1.
Lecture on Systematic Creativity,
Aalto University, School of Science

2. Contents
0. Intro, Definitions, Why it is cool to invent. Current status of tools for systematic creativity in design and science. When finding new idea means actually finding
Modelling for creativity: understanding functions and contradictions
Beauty and Power of Itselfness
Case studies
Conclusions

3.
Graduated from Physics and Mechanics Dept. of SPbSPU
1997, PhD, Doctor of Science degrees in System Theory at SPbSPU
invited Professor at Kum-Oh National University of Technology, South Korea
TRIZ consultant at Samsung Monitor Company (Seoul, South Korea)
2001 – Head of Innovatics Theory Dept. of SPbSPU
2005 – Engineer for Design Engineering Group at
LG Electronics Production Engineering Research Institute
(Osan, South Korea)
TRIZ consultant at Algorithm (Gen3 Partners, Inc.) company (supplier for GE, Siemens, Wrigley, BAT, MC,…), part-time
2011 sep-nov Research Fellowship at Politecnico di Milano (Italy)
2012 – Principal Engineer for Samsung Electronics (South Korea)
and Principal Researcher for SPbSPU
Professor at Lappeenranta University of Tech, LUT
Head of Unit “Operation Management and System Engineering”, LUT

4. Definitions What is innovation?
“Innovation is a new idea commercialized.” – Siemens
“Innovation is something …that our customer buys as innovation” – L’Oreal
«Innovation is the embodiment, combination or synthesis of knowledge in original, relevant, valued new products, processes or services.» – Luecke and Katz (2003)
«Innovation is something new and stable» – L.Chechurin (2011)

5. What means “to improve a product, technology of service”?
Optimizing within a given structure over given set of parameters (well developed of heuristic methods of optimization)
Development of new structure, new concept, idea (a few problems where a structure of new object is the result of optimizing)
”Wealth, power and influence are not gained by perfecting the known, but by imperfectly seizing the unknown.”
Wired, Kevin Kelly

6. Example. “Moon Car Headlight bulb legend”
USA’s design
USSR’s design

7. 3 steps of Industry Evolution
Are we able to produce new things?
Flexible, adaptive manufacturing + fast design + new ideas
INNOVATIONS
Are we able to produce with high quality?
QUALITY
Are we able to produce a lot?
PRODUCTIVITY
Microconclusion: “Innovation is about making something new, lasting long. It is cool to innovate”

8. Definitions. Innovations: contents and size.
1. Engineering part (design and manufacturing of the new)
2. Economical part (budgeting, marketing, financing) – MS Project, Project Expert, Market Expert…
3. Managerial part (project management, decision making support (DGSS), business-planning, legal issues of innovation activities…)
New product engineering
Invention
“Innovation”

9. Why focus on ideation?
1. Innovation is the (only) way to survive in market economy.
[1] B.Klein “Dynamic Economy” Harvard Univ. Press, 1977
[2] Export/Import balances in royalties of developed countries
2. Innovations are risky. Proper idea means a lot for the success.
[3] G. Stevens and J. Burley, “3,000 Raw Ideas = 1 Commercial Success!” Research•Technology Management, 40(3): 16-27, May-June, 1997.
3. Everything is CAD-ed, automated and/or virtualized. But conceptual design (ideation) is not still.
Ideatoin
Design
Manufacturing
“Before CAD/CAM”
Design
Manufacturing
Ideation
“After CAD/CAM” (private evaluation based on experience)
See also: [4] S.Pugh “Total Design” Addison-Wesley Publishing Company, UK, 1991

10. Product/Process Design roadmap (linearized)
Product Design
Manufacturing Process Design (Physical)
Manufacturing Process Design (Operational)
Product/
Process Data
Technical Data
Processing
Enterprise
Resource Planning
Operation
Drawing (Product)
Motion simulation (kinematics)
Motion simulation (dynamics)
Simulation of fields ?
Drawing (Equipment)
Product Data
Example mobile phone– primary mechanics design, virtual testing of kinematics and dynamics of mechanical parts, stress analysis, dynamic stress analysis, EMI analysis, thermal stress analysis, …
Example injection molding for plastic parts - fluid dynamics casting, pressing for metal parts - stress analysis chemical processing - chemical reactor analysis heat/fluid analysis
Example material resource planning capital resource planning/routing labor resource planning documentation/information management

11. Product/Process Design roadmap
Product Design
Manufacturing Process Design (Physical)
Manufacturing Process Design (Operational)
Product/
Process Data
Technical Data
Processing
Enterprise
Resource Planning
Operation
Drawing (Product)
Motion simulation (kinematics)
Motion simulation (dynamics)
Simulation of fields
Drawing (Equipment)
Product Data
All manufacturing leaders are already here
Many manufacturing leaders go “Digital Factory”
All manufacturing leaders already run ERP and PDM systems. The biggest challenge is to integrate CAD/CAM/CAE into enterprise operation system and, more aggressively, into market perspective evaluation.

