Applying TRIZ and the Theory of Ideal SuperSmart Learning to Computing Systems: Ultimate Ideal Autonomous Objects, Strategic Problem Solving, and Product Innovation

By Dr. Rodney K. King
r.k.king@supersmartnetwork.com

About a month ago, I received an e-mail regarding OOPSLA�s task of �seeking new paradigms and new thinking.� I was interested as a few weeks earlier I had published, on the Internet, my Theory of Ideal SuperSmart Learning¹. I had described the Theory of Ideal SuperSmart Learning as similar to a �theory of everything for product, personal, business, and institutional development.� The goal of the theory is �to know and understand everything from nothing and in no time.� This goal is based on utopic ideality. The Theory of Ideal SuperSmart Learning uses concepts from both utopic and practical ideality. The theory encompasses Versatile Thinking�, part of which is published in the second edition of the multi-author book, Research Methods for Postgraduates; this book is edited by Dr. Tony Greenfield.

The Theory of Ideal SuperSmart Learning is applicable to many domains. The theory presents a multi-methodology framework for pattern thinking and especially draws on ideas from Christopher Alexander�s pattern language, software design patterns, the Theory of Inventive Problem Solving (otherwise known as �TRIZ�²), Creative Problem Solving (CPS), mind mapping, and concept mapping. The theory therefore covers creativity, problem solving, and ideas management. The Theory of Ideal SuperSmart Learning in combination with TRIZ could be used as a resource for developing the following: new paradigms for computing systems; new thinking about objects; new framings for apparently unsolvable problems; new approaches to organizing ideas for strategic problem solving and innovation.

This paper presents the conceptual framework and tools of the theory as they relate to computing systems. Major concepts such as IBM�s �autonomic computing systems� and Bill Gates�s �digital nervous system� are shown to be retrospectively governed by key concepts in TRIZ and the Theory of Ideal SuperSmart Learning. Both theories are briefly applied in the area of forecasting states in the evolution of technological systems. Finally, some of the major problems, which are facing the computing industry, are framed and then strategic options proposed using tools of TRIZ and the Theory of Ideal SuperSmart Learning.

The IVY-paradigm is the conceptual framework on which the Theory of Ideal SuperSmart Learning rests. This paradigm could be applied to computing objects and systems. The acronym, IVY, stands for �IVYality� (ultimate ideality), Versatility, and �Ympossibility.� The IVY-paradigm is a triangle of paradigms, i.e., a meta-paradigm. Its interdependent elements are as follows:

• Paradigm of IVYality³ (Ultimate Ideality): �Infinity at nothingness�
• Paradigm of Versatility: �Multi-polarity� or �Infinity in all directions�
• Paradigm of �Ympossibility�: �Unforeseeable (unpredictable) excellence�

The first of the IVY-triangle of paradigms, i.e., the paradigm of IVYality focuses on ultimate ideality, which is a combination of technical ideality and emotional ideality. As a concept, ultimate ideality, in particular technical ideality, has a long history and is used in many domains. Technical ideality is a central feature of TRIZ and directly related to the concept of Ideal Final Result (IFR). Technical ideality also plays a central role in TRIZs approach to forecasting the evolution of technical systems, discovering inventive principles, and resolving contradictions⁴.

Ultimate deality is an extension of technical ideality and could be linked with the following concepts: evolution by natural selection (�survival of the fittest�) in biology; perfect information in market economics; ideal objects such as ideal gases in chemistry; ideal or utopic society in literature; ideal number of defects in quality management of products; ideal time (period) for product delivery; ideal technological and information systems in product development. The focus in this paper is on ultimate ideal computing objects. But, what is meant by an �ultimate ideal object�?

