
times while they are being grouped (83), identified (D1) (3.15)
(3.20) (3.27) (6.3) (7.9) (7.24) (8.3) (9.6) (9.12) (claim 10 of
10) and processed in parallel all together at the same time in user
queries and data arrangements. By using the invention, people will
eventually begin to describe shared information using shared codes
and gradually forget the old numbers and codes the same way word
usage changes in language (10.2). Until that point, because a
machine is compiling all of the possible descriptions
simultaneously (B3.2) [FIG. 3] (3.31) (731) (7.34) 8.11) (9.11)
before people see the information described by these codes,
numbering systems that are in tight concordance are able to be
displayed in any language, format or matrix preferred by the user
through the use of filtering and rotation techniques as illustrated
in [FIG. 8] and (Section 7, 8 and 9).
[0157] The example above in paragraph (2.3) describes immutable
facts about an object. The name of the artist is Franz Kline. This
particular object and portions of its history can be accurately
conveyed through a mere 12 descriptions. Another data group may
require 249 descriptions, only 3 of them immutable facts. There is
no standard, 1 is the minimum, and there can be no maximum. There
can be partial descriptions because partial descriptions are
necessary to interpret certain kinds of data and data groups.
However, when descriptions are precise, obvious or true, fewer are
needed which instantly tells the user something about the
information. The invention makes variations between descriptions
more observable.
[0158] There is also the real situation that some people mislead or
outright lie in descriptions, what if the painting above was a
forgery (8.3)? Descriptions, factual or not, are selected by people
for any number of reasons. For example, it is an established
international convention that has been with people since we began
keeping records to describe art by indicating the artists' heritage
because culture is an essential feature of artworks. But there are
other reasons the description American 5789034, could be used to
describe other groups of information unnecessarily, or in ways that
may not be true. All of these associations and information together
begin to illustrate a picture of what is "American". It is up to
Americans, or any other culture that is diverse and constantly
disagrees, to be able to establish our own consensus of what this
picture should look like. For example, it is not fair if the
description American 5789034 is applied to a silly movie just
because our popular culture gets a lot of attention and this movie
is silly, but in fact, this particular silly movie was made in
India 84760128. Perhaps what really matters about this movie is
that it makes people laugh, and that is the fact that will continue
to be associated and used as a description for the encoded audio
and moving image sequences that comprise this movie in a media-less
digital record form in the future. The invention and its processes
provide reliable, consistent mechanisms, described in (Section 8)
to expose and combat false or misleading descriptions by
specifically and realistically accommodating concurrent and
conflicting interpretations by getting these descriptions to
overlap, cancel and influence each other over time.
[0159] Each description is actually only an interpretation no
matter how appropriate, concise or accurate it appears to be. There
are also certain kinds of descriptions people may never be
completely sure are true. This applies equally to a 999 dimension
data array where the even the most brilliant astronomer who had the
unique foresight to even think of capturing this raw data does not
have the knowledge or machines to recognize an important
relationship hidden inside the data; or a colorless painting
created during a time of prolific color that may never be fully
understood, not even by the artist. Therefore, this type of
original information has a much greater long term value when it is
kept apart from interpretation. The inventions "placeholder" system
explained in paragraphs [FIG. 4] (2.10) (3.14) (3.26) (7.21) (10.8)
and (claim 3 of 10) and leaves originals unmarked, to accommodate
this unique knowledge and interpretation based situation.
[0160] All knowledge has been handed down word of mouth, equation
by equation, theory by theory, image by image, sound by sound etc.
even before people started keeping records. It is only now that
people have the opportunity to analyze larger, more detailed groups
of data and data relationships together. When original ideas and
knowledge are captured using the invention, this is converted into
a purely patterned, encoded manner that is able to transfer to
subsequent generations of people and machines. Therefore, the
inventions ability to reflect historical comprehension along with
improved machines, is nearly guaranteed to enable new ways for
people to understand, infer, and ask more direct questions of data
and data relationships in the future by comparing new and old
knowledge on new generations of machines (B3.4) (1.16) (2.7).
[0161] As introduced in (B3.7), machines will never be able to tell
people when data descriptions are "best" because they have no
attachment to or innate understanding of the information being
described, or the relationships people wish for them to derive. The
invention makes these attachments and understandings more obvious
by letting people, throughout time, argue about and decide which
data descriptions and associations are best.
[0162] As introduced in (2.3), it is only from a person or research
group's point of view that data can be seen in more sensible groups
if it is described separately; or more cohesive when similar groups
merge into one instead of a choppy series of semi-related pieces.
This is the largest problem with existing metadata methods--rigid,
sets of descriptions that often require leaving fields blank or
making up unnecessary descriptions for pre-defined categories are
not important or even applicable to particular works, thus gearing
current metadata systems to machines--who do not `really`
understand information, instead of people--who do. Therefore, the
invention is a better form of metadata (claim 7 of 10).
[0163] The invention, like many other existing inventions, simply
pretends all information is an object or group of objects. Various
interpretations and objects appear to scale by implying adjustable
boundaries (3.11) (3.12) (3.16) (3.18) (5.3) (7.3) (7.17) (7.24)
(7.30) (7.31) (7.32) (7.32) (7.36) (7.38) (7.39) (7.48) (8.24)
(9.11) to permit associations that may not have been possible
either in real life or machines that exist when the association is
discovered in a person or research groups imagination. The
invention provides an easier, better way for these objects to
virtually merge or be broken into individual components because
these objects are not required to function in real life or real
machines. As introduced in (2.6), they are only virtual copies of
original objects contained within the placeholder position which DO
work in real life and at least at one time functioned in a real
machine. The inventions methods for scaling data as objects and
relationships will also lead to better ways for the topologies
between advanced networks of machines to scale (claims 1 to
10).
[0164] The invention's descriptions act as boundaries around
flexible information groups rather than rigid bodies, fixed lists
or long meaningless strings. The example above is a painting. In
another case the group inside the adjustable boundary is the entire
Computer and Information Science and Engineering Research
Directorate at the National Science Foundation, or a homemade web
page with two songs and four pictures of the grandkids.
[0165] The invention descriptions and boundaries form `wrappers`
that appear (Section 8) to bind groups of information together. The
information `inside` paragraph (2.3) is one specific painting in a
specific location:
[0166] 123 4567 89 42 66.2751
[0167]
http://Hirshhorn.si.edu/collection/Artist=Franz+Kline&Title=Delawar-
e+Gap
[0168] 5789034 B1910 D1962 18701
[0169] 1524.5693 78.25.times.106.125 99508
[0170] A1966
[0171] A group that compares media and sizes may contain hundreds
of paintings `inside` and is described as:
[0172] 1524.5693
[0173] 78.25.times.106.125
[0174] (Section 3) explains how the invention links these groups
together through previous associations and descriptions, and why
these groups appear to be the same `size` to a machine.
[0175] Machines decode, encode, virtually scale and convert
mathematically based descriptions and adjustable boundaries using
techniques, software, programs and systems disclosed in (claims 1
to 10), partially explained herein (B1.5) and developed through an
upcoming project (C) and [FIG. 6].
Symbolic Characters and Their Function
[0176] As introduced in (D1)[FIG. 3] [FIG. 6] [FIG. 7] [FIG. 8]
when a user, or group of users, decides a meaningful group of
described and partially described information is arranged in a
sensible hierarchy (3.6) (3.7) (3.10) (3.11) (3.12) (3.24) (6.9)
(7.10) (7.12) (7.14) (7.16) (7.28) (7.31) (7.36) (7.46) (9.11),
these relationships are ready to be captured in a Context Driven
Topology. Machines can suggest when the arrangement may be complete
but will never truly know when an arrangement is the most sensible
or clear.
[0177] During the time people are creating, associating, reviewing,
selecting and describing groups of information presented by
machines--for example looking at images, reading text, constructing
complex drawings and visualizations, downloading music
etc.--virtual connections are being generated and continuously
updated "underneath" these groups of information in both individual
and networked machines to map these connections. These virtual
connections are used to build temporary bridges between data of any
type in any number of dimensions. The time and sequence these
bridges are built show how ideas have been built. These connections
form a conceptual map and mathematical pattern that can range from
very simple, to intricately detailed and incredibly complex. The
form and dimensions of these connections are able to vary
tremendously because they are not tied to current concepts of
machine topologies, nodes and hierarchies. For example, a
statistician may be analyzing data in 492 dimensions, graphs in 2
dimensions, and explaining these together with text and equations
in a paper, this will generate one kind of topology; an artist
generates a series of pencil drawings, they are scanned carefully,
each piece is only 2 physical dimensions but the digital
information is high resolution and very dense so this generates
another kind of topology. Generally, the viewer only sees their
information in a collage and does not see the underlying
structure.
[0178] The skeleton structure of the invented topologies is based
on arcs rather than straight lines. Some the arcs may have such a
large radius that they appear to be a straight line, but however
slight, there is always a curve. An arc's radius varies according
to the `looseness` or `tightness` of the connection between data
components. As introduced in (A1) [FIG. 8], there are no corners
between arcs, what may appear as a corner is actually a precise
rotation in the way the arcs meet. Aligning and measuring these
radii and rotations is one of the fastest ways for machines to
compare data and data relationships very generally. These same arcs
can scale when the same components are used in another context in
another structure.
[0179] Like numbering systems, some of these topological forms may
correspond to the data relationships they serve as an abbreviation
for, but in most cases they do not. Unless a particular form is
needed or regularly used for a reason, the default is a spiral or
seashell. This will force information with tight connective arcs to
be on top and looser connections to fall to the background (Section
6). It will also allow shortcuts across similar kinds of
connections in zones, yet information will still be captured
together in one continuous form.
[0180] Viewing the underlying topologies on their own creates a new
level of abstraction and simplification to see and compile data
arrangements as if they were one hierarchical whole. Modifying the
underlying topologies directly is like making a sculpture to take
data away, or put it into an arrangement that is more balanced.
[0181] These multidimensional structural topologies, like the
implied description boundaries (Section 2), could only exist in a
virtual world. They have no scale of their own and are only formed
through the human decision process of establishing information
hierarchy and context. As introduced in (1.23), Context Driven
Topologies give machines something to measure--these arcs and their
relationships--that reflects human reasoning and will allow for
comparisons over time and across different modes of reasoning.
[0182] When users have drawn conclusions, Context Driven
Topologies, become "set" into unchanging mathematical symbols. As
shown in [FIG. 9], each symbol appears to be `simplified` or
`compressed` into a map showing each information component in the
context of the new hierarchy. Context Driven Topologies as they are
used in the symbolic character, or mapping state, never change. The
mathematical machine comparison of these symbolic characters is
used to trace the historical context of each data component and its
placement within data arrangements as people have understood them
over time. Streamlining similar boundaries, descriptions,
placements, contexts and topologies on different levels by aligning
these symbols in high dimensions will help people to draw new
conclusions from complex data collections we could not understand
without machines (claim 1 of 10).
[0183] The most important and useful aspect of Context Driven
Topologies is the ability for both people and machines to
recognize, and be able to compare, both very general and very
detailed knowledge relationships by identifying proportions and
densities at the more simple, abstract level of the topologies
before `reading` the entire descriptions of each individual
component, or viewing the information itself in its entirety.
[0184] When conclusions are more obvious and the data relationships
more cohesive, the Context Driven Topology responds by becoming
tighter and more balanced. As introduced in (A1), the edges (3.18)
(3.33) (7.2) (7.35) (7.36) (7.37) (8.6) (8.10) (9.6) change to
reflect the `status` of changing data relationships. Each topology
takes on its own machine derived description to reflect the
essential properties of this particular topology. It is unlikely
people will understand the machine derived descriptions without the
underlying forms and the evolving automatic audio and visual
language disclosed in (Section 7).
[0185] Context Driven Topologies in the symbol, or mapping, state
are subsequently simplified and mathematically compacted even
further to fit within each component description as if they were a
character in the description. As shown in [FIG. 9], each map
indicates each component placement, proximity and priority in the
hierarchy as a whole in a special, self-referring way as an
"inside-out" view of the topology itself. The same component often
has different meanings in different contexts/different topologies.
Each component is a record of each topology, each topology is a
record of each component. This relationship and this history of
placement understood through the symbols allows people to trace
knowledge and association going in one direction, and helps
machines to learn better placements in the future. These
self-referring relationships and back and forth between knowledge
components (or data) and hierarchies (or data arrangements) are the
actions and decisions by people that form mathematical "knowledge
patterns".
[0186] It can be such an extraordinary challenge simply
understanding certain kinds of data or data arrangements clearly
enough to form sensible groups, that placing components in a
hierarchy to draw conclusions is something that has to wait
regardless of how efficiently this data and data arrangement is
described and annotated. Sometimes, very meaningful information
that could lead to increased knowledge and understanding is hidden
deep inside. Before it is possible for either people or machines to
derive new knowledge from this kind of complex data and data
arrangements, the conclusions or the proposed boundaries themselves
may need more discussion and review to be understood, even if they
are completely correct. Therefore, Context Driven Topologies evolve
over time to reflect changes in historical comprehension [FIG. 4].
One of their primary uses is to `fill in the blanks` (1.11) `bridge
the gap` and otherwise help people to streamline (2.2) and compare
(4.14) records of what we understand with what we do not understand
[FIG. 8]. As explained in paragraph (1.9) the flow, or pace, of
these changes directly corresponds to the pace of changes in
knowledge and comprehension idea by idea, relationship by
relationship. The history of associations captured in the symbols
does not change, the multidimensional waveforms described in
(Section 4) never change, the only way the topologies change is
through steps (3.1 to 3.7) as each description boundary and data
arrangement is interpreted over and over again [FIG. 7] by people
by making decisions to place, eliminate, and prioritize data in new
data arrangements [FIGS. 3 and 4]. Over time, this will help people
to understand data and data arrangements that are harder to
configure or draw conclusions from.
[0187] Relationships between the character-like symbols form a
shared memory (Section 7) by storing image data, text data,
mathematical data, audio data etc. along with the associated data
processes (Section 9) that have been generated or put together in a
context and hierarchy for any reason. As introduced in (3.7) (3.10)
each component description, and likewise each Context Driven
Topology, contains a traceable history of this process through the
following steps:
[0188] Using the example of the painting in (2.3) (2.12) again,
suppose the group
[0189] 123 4567 89 42 66.2751
[0190]
http://Hirshhorn.si.edu/collection/Artist=Franz+Kline&Title=Delawar-
e+Gap
[0191] 5789034 B1910 D1962 18701
[0192] 1524.5693 78.25.times.106.125 99508
[0193] A1966
[0194] is captured in a topology represented as #
[0195] and the group
[0196] 1524.5693
[0197] 78.25.times.106.125
[0198] is captured in a topology represented as +
[0199] The symbol for the first group is embedded directly into
each component description 123 4567 89# 42# 66.2751#
[0200]
http://Hirshhorn.si.edu/collection/Artist=Franz+Kline&Title=Delawar-
e+Gap#
[0201] 5789034# B1910# D1962# 18701#
[0202] 1524.5693# 78.25.times.106.125# 99508#
[0203] A1966#
[0204] Because some of the components in the second group have been
in a captured another context before they already have an embedded
symbol, and are assigned another symbol with each new
association
[0205] 1524.5693#+
[0206] 78.25.times.106.125#+
[0207] The next time a user goes back to the original group they
may notice a change to two components:
[0208] 123 4567 89# 42# 66.275 #
[0209]
http://Hirshhom.si.edu/collection/Artist=Franz+Kline&Title=Delaware-
+Gap#
[0210] 5789034# B1910# D1962# 18701#
[0211] 1524.5693#+78.25.times.106.125#+99508#
[0212] A1966#
[0213] A machine will definitely notice.