12. Product/Process Design roadmap
Product Design
Manufacturing Process Design (Physical)
Manufacturing Process Design (Operational)
Product/
Process Data
Technical Data
Processing
Enterprise
Resource Planning
Operation
Drawing (Product)
Motion simulation (kinematics)
Motion simulation (dynamics)
Simulation of fields
Drawing (Equipment)
Product Data
Some of computer-aided technologies Computer-aided architectural design (CAAD) Computer-aided design and drafting (CADD) Computer-aided industrial design (CAD)
Computer-aided engineering (CAE);
Computer-aided manufacturing capability (CAMC);
Computer-aided manufacturing (CAM);
Computer-aided material information (CAMI);
Computer-aided package selection (CAPS);
Computer-aided process planning (CAPP)
Computer-aided software engineering (CASE);
Component information system (CIS);
Coordinate measurement (CMM);
Electronic design automation (EDA);
Enterprise resource planning (ERP);
Manufacturing Process Management (MPM)
Manufacturing process planning (MPP);
Manufacturing resource planning (MRP);
Product data management (PDM)
Product lifecycle management (PLM)
Reverse engineering (RE) ? ?
Microconclusion: “Innovation in industry is more systematic. There are approaches and instruments how to innovate. It is cool to know them. One of the most vulnerable stages is still ideation phase”

13. Industrial Design: Classical view
Pugh, S Total Design: Integrated Methods for Successful Product Engineering

14. How to measure “innventiveness”?
1999 import of S.Korea in royalties 3 bln. USD
1999 export of S.Korea in royalties bln. USD
Source: Korea Industrial Technology Association (KITA), 1999

15. Export/Import of the USA in Royalties
2008 import: bln. USD
2008 export: bln. USD
S.Korea, 1999
Source: Daniel Workman “America's Secret Trade Surpluses”

16. “Standard” (“Non-inventive”) solution
The best choice over the given set (paradigm). Optimization, compromise

17. “Creative” (“Inventive”) solution
A solution out of the (given) set. New paradigm, heuristics

18. Academic way of problem solving
Give me the set of variants. Give me the criterion.
I’ll give you the best choice (solution).
(And we will publish a nice paper…)

19. “Problem solving” in reality
The set is not described. The criteria are not clear.
Give me a new idea that works. (We will at least patent it…)

20. Where does (new) knowledge live? (Where to search it?...)
New Idea as a “Know how”
New Idea as a patent
New Idea as a Public good (scientific paper)

21. Technical knowledge search
Where to look for?
Patents
Scientific papers databases
(How to automate the search? – to be discussed later)

22. Patents
(+)
Huge databases of well-structured texts on new engineering solutions.
If an idea can become an innovation (commerical!) it is not published but patented. “It is serious stuff”
(-)
No peer reviewing, no proofs. They say about 10% of modern patents are pure desinformation (!!!).
Patent is a trick, a particular solution.
If you found a patented solution on your problem… you can not use it.
Patent is a food for innovation speculators, patent trolls etc.

23. Patents Where to search: For beginners: For professionals:
1. Google Patents
2. (WO) – World Intellectual property organization
3. (EP) – EU Patent
4. Uspto.org – USA Patents
5. National patent agencies (Korean, Japanese, etc). For example: FIPS (fips.ru) – Russian patent database (what do you think, is it free?), well structured by industries
For professionals:
Multidatabase patent search systems:
For example, Delphion.com (not very expensive, standalone)
One of buid-in services of IP/innovation management software GoldFire (damn expensive)
Part of corporate infranet (Samsung, LG, General Electric…)

24. Scientific Papers
(+)
(Ideally) Open, well-presented, reproducible , proven information on new findings (peer-reviewed). Almost no speculations or intended disinformation.
Typical scientific paper is a general solution to a problem.
(-)
Mostly, unstructured texts.
Often scientific publications are correct but useless (a joke about the balloon traveler’s question)
Published openly, so the idea can be used by anyone, including your competitors

25. Scientific papers Where to search: For beginners: For professionals:
Google Scholar – Google search service in documents that “look scientific”
Some national sci paper indexes, for example “РИНЦ” (elibrary.ru) – Russian sci paper index (free, but very low impact factor and no access to the texts mostly)
Some open access collections (free but not big… or big but of doubtful quality)
For professionals:
SCOPUS database by Elsevier (expensive, but journals with high impact factor are there. Approximately half of papers are accessible)
Web of Knowledge database of Thomson Reuters (similar)

26. “This is the problem. FIND a solution!” …Find?
How to stabilize gondola swings?
Document(s) found: 26

27. What and How to automate in info processing?
Extremely hot issue: how to automate extracting the knowledge/wisdom out of data and information
(Semantic Wave)
WISDOM
KNOWLEDGE
We are good at collecting and sharing the information. But the amount of information is sky rocketing (1 mln. new patents annually!), influenced by noise (false/irrelevant information). Information itself does not help to solve a problem of creating smth new.
INFORMATION
DATA