In the Theory of Ideal SuperSmart Learning, an ultimate ideal object is a multi-level concept that is defined at three levels:

• Macro-level: A system that either infinitely demonstrates its potential functions and properties or infinitely attains its objectives under (internal) conditions of utopic ideality, e.g., using no external (additional) resources or �freely� available resource, and without causing any disadvantage or negative (harmful/undesirable) side effect.
• Meso-level: A closed (self-contained), self-organising, �self-informative�, and self-regulating system that has infinite efficiency and versatility but may not materially exist. The system could be a field, wave, or void.
• Micro-level: An autonomous system that carries out its functions or achieves its objectives under conditions of practical ideality or in the real (physical) world.

The above definitions of an ultimate ideal object are strongly related to TRIZs concepts of ideality. However, TRIZ focuses on an ideal object at the macro-level. The multi-level definition of an ultimate ideal object is especially suitable for developing paradigms or visions for the innovation and design of computing objects. Once the function of an object is ascertained or specified, the object could be reframed as an ultimate ideal object. Another advantage in using the concept of ultimate ideal objects such as in strategic system innovation and design is that it encourages �out-of-the-box� thinking, the development of breakthrough insights, and innovative design that satisfy end-users or customers. Within the framework of an ultimate ideal object, a problem-solver�s mindset is to go for ultimate ideality (�win-win�/�no compromise�) solutions rather than trade-off or optimisation (�lose-lose�/�win-lose�) solutions. Also, the macro- and meso-definitions of an ultimate ideal object indicate the evolutionary tendencies or states of objects that have enduring competitive advantages.

The above definitions indicate that ultimate ideal objects including ultimate ideal computing (hardware/software/network) objects as well as ultimate ideal �human� objects should, among others, satisfy the following set of interrelated criteria:

1. infinite functions or functionalities: ultimate ideal (computing) objects should perform their core, peripheral, and remote functions anywhere, at any time, and eternally; the functions could be technical and/or emotional
2. conditions of ideality: ultimate ideal (computing) objects should satisfy the following 6 conditions of ideality⁵: ideal (�functional�) nothingness; ideal infinity; ideal efficiency & �automaticity�⁶; ideal conflict resolution & unity; ideal simplicity, variety, & beauty; ideal identification, detection, & branding
3. no external (additional) resources: ultimate ideal (computing) objects should, when responding to perturbations or resolving problems, not use external or additional resources; such objects should exhaustively exploit not only �freely� available or redundant existing resources⁷ but also existing internal constraints
4. no disadvantage or negative (harmful/undesirable) side effect: ultimate ideal (computing) objects should neither have any disadvantages nor cause negative (harmful/undesirable) side effects
5. closed (self-contained) system: ultimate ideal (computing) objects should be autonomous, self-problem solving, self-analysing, self-maintaining, self-healing/repairing, and self-sustaining
6. self-organisation; �self-informativeness�; self-regulation: ultimate ideal (computing) objects should be self-organising, self-informative, anticipatory, self-monitoring, and self-regulating
7. infinite versatility (multi-polarity): ultimate ideal (computing) objects should be infinitely versatile, adaptive or multi-polar
8. instantaneous as well as versatile learning and knowledge: ultimate ideal (computing) objects should know and understand everything from nothing and in no time
9. ideal problem solvers: ultimate ideal (computing) objects should identify, frame, analyse, manage, and solve all type of problems without using external resources
10. no material size: ultimate ideal (computing) objects should - as in an electromagnetic field and wave or a void - not occupy physical space⁸

The 10 criteria above could be said to constitute the general operational elements of the IVY-paradigm, especially for computing systems. The set of criteria may also be regarded as the features of an �ultimate ideal autonomous object.� The criteria also provide stable �yardsticks� for not only ascertaining the level and degree of ultimate ideality (IVYality) of existing computing systems but also anticipating and designing future computing systems.

The criteria of the IVY-paradigm could be variously combined to form other paradigms⁹. For instance and in retrospect, the paradigm of autonomic computing could be said to relate to the following IVY-criteria: (ii) conditions of ideality - ideal efficiency & �automaticity�; (vi) self-regulation; (viii) (instantaneous as well as versatile) learning and knowledge; (ix) ideal problem solvers. It is important to note that autonomous computing contains some criteria not directly stated in the set of IVY-criteria.