[0214] Associations and symbols that have occurred precisely the
same way in the past are not duplicated. See (claim 4 of 10) and
(claim 10 of 10) for details. It is imperative for associations,
symbols, components and topologies that are "the same" as defined
by the user, to be eliminated on every level of the shared memory
(Section 7). This streamlining will help to search and identify
data and data relationships more clearly and accurately (claim 5 of
10).
[0215] The example above (3.12) shows only one transaction, one can
quickly imagine the number and speed of data transactions that
regularly occur and the length/depth the descriptions will grow. As
more conclusions are drawn and more symbols added, it does not take
many transactions, or even physical time as it relates to our
experience, before the component descriptions with all of their
symbols and histories become more accessible to machines.
Therefore, people using the invention will need to communicate to
their machines through Context Driven Topologies, or the forms, and
automatic language (Section 7) instead.
[0216] In nearly every case, the user does not interpret the
component descriptions with their endless associated symbols. Users
are viewing the particular encoded information contained as an
"object" inside particular hierarchies or group. The group above
is:
[0217] <INSERT IMAGE "DELAWARE GAP" BY FRANZ KLINE BLACK AND
WHITE PHOTOGRAPH IN (4 of 7) MINIMUM IMAGE SIZE 2.75 INCHES WIDE IF
POSSIBLE>
[0218] But this picture is not really contained within this group,
as introduced in paragraph (2.6) a placeholder is. What is
supposedly inside this group is fixed, taller than a person, black
and white only, and very wide. If the user wants to access the 1
object contained in this group of 12 descriptions, they need to go
to the second floor of a round museum in Washington D.C. The other
group in (3.12) contains hundreds of paintings in hundreds of
locations. They are not `in` the data any more than astronomy data
holds stars.
[0219] The invention raises a very important question about
representation. It is easy to see how 1 painting can be identified,
located and described through 12 characteristics, easy to imagine
hundreds of paintings being alike because they share 2
characteristics, oil on canvas at a certain 2 dimensions. Mapping
between these groups is very similar to high dimension statistics
corresponding to a bar graph. But the nature of certain kinds of
information makes it very difficult to distinguish between the
`information inside` and the `description outside` when the
information itself is also code, these situations need to be
handled case by case on a hand modified basis, the same as now.
These marks and captured scenarios will be very helpful for
machines (9.15).
[0220] Regardless of the number of descriptions, all objects,
groups of objects, and topologies that are `the same` as defined by
the user, appear to be the same `size` to machines so they, and
therefore we, are able to see them in fields. Like the boundaries
and underlying shapes, these scaling modifications to force perhaps
non-matching components into groups in a way that can only be done
virtually. The purpose for `seeing` this way is similar to blood
cells in a blood stream, they just need to be able to move.
[0221] Context Driven Topologies vary in density to correspond to
the `size` they need to be perceived to be to become arranged in
new information groups and topologies.
[0222] Whether the `information inside` a data group is terabytes
of code wrapped and described by one simple string, or the
`description outside` is constructed over the years into a complex,
multilayered combination of codes and symbols in infinite
dimensions all to describe a simple common object--it is the same.
There is a boundary. These boundaries become the symbols, patterns,
history and meaning surrounding each object. As introduced in
(1.23) (3.9) and explained further in (7.39), the more associations
each object accumulates, the more this changes the edges, or
texture, of each of these multidimensional boundaries [FIG. 4]. A
boundary with massive amounts of information inside yet a small
description outside looks ready to burst, a symbol with infinite
complicated and overlapping descriptions for simple information
inside is wrinkled, from far away they look the same [FIG. 7]. The
`distance away` is completely dependent on how the viewer is
arranging their information [FIGS. 3, 4, 7, 8]. One person's far is
another one's close, that is why context is the measurement used in
these time dependent topologies. Because every thing in a machine
is only virtual, objects do not have a `real size`, objects of any
size can appear to compress or expand to be placed, or fit, in any
location in any configuration. The mathematical descriptions appear
to expand and/or become extremely compacted depending on how each
boundary and object is placed. The boundaries are always compact,
or the simplest, in storage. See (Section 4) for more details about
scale and the stateless storage space.
[0223] The boundary that already exists between people and machines
is not abstract, we regularly negotiate this boundary all the time.
Machines see code, we see images; machines read code, we read
stories; machines record or hear code, we sing or hear a song. The
invention introduces a new layer of coding and decoding for
machines, and a new layer of image/story/song for us.
[0224] There are many self-referring similarities and patterns that
begin to develop on their own between the character-like symbols in
the descriptions and the interrelated ways that people develop the
underlying connective shapes. As illustrated in [FIG. 10], the
essential feature of the invention that lets these patterns be
identified and compared regardless of their state or use is the
fact that each Context Driven Topology is mathematically unique and
stays precisely the same regardless of which state, or degree of
compactness or expansion, it is perceived to be in. The patterned
space around each object is always part of each object. Variations
in interpretation of data and data arrangements over time are what
generate these forms and boundaries. Each topology is able to be
recognized at any point in time because the mathematical
relationships never change regardless of how data descriptions have
needed to stretch and squeeze to fit in new or unique contexts over
the years.
[0225] A Context Driven Topology that is updated and transformed
with new or revised interpretations is a new and revised topology.
It may have the history of previous versions inside, but it is
unique or it is automatically streamlined and eliminated in the
shared memory explained in (Section 8). The older and more changed
interpretations become over time, the earlier versions are
compressed, pushed into the background and rarely transformed into
other states for other uses. It doesn't matter if older means 780
years or less than half a nanosecond later, it is just before or
after the specified point in time and going in one direction. The
back and forth process [FIG. 8], or map, that Context Driven
Topologies form between the versions that are before and version
that are after is what generates the knowledge and display patterns
in (Section 7).
[0226] Context Driven Topologies bind together information at any
levels people are able to understand a group of data together, even
if these processing steps would not be able to occur across machine
programs or systems. As shown in [FIG. 4], each topology contains
the programming features needed to read the content it has captured
(9.4) (9.12). Over time, people will understand new reasons why
groups of information make sense together which will prompt
specific reasons to develop new content, new programs, new systems
and new networks (claim 10 of 10). These records are able to be
read by any computational machine because they "read themselves"
over a network, the presentation depends on the environment and the
equipment.
[0227] Context Driven Topologies connect source code high
dimensional visualizations to simple text as easily as they connect
images to other images, matrix to list, French to English, C++ to
C# etc. People are able to find images using similar images because
machines can be queried with images described by symbols showing
previous associations that were important for temporal reasons, yet
measurable by machines because each pattern is so precise. The
display patterns and automatic language in (Section 7) provide the
techniques and tools for mapping between languages and presentation
modes.
[0228] Only data descriptions and interpreted results have embedded
symbols. The objects and `originals` are left unmarked in a
placeholder (2.6) position where descriptions and interpretations
enclose it (3.18). There are different gateways and shortcuts to
reach the original. Portions of raw results may be extracted to use
as data components, which means they are being described and
interpreted and therefore no longer a complete, untouched original.
The interpretations begin to accumulate indelible marks and
associations in their symbols, eventually similar descriptions and
interpretations overlap and consolidate. Incorrect descriptions are
disputed and eliminated through the development of new, more
streamlined and correct topologies. All of these actions affect the
patterning and mapping of all of the topologies.
[0229] Context Driven Topologies cause data and data arrangements
to characterize. When topologies share components, histories and
other topologies symbols in their descriptions, they tend to
cluster.
[0230] The interactive process of choosing and arranging the best
data components and relationships through Context Driven Topologies
begins by users providing machines with information or by showing
and describing the type of components they have in mind. Machines
begin to compile and consolidate the topologies embedded as symbols
in each description as pathways into data stores to propose an
outline for the new structure, and begin to bring in components for
review. The more precise the descriptions are, the more complete
and relevant the retrieved information will be. Drawings, images,
waveforms, sequences, flow and mathematical relationships can be
input as easily as text.
[0231] Special commands enable the machine to interpret certain
kinds of information, and certain portions of information itself,
rather than interpreting the descriptions when this is more useful
for the user or easier for machines. For example, if the machine is
searching architectural drawings, proportions and spatial
relationships illustrated in a CAD file may be more useful than the
drawing title, however, as any person who has worked with CAD
knows, drawings that are presented in packages under a title are a
particular view with very few layers in the drawing files `turned
on`. The same drawing file may contain different views and
different layers for each drawing present in a file. This
fine-tuning and control over what is searched, identified and
presented for a particular arrangement of information is directly
tied to the users preference and quality assurance needs.
[0232] `Paper space` in AutoCAD is similar to the display patterns
in (Section 7), AutoCAD model space which is always 1:1 is similar
to stateless space where scale is manipulated by users. Context
Driven Topologies have both always related to each other (as they
are in AutoCAD) but both are `searchable` unlike AutoCAD which
cannot search for or associate measurements.
[0233] Users tell machines where to pay more attention to certain
groups of information. Information that is correct, preferred, or
meets a specific quality assurance is assigned more space,
resolution, depth and visibility in the new configuration according
to its priority (Section 6) in the new arrangement.
[0234] When machines retrieve too many choices for users to review,
more descriptions are added. In most cases these descriptions will
have previous associations but sometimes they will not, which
causes a completely new description to be created. The users and
machines go through a decision process of narrowing down, making
choices about what type of components are too similar to sort
through, creating and describing new components until eventually,
the arrangement is determined by the user to be complete and
captured into a topology with new bridges and maps to the past, to
begin the cycle all over again.
[0235] Where existing components are not available--which is
usually the case--new components are created by users either in
collaboration with machines or independently to input as
appropriate. New words and sequences are written, an exhibition is
visited and a curator identifies an artist they like, a ten year
study involving hundreds of people is initiated. There is a purpose
behind creating new materials to fill in these gaps, reasons why
existing information does not answer the question, address the same
issues, or addresses them in the wrong way to satisfy the new
purpose. Marking these new introductions and changes in course is
one of the best long term uses of the invention.
[0236] As stated in paragraphs (1.4) and (3.12), no components or
descriptions are ever duplicated, two or more locations are simply
indicated through the symbol. The degree to which components are
considered to be "the same" is directed by the user. Superimposing
or comparing two or more topologies that share components does not
duplicate matching components either. Whether a network/machine is
compiling in an even parallel across all levels of the
descriptions, or a person/research group is assigning priorities to
illustrate new conclusions, the topologies where people and
machines meet in the middle twist and rotate for redundant
components to align and cancel each other both in peoples
perception of the information, and in the machines data
compilation. These shapes, actions and relationships can be studied
through algebraic topology, knot theory and other mathematical
techniques (claim 6 of 10).
[0237] The connective shapes (and therefore symbols) are constantly
being fine-tuned, created and modified but are still recognizable
as meaningful arrangements like letters in different styles of
fancy fonts. The symbols as their own group form the longest
alphabet ever, it is constantly expanding. They become their own
language composed of all the natural and machine languages being
used (B3.2) (B3.4) (C1) (C7) (D1) [FIG. 6] (1.15) (1.20) (1.21)
(2.1) (2.3) (3.13) (3.22) (3.24) (4.18) (7.2) (7.5) (7.6) (7.12)
(7.16) (8.17) (10.2) (claim 2 of 10) (claim 10 of 10).
[0238] As introduced in (3.9), the symbols and shapes have
selectively controlled edges and an inside or outside as characters
do but people will need to get used to them because they are not
exactly like characters. They are of much deeper dimensions and
varying densities, there is related background information stacked
underneath, compressed arcs that we wish could be extended toward
the future, histories inside histories inside descriptions yet
these shapes always appear to be one solid.
[0239] Each topology has a strict inside/outside boundary, each
topology is only one continuous form with a patterned space around
it. Generally, machines understand the outside the mathematical
boundaries and people understand the information that is captured
inside. Context Driven Topologies are evolving records to draw a
picture and measure how these boundaries change over time.
[0240] Multiple topologies are able to merge into one, individual
topologies are able to break down and separate. To eliminate
redundancy (1.4), when topologies merge, shared components overlap
to cancel extra copies so only one copy of the component is
included inside the topology. When topologies break apart, all of
the components from the prior arrangement may or may not be
transferred into the new arrangements.
[0241] Better shapes have better proportions, components of
different degrees of association have different textures, close
inspection reveals histories people understand like thumbnail
sketches or maps, these are recognizable features all people are
familiar with and do not require special training to
understand.
[0242] If people have been able to do this much with music
symbols--the placement of ellipses and lines on other lines using
very few symbols--or record and illustrate an endless variety of
ideas through 26 English letters or 4,000 Chinese characters, what
could be composed through a limitless, self-contextualizing, non
redundant system provided there was a gradual way of developing and
understanding its logic?
[0243] Machines process the disclosed symbolic characters and
`understand` their function using techniques, software, programs
and systems expressed in (claims 1 to 10), partially disclosed
herein (B1.5), investigated and developed further through an
upcoming project (C) and [FIG. 6].
Evolving Mathematical Knowledge Patterns Converted into
Multidimensional Wave Forms
[0244] When Context Driven Topologies are distributed to be shared
as knowledge to be interpreted by others, the arcs inside appear to
`stretch out` or `expand` and transform into continuous
multidimensional waveform to be distributed and compared in a purer
form. As illustrated in [FIG. 10C] and (3.3), the compacted
topologies `unfold` to become a continuous irregular series of
waves. Each arc is connected to the next arc by changing
orientation from the end of one arc to the beginning of the next
(4.2).
[0245] The process of this transformation does not place or arrange
the arcs, and therefore high dimensional waveforms, in a flat
plane. The rotations vary according to both the direct relationship
between adjacent arcs, and as a series of periodicities down the
continuous length. Variations represent dimensions, time, density,
frequency and other factors.
[0246] The continuous series of arcs in a multi-dimensional
waveform may be open or closed in a loop, but each Context Driven
Topology is one continuous whole. The topologies are typically more
effective and recognizable if they are closed to allow a circular
path through the entire topology rather than starting or stopping
at a beginning or end.
[0247] When a Context Driven Topology is in the form of a spiral or
seashell (3.4), it is more convenient to make the transformations
between the connective shape, symbol and waveform.
[0248] Context Driven Topologies in the waveform state are measured
(4.11) using Fourier analysis and other mathematical techniques to
identify and compare overarching, underlying, direct and indirect
temporal connections determined by people between data and data
relationships of any type, at any scale, at any time.
[0249] As introduced in (2.2) (3.3) (3.8), portions of waveforms
can be overlapped and combined by machines before the original
information is retrieved and interpreted by people. For example, if
only tight connections are acceptable, only this specific radius or
range is recognized. Data relationships in the topology as a whole
are also specified and recognized by their rotation. Therefore,
when topologies begin to become interrelated and refer to each
other, these radii and rotations will eventually begin to
streamline and standardize the same way the descriptions do.
[0250] The waveforms reside in a stateless space in boundless
abstract cloud [FIG. 1] [FIG. 2] that is not maintained or
interpreted by any one organization.
[0251] Because Context Driven Topologies reside in a stateless
space, they are always remotely located.