28. Questions of data gathering:
Bring me all the info on the subject of the problem
Remove all the noise
Simple questions of technology intelligence:
Who (else) is working on the problem?
What the competitors are doing?
Who is the most active developer in the field?
Who is cooperating with whom? What are project teams?
Hard questions of technology intelligence: - Extract the concepts out of the texts
- Cluster the information pool according to (concepts…)
Find similar technology
- Generate new idea, hypothesis, axiom…
These and other questions can be answered by patent landscaping methodology
Some of these questions can be answered by semantic text analysis algorithms

29. Patent Landscaping: Example
Enhancing Patent Landscape Analysis with Visualization Output. May 6, 2009
Yun Yun Yang* et al. Bristol-Myers Squibb

30. Patent Landscaping: Example
Enhancing Patent Landscape Analysis with Visualization Output. May 6, 2009
Yun Yun Yang* et al. Bristol-Myers Squibb

31. Patent Landscaping: Example
Enhancing Patent Landscape Analysis with Visualization Output. May 6, 2009
Yun Yun Yang* et al. Bristol-Myers Squibb

32. Let’s to it to ”TRIZ”…
Using of interesting words/terms in Title-Keyword-Abstract fields (SCOPUS database)
Total amount of papers
(=”amount of research”)
”…and TRIZ” (%)
(=”where TRIZ is involved”)
“Computer Aided Innovation” 93
56 (60)
“C-K theory” (design reasoning) 58
7 (15)
“Synectics” 40
4 (10)
“Axiomatic Design”
740
51 (6,9)
"Kano model"
269
18 (6,7)
”QFD”
2200
95 (4,3)
“DFSS”
400
15 (3,7)
“DFMA”
260
6 (2,3)
"technology forecasting"
900
20 (2,2)
“Theory of constrains”
16 (1,7)
“Brainstorming”
2350
35 (1,5)
“six sigma”
4000
34 (0,9)
“case based reasoning”
7200
24 (0,33)
“robust design”
3500
17 (0,4)
“creativity”
31600
130 (0,4)
Quick conclusions: - closest tools are methodologies of design (AD, DFx,) and methods of industrial/product management (QFD, Kano, ToC);
- technology forecasting is among TRIZ applications

33. Commercially available tools for automated patent/paper text processing [ref]
Group 1 can work on the unstructured text:
ClearForest
Goldfire InnovatorTM
Inxight
OmniViz
TEMIS
Group 2 can work on structured text only:
QuosaTM
RefVizTM
STNAnaVistTM
VantagePoint
Thomson Data Analyzer
Group 3 can work on patents only or partially structured text (‘‘hybrid”) data:
Aureka
M-CAM DoorsTM
Wisdomain
PatAnalystTM
[ref] «Text mining and visualization tools – Impressions of emerging capabilities». YunYun Yang et al. World Patent Information 30 (2008) 280–293

34. Finally, what the idea search could be…?
Problem
FIND:
How to stabilize gondola swings?
Document(s) found: 34

35. My dream… Is it coming?
Semantic idea (concept) search (with Big Data, Literature Based Discovery, …)
FIND:
Stabilize gondola swings
Results (5):
By gyroscopic forces (4 patents, 5 papers)
By Feedback control
Passive feedback (5 patents, 3 papers)
Antiresonanse phenomena (chapter from physics reference book)
Dynamic Damping (3 patents2 статьи)
Active feedback (20 patents, 40 papers)
By parametric stabilization (1 paper, 2 patents)
Is it still IDEA SEARCH or already IDEA GENERATION?..

36. Creativity instruments: what is on the market?
“Intuitive”
“Systematic”
Brainstorming (Osborn, 1930x)
Synectics (Gordon, 1944)
Laterial thinking (De Bono, 1967) …
Morphological analysis (Zwicky, 1942)
Case Based Reasoning
General methods of Quality improvement: TQM, 6 sigma, QFD, others
Axiomatic Design
Theory of inventive problem solving

37. TRIZ: Теория Решения Изобретательских Задач <Teorija Resheniya Izobretatel’skih Zadach> = Theory for Inventive Problem Solving (TIPS)

38. History of TRIZ in brief
First publication in 1956*. A method of inventive thinking for engineers,
According to Altshuller, based on the analysis of patents,
Was widely accepted at big manufacturing plants in the USSR (Leningrad, Minsk, Kishinev, …).
Genrikh Altschuller, the founder of TRIZ ( )
*) Altshuller G., Shapiro R., On the psychology of inventiveness. Voprosy psihologii, 1956, No.6 (in Russian). Альтшуллер Г. С., Шапиро Р. Б. О психологии изобретательского творчества // Вопросы психологии. — — № 6. Available in Russian at

39. TRIZ Today
Clients: Ford Motor Company, Chrysler, Renault, Procter & Gamble, Oracle, Samsung Group, LG Group, General Motors, GE, Intel, Wrigley, AlCoA, …
Service providers: (education/consulting) Am.Supplier Institute (?), Insyntec (Holland), Algorytm (Russia) + Gen3 Partners (USA), Innotiimi (Finland), CREAX, …
Software: Knowledgist®, TechOptimizer® -> Goldfire Co-Brain® (Invention Machine Co., USA), TRIZsoft® (Ideation International Co., USA), CREAX software (CREAX, Belgium), Pro/Innovator (IWINT, China), etc
“Competitors”: Trial and Error, Brainstorming, Axiomatic Design, QFD, Morphology Analysis, TQM, 6 sigma, Lean manufacturing principles, fishbone analysis, etc
TRIZ world: International Association of TRIZ, TRIZ expert certification, TRIZ e-proceedings, web-sites and books.