As indicated above, the list of criteria in the IVY-paradigm for computing systems could be related to IBM�s (Paul Horn�s) vision of autonomic computing systems. The 8 main criteria to be satisfied by autonomic computing systems and their links with criteria of the IVY-paradigm could be summarised as follows:

• self-identification; self-knowing: IVY-criterion (viii)
• self-optimization: [IVY-criterion (ii)¹⁰]
• self-(re)configuration: IVY-criterion (vii)
• self-recovery (from perturbations): IVY-criterion (vi)
• self-protection (security)
• self-learning (including from errors): IVY-criterion (viii)
• self-regulating (to open standards): IVY-criterion (vi)
• self-resource-allocation: IVY-criterion (ix)

The criterion of self-protection (security) is unique to autonomic computing systems. The similarity between the criteria of autonomic computing and the IVY-paradigm is mainly due to the assumption - inherent in Paul Horn�s paper but explicit in TRIZ and the Theory of Ideal SuperSmart Learning - that technological and information systems evolve towards ultimate ideality (IVYality). Although the set of 10 criteria in the IVY-paradigm was developed after reading Paul Horn�s paper, Autonomic Computing: IBMs Perspective on the State of Information Technology, nearly all criteria in the IVY-paradigm could be traced to the multi-level definition of an ultimate ideal object that is contained in the Theory of Ideal SuperSmart Learning.

Despite the strong similarities between the paradigm of autonomic computing and the IVY-paradigm, there are some important differences. The IVY-paradigm is explicitly rooted in TRIZ as well as ultimate ideality and is conceived not only for technological systems but also for human-activity and learning environments. Thus, the IVY-paradigm could be applied to computing as well as domains outside of computing. According to concepts in the Theory of Ideal SuperSmart Learning, the paradigm of autonomic computing deals with practical ideality; the 8 criteria are meant to be achieved, however long the time frame. In contrast, criteria in the IVY-paradigm deal with utopic ideality. The list of 10 criteria is therefore normative. Some criteria are not meant to be achieved in the foreseeable future. Although some criteria may not be achieved in generations to come, the 10 criteria provide a means for benchmarking existing products as well as evaluating future designs. Finally, the paradigm of autonomic computing focuses on what is referred to as �ideal automaticity� in the Theory of Ideal SuperSmart Learning. Ideal automaticity is one of 6 conditions of ideality in the Theory of Ideal SuperSmart Learning. The paradigm of autonomic computing does not directly focus on conditions such as ideal (�functional�) nothingness; ideal infinity; ideal conflict resolution & unity. As in TRIZ, the latest condition emphasises the concept of �win-win� or �no compromise� solutions, while the paradigm of autonomic computing explicitly deals with �self-optimization.� It may be noted that a system that continually self-optimizes will steadily progress towards technical ideality.

The paradigm of autonomic computing is based on an analogy of the human nervous system. So also, is Bill Gates�s concept of a digital nervous system. While the concept of autonomic computing focuses on computing objects (networks, hardware, and software), the concept of a digital nervous system deals with designing a business or enterprise information system with a view to �instantaneous as well as versatile learning and knowledge�; this is IVY-criterion (viii). The target of the vision of autonomic computing ranges from level of the computing industry to product design, while the target of a digital nervous system is an enterprise that may be at local or global level. A digital nervous system may be said to focus on practically ideal information gathering, processing, and distribution (flow). The paradigm of a digital nervous system deals with an operational rather than an abstract framework. The digital network paradigm may therefore be expanded using the listed criteria of the IVY-paradigm.

