Contents of
Heuristic Innovation
Dedication iii
Table
of Contents v
Table
of Examples ix
To
all problem solvers xi
Preface xv
Organization
of heuristic innovation in three parts xvii
Part A Mental Problem
Solving – How the Mind Solves Technical Problems 1
Goal of heuristic innovation 1
Procedure 2
Assumptions 2
Analogy of visual cues and problem-solving seeds 7
Using seeds 8
Solutions 11
Causes and effects in a well-defined problem 12
Plausible root-cause analysis 17
Forms of the proforma graphic 21
Focus on attribute → unwanted effect
→ attribute units 21
Orphan attributes 23
Questions having answers / Problems having solution
concepts 23
Inventing problems 23
How do seeds work? 24
Generification of problem definition 27
Iteration in mental problem solving 29
Natural thinking in problem solving 30
The neural chemistry of problem solving 32
Brain lateralization 34
The struggle between intuition and logic 34
Resolving the struggle between intuition and logic 36
Brain divergence 37
Transition from structured to unstructured problem
solving 38
Part B Application of
heuristics 41
Preface 41
Origins 43
Proof of
efficacy 44
Introduction 45
Structured
problem solving from TRIZ to USIT 45
The model of heuristic innovation 48
Logical problem solving – a linear path 51
Problem definition – the heart of heuristic
innovation 52
Engaging both hemispheres of cognition 55
Metaphors – thought starters 54
Awareness images 55
Metaphorical images 56
Hemispheres of cognition 57
Goal of studying cognitive-hemisphere modes of
thinking 57
What our two cognitive hemispheres have to offer 58
When do we use both logical and intuitive thinking
traits? 61
Ambiguous metaphors 63
Filters 64
Two objects – ultimate focus 66
Introduction of thought paths 67
Examples of thought paths found through attribute
paring 68
Depth of understanding of an effect 74
Using thought paths 74
Attribute pairing in ambiguous effects 75
Thought paths fond through attribute triplets 80
Images in problem solving 82
A real-world problem 82
More on cognitive-hemisphere thinking traits 90
Heuristics 94
Strategy for heuristic innovation demonstration 94
Demonstration problem: the loose wire-harness
connectors 96
Construction of a problem statement 96
Simple sketch 97
Discussion 99
Iteration of problem statement 100
Iteration of heuristics 103
Utilize an unwanted effect 103
Eliminate an unwanted effect 104
Nullify an unwanted effect 104
Challenge assumptions 105
Take objects to extremes 105
Take attributes to extremes 106
The
transition from USIT to heuristic innovation 111
The USIT plausible root-cause heuristic 111
The heuristic-innovation transition 113
How to invent from an unwanted effect 114
Left behind? 118
In
the end, it is problem analysis 118
Conclusion 120
Part
C Theory, Derivation, and Application
of Heuristics 123
Preface 123
Overview 124
I. Theory for Derivation of Heuristics 125
Introduction 125
Heuristics in mathematics 125
Definition of heuristics and
intuition 126
Table C1. Examples of
heuristics used by technologists in problem solving 127
Heuristics seed the
subconscious 127
The use of heuristics in
problem solving 128
Unstructured brainstorming 129
Background 130
Structured, problem-solving methodologies 130
Origin of heuristics 130
A simple model of cognition 130
Perspectives and biases in problem solving 131
Abstraction of heuristic 133
Comments on the method 133
The Method
for Derivation of Abstract Heuristics 135
Application of heuristics to a physical-world problem 135
Problem-definition phase 135
Problem-analysis phase 137
Problem-solution phase 141
Table
C2. Summary of heuristics used 146
Abstract
heuristics – no physical-world references 147
Application of heuristics to an abstract problem 148
Problem-definition phase 148
Problem-analysis phase 149
Problem-solution phase 149
Table C3. Summary of new graphic heuristics for an abstract
problem 155
Abstract
heuristics for abstract problems 155
Graphic
representation of heuristics 156
Comments on the adaptation of derived heuristics to other
fields 157
Object 159
Information as an object 159
Attribute 160
Function 160
Object abstraction 161
Note on Mathematical Heuristics 162
Table C4. Comparison of
twelve mathematical heuristics with known and derived heuristics 162
II.