40. Industry TRIZ users
TRIZ has shared in the success of hundreds of companies and individuals worldwide. Our position as a technological leader in the inventive problem-solving arena is the result of their visions, their ability to reach beyond the obvious, and their unwavering commitment to excellence. Collectively, they serve as the catalyst that challenges us to continue to develop and advance our products and services. A few of them are: 40
A.Kynin Examples MLCC 40

41. Some TRIZ Success Cases41

42. Boeing 767 refueling system by Boeing
TRIZ helped to develop a new refueling system for
Boeing 767 aircraft, which resulted in extra sales of 1.5 billion US dollars.
“A TRIZ workshop solution was developed for the 767 Tanker (air-to-air refueling) aircraft project. As a result of that TRIZ solution, the program was successfully launched with a customer who preferred the TRIZ solution over the competitions solution for the same system, thereby ordering aircraft from Boeing. “
Don Masingale
Advanced Research Engineering Program Manager,
Boeing, USA 42

43. Crest Whitestrips by Procter & Gamble (Gen 3 Partners Project)
“TRIZ was used to develop Crest Whitestrips™ for Procter & Gamble (P&G). From a TRIZ perspective, the key problem was that tooth whitener should be on the teeth to bleach, and it should not be on the teeth to avoid contact with saliva. A TRIZ concept, a thin flexible film saturated with whitener that selectively adhered to teeth, proved to be the answer. Whitestrips was P&G's most successful product launch ever, generating $130 million dollars of sales in the first year of operation while capturing over 45% of the whitening market.”
Larry R. Smith
President, Altshuller Institute for TRIZ Studies, Inc, USA 43

44. What is inside TRIZ?
A way of (functional) modeling of a technical system or a product.
Formalism of modifying the models to improve them.
A set of best engineering practices to apply the required modification to real system (Contradiction Elimination Techniques, Ideal Final Result Concept)
Trends of Engineering System Evolution
Algorithms for better search and analysis of patents and technical information.

45. Example of Function Modeling
Paint Filling System
Barrel
Part
Lever
Paint
Paint
Float
Pump
Switch
Tank
Motor
Slide from Gen 3: ID

46. Function Model (Graphical) of Paint Filling System
Holds
Moves
Controls
Controls
Holds
Holds
Moves
Tank
Holds
Rotates
Contains
Paint
Moves
Solidifies
Contains
Barrel
Air
Slide from Gen 3: ID

47. 1. Modelling for creativity 1.2 Contradictions

48. Outlines How to see a problem as a contradiction.
Invention as Contradiction elimination.
Typical contradictions and contradiction elimination practices. Altshuller’s Matrix.
Physical Contradictions and elimination patterns. Separation principle.

49. Contradictions Contradictions in philosophy Chinese In and Yang
Hegel, Dialectics 49

50. Definition
Contradiction is a situation when two, mutually excluding requirements are exposed to one part or different parts of one technical system 50

51. Contradictions 1. Typical conflicts in system design:
Construction mass and construction strength
Manipulator speed and manipulator accuracy
Sensitivity to the control signal and robustness to the noise
High thermal conductivity for heating and low thermal conductivity for the insulation
Short aging operation time and low thermal stresses
Etc
2. Contradictions may be seen as a situation of Pareto optimum in product parameter improvement, when we reach the situation when no parameter can be improved without worsening other(s).
3. Contradiction is the conflict in parameter requirements. Standard approach in the situation of contradiction is finding the compromise. It is done by optimization. If optimization is successful and brings acceptable results, it is called optimized solution, compromise solution.

52. Contradiction: Altshuller’ s findings
Inventions are characterized by contradiction elimination
only!
Regular problem becomes inventive problem, when we are required to eliminate a contradiction.

53. Contradiction: Formulae “If-then-but”
If <justified action> then <required result> but <unwanted consequence>
If I improve <a parameter> by doing <action, design> then <another parameter> worsens

54. Advice to formulate the contradiction
1. It is possible to formulate many, not one contradiction in any system. Focus on the contradiction(s) that appear when you try to improve your critical parameter, parameter that is believed to be crucial for system performance improvement (given by the management, marketing or physics).
2. Dramatize the conflict! (Use Size-Time-Cost operator), think of impossible. ”Immediately” instead of ”5 seconds faster”, ”5 km” instead of ”need to increase the length from 3 meters to 4 meters”, “zero stress” instead of “small stress” etc. 54