Combined with TRIZ, the Theory of Ideal SuperSmart Learning contains a menu of tools that assists in generating ideas, obtaining breakthrough insights and innovative products, (personally) managing ideas, solving problems, and planning scenarios . In this paper, only a selection of tools is presented. On the one hand, there are tools that could be used for anticipating patterns in the evolution of computing systems (hardware/ software/networks). On the other hand, tools exist for framing and solving emerging problems in computing. The tools could also be used to obtain multiple perspectives on a given problem.

The 3 �laws� of IVYality are in fact, hypotheses¹¹. They are interdependent and applicable to designing objects in computing as well as other domains. The hypotheses are as follows:

1. �Law� of Infinite IVYality
   
   Technological and information objects (systems), which acquire and maintain high competitive advantages, develop towards infinite IVYality.
   
   (Note that: Level of IVYality = Advantages - Disadvantages
   
   &
   
   Degree of IVYality¹² = Advantages/Disadvantages)
2. �Law� of Infinite Versatility
   
   Technological and information objects (systems), which acquire and maintain high competitive advantages, develop towards infinite versatility (multi-polarity or adaptiveness).
3. �Law� of �Ympossibility�
   
   Technological and information objects (systems), which acquire and maintain high competitive advantages, develop towards �ympossibility�, i.e., apparently impossible (unpredictable) states with excellent �emergent� properties.

The laws of IVality (ultimate ideality) seem like truisms. And indeed, they may well be. The laws would be true whether one takes the viewpoint of a consumer or producer. The laws of IVyality could be regarded as the �invisible hand� that guides the choice of consumers and is increasingly driving the business of suppliers.

According to the law of infinite IVYality, computing systems, which are likely to have great competitive advantage, will be those that have the highest level or degree of IVYality. Products that violate the law of IVYality are likely to suffer �death.� Examples of some variables or resources, which could be maximized, are presented in the IVY- Matrix of Bipolar Variables in table 1¹³. In the compilation of information in table 1, TRIZs patterns of evolution as well as literature on the evolution of technical systems were used¹⁴.

Table 1: IVY-Matrix of Bipolar Variables (Resources)

Name of system (�object�):

Main function(s)/objective(s):

Supersystem(s):

Cells, the contents of which are embolded in table 1 reflect ultimate ideal states in systems. For instance, the row for efficiency (variable #3) indicates that objects with a tendency towards ultimate ideality would overwhelmingly display very high efficiency. Other variables in table 1 are �bi-directional�; this means that there is no unique direction for ultimate ideality. By vertically profiling, i.e., vertically plotting the characteristics of a given computing system, one could see possible states that the system, subsystem, or supersystem could adopt in the future. Scenarios for the evolution of systems could therefore be facilitated using the laws of ultimate ideality and the IVY-matrix of bipolar variables.

As an example of the use of table 1, a list of highly probable states in the evolution of the personal computer is presented below.

• �Efficiency� state: extremely highly efficient and recycleable
• �Automaticity� state: extremely high degree of automation; minimal manual operations
• �Unity/Integration/Structure� state: extremely networked and largely integrated system
• �Simplicity� state: absolutely simple to use
• �Beauty/Ergonomics� state: multi-colored; awesome beauty; easy to handle
• �Versatility� state: ubiquitous; adaptive; responsive to needs of user
• �Function� state: multi-functional; integrated with other systems in core, peripheral, and remote domains
• �Material/Substance/Physical� state: predominance of �invisible materials� such as fields and waves; also, the use of liquid-like materials
• �Flexibility� state: extremely flexible or mobile; foldable; nestable; wearable
• �Weight� state: almost weightless
• �Energy (Power) Required� state: minimal energy; self-powered
• �Cost� state: minimal (readily affordable) cost; almost free
• �Safety� state: extremely high
• �Length� state: miniature length; tending to invisibility
• �Quality/Advantages� state: total quality; enormous advantages to consumer as well as high constumer satisfaction
• �Colour� state: offered in colors covering the range of the color spectrum
• �Coordinates (Position)� states: multi-dimensional; could be placed in any position and place at any time.