[0252] Context Driven Topologies are user defined pathways in and
out of the stateless space or boundless abstract cloud [FIG. 1]
[FIG. 2] that are given by mathematical relationships between the
symbol/map use, the connective/changeable state, and the
simpler/expanded waveform state. Neither the stateless space nor an
unformed topology have any "scale" until this is determined by
users through the act of interpreting information, drawing
conclusions and creating a topology.
[0253] Comparing, optimizing and streamlining these pathways
themselves independently from each other is at a level that people
are not capable of comprehending without advanced networks of
machines. Managing shared knowledge, information and data
arrangements at this level is one of the many ways the invention
will be useful in the future.
[0254] Synchronizing Context Driven Topologies in the waveform
state with machines and other display devices is a machine based
calibration using groups of the mathematical patterns, or Context
Driven Topologies, themselves. As introduced in (A1), because the
information contained within each topology is eventually simplified
and transformed (Section 9) to be presented through light and sound
(Section 7). The measurement and calibration techniques most often
used are similar to current optical and acoustical frequency
techniques with added variables for density and other customized
features (B1.4) (4.5) (9.11).
[0255] Context Driven Topologies, including the history and
knowledge they contain, are distinguished from each other in a
dialogue and decision process between people and advanced networks
of machines over time. As indicated in (B3.7), these distinctions
are directly related to the nature and interpretation of the
information itself, the way the user is looking, the similar
examples each user or research group provides, knowledge describing
the information, the era which the information originated and the
era the knowledge is being re-interpreted. For example, a teenager
downloading music may input slang words that disappear over time; a
mathematician may input very precise equations that have not been
examined for 142 years and also unexpectedly retrieve all the
arguments from the original era as well; a mechanical engineer
inputs a flow sequence and accidentally retrieves similar flow
sequences that illustrate about shopping trends. Through a
knowledge based interactive process where people provide machines
with similar examples, in similar dimensions, with similar pacing
or evolution, non-relevant information is eventually weeded out. It
is `cleanest` or `easiest` for machines to search, identify,
compare and retrieve groups of Context Driven Topologies with each
other when they are in the multi-dimensional wave form state in the
abstract data cloud because this is when the topologies are most
pure and machines are able to rearrange them in ways we may not
understand (claim 10 of 10). These operations are transparent to
users, the more knowledge they have to specify the information they
seek, the more direct the connection is to the original objects and
ideas (7.22).
[0256] Because Context Driven Topologies automatically overlap and
conceal like components whether they are text, simulations,
mathematical relationships or any other type of encoded material;
search and retrieval will be more efficient and precise than
keyword and key code searching is today (claims 1 to 10).
[0257] Unrelated data arrangements with similar data relationships
are able to be compared to see new ways to form relationships that
are hard to understand. People are able to arrange and to identify
data relationships up until the point where they no longer
understand, then send this "topology sketch" into the stateless
cloud and limitless collection of shared memory and shared
knowledge where this early form, proportions, and densities could
be compared and possibly corrected or given a better direction to
continue developing the idea. For example, if a field ecologist is
documenting the habits of unfamiliar birds in an unfamiliar
environment, the ecologist may only feel comfortable gathering a
minimum amount of data that is the only data, and data collection
method, they are sure is accurate. It may be far less data than is
typically gathered. The basic structure is sent out to the
stateless space to be compared in a very raw, early state. When
similar relationships are retrieved by machines, there may be
something unquantifiable the ecologist knows that makes some of the
unrelated results appear to be appropriate. When this is the case,
the coded data is extracted to see what it is and it could be
anything--movements in a dance, algebras from a high school text
book, etc. any type of information in any form which the future
user may or may not understand. However, where an extensive body of
data and data relationships exists for the other, unrelated data,
the "better" topologies may provide direction for the completion of
ideas in early stages, saving time and focusing the efforts to a
pattern of relationships known to be simple and effective. Data
arrangements that are developed through Context Driven Topologies
are able to be built-upon, refined and developed further over time,
as long as each step along the way is accurate, the evolving
topologies are a more valuable use of shared knowledge resources
(7.18) (9.1) (claims 1 to 10).
[0258] Redundancy (1.4), noise (9.10) and crosstalk are eliminated
through the process of looking very precisely for very precise,
mathematical arrangements. What is not there is equally, if not
more, important than what is there (claim 4 of 10).
[0259] Context Driven Topologies are graceful and continuous like
music.
[0260] Context Driven Topologies exist only in a virtual world,
they are able to twist, fold, transform, align and associate
waveforms [FIG. 10C], components, structures and symbols that may
only able to be captured in our imagination, before these same
relationships are able to be realized through art, science,
machines and advanced networks (claim 1 of 10).
[0261] Machines decode, encode, convert and calibrate waveforms and
relationships between waveforms using mathematical patterns,
imagery, sounds, techniques, software and programs prototyped in an
upcoming project (C) [FIG. 6] and future technologies listed (B1.5)
and (claim 10 of 10). The same way that people seek, retrieve and
associate similar concepts in their heads (claim 8 of 10),
topologies in the stateless space begin to streamline and organize
themselves by sharing component descriptions, knowledge objects,
algorithms and measuring techniques, backgrounds and histories.
Together the topologies and the patterned spaces around them form
knowledge patterns moving in a multidimensional abstract "stream"
that is difficult for people to understand until it is transformed
into the evolving automatic audio and visual language and display
patterns disclosed in (Section 7).
Metaphors
[0262] A Context Driven Topology is like a person, each one is
unique, born and was not here before. Even if each individual
shares preferences and characteristics with other people, they have
their own age and circumstances, even twins sharing practically the
same lifetime and most circumstances rarely behave the exact same
predictable way. A Context Driven Topology has a life and interacts
with or influences others even if they never meet. When you want to
understand more about a person, you can see how their friends,
family or culture has influenced them. These factors may be
invisible to the person themselves, an outsider may identify
influences more clearly but they may be incorrect. If you want to
know why this person has blonde hair, the topology is like DNA
efficiently mapping out everything about their ancestors in an
accepted, proven structure. If you want to know why the person has
dyed their blonde hair black, the set of descriptions contained
within the topology are pathways that lead to images of the popular
culture of that time, pictures with this person's friends who have
also dyed their hair, the person's online diary. The person
themselves may not understand or care why they made this decision
therefore this persons reasoning is not accurately measurable using
an accepted structure such as DNA and can only be inferred through
patterns such as trends in popular culture. Some day the person
will die, it is not fair when they are taken away too early or
linger too long. They will be remembered by people who knew them,
people they influenced, they may have children and grandchildren.
There are unique traces that are not the person and at some point
even these traces will be gone. A Context Driven Topology is not
like a person because it can be specifically tailored to keep and
use only certain aspects of its personality.
[0263] A Context Driven Topology is like a song, sometimes there is
only one clear note you remember.
[0264] Using similarity measures to retrieve Context Driven
Topologies is like raking leaves, there are different kinds of
rakes with different styles of prongs with varying distances
between, different material strengths or flexibilities. When they
are used too often or left unattended in unstable conditions they
need to be replaced. Information that is not the right proportion
or density passes through or will not be picked up, sometimes
foreign objects that do not belong are also retrieved but they are
easy to recognize and remove because the first level of separation
has occurred. Using similarity measures are not like raking because
the leaf pile can be compressed into one piece and easily handled
because users can identify one boundary and shrink it, rather than
looking at all of the individual boundaries around all of the
leaves, and clusters of leaves, scattered around separately.
[0265] Context Driven Topologies put in groups together are like
chemical reactions, some simply cannot be together, other kinds
transform, unite and become something else. Context Driven
Topologies are not like all chemicals because they are only man
made.
[0266] Context Driven Topologies are like water, they can be in
different states that have certain thresholds. When they are ice
they are different than steam but they can transform from one to
the other without becoming something that is not water [FIG. 10].
When a Context Driven Topology is captured, it takes the shape of
the vessel that carries it, when it is moving it changes the
non-water landscape around it. Context Driven Topologies are not
like water because they are not real and do not have physical
properties that constrain them to certain structures or states to
recognize what they are.
[0267] If Context Driven Topologies were a library the levels and
information components would be: this library compared to other
libraries, a section based on a subject matter, a book, a chapter,
a phrase, a word, a letter. When a Context Driven Topology
recreates a book it does not have the word "the" in its data stores
over and over again, it is a map to each word saved only one time
and assembled in the proper sequence even if there are
duplications. One level down this is also true for the letters but
this particular user is not looking at that level so these maps are
hidden. At the level of library to library, there is of course only
one book also. Because of the way the book was initiated as
information (8.3), the system will automatically and always defer
to the original writings of the author. The user can switch
`dimensions` to read a French translation which naturally has
different maps because the words are often in different sequences,
but at the chapter level, the ideas are cohesive. The identity of
the book is a title, a much shorter description than all of the
words. The words are used in other books, this is another map that
leads to other points of view that be compared if the user is
interested in the concepts conveyed by this word. Context Driven
Topologies are not like libraries because there is not a better
library with everything in mint condition including a knowledgeable
staff to direct you versus an unattended trailer with 28 dog eared
books to choose from, most which you have already read--there is
only one original book mapped out in a sequence, anyone can get a
copy, read it in their language, and it is never checked out.
[0268] There is an old wooden roller coaster at Kennywood Park in
Pittsburgh Pa. called the "Jackrabbit" built in 1921. Over time,
piece by piece, the wood and tracks and cars have been repaired and
replaced yet there was never a time the Jackrabbit wasn't there, as
a whole, the ride has not changed. It clacks and shakes and people
fly up or hold onto their kids on the double dip the same way their
parents held them before. Darts is a game that persists through
time because of its geometry--circles of specific diameters, a
fixed distance to stand away, endless styles of darts and boards.
The popular game "Cricket" includes an efficient scoring system
that does not waste time writing down what `might have been`.
Perpetuating and precisely recreating Context Driven Topologies of
shared knowledge across advanced networks of machines over time is
like this ride and game, the components may be replaced and
updated, but through each topology's geometry, and an efficient
annotation system, the whole continues to have meaning on its own
regardless of the rate the components are replaced, or the number
of variations that occur.
Monitoring, Controlling, and Influencing Information Placement and
Proximity using Pace, Flow, and Changes to Human Knowledge Over
Time
[0269] Every idea has a pace [FIG. 10].
[0270] The invention is used to record and monitor information
component placement and proximities through a record relative to
the pace, or flow, of changes to related component placement and
proximity over time. These records, including the pace of changes,
help both people and machines to place and prioritize future
components in future topologies. The Context Driven Topology
placement process is a system of, indexing and mapping that is a
mathematical process translated into an evolving automatic audio
and visual language (Section 7) which places more important
concepts in the front and makes them bigger.
[0271] The only control or influence machines have on component
placement or proximity is by compiling and consolidating patterns
of previous associations contained within each component
description, patterns identified within each topologies uses, and
groups of optimal paths (6.8) identified by people into and out of
a stateless space [FIG. 1] [FIG. 2], that is understood more
clearly by machines. Both people and machines will eventually get a
feel for components that are often near each other during certain
eras.
[0272] Address data is a mathematical identity of placement and
priority for both the source and destination as each of these
changes over time.
[0273] Addressing within and around Context Driven Topologies is
the same as many existing processes for addressing a character. It
is a map or graphic memory that also stores image data.
[0274] Configurations that are consistently used to arrange like
data components will become like a template or standard over time.
As explained in paragraphs (3.10) and (3.26), machines typically
propose the first outline arrangement for new data arrangements
based on a convenient arrangement of all the histories/pathways
from the shared memory (Section 8) to the current arrangement.
Components initially tend to be located (and therefore addressed)
in the new arrangements similar to the way they have been located
in previous arrangements. However, this is not always true, a new
interpretation may have a greater emphasis on certain components
that may have been less acknowledged in others and vice versa. That
is why it is up to people to decide which arrangements and
priorities are best.
[0275] As introduced in (2.3), the registry of new data
interpretations is generally assigned by people who create or are
responsible for the information. For example, a university that
undertakes a large multi-year study will determine how to describe
and arrange this data. The agencies that funded the study will
apply their own descriptions and categories to it and put these
data groups into their own groups, for example, with other studies
funded in the same program. Each of the description and arrangement
processes that data undergoes during the course of its life will
affect its history descriptions, categories and locations--which
will in turn affect its initial placement, priorities, and
proportions in new configurations. This is a significant
improvement because these are people, idea and progress driven
arrangements that change this registration over time. If records
are not reviewed, or too close to other records, they are
eventually compressed and consolidated (Section 8). The inventor
believes the greatest danger in large data collections--whether
they are modern, complex and dynamic or handwritten on cards--is
when stored information is not reviewed (7.37) it is possible these
records could imply priorities that did not actually exist and/or
certain records may seem more important than they actually were
simply because they are old.
[0276] Selecting the optimal path to review large scale records to
access targeted information is determined by each user deciding
[FIGS. 3 and 4] which components and which era they would like to
review. Components may be recombined, re-prioritized and
re-addressed in each new arrangement. Components that are regularly
used together come together in context. These `pre-existing` groups
[FIG. 8] affect the agility and direction of the optimal path. Some
paths are more efficient than others because they have been made
`thicker` to accommodate these pre-existing groups. Over time,
something similar to a channel will wear down.
[0277] The address and priority of each component is a reflection
of their placement in the hierarchy of each Context Driven Topology
and their journeys in and out of the stateless space. Each
component, whether a word, or all of the collection records of the
British Museum since 1753 will gradually accumulate many addresses
and priorities according to the different associations it has had.
This starts to "pull" like components and topologies toward each
other deep in a background that is difficult to imagine without the
use of mathematics. As described in (3.26), when a placement within
topologies begins to become typical, or reliable, it is
statistically and methodically streamlined by machines to
`gravitate` towards this position in future topologies.
[0278] Machines monitor, propose and record addresses and
priorities for data in data arrangements as disclosed using
software, programs and systems specified in (claims 1 to 10),
partially disclosed herein (B1.5) and developed into an enhanced
prototype through an upcoming project (C) and [FIG. 6].
Use of the Automatic Evolving Audio and Visual Language and Display
Patterns
[0279] The invention maps between the mathematical description
framework, symbols, signs, priorities, data and data arrangements
through the use of an automatic audio and visual language that
evolves over time. The invention, like many existing inventions,
"sees" all of the data, data arrangements and boundaries (3.18) as
an object. However, unlike most prior art in data processing (as
opposed to data display) subject matters, these objects appear to
compress and expand so machines can compile and process these
objects in groups where they do not typically "belong". The edges
(3.9) of these objects are driven by their history of different
interpretations and the layers of thickness that result to reflect
each object's correctness (1.15) (7.25) (9.22), their status in
disputes or challenges [FIG. 4] (7.32) (83) (8.6) (8.10), and other
processes that gradually push these objects into aggregated
generative shapes and streams that evolve over time--these are
knowledge patterns. A second kind of pattern, that in many ways is
an opposite pattern or the same pattern rotated or otherwise
transformed in a different direction, is used to present the
knowledge patterns and new ideas in each particular era's machines
and display devices--these are display patterns. Together both
patterns form the basis for the inventions improved human computer
interaction process expressed in paragraphs (B1 to B3) and (claims
1 to 10).