55. Advice to Eliminate the contradiction
1. Use Altshuller matrix (see below) 2. Use separation principle 55

56. According to his own report, G.Altshuller
analyzed some patents in many industries, building the database of typical contradictions and typical practices of their resolving. Only patents where contradiction had been resolved were selected for further study. (no incremental improvement, no optimization, no pure heuristic, no design patents)
listed all possible conflicting parameters and generalized them into 39 groups
generalized best engineering practice of contradiction elimination into 40 inventive principles
according to the patents, linked each possible of 39x39-39 contradiction with 3-4 principles that resolve them. 56

57. 39 generalized properties
1 Weight of movable object
2. Weight of fixed object
3. Length of movable object
4. Length of fixed object
5. Area of movable object
6. Area of fixed object
7. Volume of movable object
8. Volume of fixed object
9. Speed
10. Force
11. Stress, pressure
12. Shape
13. Object’s composition stability
14. Strength
15. Duration of moving object’s operation
16. Duration of fixed object’s operation
17. Temperature
18. Illumination
19. Energy expense of movable object
20. Energy expense of fixed object
21. Power
22. Waste of energy
23. Loss of substance
24. Loss of information
25. Waste of time
26. Quantity of substance
27. Reliability
28. Measurement accuracy
29. Manufacturing precision
30. Harmful action at object
31. Harmful effect caused by the object
32. Ease of manufacture
33. Ease of operation
34. Ease of repair
35. Adaptation
36. Device complexity
37. Measurement or test complexity
38. Degree of automation
39. Productivity 57

58. 40 Inventive Principles 1 Segmentation 2. Extraction 3. Local quality
4. Asymmetry
5. Combining
6. Universality
7. Nesting
8. Counterweight
9. Prior counter-action
10. Prior action
11. Cushion in advance
12. Equipotentiality
13. Inversion
14. Spheroidality
15. Dynamicity
16. Partial or overdone action
17. Moving to a new dimension
18. Mechanical vibration
19. Periodic action
20. Continuity of a useful action
21. Rushing through
22. Convert harm into benefit
23. Feedback
24. Mediator
25. Self-service
26. Copying
27. Inexpensive, short-lived object for expensive, durable one
28. Replacement of a mechanical system
29. Pneumatic or hydraulic construction
30. Flexible membranes or thin film
31. Use of porous material
32. Changing the color
33. Homogeneity
34. Rejecting and regenerating parts
35. Transformation of the physical and chemical states of an object
36. Phase transformation
37. Thermal expansion
38. Use strong oxidizers
39. Inert environment
40. Composite materials 58

59. The Matrix 1. Weight … 12. Length 4,15,40 30. Speed 2, 11, 18
Conflicting Properties
1. Weight …
12. Length
4,15,40
No. Standard Solution
30. Speed
2, 11, 18 59

60. Algorithm for using the Matrix
Find a conflicting pair, a contradiction
Define a property that has to be improved and a property that deteriorates, worsens
Generalize the property or find the best fit from the list of general (of 39)
Find corresponding principles (of 40)
Try to interpret the general principles in terms of real engineering solution. 60

61. Physical Contradiction
If there is a parameter ”A”, physical contradiction is described by a situation when we want opposite properties: A and -A

62. Physical Contradiction: Formulae “If-then-but”
<A parameter> has to be <justified requirement> but it has to be <opposite justified requirement>

63. Inventive principles to resolve Physical contradictions (11 in total)
Separate in space
Separate in time
Separate in system level (scale)
Separate in relation… 63

64. Separation in Space
”WHERE the parameter has to have the property A and WHERE the property -A ?”
A Saw
At the beginning of cutting the saw teeth are to be small, because it is easier to position the saw and start cutting carefully
BUT
in the middle of cutting the teeth are to be big for the fast cut.
The teeth can be small IN ONE PART of the saw and big IN ANOTHER 64

65. Separation in Time
”WHEN the parameter has to have the property A and WHEN the property -A ?”
A needle eye is to be big to get the thread through easily BUT the needle eye has to be small to avoid tissue damage.
The needle eye can be large BEFORE it is used in stitching and can be small DURING the stitching 65

66. A boxer was known to be not very good
A boxer was known to be not very good. But suddenly he started performing outstanding punches, knocking out opponents one fight after another. They all reported that his knocks were “as hard as stone”. It lead to the double check his gloves before the fight, if there were any stone hidden in gloves. But no stone was found. But there were definitely stones in his gloves. Stones must exist (to make your punch hard) and Stones can not exist (to be allowed for the fight by referee)…

67. Separation in System level (scale)
”AT WHICH LEVEL the parameter has to have the property A and AT WHICH LEVEL the property -A ?”
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes 67

68. Separation in Relation
”FOR WHOM the parameter has to have the property A and FOR WHOM the property -A ?”
For the high performance (good tracking speed) the controller gain is to be high BUT for low sensitivity to the measurement noise the controller gain is to be low.
Since typical reference signal is of low frequency and noise is of high frequency, we can separate the gain requirement in the relation to frequency.
Gain 68

69. Contradictions
There are MANY contradictions everywhere, not one. But some contradictions are important, others aren’t
Contradiction is the modeling technique. It is the language we describe the situation in order to look for inventive solution.