It is possible that the personal computing industry may have recognised some of the above pathways, but not all of them. Not-yet-recognised or unused pathways indicate directions as well as opportunities for further development of the personal computer.

The laws of versatility and �ympossibility� are not separately discussed since they are subsumed in table 1 and consequently in the above example.

Although the primary use of the IVY-pyramid of innovation is to rapidly evaluate and classify alternative innovations, it could be used to anticipate patterns in the evolution of computing systems. The IVY-pyramid of innovation is shown below in table 2. It is important to note that the IVY-pyramid of innovation is based on TRIZs five levels of invention (solutions¹⁵). In contrast to the focus of TRIZ on inventions or highly inventive solutions, especially in the manufacturing sector, the IVY-pyramid of innovation presents a general framework for categorising and evaluating innovations.

Like in TRIZs level of invention, the IVY-pyramid of innovation shows five levels of innovation. In terms of the number of innovations that could be found at each level, the pyramid could be visualised as an inverted pyramid. The large majority of innovations occur at level 1 and gradually reduce until level 5, which contains the least number of innovations. The evolution of an enduring system is like a series of spirals or S-curves moving from levels 1 to 5. Computing networks are currently considered to be moving towards the peak of level 4 in the IVY-pyramid of innovation. Thus, computing networks will - in the not too distant future- possess �matured� mega-problems.

When mega-problems emerge in computing networks, the circle of resources required for solving such problems will include professionals from peripheral domains as well as technology being used in more advanced systems. Tools, technology, and resources in apparently disparate domains would have to be combined in order to resolve mega-problems. Also, solution of such mega-problems would need international cooperation.

The IVY-pyramid of innovation indicates progressive scalability of problems. Thus, after mega-problems have been solved, computing networks would perform first with increasing IVYality and then with decreasing IVYality, probably due to increased complexity as the functionality of networks increase. The next generation of problems will therefore be �giga-problems�, i.e., problems of a global order of magnitude. Solving such giga-problems would require global cooperation as well as a paradigm shift (e.g., as in ultimate ideal autonomous objects) combined with the discovery or application of new (�original�) technology. Hitherto remote disciplines could be valuable resources for knowledge. The result of solving giga-problems will be a new system (supersystem) with completely unforeseen (�emergent�) properties. This supersystem will form a new genus at level 1 and the spiral of increasing IVYality, problems (decreasing IVYality), and innovation would continue down the pyramid.

Table 2: IVY-Pyramid of Innovation

Name of system (�object�):

Main function(s):

Supersystem (Family of products):

3.2 Framing and Solving Problems of Strategic System Design in Computing

In the Theory of Ideal SuperSmart Learning, the approach to solving problems of strategic system design is based on resource archetypes, in particular problem-, opportunity-, and solution-archetypes. Problem-archetypes are universal patterns of problems in systems; opportunity-and solution-archetypes could be similarly defined. An opportunity is regarded as being on the reverse side of a problem. Problems and opportunities are therefore complementary.

With a view to facilitating creative problem finding and problem classification, the Theory of Ideal SuperSmart Learning distinguishes 8 problem- archetypes as follows:

- What are undesirably large or present?¹⁶

- What are undesirably small or absent?

- What are undesirably inefficient, sub-optimal, or wasted?

- What are undesirably conflicting, contradictory, bipolar, paradoxical, disunited, or discontinuous?

- What are undesirably complex, uniform, standardised, or symmetrical?

- What are undesirably identified, detected, or branded?

- What are undesirable dimensions, properties, parameters, or attributes?

- What are undesirable situations, side effects/consequences/ systems, elements, or super-systems?