[0280] The evolution of these interrelated patterns, the Context
Driven Topology forms and these streams, or threads of
understanding through time, each have defined values reflected in
the evolving automatic audio and visual language. The best way to
review and interpret information contained in these patterns is
visual, conceptual and related to people's natural understanding of
objects in spaces. Data and data relationships may also be
evaluated by ear or machine acoustic measuring devices, a
combination of optics and acoustics, tactilely, in words, or any
other mode where the machine has a pathway in a topology to show
how this knowledge object has been placed in context and displayed
in the past.
[0281] Each of the knowledge and display patterns have unique
mathematical identities, recognized primarily by machines, by
comparing and measuring the infinite ways historical knowledge and
new ideas come together, separates and the pace, or flow, of these
changes over time. Using the automatic language to compare the
knowledge and display patterns with the flow of these changes over
time will lead to deeper insights and predictions people may not be
even be aware of until we start using a system like the invention.
One of the most useful long and short term benefits of the
invention will be machine detected errors, interesting details,
simplifications and deep background (8.7) patterns detected over
longer periods of time than one person, research group, individual
machine, or small network of machines normal lifetime.
[0282] The knowledge patterns and multi-dimensional waveforms
(Section 4) are translated into the evolving automatic audio and
visual language using the both the knowledge patterns themselves,
and their related display patterns (7.1), as a new very specific,
very temporal technique to search, identify and interpret data and
data relationships a new way (claim 1 of 10).
[0283] The general use of the display patterns is to present the
knowledge patterns through light and sound even if the knowledge
patterns and multidimensional waveforms themselves do not
correspond to light and audio waves people are able to perceive
and/or machines are able to present. The display patterns and
automatic evolving language `make the waveforms fit` into light or
sound, or digital units that are able to be displayed as light or
sound, as preferred or specified by the person or research group
interpreting the objects in the future. These user defined displays
could mean writing in any natural language, images, sounds, music,
drawings, flows, complex sequences and any other data arrangement
that is able to be displayed on a backlit screen with speakers
similar to current computers; or projected image sequences and high
quality `surround sound` similar to films in current theaters; high
dimensional ideas similar to current scientific visualizations;
recreated environments similar to current virtual reality caves;
layered imagery similar to current holograms; and any other display
and interpretation technique people are able to dream up in the
future and present through machines connected to a network.
[0284] The evolving automatic audio and visual language of the
topologies themselves is disclosed in the remainder of this
section; the language of the data components, groups and
arrangements is introduced in (Sections 2 and 3); machine
interpretation of the language is disclosed throughout the
remainder of these specifications and in (claims 1 to 10) at the
conclusion of these specifications.
[0285] As introduced in (Section 6) knowledge, data components,
groups, arrangements are given audio or visual intensity,
prominence, deferral or other priority addressing based on their
user defined placement and proximity in a Context Driven Topology.
This is automatically reflected by their placement and priority in
each corresponding display pattern.
[0286] There is something important (Section 1) that happens in
data visualization, the act of summarization and the creative
process "before" an annotation system (Section 2) (7.37), context
(claim 1 of 10), or frame of reference (Section 3) can be
introduced. Translating and mapping ideas that originate in this
place into data and data arrangements is where the invention is
intended to work best. It is also, as enumerated in section (B3)
and (claims 1 to 10), a place where other inventions do not exist
to search, interpret, compare, streamline, preserve, share,
translate and map knowledge at all levels over long periods of
time.
[0287] Variations between knowledge patterns, display patterns and
these two related sets of patterns, may be detected infinite ways
according to the way each stream of topologies is identified (2.3),
extracted [FIG. 8] (3.25) (4.14) and presented. In the invention,
the frame of reference is not only the original context of the
topology, it is also the users' choice of presentation (7.4). In
other words, the identity of this knowledge as it is being
evaluated by another person either remotely or at a future point in
time, even a fraction of a second later, takes on both of these
addresses, placements and priorities as they are interpreted by
both the original and new users. This fine tuning and calibration
is first accomplished through the display patterns, and second
through their opposite or rotated knowledge pattern. Of course
feedback loops between these patterns can continue into infinity
but through the use of the invention, will eventually delete far
more information than it adds, the number of objects is intended to
stay the same because these are what is actually important and
needs to be preserved.
[0288] The same objects can be represented many different ways to
many different people over time, therefore it is at the sole
discretion of future users to decide and specify (Section 9) if
they would prefer for the knowledge to remain in its original
state, or if they would like to `dissolve` portions of the topology
to change the natural or machine language (3.32) to be compatible
with the languages they and their machines understand most clearly.
The topologies and their associated patterns can also map into new
modes [FIG. 6] (3.24) (7.10) (7.16). Like existing internet
searches, the more context maps that are created the more effective
this mapping process will be. See paragraph (C) for the inventors
plans to introduce the mapping process to potential future
users.
[0289] Certain areas of each Context Driven Topology and its
associated patterns will be more complex than others. See [FIG.
10A] for an illustration using the content of these specifications
to show the complex subject matter of unique identity as it is
disclosed in paragraph (8.3). Explaining the use of this concept
requires the use of many overlaps, circles, loops and references
within the same paragraph. This is one illustration, other more
complex concepts that are better expressed or investigated through
epistemological circles, references, and feedback loops leave marks
on data and data arrangements in their histories (Section 3). These
marks, cycles and multiple interpretations exist in the knowledge
patterns, are filtered through the display patterns, and
illustrated through the evolving automatic audio and visual
language.
[0290] The evolving automatic audio and visual language easily maps
between audio, visual, both, text, images, mathematics, tables,
clusters of data and data arrangements, or whatever hierarchy
(3.1), context, configuration, placement and proximity (Section 6),
and mode of presentation the person or research group either
prefers, is obligated to use, or is a standard procedure to
understand this type of knowledge. As previously stated in
paragraph (1.12) when knowledge is not evaluated or used, it
gradually disappears. Gradually in this sense is over 1,000 years.
Current methods of knowledge preservation, such as books are
typically between 200 and 500 years, and although few people like
to admit it, the lifespan of most current digital information is
far less than a persons lifetime (1.16) (Section 9).
[0291] Mapping back and forth between the knowledge and display
patterns is initiated and directed by people [FIG. 8], then
recorded and presented by machines.
[0292] Machines do not automatically "know" how to map between
modes, for example from an image to words that describe this image,
except by following the pathways captured within Context Driven
Topologies. Networks of machines are able to follow these pathways
to trace a history of reasons why this image and these words have
been associated in the past. Retrieved objects are typically
presented in the same mode which they were created (i.e. text for
text, drawing for drawing, image for image) however, sometimes it
is more useful for the person interpreting the objects to see them
a new way. If this is a completely new image, as most images often
are, it can only be interpreted into words by a person because the
machine has no associations to create outside of marking the
particular source and time when the new image was created. As
introduced in (Section 2) and further explained in paragraph (8.3)
it is at the discretion of the author to describe, identify and
associate data and data relationships used within the inventions
holistic system. The description of a new image, or any other
knowledge object, will be much easier for the machine to compile
and consolidate into the patterns if users provide associations,
such as words, that already have many of their own maps,
connections and interpretations available to advanced networks of
machines.
[0293] It is possible in that not one detail about a particular new
image's dimensions is important, or, maybe it the new image is an
architectural drawing and the dimensions, proportions and
references to other documents is more important than what the image
`looks like`. In the case of an architectural drawing, specific
spatial relationships and proportions need to be conveyed without
ambiguity; on the other hand an image, cultural artifact or
sequence of words in a story may raise many questions. It is the
inventor's position that either of these can be documented and
measured (B2.2) (10.6) because each object and their placement
within Context Driven Topologies and their associated knowledge and
display patterns, is unique. When the mathematical and display
patterns are used to analyze objects that require both quantifiable
and unquantifiable descriptions and interpretations (7.12), the new
bridges (1.13) (3.2) (3.29) (7.42) (8.3) (8.12) (8.19) that are
built, and the resulting changes this may lead to in both knowledge
and display technology, is the purpose of the invention (A1) (B1.2)
(B3.2) (1.18) (1.23) (7.9) (7.12) (7.21) (7.26) (7.37) (Section 10)
and (claims 1-10).
[0294] The variation between images, or any other type of knowledge
object or information component as defined by a user given boundary
(3.18), is assigned a value and prominence in the Context Driven
Topology system by: the person who creates the information; the
people who evaluate and interpret the objects in the future; and
any histories of previous associations for each component as
reflected in the Context Driven Topology and the knowledge and
display patterns generated by the topologies interpretation and
use. These values are mathematically based and mapped into the
display patterns. The use and evolution of the display patterns
will improve the mathematical framework and mapping procedure over
time.
[0295] Definition: For the sake of simplicity, the word "image" is
used, however, any encoded information component of any type, such
as text, a matrix, one sound, recordings of a symphony over the
years, a data array and any combination of any modes to portray an
object from any number of--interpretations over any period of time,
may be substituted for the word "image" for the steps and
procedures in the remainder of this section.
[0296] The invention allows digital information about knowledge
create its own metadata and describe itself. This takes place
through the process of creating, using and reusing the mathematical
descriptions (Sections 1 to 3), and the perceptual process which
includes the "appearance" of these boundaries, information groups,
and data arrangements as they are expressed through the evolving
automatic audio and visual language.
[0297] Each created or existing image has information that has
meaning to the person who creates, understands, or is trying to
comprehend this image. When there is an image that is also a
measurement (7.14) yet is still only partially described, for
example space imaging, these descriptions need to be able to
accumulate together over time so that machines can help us to
derive more associations, meaning and new, more complete or
streamlined knowledge from shared data stores. Shared stores will
be more useful if they are inherently dynamic, can be fixed into
specific views and reliable mathematical configurations of
knowledge patterns, and mechanisms such as the inventions display
patterns, to streamline and weed out incorrect, temporary, or
misleading information (claim 2 of 10).
[0298] Two scientists arguing over a measurement are having a
different argument than two curators arguing over a painting,
however, they may both saying that the other is "looking at it the
wrong way". This dispute has value reflected through a display
pattern.
[0299] The invention will help people who create and interpret
complex information to share their resources. It is a reliable
place and a shared language to consolidate related studies,
particularly studies that are accomplished in phases and depend on
federal or other funding to be accomplished step by step over time.
There may be occasions when investigators are "almost there" but
their program is canceled on a larger level that individual
investigators or program officers are unable change. The slightly
incomplete knowledge, information and data that is generated by
partial studies still has value reflected through a display
pattern.
[0300] One problem in current shared data stores is an
overdependence on natural language to describe objects. There are
some objects, such as art, that require either long or short
descriptions that cannot easily `fit` within the linguistic
constraints imposed by many current shared knowledge systems.
Specific terminology, such as chemical names, work well with both
existing shared data systems and natural languages because specific
terms are typically not translated. The inventors question is, how
extensively are shared knowledge systems intended to be shared? If
it is across domains and across natural languages, as the inventor
believes they should be, neither specific terminology nor
constrained natural language will be able to describe images in the
future as well as the images themselves can. See paragraph (2.6)
for a more detailed explanation of the placeholder concept and its
essential role in long term data curation and digital knowledge
preservation.
[0301] As historical comprehension changes, so will information
about the images people and their machines are trying to
comprehend. This discussion takes place and is recorded back and
forth between the knowledge and display patterns over time [FIG.
8], and is reflected in the new topologies and new patterns that
emerge.
[0302] The more streamlined the topology is, the more true it is,
the more entangled it is may mean it is more interesting. It all
depends on the information captured within the object, the viewer's
knowledge, and the way the viewer is looking. The aim of the
invention is to enable viewers, and people who guide viewers, to
make connections to objects as direct as possible (B3.7).
[0303] Description layers and knowledge patterns of any complexity,
and the effect this has on the display patterns, is able to be
identified, selected and controlled by each user or research group
THROUGH the patterns themselves. As explained in (Section 2) an
object in one sense could be one painting, in another it is all
paintings, all art, all things made by peoples hands, painting
number 12345 held in the collection of the 6789 museum--the
boundaries are only set by users to explain their ideas. The
patterns, or behaviors, of these boundaries and the pace they
change have values that are reflected in the inventions
presentation of information retrieved from shared data stores by
creating a display pattern.
[0304] Unquantifiable, non-machine based factors influence changes
to the mathematical and perceptual structures of Context Driven
Topologies over time because this is necessary to understand what
people think about and invent over time on many different levels.
People need their cultures as a way of understanding certain kinds
of information regardless of which machines or networks they are
currently using. The invention converts these cultural and group
understandings into an overall pattern, or filter, between
networked cultures and multi-cultural shared data stores as a value
that is reflected in the inventions presentation of information
retrieved from shared data stores through the display patterns.
[0305] By most definitions, what most users generally want from
information is for it to be correct. But when the "correctness" can
vary because the information is related to, for example a series of
high dimensional theories or humanities content, the purpose of the
invention is to allow these conflicting and concurrent
interpretations to gradually "push" or "guide" the patterns towards
more documented, higher density, long term, evaluated, deeper,
denser channels--which therefore are automatically given a higher
resolution in the display patterns. Use of the invention will also
make unusual occurrences or rapid changes in direction more obvious
because the display patterns provide more ways to "jump to a new
view", go to the overall [FIG. 8] and jump back and forth between
what is and is not there by comparing the knowledge and display
patterns in quantifiable and non-quantifiable ways. These
quantifiable and non-quantifiable scenarios would be captured
mathematically, as patterns that are measurable by machines, which
is new (claims 1 to 10).
[0306] Currently, most shared knowledge systems are geared toward
fixed metadata (2.3) with linguistic constraints to ensure data and
data arrangements are in equal regular packets for machines.
Therefore, high dimensional complex evolving content is being force
fit into systems that do not express the complexity inherent to
this kind of data and data arrangements. The purpose of the
invention is to provide new ways to see these patterns without
constraints imposed for machines but of little benefit to people.
Context Driven Topologies, including the languages and patterns
they generate, exist only in the virtual world, they do not need to
"work" in real machines. These visualizations begin with, and are
only formed by, people's imagination. Context Driven Topologies,
including the languages and patterns they generate, are used to get
this dialogue and knowledge about what we are looking at into
machines using the display patterns.
[0307] The inventions mechanisms of abstracting and simplifying
complex high dimension knowledge enables overall patterns between
the knowledge and display patterns to be "colored" by a history of
interpretation. Naturally components that share these same "colors"
will blend, and components that disagree will contrast. Until a
Context Driven Topology is fused into one cohesive whole, it
appears to be the colored individual components in their current
context and hierarchy--dots. When knowledge or ideas are fused into
a topology they are made continuous and blend (9.3). There are
countless variations of intensity, saturation, density and hue that
are able to be controlled to fine tune these distinctions. (claim
10 of 10).
[0308] Certain components, interactions and histories are assigned
colors both as components and in fields. People generally
understand colors and fields without special training, therefore,
this blending and contrast will make sense of the patterns on
levels where the detailed information itself may not be fully
understood. All color assignments and other classifications are
arbitrary and intellectually assigned, for whatever reason, some
people making these kinds of assignments choose only vibrant
colors. The invention's palette relies on neutrals and colors that
correspond to unlearned responses. For example in nature, the color
yellow is observed in temporary phases such as a baby chick,
dandelion or sunrise. Therefore, yellow is ideal for data and data
arrangements that are known to be subject to change. Reading these
colors, topologies, textures and surfaces is conceptually very
similar to reading maps. Interpreting these maps may be confusing
at first but people, like they always have, will adapt and learn to
understand.
