Directed Evolution, Innovation and TRIZinnovation.technion.ac.il/upload/_dtdfr51r6v.pdf · TRIZ Ido Lapidot & Amir Roggel 1 Directed Evolution, Innovation and TRIZ Genrich Saulovich

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Ido Lapidot & Amir Roggel

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Directed Evolution,

Innovation and TRIZ

Genrich Saulovich Altshuller

October 15, 1926 - September 24, 1998

Introduction to

Systematic Innovation

with TRIZ

Amir Roggel & Ido Lapidot

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System thinking

Laws of Manmade Systems Evolution

World’s knowledge

Scientific effects DB

Patterns

Defy psychological

inertia

resolve contradiction

TRIZ - a New Science

A

B

1 2

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1

2

3

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5

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n

To

Corresponding

Solutions

Many Standard

Problems Many Standard

Solutions

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TRIZ - What Is It Good For?

Directed evolution

Strategic planning

Overcome challenges/Problem solving

Setting Vision, Mission, Targets

Failure anticipation

Patent strengthening/Overcoming

Integrating Knowledge

And more

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Innovation – What is it?

Innovation:

Revealing new methods of utilizing Resources, achieving

new or improved Functionality

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Gain:

Value Expectancy

(Risk, Cost, Time)

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"The best way to predict the future is to invent it.“

Alan Kay

If we knew the Future we could invent it

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Moore’s law (1965)

Law of Accelerate Return (Ray Kurzweil 1990)

7 Is it possible to predict the future at the micro level?

“Since the destruction of the Temple....”

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Is there a Common Denominator?

Line Plane

Mng.1 Mng. 2

Dot Volume

Two in a Box Matrix Management Spherical Management

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3-D

curve Complicate

Point

Line

Tra

ns

itio

n “

Po

int

– L

ine

-

Su

rfa

ce

– V

olu

me

”

Lines

evolution

Surface

evolution

Volume

evolution

Complication of geometrical shape

Volume Cylindrical Spherical Complicate

One

curvature Complicate Surface Double

curvature

2-D

curve

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System Human

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Tool Transmission Energy Source Control


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Elimination of Human Involvement


Auto-Adjusting Brightness

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Next?

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What if we change to order?

New invention

Cucumbers Picking

Machine

Tool

Transmission

Energy

Control

?


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Mono

system

Bi

system Poly system

16cen 17cen 18cen 19cen

1970 2005 2007


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Monolithic

System 1976

One joint

1990

Many joints

1994 Elastic Bar

Monolithic

System

Completely

Elastic System System with

many joints

System with

one joint

Field Liquid, Gas

Mono – Bi - Poly

Flexibility - New vector of Value

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Sequential

Processing

In-Order

Pipeline Cycle 1 2 3 4 5 6 7 8 9

Instr1 Fetch Decode Execute Write


Instr3 Fetch

Cycle 1 2 3 4 5 6 7 8 9

Instr1 Fetch Decode Execute Execute Execute Write

Instr2 Fetch Decode Wait Wait Execute Write



Instr5 Fetch Decode Wait Wait Execute

Instr6 Fetch Decode Wait Wait

Out Of Order Hyper-Threading

Cycle 1 2 3 4 5 6 7 8 9

Instr1 Fetch T1 Decode Execute Execute Execute Execute Execute Write

Instr2 Fetch T1 Decode Wait Wait Wait Wait Execute Write

Instr3 Fetch T2 Decode Execute Write

Instr4 Fetch T2 Decode Wait Execute Write



Cycle 1 2 3 4 5 6 7 8 9




Instr4 Fetch Decode Wait Execute Write



Cycle 1 2 3 4 5 6 7 8 9


Instr2 Fetch Decode Wait Wait Wait Execute Write


Instr4 Fetch Decode Wait Wait Wait Execute Write





SuperScalar

Cycle 1 2 3 4 5 6 7 8 9







Pipelined

Monolithic

System

Flexible system Poly-System Bi -System Field Liquid, Gas

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?