The above problem-archetypes constitute a system for classifying and organizing (design) problems in a domain. Problem archetypes could provide different perspectives as well as obtain an array of inventive problems in a system. The classification of problems as archetypes facilitates analogical problem solving. This implies that families of solutions could be accessed and used as a resource for solving particular problem-archetypes, especially in strategic system design of computing systems. Problem-archetypes also indicate a need for having a catalogue of tools and multiple mindsets for tackling multifarious problems. Although problem-archetype 4 is recognised in computing systems, there seems to be inadequate formal tools for dealing with this type of problems; examples include apparently impossible conflicts, contradictions, bipolarities, dilemmas, paradoxes, and discontinuities. The prevailing mind set for example when dealing with technical conflicts is to go for trade-off or optimization. Why not go all out for a win-win solution, in the first instance? According to TRIZ, inventive or �patentable� solutions emerge when hitherto technical contradictions are resolved.¹⁷ TRIZ has documented 40 �Inventive Principles� that are inherent in highly innovative product solutions. These 40 Inventive Principles have recently been adapted for software systems¹⁸.

According to TRIZ, �inventive problems�, i.e., apparently impossible problems that involve technical and physical contradictions, constitute the most difficult category of problems in design. Within the framework of problem-archetypes, inventive problems belong to problem-archetype 4. Inventive problems in computing systems cover the following conflicts:

• Speed vs. Reliability

• Automation vs. Complexity
• Computing �Functionality� Power vs. Storage Capacity
• Computing �Functionality� Power vs. Sophistication of Computer Architecture (Lines of Code)
• Computing Power vs. Power (Energy) Consumption

A few questions come to mind when looking at the above conflicts. For instance, what formal technical and thinking tools exist to deal with the technical conflicts?¹⁹ How are these inventive problems to be solved? Using ideas from TRIZ and the Theory of Ideal SuperSmart Learning, some of the above technical conflicts are illustrated in Fig. 1. These technical conflicts could be described as sub-archetypal problem 4. The approach to dealing with problem-archetypes is outlined in the following sections.

Fig. 1: Examples of Technical Conflicts (Contradictions) in Computing Systems

A: Type I - Technical Conflict (Decreasing Pattern)

B: Type II - Technical Conflict (Increasing Pattern)

The Theory of Ideal SuperSmart Learning proposes a Creative Web - ARIZ (Multi-methodology) Framework for solving problems, especially in strategic system innovation and design. This framework is illustrated in Table 3 and mainly refers to tools in TRIZ, the Theory of Constraints²⁰, and the Theory of Ideal Supersmart Learning. Details on the use of the Creative Web - ARIZ framework could be obtained from the booklet on the Theory of Ideal Supersmart Learning²¹. Briefly, the table provides a framework that links steps in ARIZ with more detailed tools in TRIZ, the Theory of Constraints, and the Theory of Ideal Supersmart Learning. Within a particular �space� of the creative web, tools of TRIZ could be mixed and matched with �functionally equivalent� tools in other methodologies. The section, �Solution-Archetypes,� reflects an application of the Creative Web-ARIZ framework but with an emphasis on tools from TRIZ and the Theory of Ideal Supersmart Learning.

An important step in solving problems in strategic system design is to identify internal and external resources. The concept of opportunity-archetypes facilitates the identification of resources that could be used in providing �closed-system solutions� to design problems.

Opportunity-archetypes are perceived as problem anti-archetypes. Consequently, the description and checklist of questions for opportunity archetypes are based on problem-archetypes. A list of opportunity-archetypes is presented below.

- What are desirably large or present?²²

- What are desirably small or absent?

- What are desirably inefficient, sub-optimal, or wasted?

- What are desirably conflicting, contradictory, bipolar, paradoxical, discontinuous, disunited, or discontinuous?

- What are desirably complex, uniform, standardised, or symmetrical?

Table 3: The creative web - ARIZ (multi-methodology) framework

- What are desirably identified, detected, or branded?

- What are desirable dimensions, properties, parameters, or attributes?

- What are desirable situations, side effects/consequences/ systems, elements, or super-systems?

