**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
heuristic**s 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

N_{2} and O_{2} (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)