[0309] When looking at information on the deeply detailed and
meta-levels where the invention is most useful, it is clearly
recognizable where, and when, each object originated and each point
of view was generated--a simple way to see this is by looking at
the arcs (A1) (C1) (33) (3.4) (3.6) (3.33) (4.1) (4.2) (4.3) (730)
and [FIG. 4]. Analyzing why these creation points and concurrent
and conflicting views cross back and forth, or come and go [FIG.
3], is an area where people have much better understanding than
machines because these are OUR human dynamics, displayed at a scale
or level of detail we are not able to see without machines [FIG.
8]. The invention is used to negotiate new boundaries for human
understanding, machine understanding and our interactions with each
other. These human/computer interactions are currently being
redefined on a very short time scale that could affect a very long
time scale. For example, the people of Saudi Arabia have been
looking at the stars very carefully, and keeping records, for a
very long time and therefore have unique detailed knowledge. It is
dangerous to think that today's large scale digitization projects
and shared knowledge stores will ever actually digitize all of the
most important human knowledge and observations. In the long term,
broader, more permanent view it will become increasingly important
in our connected digital age to clearly understand where shared
knowledge originates in time, culture, domain, or other area of
background knowledge. We have handed down knowledge and kept
records as long as we have been handing down knowledge and keeping
records, these existing ways have all changed with the advent of
computational machines and advanced networks, therefore, our
methods for handing down knowledge and keeping records must also
change. It is critical at this stage that these methods and record
keeping styles change in a way that is geared for people because we
have the better imagination and ability to see and interpret what
these patterns mean.
[0310] All boundaries imagined by people are scale free to a
machine or network of machines. Each boundary is "stretched or
squeezed" (3.16) (3.18) (4.1) (9.11) to fit on various levels of
the hierarchies perceived by users until context is assigned and a
conclusion is drawn in the form of a new Context Driven Topology.
During the time users are manipulating, controlling and selecting
context for objects and the patterned space around them, machines
never "see" these objects as people do--in a hierarchy with
foreground and background--machines always process all of the data
in the current arrangement in parallel. As introduced in (B3.1) the
machine uses mathematical processes to order these arrangements as
a reflection of the user's hierarchical structures but machines
only "see" groups, not hierarchies.
[0311] Sets and subsets of data components, groups, arrangements,
objects and topologies that are viewed together in groups together
tend to "drift" "gravitate" and "snap" towards each when they share
common backgrounds or features such as a measurable scale, or key
words etc. See (Section 10) for examples. The histories contained
as symbols (Sections 2 and 3) within each components description
are drawn together because machines are always processing the whole
set of component descriptions in parallel, as duplicative
components are gradually consolidated this shifting and moving
within the components acts as if it is "pulling" components that
share aspects of their histories together. This is caused deep in
the background an unseen by most users except how these actions
affect the display.
[0312] Each group as a whole is treated as one object because it is
"held together" or "wrapped" inside one description boundary.
Ultimately, the reason a Context Driven Topology is formed is so
this set of information groups and objects can share one new
boundary as one new whole. The perceived appearance and
mathematical configuration of a completed topology is markedly, and
obviously, different than a collection of components that have not
become "set" in their context. This whole is more than a sum of the
parts. The behavior of these boundaries, and patterns that may be
recorded about boundaries as they are initiated from different
points of view, has value that is reflected in the inventions
presentation of information retrieved from shared data stores
through the display patterns.
[0313] Generally, these configurations, histories and patterns are
treated as objects in spaces where both the object and the space
around it have meaning (1.1). There are ideal objects, spaces,
proportions, densities and other measurements used by the invention
that are able to be regularly observed by all people when they
observe nature, art, music, design and mathematics.
[0314] Some patterns and sets of information may share so many
components in common, it is up to the viewer to decide what the
best way is to view and arrange like or repetitive components in
the background or to pull information of priority to the front
(Section 6). When these locations and proximities are set and the
arrangement is captured in a Context Driven Topology, the priority
addresses and locations are marked within the symbols as shown in
[FIG. 9]. Each component and each topology has its own story
presented through the display patterns.
[0315] When it is decided by a person or research group that the
boundaries, proportions, density and hierarchy of the information
is in its preferred embodiment, the edges (3.9) or texture (1.23),
of the topological form reflects only the final decisions
regardless of how many variations, reconfigurations and changes to
the components there were along the way (5.7). Only the final group
and final arrangement is captured.
[0316] Topologies that are merged together eliminate duplicative
components, eventually, the intention is to streamline down to only
one very high quality original for each component and each
topology, changing our dynamic shared data stored into an unlimited
quantity of high quality maps instead of duplicative components
(5.6). This will permit enormous quantities of unnecessary
background information from being duplicated or displayed as a
result of searches and other tasks (1.15). It will also ensure the
information in dynamic shared data stores is of higher quality, the
maps themselves are reviewed (6.7) and will greatly simplify the
data arrangements that are preserved. These simplifications and
improvements to quality are the purpose of the invention (claim 2
of 10).
[0317] One way to see these simplifications is by looking at and
comparing the edges and overall proportions of the various
topological forms at an abstract level that is "before" the
information itself (7.8). This becomes a new kind of object in a
new kind of collection. Moving through the objects uses these
edges, and has a palette (7.29), that is similar to walking through
nature (A1).
[0318] Each hierarchical configuration, symbol, and waveform will
reflect details in the object boundaries, or edges, as textures
across the entire form. For example, information that is used over
and over again will have a texture like sand on a busy beach,
information that is rarely used has a texture like fresh snow. No
people need special training to understand these textures and
machines can be easily trained in this automatic language. These
edges, proportions and textures are the first identification that
makes this particular one whole group of objects distinguishable
from similar objects. Details in these edges are measured by a
machine or group of machines mathematically, people may only be
able to understand the automatic language (colors, textures, and
forms) because the forms and edges themselves may be so complex it
will take people some time to learn how to understand them, know
where to zoom and how to best negotiate the relationship between
the knowledge patterns, Context Driven Topologies, and display
patterns. The automatic audio and visual language itself evolves
over time to reflect these new relationships.
[0319] As introduced in (3.18) a boundary with massive amounts of
information inside yet a small description outside looks ready to
burst, a symbol with infinite complicated and overlapping
descriptions with simple information inside is wrinkled, from far
away both look the same. Whether each component is "far away" or
"close up" depends on how this component is typically used in other
configurations, people can usually only see this through the
automatic language. The boundaries and details themselves are far
more detailed then most people will ever review up close. The
boundaries are made of vectors to scale without pixilation, they
are continuous and never break down. This enables user to interpret
information of different scales through the topologies directly
(3.5) rather than through the original information itself. The
boundaries have no scale because they are every scale. The
boundaries have no inherent thickness, they are built layer by
layer or initially connected arc by arc. The tools and controls for
drawing the boundaries is related to computer graphics processing,
operator interface processing, and selective visual display; the
causes and effects of these changing boundaries is related to data
processing.
[0320] As introduced in paragraph (1.4), no components or
descriptions are ever duplicated. Two or more locations are simply
indicated as multiple locations as a map of the context (5.6). If a
user is not ready to choose one component over another yet, only
the preferred component is shown on top, the similar choices are
indicated either as a stack waiting behind the preferred component,
or as a transparent component that gradually steps back in
resolution as the similar objects recede in priority until the user
decides to cancel the redundant and similar background elements.
The user may define a limit on the number of levels and layers to
be presented, compiled, or retrieved at any one time turning layers
of information on and off using special controls. When the final
Context Driven Topology is captured, all of the layers that are
turned off or options that were not chosen are eliminated
completely.
[0321] The last way a topology is viewed is also always the first
way it is viewed by a subsequent viewer. When subsequent users wish
to examine the information more closely, add or subtract
components, the topology structure is dissolved and the components
become individual objects again. The subsequent interpretation is
then made into a subsequent topology which may be very closely
related to the original topology. These small variations can be
recognized mathematically and perceptually using the automatic
audio and visual language and display patterns.
[0322] The knowledge and display patterns are controlled through
techniques that twist and rotate in higher dimensions than people
experience in daily life. These shapes and relationships can be
studied through algebraic topology, knot theory topology and other
mathematical techniques and are portrayed through an evolving
language of colors, textures, forms and spatial relationships.
[0323] The inventions special commands and visualization techniques
enable people and machines to evaluate complex information at a
simplified, more abstract level. The connection of these
visualization techniques, or display patterns, to the mathematical
framework, or knowledge patterns, allows redundant information to
be machine deleted without a person or research group expending
resources to review it. The thresholds and tolerances for these
controls vary by user and research group. The indelible
recognizable marks provided by the infinitely detailed topologies
will enable a greater trust to let machines decide and consolidate
redundant information on their own. Certain standards will become
typical across certain fields. For example, an organization such as
the American Institute of Architects (AIA) could create their own
pattern of drawing standards, up to date building codes in each
state etc. This pattern would be meticulous, detailed, current and
shared. This group initiates their own pattern, is responsible for
it, and maintains it over time, they can be the only ones able to
change it in a way that it can remain in this particular pattern
(8.3). However, the methods of architects could be useful to other
fields and used to begin another template, architects could borrow
templates from engineers to incorporate into their own etc.
[0324] The pattern uses and activities will begin to define where
information patterns will tend to settle in the stateless space
[FIG. 2]. Shared components, shared histories and shared patterns
will "pull" similar topologies together to begin to self-organize
because this is the way they are accessed in and out of the
cloud.
[0325] A Context Driven Topology is a memory store that is also a
multidimensional image store. Look up generally begins by providing
the machine with related information or a set of
specifications.
[0326] Images and other encoded data are recognized by machines and
people in different ways, people see the manifestation of the
information itself, for example, by reading or writing text. A
computer only sees mathematical descriptions in mathematical
arrangements. Detecting structure, design and variation is a very
detailed human computer interaction process using the invention in
between. Persistent or "true" information has a different
appearance and characteristics than information that is in
development or dispute.
[0327] As introduced in (Section 6), components are addressed as a
bit map or graphic memory with priority based on their location(s)
in each hierarchy. One output of these knowledge patterns is image
data. These locations, adjacencies and circuitry are a combination
of the data processing and display. It is in a sense, optical
addressing, however the true locations are known mathematically and
may be presented through any mode. The reasons behind the registry
of most information components within a topology are described
throughout (Section 2).
[0328] Machines affect the arrangement, grouping and processing of
data and data arrangements by drawing and displaying virtual
boundaries that twist, rotate, scale and impose hierarchy on
knowledge objects to fit these objects into user defined
configurations using new combinations of improved data processing
techniques with Computer Aided Design (CAD) and scalable,
non-pixilated continuous tone imaging techniques for bounded
mathematically defined forms using techniques and processes
partially disclosed throughout these specifications including
paragraph (B1.5), further documented and developed through an
upcoming project (C) [FIG. 6] and enumerated in (claim 10 of 10)
including, but not limited to, the USPTO and corresponding
International classifications listed below:
[0329] USPTO Class 326 Digital Logic Circuitry
[0330] USPTO Class 327 Nonlinear Devices, Circuits and Systems
[0331] USPTO Class 341 Coded Data Generation and Conversion
[0332] USPTO Class 345 Computer Graphics Processing, Operator
Interface Processing and Selective Visual Display Systems including
Subclass 77 brightness or intensity controls and others
[0333] USPTO Class 347 Incremental Printing of Symbolic
Information
[0334] USPTO Class 356 Optics: Measuring and Testing
[0335] USPTO Class 360 Dynamic Information Storage and
Retrieval
[0336] USPTO Class 367 Communications: Acoustic Wave Systems
[0337] USPTO Class 368 Horology: Time Measuring Systems
[0338] USPTO Class 369 Dynamic Information Storage and
Retrieval
[0339] USPTO Class 380 Cryptography (also see Section 8 Shared
Memory)
[0340] USPTO Class 382 Image Analysis, including procedures for
analyzing and categorizing patterns
[0341] USPTO Class 385 Optical Waveguides
[0342] USPTO Class 434 Education and Demonstration
[0343] USPTO Class 700 Data Processing: Generic Control Systems or
Specific Applications
[0344] USPTO Class 702 Data Processing: Measuring Calibrating or
Testing
[0345] USPTO Class 703 Data Processing: Structural Design,
Modeling, Simulation and Emulation
[0346] USPTO Class 704 Data Processing: Linguistics and
Translating
[0347] USPTO Class 705 Data Processing: Specific to Business
[0348] USPTO Class 706 Data Processing: Artificial Intelligence
[0349] USPTO Class 707 Data Processing: Database and File
Management and Data Structures
[0350] USPTO Class 708 Data Processing: Arithmetic Processing and
Calculating
[0351] USPTO Class 709 Data Processing: Multicomputer Data
Transferring
[0352] USPTO Class 710 Data Processing: Input/Output
[0353] USPTO Class 715 Data Processing: Presentation Processing of
Document
[0354] Templates and standards for many of the colors, textures and
other characteristics described above are available for review
separately from this document and will be further documented and
investigated through the upcoming project (C) and [FIG. 6].
[0355] The invention will raise questions about aesthetics. The new
dialogue with machines may change people's aesthetics in ways we
are not familiar with. The process of using the invention will give
people new ways to recognize patterns, reasoning, classifications,
hierarchies, purposes, designs and aesthetics we do not already
know (claim 6 of 10) (claim 10 of 10).
Shared Memory
[0356] Context Driven Topologies reside in a stateless space as
interlocked threads of knowledge woven together by people over time
to be simplified and streamlined by machines through the use of a
shared memory.
[0357] As introduced in (B3.2) (B3.5) [FIG. 6] (1.2) (1.7) (1.20)
(2.5) (2.10) (3.21) (3.25) (6.7) (7.1) (7.11) (7.14) (7.15) (7.16)
(7.26) and explained through (claim 4 of 10), different
interpretations of the same information may lead to different
conclusions and therefore variations in each Context Driven
Topology's identity, form, boundary structure, perceived
appearance, mathematical properties and other unique
characteristics and defining features. The decision whether or not
to create a permanent context driven identity for a data component,
group, arrangement or topology is a process (Section 2) by the
author, whether the author is an individual, institution or
research group. Machine generated identities, although they are
also unique, machine generated identities are only patterns.
Machines are not able to define components, groups, arrangements
and the conclusions represented in topologies, only streamline what
we have defined.
[0358] The process of creating and identifying a Context Driven
Topology is defined below and further disclosed in (Section 9).
When information is proprietary, sensitive, private or should not
be interpreted out of context, the protection of that context and
identity follows any necessary combination of the steps below:
[0359] When there is a dispute about data authorship or
originality, two or more different topologies are precisely
compared by machines and interpreted by more people than the
original author and challenger(s), through the use of a shared
memory.
[0360] The first topology has already been produced and made into a
fixed form by the author including the marks (D1) (3.15) (3.24)
(7.11) (7.44) (8.10) (8.16) (8.23) (9.15), history (A1) (B3.4)
[FIG. 5] [FIG. 6] [FIG. 10] (1.4) (2.4) (3.10) (3.11) (3.18) (3.21)
(4.12) (6.7) (7.1) (7.14) (7.28) (8.3) and arrangements that show a
"background" [FIG. 5] where the idea originated [FIG. 6] (1.22)
(4.12) (7.30); subsequent topologies are the challenge(s) posed by
the person, or group of people questioning the validity of the
author's claims, originality, conclusions, work methods, conceptual
processes, knowledge, or any other aspect of original work that may
be challenged.