The search space for opportunity-archetypes could be further extended by replacing, in each archetype and question, �are� with �could be.� Thus, for opportunity-archetype 1, one could also ask: �What could be desirably large or present?� After identifying problem- and opportunity-archetypes, attention could be turned to resolving identified problems, especially using internal resources. Solution-archetypes offer prompts for brainstorming on strategies and mechanisms for resolving more well-defined problems.

Solution-archetypes are presented in table 4 as the �SCAMPER-DUTION� matrix. This matrix includes solution-patterns from Osborne-Eberle�s SCAMPER²³ as well as the 40 Inventive Principles and Separation Heuristics from TRIZ. Numbers in bracket in the table refer to TRIZs inventive principles. Only patterns at level 1, i.e., keywords (idea prompter/trigger/hint) are shown in table 4. In a software application, patterns at level 1 could be hyperlinked to patterns at level 2, i.e., heuristics (descriptions or exemplars using phrases, sentences, paragraphs, diagrams, and/or multimedia). Software design patterns could be organized in the form of a SCAMPER-DUTION matrix.

If we are to consider the technical conflict that is illustrated in Fig. 1A, i.e., speed vs. reliability, we could say that our objectives for design should be as follows:

• Practical ideality-objective: to maintain the best available level of reliability while increasing the speed of computing systems
• Utopic ideality-objective: to increase the reliability of computing systems while increasing their speed

Table 4: SCAMPER-DUTION matrix of patterns for solution-plots, properties, and devices

The IVY-paradigm and laws of IVYality would suggest that the designer focuses on the utopic ideality-objective. Consequently, the SCAMPER-DUTION matrix would be consulted with a preference for �ideal infinity patterns.� From table 4, the following principles from TRIZ²⁴ are recommended for resolving the conflict between speed and reliability:

(1): Segmentation/Division - �Segment, divide, fragment, modularise, or �granularize� [object] and/or [functions and attributes of object] into parts�; especially using database of opportunity-archetypes

(17): Dimensionality/Orientation/Tilt - �Change orientation or dimensions of existing physical space that is occupied by [object] and [parts of object], e.g., from horizontal to vertical; from 2D to 3D; from inside to outside; from uni-lateral to bi-lateral to multi-lateral; from single layer to multiple layers ( vice versa)�;

(20): Continuity - �To maximize continuity of operation as well as eliminate idle time, exhaustively use distributed, parallel (synchronised), multi-level, and/or �multi-polar� processing on [object] and/or [functions and attributes of object]�;

(26): Copying - �Use simpler and inexpensive copies as replacement for unavailable, expensive, fragile [object]�;

(34): Recovering - �Discard, modify, release, or eliminate (before or during main operation of system) portions of an [object] that have performed auxiliary functions�;

The above list contains generic solution-patterns, pointers, or strategies that could be further developed in the specific context of the problem, for instance by asking �How? How?� or �In how many and different ways �?�. It is important to note that the above strategies are derived from a small subset of the �ideal infinity patterns� of the SCAMPER-DUTION matrix. More ideas could be generated using ideal infinity patterns as well as other patterns. Alternatively, relevant inventive principles could be obtained from TRIZs contradiction matrix in the cell for the engineering parameters: speed (+: increasing) and reliability (-: worsening). An advantage of the graphical approach using technical conflicts is that it is independent of TRIZs �39 engineering� parameters and consequently, could be more easily applied to problems in technical, administrative, and social systems.

The IVY-template could be regarded as a dynamic and multi-level structural description of a system, including categories of its impacts. The template could be used for documenting ideas and problems as well as solving problems, particularly those relating to strategic system design. The Basic IVY-Template for Strategic Problem Solving is shown in Fig. 2. The technique of object mapping²⁵ is recommended for recording information on the IVY-template. The template facilitates holistic problem solving as it visually shows and integrates the problem-definition, methods, and solutions-space relating to a given task. The template illustrates the fact that there are 2 categories of solution-systems, i.e., open- and closed (self-contained)-system solutions and 3 generic ways of solving any problem.