70. Advice to formulate the contradiction
1. It is possible to formulate many, not one contradiction in any system. Focus on the contradiction(s) that appear when you try to improve your critical parameter, parameter that is believed to be crucial for system performance improvement (given by the management, marketing or physics).
2. Dramatize the conflict! (Use Size-Time-Cost operator), think of impossible. ”Immediately” instead of ”5 seconds faster”, ”5 km” instead of ”need to increase the length from 3 meters to 4 meters”, “zero stress” instead of “small stress” etc. 70

71. 2. The beauty of Itselfness
Ideality Principle for Systematic Creativity

72. Definitions “Creative Solution” vs “Standard Solution” and “Ideality”

73. Definitions. What is “Ideal System”?
“ideal teacher”
“ideal car”
“ideal watch”
Web Images News Video

74. “The less order in a State, the more regulations appear…”
“Wise (ideal) ruler acts by non-acting…”
Lao Tze (노자,老子), Chinese philosopher, (500 BC)
“Ideal ruler is the absence of ruler, but there is a perfect order in the state (university, company, …).
The institution operates itself.” (my translation  )

75. “Ideal (engineering) system is the absence of system
“Ideal (engineering) system is the absence of system. But its function is performed”
Genrikh Altshuller, the founder of the “Theory for Inventive Problem Solving” (TRIZ) ( )

76. IFR Mantras
“Ideally, the product appears itself” or “function is performed itself”
“Ideally, the product appears itself only when it is needed and only where it is needed”

77. Example. Bug temperature?

78. Example. Humidity sensor sealing problem
outside
inside
sensor
sensor case +
lint

79. Example. Spacecraft rocket launch system
Apollo 11 launch (1969)
Watch from 1:40 of the footage
Soyuz TMA-15 launch (2009)
Watch from 0:15 of the footage
Close up:

80. Russian Solution
Counter-weight
Counter-weight

81. Patent RU2220887 Launching system

82. Example. “Moon Car Headlight bulb”

83. Example. How to extract ice cubes from the tray?
Twisted (flexible) trays
Automated trays with… heater

84. How to extract ice cubes from the tray?

85. Ideal Final Result: hints
IFR is an ideal system that operates itself .
Do not think in advance that IFR is unreachable!
Do not think in advance about a way to reach IFR.
IFR indicates you rather a direction to go than a place of final solution.

86. EXAMPLE: Acid test tank wear problem.
Material to be tested
Specimen sample cut
Useful action: acid etches the specimen (experiment)
please, suggest standard solutions for this problem.
Material science engineer: -> change coating, change liquid etc., optimize experiment schedule.
Mechanical engineer -> shape?
Yes, these are possible standard concepts for design.
However, it is always a kind of optimization. Indeed, if we change the acid to another one – than our coating could be not protective any more.
More problems: Sample in gas. Large scale photo development.
Acid
etching
etching
Harmful action: acid etches the tank (side effect)
etching
Test tank

87. Acid EXPECTED RESULTS:
etching
Test tank
Ideas of Standard Solutions:
Mechanical Engineer: Improve optimize the shape/thickness of the tank
Material Science Engineer: Find a better material for the bath, or better coating layer
6 sigma Engineer: Optimize (minimize) the time of the test. Optimal test planning
Chemists: Find a better acid for the test that does not damage the tank
EXPECTED RESULTS:
Optimal bath thickness, shape, material. The best material for the bath coating. A compromise between cost and efficiency, between time of experiment and quality, etc. Quantitive: the best parameter found, e.g. the optimal thickness is 10 mm etc General: the designed coating can be used for other needs, not only for the specimen test
please, suggest standard solutions for this problem.
Material science engineer: -> change coating, change liquid etc., optimize experiment schedule.
Mechanical engineer -> shape?
Yes, these are possible standard concepts for design.
However, it is always a kind of optimization. Indeed, if we change the acid to another one – than our coating could be not protective any more.
More problems: Sample in gas. Large scale photo development.

88. Make the bath of the material of the specimen, no tank is needed
Inventive Solutions:
Make the bath of the material of the specimen, no tank is needed
2. Apply the electrical current. The metal ions will flow to the tank wall and make it even thicker
Acid
Me+
Me+ + +
Me+ _ +
Acid
freeze
3. Freeze the walls of the tank. The acid will form the ice and protect itself the walls from etching
EXPECTED RESULTS:
Break through “tank” concept. No tank is needed. No compromise between thickness and cost or time and quality, etc. Qualitive: the best structure is found, conceptually new design Specific: these ideas will not possibly work for another material or another acid etc
please, suggest standard solutions for this problem.
Material science engineer: -> change coating, change liquid etc., optimize experiment schedule.
Mechanical engineer -> shape?
Yes, these are possible standard concepts for design.
However, it is always a kind of optimization. Indeed, if we change the acid to another one – than our coating could be not protective any more.
More problems: Sample in gas. Large scale photo development.

89. Example. Etching of chip contacts and needles
…We need robot, right?

90. Example: Leonardo’s invention?
Ancient example: How to grill a piece of food properly while the fire is not uniform (sometimes weak, sometimes strong)?