? T

Monolithic

System


One

Channel

Two

Channels

Multi

Channels

Flexible

Channel

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?

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T

Monolithic

System


Regular

Lanss

Bifocal Multifocal Flexible?

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Evolution Mechanism

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A process of passing traits from one generation to the next, while

introducing mild changes, aiming to improve Adaptation

The process is based on a combination of these elements:

1. Variety

2. Competition over resources

3. Selection

Selection

Gen 1 Gen 2 Gen 3

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Development of an Engineering System is analogous to the

growth of a bacterial colony

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Law of S-Curve Evolution

Adaptation

Rapid growth

Stabilization

Death

Time Time

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Law of S-Curve Evolution

Development of an Engineering System is analogous to the

growth of a bacterial colony

Innovation &

Adaptation

Rapid growth

Stabilization

Death

Time Time

Main

Para

mete

r o

f V

alu

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Ideality

Law of Idealization:

Systems evolve towards increased ideality

Performing the Function with no Cost and no Harm

V = Value

F = Functionality

C = Cost (Resources + Harmful)

Ideal system – should appear at necessary moment in necessary place, to do 100% of work

C

FV

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Vertical Evolution

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Vertical Evolution

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Horizontal Evolution - Importing Solutions

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Evolution Mechanism

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Horizontal

Solutions

Library

Parent

Parent

New

variants

Pre –Filtration

& development

Vertical

Invention /

Mutations

Natural

Selection

Survivors

Manageable

Co-Evolution

Generating Idea Implementation

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Innovation Strategy

Scenario 1 The Environment is changing slower than the rate of introducing

new generation

Typical Strategy:

Introduce as many as possible new versions, the “Natural

Selection” will present the winner

Scenario 2 The Environment is changing faster than the rate of introducing

new generation

Typical Strategy:

Introduce small number of highly versatile new versions

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1. Innovation is an Evolution

Mechanism

2. Manmade Systems were

design to serve Humans

3. Manmade Systems are

extension of Human Evolution

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Evolution, Innovation and TRIZ

C. Darwin G. Altshuler J.B. de Lamarck

S

S +

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TRIZ

Pronounced “treez”

“Teoriya Resheniya Izobretatel’skikh Zadach” = “The Theory of

Solving Inventor Problems”

The thinking that got you into a particular

problem, will not get you out of it.

- Albert Einstein

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Genrich S. Altshuller - TRIZ

Based on patents research

“There is regularity and repeated patterns in

invention”

1. 99.7% of inventions used known solutions principle

2. Solutions patterns are universal across different areas

3. Evolution of man-made systems is governed by certain

regularities and trends

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Laws Of System Evolution

Law of

Transition to a

Supersystem

Law of Increasing

Ideality

Law of

Increasing

Degree of

Trimming

Law of

Optimization

of Flows

Law of S-curve evolution

Law of

Increasing

Coordination

Law of Increasing

Controllability

Trend of Increasing

Dynamicity

Law of Non uniform

Development of

System Components

Law of System

Completeness

Trend of

Elimination of

Human

Involvement

Young

Mid

Mature

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Evolutionary Potential Analysis

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0

10

20

30

40

50

60

70

80

90

100

Human Involvement

System Components

IDegree of Fragmentation

Field withHigher Degree of Control

Uncontrolled to SelfControl

Control Logic

Coordinationof Shape

Coordinationof Rhythm

Process Trimming

Product trimming

Dynamicity of field

Dynamicity of substance

Optimization of Action

Optimization of Materials

Optimization of Space

Optimization ofSubsystem

Optimization ofSupersystem

Optimization of System

Mono-Bi-Poly

System integration

New

Current

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Display Evolution – Samsung 2003

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Documents

Directed Evolution, Innovation and TRIZinnovation.technion.ac.il/upload/_dtdfr51r6v.pdf · TRIZ Ido Lapidot & Amir Roggel 1 Directed Evolution, Innovation and TRIZ Genrich Saulovich