Derivation of Heuristics 163
Introduction 163
Common rules / uncommon language 163
Derivation 164
Definitions 164
Axioms 165
Known Heuristics 166
Abstraction 167
Problem state 167
Problem-state – to – Solution-state strategies 169
Problem State graphic model 170
Solution State graphic models 170
Characterization of Attributes 171
Analysis of solution states with example
solutions 174
Solution by utilization 174
Table C5.
Space-time attribute modifications for solution by utilization 175
Examples of solution by utilization 177
Solution by A-F-A linking 179
Solution by nullification 181
Solution by elimination 184
Graphic metaphors as solution heuristics 185
Table C6 Random
two-attribute arrangements and their metaphorical implications. 186
Spatial and temporal heuristics 188
Solution by transposition 190
Table C7.
Paired spatial | temporal attributes 191
Table C8. Summary of Heuristics for Problem Statement,
Analysis and Solution 193
Summary of heuristic strategies for problem
solving 196
Solution strategies 196
Phraseology in words and graphics 198
Conclusion of Derivation of Heuristics 199
III.
Application of Derived Heuristics 201
Introduction 1201
Inventing a belt – a problem
to be solved using the newly derived heuristics 202
Deduction of problem
definition information 202
An unwanted effect as a
strategy for invention 203
Graphic problem statement 205
Solution by utilization 207
Solution by utilization using A-F-A linking 210
Solution by nullification 212
Solution by elimination 214
Conclusion of Application of Derived
Heuristics 216
Appendices
A1. Infovores crave information 217
A2. For managers: Strategic partitioning of
problem-solving resources 219
Glossary 223
References 231
Exercises 233
Acknowledgements 237
About
the Author 239
Index 241
Examples – Ideas, partial
demonstrations, completed exercises, etc.
Complete
problems:
Erasure smudge 5, 8–17, 19, 23-29,
Pin and balloon 49-55, 64-81
Loose wire-harness 96-110
Hand-held binoculars 135-145
Engineering
scale-up:
Audio speech compression 2
Graphic
proforma:
Trunk lid and airbag 3
Erasure smudge 24, 25-26, 28
Pin and balloon 70
A law and a suspect 160
Specimen and glass slide 166
Rod and solid 168
N2 and O2 (speed
control) 177
Polymer and location 182
Belt and swabs 182
Front wheel and rear wheel 183
Cell and blood 183
Belt and buckle 205, 207
Belt: stress and creep 210, 211
Images
and metaphors
Laundry room leak 81-89
Introspection
Jigsaw puzzle 91
Volume of a sphere 92
Inventing an electric motor 92
Invention:
Computer mouse 115-117
Men’s trousers belt 202-215
Problem
statements (well defined and not so well defined)”
Pin and balloon 497, 51-53,
Four saloons 61-62
Two trains and a bumble bee 63
Solution
by utilization:
Nitrogen and oxygen 177
Solution
using A-F-A links:
Pedal and driver (speed control) 180
Solution
by nullification:
Polymer birefringence 182
Conveyor belt and swabs 182
Turn radius of a vehicle 183
Pancreas cells in silicon holes 183
Solution
by elimination:
Car radio temptation 184
Exercises
Practice metaphors 6
Sticky asphalt 39
Flag pole invention 39
Solution vs. concept 39
Balloon sketch error 53
Two trains and a bumble bee 63
Reactions to Fig. B.3 64
Ice cream 67
Reactions to language 121
Problem from one’s own field 121
Apples in a box 121
E1– A fix-it problem 233
E2 – Reverse engineering 233
E3 – Attributes 233
E4 – Generification of objects 233
E5 – Points of contact 234
E6 – Invention 234
E7 – Well-defined problem 234
E8 – Functions 234
E9 – Object minimization 234
E10 – Solution strategies 235
E11 – Attribute pairing from lists of
randomly selected attributes 235
E12 – Attribute pairing in ambiguous
effects 236
(More examples are found in Ref. 1)