91. Example: Leonardo’s invention
Standard Ancient Solution: Hire Operator

92. Example: Leonardo’s invention
Standard Automation Era solution: Sensors, Controller, Drive.

93. Example: Leonardo’s invention
Invention of Leonardo da Vinci (XVI century): Roasting jack rotates itself

94. Example 1: History of the Problem of Rotation at required rate
Industrial era example: How to ensure constant rotational rate of a steam-driven machine while load profile is not uniform (sometimes idle, sometimes loaded)?

95. Example 1: History of the Problem of Rotation at required rate
steam supply valve
rotation at a certain rate
Invention of James Watt (1788): Automatic (“itself”) steam supply governing
ω(t), rotational rate
No mathematics involved, but… it turned out to be unstable:
t, time

96. Example 1: History of the Problem of Rotation at required rate
Only analysis of mathematical model of governed (closed loop) machine dynamics by J.Maxwell and I.Vyshnegradsky (1868—1876) lead to inventions that changed the structure of the governor (introduction of additional friction element, changing the geometry of governor). Photos below: Collection of improved Watt governors at Baumana State Technical University Museum (Moscow)

97. Example 2: History of the Problem of Rotation (at required rate)
Vibration reduction: How to reduce rotating shaft vibration while any shaft has an offset?
Mathematical model of Rotating body dynamics is complex (nonlinear) and full of interesting phenomena. These phenomena could be used for rotor self-stabilization (Ideal Final Result), like rotating beyond the “critical speed” or cavities with small masses (balls) in the rotor body.
A Brief History and State of the Art of Rotor Dynamics, By Dr. Rajiv Tiwari, Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Short Term Course on Theory & Practice of Rotor Dynamics (15-19 Dec 2008)

98. Signal Amplifier (Ideal)
Example 3. Harold Black invention of feedback amplifier. Description of Problem
y, output signal
t, time
c, input signal
Signal Amplifier (Ideal)
t, time
y=Kc

99. Signal Amplifier (Real)
Example 3. Harold Black invention of feedback amplifier. Description of Problem
y, output signal
t, time
c, input signal
Signal Amplifier (Real)
t, time
y=Kc + <high frequency Noise due to amplifier distortions>

100. Example 3. Harold Black invention of feedback amplifier
Example 3. Harold Black invention of feedback amplifier. Step 2: Invention of Feedback Structure c
y=Kc
Amplifier 1, K
Kc+N _ N
“Anti-Amplifier”, 1/K
f=c+n
Amplifier 2, K _ n
…but it required absolutely identical Amplifiers 1 an 2, that turned out to require frequent tuning

101. “Anti-Amplifier”, 0<β<1
Example 3. Harold Black invention of feedback amplifier. Step 3: “Hudson River Ride” Invention c
Amplifier, K>>1 y _
β<1 is the “amount of amplifier distortion reduction”
β=1 : “perfect distortion attenuation, no amplifying” β=0 : “perfect amplifying, no distortion attenuation”
“Anti-Amplifier”, 0<β<1
Patent application was filed in 1927, patent granted in1937 only (!). Required a lot of mathematical work of Harry Nyquist to prove the closed loop system stability (Famous Nyquist stability criteria)

102. Example. Synchronization Problem
“External Synchronization”
“Internal Synchronization”
Vibrational Screening
Cardio Stimulating

103. Example. Synchronization Problem: Generalization
“External Synchronization”
“Internal Synchronization”
F(x)
No synch
Synch

104. Example. General Synchronization Problem
“External Synchronization”
“Internal Synchronization”
Old design
F(x)
Innovation!
© I.I.Blechman, Institute of Machnie Building Problems, Russia
In all cases, careful analysis of nonlinear dynamics can help to find self-synchronizing conditions
Readings: in Russian:
In English: Blekhman I.I. VIBRATIONAL MECHANICS.  Nonlinear Dynamic Effects, General Approach, Applications. English translation (revised and enlarged edition) World Scientific, pp.509. Pub. date: Jan 2000

105. Example. General Synchronization Problem (continued)
“More amazing facts on Synchronizing”
1. Dynamic chaos is irregular periodic process… But self-synchronizing can exist between … chaotically oscillating systems!
Applications in Signal Encrypting
2. So many periodic processes are synchronized… in “healthy state”!
Is there anything beyond “4 Physical Fields?”

106. Case study. Load swing stabilization
- Concept design based on math modeling
- Primary patent search (by Sergej Shalnev)

107. Inventive solutions with mathematical models
t, time
x(t), position

108. Inventive solutions with mathematical models
t, time
x(t), position

109. Inventive solutions with mathematical models
Solution in theory: (active) feedback concepts
Embodiment
pendulum x _
controller
ACTIVE CONTROL SYSTEM TO STABILIZE SUSPENDED MOVING
VEHICLES IN CABLES
Vieira, Danilo Martins, Ibrahim, Ricardo Cury, Torikai, Delson,
Escola Politécnica São Paulo University.ABCM Symposium Series in Mechatronics - Vol. 4 - pp 1.
Mechanism for swing damping and leveling of the load RU 2. … N.