[0361] The original topology created by the author naturally
includes obvious indications of new connections that did not exist
before. New bridges were created (1.13) (3.2) (3.29) (7.15) (8.12)
(8.19), new techniques were created, the algorithms streamlined and
encapsulated (9.4), non-essential components and techniques are
eliminated, and all of the background information consolidated into
a patterned space. Each topology's content is unique, each
background is unique, two topologies created at the same time that
share the same components and same context are considered to be the
same topology regardless of the fact that, theoretically, it would
be possible for two precisely matching topologies to exist. The
invention is a form of record keeping it serves no purpose to keep
exact duplications, only unique variations. Therefore, two authors
cannot create the same topology, they each have their own version
and one would be first, even if they were at the same time, it
would not be possible or worthwhile (using this system) to also be
in the same place, or have the same content and context, without
being automatically consolidated into the same topology.
[0362] The proper placement and definition of new data components,
groups or arrangements is therefore the most difficult aspect of
introducing new ideas because each arrangement does actually need
to be unique, even if it is simply "before" or "after".
[0363] Each Context Driven Topology is formed to convey the
author's idea most clearly by being described and arranged to
reflect the author's reasoning and meet the author's technical
specifications and aesthetic preferences. This unique configuration
and identity is introduced to the stateless space and automatically
gravitates toward a certain zone because of `threads` and
`connections` to related patterns, placements, histories, and
configuration types.
[0364] If an author has drawn new conclusions they wish to publish,
the author may try to place their ideas into the shared memory
without review by claiming the ideas have certain characteristics.
However, the author's own arrangement may not have as much meaning,
or be as recognizable, without the additional marks and connections
provided by reviewers, publishers and peers that understand this
complex information. Each of these connections adds `weight`
`density` and definition to the ways each topology is prioritized
and placed (Section 6). Typical users of Context Driven Topologies
and the shared memory upon which they depend generally aim to
provide their peers with access to new ideas and new knowledge
because they would like to receive credit for these ideas, defend
their ideas [FIG. 4], and be able to continue the discussion.
[0365] The author uses special language develops techniques and
other knowledge related skills to arrange their configurations to
be recognizable and unique because there is no reason to create a
new one that is too similar to another version. Advanced networks
of machines and the shared memory area in the stateless space will
automatically merge matching versions in a very cold hearted,
automatic way. If the author did not `make` this arrangement it is
obvious. Whether the arrangement is `true` or `accurate` or `the
best` arrangement or technique is up for discussion. This procedure
is no different than conventions for introducing or sharing
knowledge that already exist. However, it is very different than
the easy introduction and lack of review that exists for unmapped
data and information proliferating on the internet today.
[0366] If the future person, or people, challenging the ideas
captured within a topology wish to file a dispute, they "pose their
question" using another topology going straight to the essence of
why they feel the data or the data arrangement is either incorrect
or a "set up". The challenging topology may be either more simple
or more complex than the author's topology because it is
constructed for a different reason and therefore has a different
set of marks, arrangements and histories--it may be a very simple
question that could resolved or corrected by the author in an
amended version, or it could be a detailed point by point argument
that requires multiple proofs and citations to explain. Neither the
original nor the questioning topologies is automatically `shorter`
or `longer` than the other or in any specific order (2.3). Machines
always analyze all of the descriptions together in one group. The
original and the challenge(s) may be considered as: separate
wholes; these wholes as they compare to each other; these wholes
against limitless others introduced to decipher whether or not this
group of data and data arrangements is unique, accurate, or any
other knowledge based question. Using a combination of the
knowledge patterns, display patterns and the shared memory, there
is a point that is eventually reached, and determined to be the
correct point according to the users and challengers knowledge,
where the issue at hand is able to be isolated and accepted or
rejected.
[0367] Data authenticity and error or fault detection generally
begins with both people and machines interacting to investigate
where conceptual leaps (1.25) and connections have occurred. It is
a mathematical statistical process for machines to compile the
histories and pathways contained within the symbols (Section 3),
and a judgment process for people to evaluate the placement of
these bridges, and the logic of these connections, to discover if
the conclusions are new, true, or already concluded by someone else
(7.12). This comparison is only possible because the conclusions
are able to be compared on many levels in their entirety against a
background of existing historical knowledge [FIG. 8].
[0368] When information takes on completely different meanings out
of context, one of two things can happen, either this data
arrangement and patterned space around it cannot be broken
therefore all of the components automatically come into new
topologies as group permanently locked together, or through a very
detailed and time consuming process, individual components can be
reassembled one by one up to a certain point defined by the author.
Just as in real life today, some people may spend this time to fake
a `real thing` but using the invention, especially the shared
memory feature, question by question, challenge by challenge,
either this `fake thing` will persist or it will not. Blending in
without disrupting the flow does not enable a topology to persist
either. This time consuming and tedious process to construct a
`copy` `forgery` or `fake` would leave bridges and marks easily
identified and exposed by both people and machines, it is usually
not worth the effort.
[0369] Due to the history of each components prior context and
uses, and in certain highly specified future cases, once a certain
threshold of components, defined by the author, begins to be
arranged or grouped together by another user, either the entire
group of them is automatically retrieved and shown in context which
would naturally include the authors marks, or the components repel
each other (5.4) and will not stay in the same group unless new,
out of context, bridges and components are also included to hold
them together.
[0370] Time in this system is one way for most purposes. The
context and specifications to define each topology are able to be
established so the form collapses or cannot be seen when it is
accessed from the future in the wrong way. Privacy specifications
are typically only applied to the display patterns. As it is today,
if someone invents a new way to hide, another person will invent a
new way to detect and vice versa. This back and forth dance is part
of the human experience.
[0371] If, for example the possible forgery in (2.5) or the court
records in [FIG. 4] ever show the indelible mark of a falsified
identity related to this person or object, this information is
permanently and engraved into this set of components. It is not
possible to put these same components together in the future
without also, automatically, bringing this permanent indelible
mark.
[0372] The invention is a new way for more people to include their
interpretations about ideas that have a more wide reaching affect
on more people than some of our older methods are able to
handle.
[0373] When information is private, for example a person's
identity, these components are specified by the author to have edge
conditions (3.9) that are not able to be bound within other
arrangements. Each specific attempted use is transmitted to the
author via the shared memory. If the use of the information occurs
after the author's life time, or if the author is not paying
attention, it is a special matter to be handled case by case using
people's judgment about machine derived histories.
[0374] Context Driven Topologies and the shared memory space will
allow for both broader and narrower interpretation at both deeper
and shallower levels. The inventions component based system
(Section 3) will permit certain PORTIONS of data, for example a
persons identity, to be protected and removed from other, less
invasive or sensitive, legitimate arrangements, such as a
scientific study of peoples travel habits and expenses, where an
individuals personal identity is not important except in the most
extreme cases, for example monitoring travelers that are a known
security risk.
[0375] If, for example the detective in [FIG. 5] or the possible
forgery in (2.5) involves interpreting information where the author
wishes to conceal or otherwise mislead others on their identity, or
the identity of the objects, or histories; as indicated in (8.3b)
above, this is a construction that advanced networks of machines,
peoples awareness, and the shared memory space will detect through
patterns such as excess challenges, irregular challenges and lack
of challenges in the histories; or specific tracking, specific
similar arrangements, and specific new questions established in a
new topologies and new patterned spaces until eventually these
constructed, as opposed to evolving, patterns will be stripped away
and revealed by the process of not moving ahead, not changing or
otherwise not matching infinite other patterns that have been
created over long periods of time for legitimate reasons that are
far too intricate and detailed to be falsified within one person or
machines lifetime.
[0376] Context Driven Topologies can be viewed from every angle,
taken apart, put back together until the ideas wear out and we get
tired of them. When we do not need or want to look at them anymore
they do not clog the top levels of the shared memory space. Ideas
that have fallen out of fashion, or proved to be incorrect and are
abandoned, are compressed in the background and occasionally
retrieved in the future when someone has the knowledge to abstract,
describe, and extract the essence of the idea (i.e. the object
Sections 1 and 2) by cutting through the existing descriptions in a
way that could not happen without advanced networks of machines and
a shared memory. Naturally, these shortcuts would bring the older
information to the front and the quicker path to the object, in the
context of current knowledge, is the new description.
[0377] If it is an incorrect idea from the past, it would be
dragged into the future along with the reasons why it was
abandoned. There may be new knowledge and new machines to interpret
previous ideas a new way, however it is very important to know WHY
the idea was dropped (4.12).
[0378] Information in the shared memory space is subject to intense
scrutiny (8.3). Information that cannot withstand such scrutiny has
difficulty becoming attached to other objects and will eventually
be forgotten without clogging up shared data collections or being
recognized in specific searches. Most areas of the shared memory
are too deep to be "accidentally" retrieved (1.22) (9.1).
[0379] As knowledge evolves, is gradually accumulated and
streamlined into in the shared memory, over time it will become
more obvious which information belongs together versus which
information is difficult to force into the same arrangement (8.3).
It will also allow people in groups, with specific knowledge,
techniques and machines to decide which complex shared information
is not important or worth keeping (claim 5 of 10).
[0380] The disputes, bumps, and irregular edges (3.9) on a Context
Driven Topology are its characteristics and defining features. As
knowledge and ideas become more accepted, even old, these edges
eventually wear down over the course of time. Ideas that have
inherent deep textures, or histories, that have become worn down
are more easily streamlined with other ideas because the deep
background textures are always compiled by machines, as all
topologies always are, in an entire group many levels beyond the
levels where people are evaluating or understanding the data
arrangements and the histories they contain.
[0381] The priorities (Section 6), characteristics and features
that people identify, create and extract through use of the
invention extend many levels beyond the era or knowledge domains
where the data was created (8.7).
[0382] If a future query originating at any point in time from any
knowledge domain is able to describe if the essence of knowledge or
data arrangements using any technique that captures this
essence--the knowledge, its objects, relationships and histories
will all be recognized and retrieved. Each subsequent retrieval and
interpretation leads to the preservation and clearer understanding
of this knowledge. Faster, more direct pathways to each object are
permanently bound to each object and recognized by machines in
future searches. These new pathways are, is in a sense, a machine
created identity for the object that may not have been initiated in
the past or within the domain where the objects are normally
preserved. Nevertheless, machines do not "know" how to create a new
topology, nor is it to people's advantage to show them. Deriving
new relationships and patterns, streamlining knowledge in
dimensions we cannot perceive, and recording our knowledge over
time is the only role machines serve in the invention (claims 1 to
10).
[0383] Searching the shared memory space typically begins by
providing similar versions, outlining or sketching arrangements,
running tests (4.14) and other user defined techniques to define
features, irregularities and precise variations of the information
being searched, retrieved and extracted by feature.
[0384] The marks and histories embedded in each Context Driven
Topology in the shared memory space show that the data arrangement
has faced challenges and persisted. Likewise, data arrangements
without these marks does not persist in dynamic shared data stores
(D.1) [FIG. 6] (1.1) (1.6) (5.7) (7.47) (8.3) (8.20). Therefore,
the data and data arrangements maintained in the shared memory will
be higher quality (claim 2 of 10) and the storage space itself will
have mechanisms to self-organize (claim 5 of 10) as data
components, groups, arrangements and whole topologies move in and
out of the stateless space over time as initiated and sent back
into storage driven by human questions and interpretations from
backgrounds of varying knowledge and understanding. Simplifications
and streamlining between ideas, having a record and being able to
see how ideas and interpretations change, or are interpreted
differently from different points of view are all made possible
through the use of the inventions shared memory (D1) [FIG. 6]
(1.22) (9.2) (claim 3 of 10).
[0385] As data components, groups and arrangements are retrieved or
created to be used in new topologies, the object's meaning and the
processes required to read information about this object is
automatically updated and preserved to meet current technical
standards and knowledge requirements. Therefore, knowledge and the
techniques required to read this knowledge are preserved through
evaluation and use (claims 1-10).
[0386] Whether a person creates an identity (2.13) (8.3) or a
machine generates a pattern (8.9) used in the shared memory space,
there always be will non-continuous places and edge conditions that
distinguish each identity and pattern from similar versions. The
placement of these bridges and the ideas they are bridging, the
edge conditions, and the aesthetics of the topology itself will be
what is interesting, and of value, in the future (claims 3, 5, 6,
7, 8, 9 and 10 of 10).
[0387] The invention, including the shared memory space, is
intended for information that has unlimited variables,
configurations and essential characteristics that people spend time
to discuss, evaluate and argue.
[0388] As historical comprehension changes (B3.2) (3.11) (7.22) so
do the topologies.
[0389] Because Context Driven Topologies rely on shared memory to
self-organize (7.45) (8.11) and self-perpetuate (9.19), the
invention is an interaction without a medium (claims 2, 6 and 10 of
10).
[0390] Because Context Driven Topologies are evaluated from many
points of view over time they rely on shared memory; because this
memory is without a medium (8.16), Context Driven Topologies are
automatically passed from generation to generation as if they were
stories. These cycles and interpretations are an aggregate of all
of these descriptions and views over time. Therefore, the invention
is independent of specific natural or machine languages because
each of these languages would naturally be included within each
topology to continue to evolve, and be periodically updated or
preserved. Most patterns are detected by advanced networks of
machines, and even though each machine or network of machine may or
may not be dependent upon electricity now or in the future, Context
Driven Topologies and the purpose for them, are passed like songs
or stories through time and therefore independent of electricity
(claim 2 of 10).
[0391] It is only mathematical patterns and an automatic language
that is created and reviewed by machines. A Context Driven Topology
is a time capsule of ideas and data processing techniques in a
unique configuration that only makes sense in the stream (A1) (D1)
[FIG. 3] (B1.5) (1.1) (1.7) (2.2) (3.7) (3.11 and 3.12) (3.16)
(3.21) (3.24) (4.6) (4.10) (4.18) (6.9) (7.1 and 7.2) (7.8) (7.18)
(7.23) (7.37) (8.1 to 8.3) (8.6 and 8.7) (8.9) (8.11) (8.29) (9.1)
(9.17) or as part of its original collection. The `real` or
original information resides protected, or degrading, in another
place. The information used in Context Driven Topologies is copied
from the original (2.6), these data arrangements are only virtual
and man made, and no one organization is responsible for its
accurate interpretation because everyone is responsible for its
accurate interpretation. Context Driven Topologies reside in a
stateless space that is a media-free shared memory (8.16) (8.17).
Context Driven Topologies, especially in the symbol form (Section
3), are used as maps in and out of this one shared memory or
dynamic set of interpretations and records. Each map itself is an
interpretation. The purpose of the invention is to consolidate and
streamline these records, make them available at deeper levels to
experts in different domains, and to preserve this information over
longer periods of time independent from individual media, machines
and electricity (Section 1) (claims 1 to 10).
[0392] The registry of information within Context Driven Topologies
is determined by the person or research group who authored and
engineered each arrangement (2.12) (8.3). This registry, identity
and placement are the only ways a machine or network of machines
knows how to begin to retrieve and identify information within the
abstract data cloud [FIG. 1] [FIG. 2]. Machine created identities
are generally patterns not individual identities such as
components, groups and topologies (Sections 1 to 3) and (claims 1
to 10).
[0393] Each person or research group that creates data and data
arrangements has control over their data ownership and
responsibilities. Generally, the Context Driven Topology system is
intended for information where each author assumes full credit and
responsibilities for each arrangement (1.14) (8.3). It is for
information that is meant to be formally introduced, challenged,
argued, examined closely, then determined by the shared memory
between the group of users who understand this information (1.7),
whether or not it is correct or worth keeping (8.6). The more
information is reviewed and used, the longer it persists in the
shared data store maintained by people who understand the
information in this store. When the same examples keep being used
over and over without introducing any thing new, this is able to be
perceived through topology itself due to a lack of virtual bridges.
If users creating the new topologies wish to have this be viewed as
a truly new idea, over used components are able to be limited
through the configuration specifications from the beginning.
[0394] Context Driven Topologies are a shared memory combined with
a storage means independent of specific display devices because it
is assumed these devices will improve over the time each topology
exists.
[0395] When the Context Driven Topology system is used to detect or
correct errors, these errors are able to be isolated by either
working with the space the topologies are perceived to be in, in
the topologies themselves, or in a combination back and forth [FIG.
8] between the two until the error has been isolated and corrected
to meet the originating author's specifications. The Context Driven
Topology system will allow users to compare complex, different,
even unrelated, information to get a faster idea about the areas
that have become entangled and should be reviewed to be more
streamlined and freed of their errors.
[0396] Sharing information by using the invention will lead to a
new politics of data description and presentation and, more
importantly, a new aesthetic for what is perceived as designed,
balanced, or purposeful (A1) (B1.2) (B3.5) [FIG. 6] (7.49) (8.13)
(claims 6 and 7 of 10).
[0397] Data authenticity is reliably and quickly evaluated through
a cooperative agreement between people and machines by an analysis
of the indelible marks within each topology. If it has a mark from
a certain lab, which cannot be duplicated, this adds to the data's
authenticity. If a mark is forged, not only the (false) mark will
be there, but the leap or bridge and steps needed to create the
mark since it was not already present, will be inseparable from
each other in this particular topology, or it will be a new
topology because actually, it is a new arrangement no matter how it
is propped up or posed, it is a new unique arrangement or it is
drawn into the void (8.3). The invention forces accountability upon
those who use this system.
[0398] There will always be an implied trust that the people who
generated archived information understood what their words,
mathematics or images imply, that this information was constructed
on purpose and their machines were functioning as intended. If we
believe and understand the images, works, claims, stories, proofs
and simulations displayed in the future, we can either "save as"
the whole arrangment, or the bits and pieces that are relevant or
interesting to current modes of thinking.
[0399] Collective data fault and recovery made possible through the
shared memory is a process of defining consistencies, specifying
constraints on the arrangements themselves and other steps
explained throughout this section and in (claims 1 to 10).
[0400] Machines record and measure human reasoning, evaluation
techniques, knowledge based discussions and interpretations as they
occur over time using a shared memory that falls within, but is not
limited to, the USPTO and corresponding international
classifications listed below:
[0401] USPTO Class 380 Cryptography/42 data stream/substitution
enciphering
[0402] USPTO Class 709 Multi-computer Data Transferring
[0403] USPTO Class 711 Data Processing: Memory
[0404] USPTO Class 712 Data Processing: Processing
Architectures
[0405] USPTO Class 968 Horology/47 acting in both directions/290
for extremely long running times and others as enumerated in (claim
10 of 10), using techniques partially disclosed herein (B1.5),
further investigated, documented and developed through an upcoming
project (C) and [FIG. 6].
Data Curation and Digital Preservation
[0406] There are different organized areas in the stateless space.
The shared memory area where theoretical physicists perpetuate and
preserve their ongoing discussion is in a different, deeper place
than the area where the latest on web art is displayed, and
different than the locations of the best pathways and circuits, or
rings, to unique little shops making handbags no one else has. The
display patterns and knowledge patterns are different to and from
each of these areas. The shared memory is not the `only`
information space, just the areas and objects that are preserved
and accessible over extremely long periods of time. The shared
memory of continually updating information, for example a product
like a computer that will be replaced with an improved version as
soon as possible, uses different kinds of patterns to record what
is happening overall and position itself in the stateless space.
Some aspects of these products and patterns, for example
information about what each operating system actually does and the
ways this group of computers evolves, will eventually form a long
term shared memory with overall patterns but generally, just like
today, this information is private or belongs to the company
creating the product and the competitive jockeying for position to
stay on top, be the first one seen in all the competing
information, is all that is displayed to others (8.3). This
positioning, history and pace of change is expressed through the
automatic language (Section 7), tracked overall using the knowledge
patterns, viewed using the display patterns, and interpreted or
otherwise brought to new conclusions by constructing new topologies
(claims 1 to 10). The mapping between the knowledge patterns and
the display patterns is accomplished differently by each user which
begins to form its own set of patterns and meta-patterns. The
patterns on all levels are infinitely connected and detailed. Since
the display patterns are an "opposite" or known "twist" to the
knowledge patterns, the two are often used in combination.
Standards or rules, such as architectural drawing conventions
(B2.2) (B3.6) [FIG. 5] (3.27) (7.15) (10.3) are easily combined
with other patterns to ensure that information is structured and
presented as accurately as is known to be possible. Varying
presentation through these patterns also applies to preferred
natural languages (10.2), cultural interpretations (7.25), new
aesthetics (7.50) and any other interpretive reasoning or control
features that can be gradually accumulated to form mathematically
based patterns illustrating knowledge objects and areas within a
stream of patterned spaces.
[0407] The knowledge and display patterns interpreted through the
invention reside in boundless abstract cloud, or stateless space
that does not exist in a "place". It is a shared problem solving
space that is needed for our shared knowledge systems. Context
Driven Topologies are not physical or real, through the use of the
shared memory space and advanced networks of machines people can
`borrow` both data and data techniques over long periods of time,
and individual machines and data processing techniques can continue
to advance. For example, if the current machine needs the 2004
Universal RDF Schema Namespace or the user needs to access a German
1639 dictionary, the topology indicates a wider context to borrow
tools or purchase access to the broader knowledge.
[0408] Many institutions, such as museums and libraries, do not
have the resources to stay current with the changing pace of
technology. Only the most essential commercial programs are
purchased, otherwise it is generally an open source system.
[0409] There is no place, no reason, or no one organization where
the topologies could reside outside of our imagination, reliable
non-ambiguous mathematical codes, and a rendering of the form where
we communicate in between. They just exist, people make them, find
them and monitor their use. There is no benefit in them being made
physical or "real" at this, or probably any other, time because if
they were someone would just have to take care of them. This
individual or institution would start to be the only one who
understands the topologies and, in effect, they would no longer be
shared or discussed which is their purpose. The topologies need to
degrade and eventually be lost (5.1), just not at the rapid pace
that current technology degrades or is lost (1.15).
[0410] If there was a material that could be used to capture the
topologies as a fixed sculptural form the inventor is not aware of
this material but intrigued by the possibilities and this may be
pursued (C). Generally, topologies people look at or are next to
are `made of` light and sound (B1.2) (B1.5) (B2.2) (B3.6) (4.11)
(7.5) (7.20) that naturally need to be presented through machines.
The inventor believes many existing techniques and conventions can
be married together as will be prototyped, introduced and
distributed through an upcoming project (C) [FIG. 6].
[0411] There are efforts underway (for example at IBM, Aprilis,
InPhase Technologies and research universities) to develop
holographic data storage which may prove useful for representing
and archiving Context Driven Topologies if a media was determined
to be necessary in the future. However, many of these techniques
like all machines and media, face technical difficulties, for
example image distortion and ghosting, but more importantly most do
not allow new information to change the image/memory on the media,
or any change violates the authenticity of this record.
Nevertheless, the implications for "shortcuts", "overlapping" and
higher dimensions of data access or storage using holograms may
have possibilities and may be briefly investigated (C) [FIG.
6].
[0412] When an idea is captured or a conclusion is drawn--science,
art, language, image, human understanding and machines all
cooperate and intersect being formed into a Context Driven
Topology. The unique user interpretations and techniques are in an
arrangement of only essential components. This particular group of
components is squeezed together so hard that it is fused into a
continuous form, the only extra space is on the outside, this space
and the time which the topology was created is also patterned. The
outside description is composed of mathematical patterns with
cycles that never exactly repeat. Machines only understand the
outside. This shape becomes the Context Driven Topology that is
converted into the symbols as "pathways" into multidimensional
waveforms to be distributed. The content inside is merged, one
component of the idea seamlessly leads to another, people only
understand the inside. The boundary in between is the shared
identity of this idea for the future, it can be simple, complex or
anywhere in between (claim 1 of 10).
[0413] When a Context Driven Topology is formed, advanced networks
of machines automatically "vacuum up" the techniques and algorithms
needed to read the users content from the ORIGINAL data stores, for
example the lab that created the work, a writers desktop, a
museum's high quality digital images from several views bound
together with a scale icon [FIG. 6] curators descriptions about
context, and any other combination of ideas and techniques used to
create, interpret and represent knowledge. Redundancies are purged
between the techniques and algorithms the same as the shared
components and shared descriptions are streamlined and combined.
Simplifying these algorithms and techniques will be a useful,
purposeful way for machines to help us organize and structure the
shared information space using very straightforward methods people
may not be able to recognize or understand because we have a
different relationship with data arrangements. The Context Driven
Topology of arcs, data, sequences and arrangements contains only
specific components scaled to fit this exact user defined
configuration. The process of compressing and compacting ideas and
techniques into a topology does not save the whole dictionary of
the French language, only this set of words in context; there is no
space allocated for every image processing technique available in
Photoshop Version 6 on Windows XP Version 5.1 Build
2600.xpsp2.030422-1633: Service Pack 1, only what is needed to see
this image, with these words in this order, arrangement and context
where the author has them placed [FIG. 2 to 4]. The invention
treats original ideas and the techniques employed to express them
like something real that is only truly accessed from the original
source, like borrowing a painting for an exhibit, when it is
stamped in context, in a topology and made into a perfect,
readable, copy for the future.
[0414] During the interactive data curation process, people can ask
for objects and measurements to be automatically aligned,
proportional, stacked or displayed in a preferred or known order.
The underlying structures themselves are never automatically
"aligned" or "placed" [FIG. 2] without the users knowledge because
this is where the adjustments occur to make each configuration
unique. As retrieved and placed information gets more attention
from the viewer, it is automatically allocated more space and
higher resolution by the machine. The display patterns and
automatic language this results in a new type of collage that is
the easiest for the user to read [FIG. 8], the actions,
transformations, techniques and controls rely on the patterns
themselves.
[0415] The topology boundary is a continuous edge (3.9) that is
displayed as smooth curves at any scale (7.40). Sometimes adjacent
and distant curves are so overlapped and tangled it does not look
like one continuous boundary but it is always is, otherwise it
would not be one topology. Mathematically, this is similar to knot
theory topology (1.1); in people's imagination it is a drawing of a
hard problem that has not been unraveled. Distinguishing between
and describing the properties, relationships and rationale that
define each boundary and therefore each topology is an interactive,
high dimensional time dependent process between people and machines
over such long periods of time that both the people and machines
will come and go over the course of one topologies simplification,
or unraveling process. Generally, machines are able to `understand`
complicated boundaries and larger groups of bounded objects easier
than people except in this system where each topology has been
constructed, and each boundary is defined, for people to figure out
a new idea, decipher and put together a new picture, or show
existing ideas a new way using new techniques. Each data component,
group and topology boundary has been carefully placed [FIGS. 3, 4,
9, 10] by a person for a reason. Boundaries are always composed of
curves without corners (A1) (B3.2) (B1.5) (D1) [FIGS. 6, 7 and 8]
(2.10) (3.3) (3.18) (3.27) (4.5) (4.9) (7.30 to 7.32). Each
topology is an evolving continuous whole in a stream of patterned
spaces. Each configuration can be viewed from every angle until
this set is dissolved to use as individual components and smaller
groups in new topologies (8.3). There are important areas around
these bounded evolving memory forms where there is "nothing". These
empty spaces have as much or more meaning than the areas where a
boundary and form is perceived (A1) (B1.2) (B1.5) (D1) [FIG. 2]
[FIG. 3] (3.17) (3.20) (7.5) (731) (7.33) (8.2) (8.9) (8.20) (8.22
and 8.23) (9.11) (9.13) (claims 1 to 10).
[0416] When using the archives in the shared memory space, people
begin the interactive process by asking machines to see through an
infinite field of unrelated data [FIG. 1] to systematically
recognize previously unpredictable or temporal alignments a new,
more predictable way using the invention's detailed mapping,
filtering and patterning techniques. There is not one centralized
source or starting point to begin looking through the archives in
the stateless space except that each view always originates in the
middle. Placing the view, defining the edges and boundaries is
accomplished step by step using the inventions knowledge patterns,
mapping, organizing and display techniques (claims 1 to 10). The
information captured in a topology is initially seen as if it were
a photograph, the last way it was arranged and recorded (7.42).
However, unlike a photograph, future viewers can turn the image new
ways to see and create new views.
[0417] If the retrieved information is not quite what the future
viewer had in mind, the group of topologies in symbol form within
the descriptions are consolidated into pathways leading to the
original data collections which are certain to be broader, more
complete, and more up to date than the knowledge and techniques
captured within any one topology. It is very likely that similar,
potentially better information will be in the original sources. Or
it may be backwards and there is an area that is very important in
the new arrangement and no existing components have enough clarity
or depth to expand into the "space" that should be filled, in that
case the new person needs to build bridges, fill in gaps, make or
otherwise complete what is missing.
[0418] As introduced in (Section 1) it is not possible,
theoretically or practically, to predict what may be interesting or
we will want to look at in the future. People need to identify,
preserve and be able to accurately search problems and ideas that
may require further contemplation, or better machines, to figure
out later.
[0419] Using Context Driven Topologies is like writing down,
recording and playing music. Mapping between the knowledge and
display patterns eliminates noise and fine tunes the music to the
kind you like or the composer had in mind. Noise in this sense
could also mean more abstract noise in scientific data.
[0420] All boundaries are scale free to machines. Each boundary is
"stretched or squeezed" (7.31) to fit in hierarchies and levels
constructed by people until a meaningful context is assigned and a
conclusion is drawn [FIG. 2] [FIG. 4] [FIG. 6] (3.7) (3.13) (7.31)
(7.32) (8.3). During the time people are manipulating, selecting
and determining the priorities and adjacencies of data components
and groups in the new configuration, machines never "see" these
arrangements as people do--in a hierarchy where portions of the
background are completely blocked by the foreground--machines
always process the whole group of techniques in the current
arrangement as if they were one technique by borrowing from the
background, updating with current techniques on the network, and
folding this set of techniques over to consolidate, mix, simplify
and weed out algorithm by algorithm until machines can establish
their own pattern defining simpler ways to do the calculations and
simplifications that eventually get this group of techniques to
work together. The only way people can check this work is to see
how it compares to other calculations and simplifications that are
known to be correct. The vast majority of topologies use the same
technique throughout and it is not an issue. Machines keep
techniques separated and just `pretend` to run them together at the
same time to temporarily show the images, words, drawings and ideas
people are would like to see together at the same time for reasons
machines can't understand and people are not able to describe yet.