110. Inventive solutions with mathematical models
Solution in theory: passive compensation concept
Embodiment m M
See: Matsushita Lab at Kyoto University

111. Inventive solutions with mathematical models
Solution in theory: gyroscopic stabilization
Embodiment
M, J
Example

112. Inventive solutions with mathematical models
Solution in theory: Feedback control
Embodiment
Method for damping the load swing of a crane US A
load swing results
ABB Industry Oy
Standard classical stabilization by suspension point motion (feedback control). Optimization: Pontryagin’s principle and “bang-bang control” scheme.

113. Inventive solutions with mathematical models
Solution in theory: Parametric Resonance
Embodiment
A method to design self-adaptive parametric compensation
IFR, system/antisystem concept support
Even more ambitions: no controller?! Think of spring pendulum:

114. Practice: French Door Refrigerator DIOS
114

115. Problem Set Up
Drawer sliding rail (~2 kGf)
Problem decomposition
High drawer opening force (6..7 kGf)
Drawer sealing with magnet strip (~3 kGf)
Contradiction: For good energy efficiency the drawer sealing should be strong. But for easy opening it should be weak.
IFR: Perfect sealing being closed, zero resistance being opened. No changes in manufacturing
115

116. 1. Drawer sliding rail Virtual prototyping Contradictions
Easy Door Open
Present
Contradictions
Rail Slider Spring
- should be strong to prove complete closing
- should be weak to open door easily
TRIZ principles
Separation in time & space
New Idea
Effect
Reduce 17% opening force
New idea can satisfy both open and closing situation
Virtual prototyping

117. 2. Drawer Sealing design Current design Breaking the symmetry idea
Door
body
Breaking the symmetry idea
Virtual prototyping
New design
117

118. Case study 2. Inhaling device conceptual design

119. Function Model Possible Patent Circumvention Approach: Trimming
Control
Control
Power Source
Generate
Electric
current
Heat
Heater
Activate
Wiring
Directs
Heat
Please
Generate
User
Aerosol
Tobacco

120. Possible Patent Obstacles: Typical Example 1
Patent details
Independent Claim
Electrical smoking system
A combustionless tobacco smoking system comprising: a) a tobacco mass (24); and b) heating apparatus, substantially enclosing the tobacco mass, electrically operated, operable to heat the tobacco mass to a temperature in the range of 150 to 220° C., defining a heat transfer channel through which air is directed.
US 2007/ A1
2007
Philip Morris USA Inc.

121. Possible Patent Obstacles: Typical Example 2
Patent details
Independent Claim
Aerosol delivery articles utilizing electrical energy
A disposable portion of an aerosol delivery article (12) containing a drug or flavour substance for use with a source of electrical power comprising: a) an electrical resistance heating element (18); and b) aerosol forming substance carried by the heating element prior to use.
EP A2
1990
R. J. REYNOLDS TOBACCO COMPANY

122. Possible Patent Obstacles: Other patents
Electrical resistive heating
US 5,249,586, US 5,269,327, US 5,322,075, US 5,443,560, US 5,505,214, US 5,666,978, US 5,708,258, US 5,730,158, US 5,750,964, US 5,865,185, US 7,726,320

123. Function Model Possible Patent Circumvention Approach: Trimming
Control
Control
Power Source
Generate
Electric
current
Heat
Heater
Activate
Wiring
Directs
Heat
Please
Generate
User
Aerosol
Tobacco

124. Model transformation Control Control Power Source Generate Electric
current
Heat
Heater
Activate
Wiring
Directs
Heat
Please
Generate
User
Aerosol
Tobacco

125. Model transformation Trimming Model Solution Control Control
Power Source
Generate
Electric
current
Heat
Activate
Wiring
Directs
Please
Generate
User
Aerosol
Tobacco

126. Conclusions
Ideality-driven design brings concepts of inventive and simple/robust solutions.
Ideality can be used for concept evaluation
Modelling (mathematical, functional, …) can make idea generation more systematic.
Advanced professional knowledge (physics, chemistry, engineering, math) helps to turn inventive ideas into technical solutions. It enables the magic of ideality.
“Ideal system is no system, but the Function is performed”

127. - What is Ideal Lecturer?
Thank you.

128. Contents
0. Intro, Definitions, Why it is cool to invent. Current status of tools for systematic creativity in design and science. When finding new idea means actually finding
Modelling for creativity: understanding functions and contradictions
Beauty and Power of Itselfness
Case studies
Conclusions

129. Experience-based Speculation: New product design roadmap
Business
Industrial management
Engineering domain
Manufacturing
Idea generation tools
Idea verification, optimization tools
Decision making tools
Market analysis tools
QFD
TRIZ
Basic sciences, Simulations, Experiments, Engineering feasibility assessment methods,
Optimization
Axiomatic Design
Kano
data analysis, artificial Intelligence
Theory of Constrains
Analytical Hierarchy Process
C-K theory
Case based reasoning
Six Sigma