Diverse, potentially incompatible techniques only appear to be
combined when they are compressed and captured together in a
topology. Each data compression and technique consolidation may
need to leave sets of techniques separated until they can be
simplified, streamlined and consolidated over longer periods of
time. These separations could be compared to natural languages and
cultures, people can still communicate and share common interests
even if we do not speak one shared natural language, each culture's
ideas and personality is expressed best in their own language, the
same might be true for machines, how would people know? Advanced
networks of machines use mathematical processes to help us
understand, maintain, organize and simplify dynamic shared data
stores by translating these actions, groups and relationships into
an automatic language (7.1 to 7.50) that is a new application for
Graph Theory; Knot Theory Topology; Algebra, Group Theory,
Combinatorics, Fourier Analysis, and various interrelationships
between these fields that is most clearly captured through
mathematics but understood through words, sounds, and images and
other modes (B3.1)(claims 1 to 10).
[0421] Each topology will be easily identified by either people or
machines because the overall description has been vastly reduced
from the expanded descriptions of each object and group inside.
Each theme has been established for a reason and the algorithms
have been arranged and consolidated in a way that was logical to at
least one machine at one time. Therefore, this compacted united
knowledge object can be recognized using both object itself
(peoples understanding) and the patterned space around it (machines
understanding) to recognize the object either by its form, or the
mathematical descriptions that pattern the space around it.
[0422] The complexity of these patterns and forms will require a
higher level of sensitivity than people usually have; each one is a
challenge to measure, there are too many topologies, objects and
spaces to choose from, the details blend, people are impatient, get
distracted and are not able to perceive enough depth to see all of
these configurations, symbols and forms as if they were a
collection or group like we are used to, but together with
machines, we can collect and analyze these new kinds of activated
objects as if they were fingerprints that could point to the
persons current location, fossils that come alive again, or
sculptures that could be tried in different materials under
different lighting in different sizes and different places.
Noticing or creating relationships between these objects, patterns
and new collections can be initiated by people or machines, but
people are the only ones who set the pace by deciding where to mark
and place new boundaries over time.
[0423] Of course data privacy, security and authenticity will
become even more critical as remote or unverifiable information
continues to grow and connect. As introduced in (8.3), the display
patterns can be used to cause data arrangements to utterly
collapse, disappear or present only as permitted by making certain
components and combinations "one-way". Deciding what the most
reliable techniques are to block and filter portions of knowledge
and objects in the stream will be developed, documented and
investigated further by the inventor as indicated in (B1.5) and
(claims 1 to 10).
[0424] The histories, evolution and changes contained within groups
of Context Driven Topologies over time are not only helpful for
people, they are also helpful for networks of machines (9.11). The
group of techniques captured and simplified as much as possible
into one group in each topology and each set of pathways can be
packed with countless instructions and difficult, carefully
reviewed scenarios engineers have worked through, thought about and
discussed with other engineers to get this set of techniques to
work together in the past. Networked machines using the invention
could access and try some of these more creative, innovative
algorithms and calculations to see how they work to simplify this
group. Because machines generally improve as time goes by, it is
very possible that new machines will be able to show us simpler
ways to organize data and data arrangements IN THE ENCODED VIEW
faster than people will ever be able to see from "inside" each
topology (9.3). Through this continued back and forth dialogue
[FIG. 8] at the changing boundary between what people understand
and machines can show us will eventually lead to sets of data
arrangements and techniques that are difficult to get to work
together and therefore kept separately (9.11) will start to be
compressed into tiny little records tucked inside more simplified
arrangements that DO work. All of these embedded, small slightly
incorrect records and techniques can be evaluated by machines
together over time, there may be details that appear insignificant
to us but in fact, are what the problem was, therefore, the
invention will also help simplify the topology and techniques of
advanced networks of machines (claims 1 to 10).
[0425] The inventions new logic must be developed in collusion with
machines, because it is constantly updating and being fine
tuned.
[0426] Data collections that exist in the same physical location in
the future are loaded into a compiler/broadcaster that continually
simplifies and streamlines this one collection individually, these
simplifications will feedback into the shared memory as specified
by each user, research group or institution that creates or is
responsible for this information.
[0427] Regardless of how compressed and "pushed into the
background" each Context Driven Topology becomes it never affects
the shape and pattern recognition, searches are still initiated by
people sketching out, describing and trying to define and "see"
either this form or the space around it (3.8).
[0428] The invention is an automatic knowledge distribution system
to store, organize, perpetuate, and retrieve dynamic information
without a medium (8.16).
[0429] Interface and conversion between diverse techniques,
languages, systems, and formats is handled through the mathematical
patterns (7.1 to 7.4), the automatic language (7.1 to 7.50) and the
maps in related Context Driven Topologies.
[0430] By most definitions, what most users generally want from
information is for it to be correct. But when the "correctness" can
vary because the information is related to, for example high
dimensional humanities content, the purpose of the invention is to
allow these varying interpretations to gradually "push" or "guide"
the knowledge patterns towards more documented, long term,
evaluated beliefs rather than rash or judgmental short-term
beliefs. Use of the invention as a record keeping system for ideas
will be able to pinpoint locations of unusual occurrences or rapid
changes in direction, this will help us to identify other kinds of
unusual occurrences or changes in direction. These scenarios are
captured mathematically, interpreted as a pattern, transformed and
evaluated through the presentation mode preferred by the user.
[0431] All data curation and digital preservations actions are
accomplished through the human decision and evaluation process
(9.1) (9.5 to 9.7) (9.11). All data processing is done with
machines using the techniques partially disclosed herein, paragraph
(B1.5), investigated and documented further through an upcoming
project (C) and [FIG. 6] and specified in (1.25) (2.13) (338)
(4.18) (6.10) (7.49) (8.26) and (claims 1 to 10).
Specific Embodiments and Applications
[0432] Specific embodiments and applications for the invention
include but are not limited to: large scale museum and library
digitization; online publishing; object, pattern, shape and
sequence generation, identification and recognition; priority
addressing and mapping; network and machine topology; identifying
current locations of genuine objects, events or living beings;
measurement; evaluation, testing, authentication, calibration,
analysis, interpretation, exploration, vision, creation,
conversion, translation, transformation, logic, purification, error
and consistency detection, tuning, classification, registry,
harmonization, composition, consolidation, masking, precise
similarity measures and better redundancy elimination techniques,
visualization, design, imaging and modeling, simulation, games,
drawing, recording, processing, compiling,
compression/decompression, distribution, cryptography, navigation,
multiplex and global communications, transmission, signaling, and
other research, educational, entertainment or business products and
practices (claims 1 to 10).
[0433] The invention can also be used to improve machine
translation of natural languages. Words in natural languages are an
intricate web of associations. The inventions patterns will show
each word, phrase, concept and story surrounded by the layers of
interpretation and meaning each word has had over time. In most
languages, the word usage is strongly related to its association
within a particular phrase or other context. Identifying subtle
context, such as word meaning, is one purpose of the invention.
Also, because the invention is primarily presented through (light
and) audio using measurements like intensity, inflection and
particular emphasis which is so critical to the correct
interpretation of most natural languages. In the upcoming project
(C) [FIG. 6] Chinese Mandarin (and nine other languages) will be
recorded and used in combination with Chinese characters in model
showing relationships between word usage, symbol and inflection
using the real words in context that are generated by this project
as a "set". Natural languages are translated by turning and
manipulating and realigning [FIG. 8] each word, phrase and concept
meaning using the patterns until this group of objects presents in
the natural language preferred by each culture, research group or
user. The more widely the invention is used, the more complex,
dense and correct the web of word associations will become. The
better, more meaningful, story telling machine translations enabled
by the invention are more useful for international research than
the linguistic constraints imposed by current metadata methods.
Over time, the invention will gradually be able to quantify
unquantifiable factors such as expression and other differences
that exist between natural languages and computational
machines.
[0434] The invention is perfect for games and amusements.
[0435] The invention will present art as intended by the artist,
science as intended by the scientist, and other creative fields
where people struggle to define work that is often not measured by
words. The invention permanently places the originators
instructions as the first, closest definition around each object.
The knowledge patterns form themselves through the concurrent and
conflicting insights, opinions and knowledge continually developing
about and around each object over time. Therefore, the invention
provides machines with something to measure that is closer to the
way people think.
[0436] The invention is a better way to measure people's ideas and
activities across cultures and knowledge domains over time. The
invention is a new mechanism to track, measure and compare ideas
and activities expressed through natural languages, images, sounds,
events and other evolving patterns that allow for each culture, or
knowledge domain, to define itself (2.5).
[0437] The invention is able to combine data and data arrangements
that are created and maintained separately. For example,
information about a museum's case interiors is typically
constructed from the following:
[0438] Drawings of the museum architecture, typically in AutoCAD as
an external reference shared with the architect and all of their
subcontractors such as engineers.
[0439] An artifact schedule, sometimes maintained by the inventor
herself and sometimes developed back and forth with the owner of
the objects, usually never finalized until the museum opens.
[0440] If the objects are photographed for the artifact schedule,
these are typically digital images that are not distributed
digitally, rather these images are pasted in matching boxes in the
schedule program, for example page maker, that is not able to
indicate the scale of these objects with each other as the drawings
or the objects themselves do, the images of these objects are not
isolated from their backgrounds to distinguish their unique profile
and proportions. This group of images that matches dimensions that
are not required to match while ignoring more important
relationships is then photocopied as if the object and background
were continuous and all of the objects were the same size. The
first black and white generation of colored dimensional objects,
and definitely subsequent generations, means these (once digital,
scaled, colorful) images are no longer useful.
[0441] There is a scope of work, materials, and
object/architectural area numbering matrix in Excel, often
maintained by the inventor herself sometimes back and forth with
the owner, but more typically a deliverable to the owner.
[0442] There are graphics using their own images, text developed
with a writer in another program, sometimes back and forth,
sometimes as a deliverable. Fitting the objects, labels and mounts
into a case is something that can only be done as a mock-up, or
detailed drawings using several views.
[0443] There are new CAD drawings of the objects, these drawings
are very time consuming because each object is unique, the exact
proportions and dimensions are very specific. It is not possible to
find these drawings in a library however, it would be possible to
have each object only drawn once and shared regardless of which
case, museum, or other location where the object travels to in the
future. The invention permanently binds this digital drawing to the
object as part of its description.
[0444] There is usually a plan drawing, at least a front elevation,
sometimes sections and other CAD drawings of the case with the
objects, graphics, mounts, lighting, materials, construction
details, access, security, environmental controls and other items
generally received from others that need to fit within the case
without interfering with the visual presentation and didactic
understanding of the objects and labels.
[0445] Sometimes, all of the cases throughout each museum have the
same lighting fixtures and equipment, glass, security and other
requirements. Other times, each case or group of cases is unique.
Lighting fixtures and other equipment are generally available in
CAD from the manufacturers, but these files rarely match the design
drawings. Manufacturers top priority is their product, therefore
their drawings are typically too detailed and in "blocks" which, if
"exploded" to remove information or change the line weights, adds
many, many layers to a drawing that is trying to manage the layers
as a useful technique to see relationships within the case. The
`real` lighting itself can only be adjusted at the final
installation, outside of very obvious exceptions such as a very
large object that should go towards the bottom of a case instead of
the top, it is unlikely to be able to prevent shadows and other
lighting conflicts until the case and the objects are
installed.
[0446] If the number of objects simply cannot be narrowed down any
further, they will not fit in the case and the case dimensions need
to change, this will affect the dimensions of the walls, the
architects and electrical engineers drawings, the contractor with a
tools and materials in his hands ready to build the case at the old
size, and other factors that need to go through the cycle again
starting at 10.6a.
[0447] Therefore, especially because all that results from each of
these drawings, images, schedules etc above is ONE case in ONE
museum, the inventor would like to consolidate these programs,
drawings, images, knowledge, skills and expertise into one place,
even if it is only temporary [FIG. 8] similar to an AutoCAD
external reference. Because most museum owners do not know how to
use CAD and other programs, the deliverables are usually blueprints
and photocopies that anyone can keep or read but do not take
advantage of the imaging and detail that is available in
(expensive, constantly updating) design programs. Because the owner
usually does not have office space for all these paper and ink
packages and people new to the museum may not understand how these
packages are organized, the best way to see the most recent
condition of each object and space is by going to see, measure and
evaluate it in person rather looking through all of the
complicated, detailed design packages only to retrieve a detail
that changed after the design was delivered. The example above is a
typical scenario that prompted the invention being realized through
machines and networks. The invention is for the long term sharing
of knowledge, ideas, objects, drawings, images, processes and
spaces.
[0448] Future `temporary` combinations of detailed drawings,
images, measurements, lists etc have applications to many fields.
For example, there is astronomy and physics where the arguments are
passionate, mathematics is the language, time itself and dynamic
processes are measured, and most importantly there are specifically
new and different views that are able to be shared and seen
together which therefore requires a new form of drawing, imaging,
description, data curation and digital preservation. Like the
cultural objects above, if new objects/ideas in astronomy and
physics could be superimposed onto known "patterns" or "signatures"
these objects/ideas might or might not correspond to, this could
lead to the objects/ideas gravitating and snapping into their time
or location as introduced in (A1) [FIG. 2] (7.32) (10.7). Being
able to see and discuss where something "belongs" would be useful
in astronomy, physics and other fields where imagery, drawings and
measurements are carefully constructed, analyzed and discussed over
long periods of time and from different points of view (B3.3)
(1.22) (2.6) (3.14) (7.30) (10.7).
[0449] Because of the invention, we will understand more about how
`things` react to each other, become mixed and separate, therefore,
there are also applications to theoretical and applied chemistry.
Similarly, because of the inherent time and sequencing
characteristics and overall patterns that are made measurable in
the invention's records, there are also uses for theoretical and
applied biology, geology and other natural sciences. The best
scientific and humanities use for the invention is for building
theories over time. Any of these pursuits that involve mathematics,
can take advantage of the `placeholder` concept (2.6), rely on
visualization and other detailed imagery to draw conclusions, are
in line with the purposes of the invention.
[0450] Because of the examples illustrated above, the invention
also has uses for Education and Demonstration.
[0451] Because the inventions mathematical processes are actually a
form of counting or statistics that are able to reflect
preferences, the invention will also be useful for business
practices and value determination.
[0452] The invention is a better method for both search and
organization because it combines mathematical and temporal
relationships.
[0453] Because of the privacy features specified in (8.3); and the
type of detailed and shared records, images, trends, patterns,
characteristics and behaviors the invention is able to efficiently
update and organize; and the recognition techniques for data
features that could easily be applied to physical features (for
example faces and fingerprints); tracking of current locations; and
reflections of cultural characteristics (10.5) or other theoretical
and observable real patterns, there are also timely applications
for security and law enforcement.
[0454] Use of the invention with research or other efforts that are
supported by an agency, such as the US federal government, could
easily add a requirement to save the "raw results" "drawings in
progress" "knowledgeable observations" and other high quality data
and data arrangements created through these funded efforts to be
delivered in a way that is compatible with the shared memory system
in (Section 8).
[0455] It is the inventor's belief that our unique age holds many
opportunities to integrate processes such as those described above
to inform and learn from each other in new ways because we all
understand art, science, mathematics and music the same way, and
now have the added benefit of being connected through shared
networks. Sharing carefully evaluated knowledge and preserving it
for the future so we may increase our understanding of each other
and the natural world around us is the most important use of the
invention.
* * * * *
References