Each description on the IVY-template could be regarded as an �object.� Letters on the IVY-template could have the following interpretations:

• O: �Object� (in the sense that everything is an object)
• F: Factor(s); Field(s); Force(s); Function(s); Failure(s)
• P: (Solution-) Pattern(s); Principle(s); Procedure(s); Process(es);

Properties; Parameter(s); Prompter(s); Paradigm(s)

• O1: Problem-archetype; Substance; Constraint; Weakest Link
• O2: Opportunity-archetype; Tool; Agent; Means
• O3: Given System; Super-agent; Super-system
• O4: (Ideal) Final Result
• O3.1: External elements
• O3.2: New (analogical/substitute/replacement) system
• O(-): Undesirable (harmful/negative/) effects; Disadvantages
• O(+): Desirable (useful/positive) effects; Advantages; Opportunities

Fig. 2: Basic IVY-Template for Strategic Problem Solving

The IVY-template is directly related to the concept of an ideal object, IVYality, and TRIZs ideality as well as resource (problem, opportunity, and solution)-archetypes, SCAMPER-DUTION matrix, and creative web (versatile map). The IVY-template could therefore be regarded as the embodiment of the IVY-paradigm. However, the template is restricted to strategic problem solving.

The creative web is a tool that focuses on ideas management as well as holistic problem solving in any discipline. The creative web²⁶ consists of five spaces. Generic activities in the spaces of the creative web are listed below:

1. Creative (�inventive�) Problem Finding
2. Preparation and Immersion

3. Reengineering, Exploration, and Generation/Incubation
4. (Unexpected) Synthesis/Illumination

5. Execution (Experimentation) and Testing
6. Evaluation and Verification

7. Presentation, Acceptance, and/or Implementation

8. External or �environmental� interaction

The numbers above are nominal rather than ordinal; their main purpose is to identify the activities as modules rather than elements in a chain. Activities in the creative web are recursive and involve �trial-and-error� (feedback). The problem-definition, methods, and solutions-spaces constitute the versatile map; see Fig. 3.

Fig. 3: Versatile Map

Both the creative web and versatile map are especially useful for solving open-ended or �wicked� problems. Using the creative web or versatile map, a designer could strategically plan a design project. Another use of the creative web is as a framework for using multi-methodologies as is demonstrated in table 3. Solving complex problems often requires the matching and mixing of methods from disparate disciplines and domains. The creative web provides a platform for assembling a �menu� of tools from various disciplines.

To date, there is no standard template or structure for documenting software design patterns and anti-patterns. Due to the level of abstraction of the creative web, it could be used for ordering various design patterns. A scheme is presented below:

• Problem-definition space
  (Pattern) Name/Problem/Context/Forces
• Methods-space
  Rationale
• Solutions-space
  Solution
• Implementation-space
  Resulting Context (Consequences)/Known Uses/Examples/
  Related Patterns
• Creative lifeSpace
  Not available

The advantages of documenting patterns and anti-patterns are well described in the literature and are therefore not addressed in this paper. Suffice it to say that a library of design patterns and anti-patterns could be linked with the SCAMPER-DUTION matrix and IVY-template.

As a response to the task of finding new paradigms and new thinking for computing systems, this paper introduces many concepts including ultimate ideality, ultimate ideal object, IVY-paradigm, IVYality, and tools of the Theory of Ideal SuperSmart Learning. The key proposals of this paper are more widespread use of the model of �ultimate ideal autonomous (autonomic) object� for computing systems as well as case study applications of tools of TRIZ and the Theory of Ideal Supersmart Learning in the area of computing. IBM�s (Paul Horn�s) paradigm of autonomic computing systems and Bill Gates�s digital nervous system could be derived from the meta-paradigm of ultimate ideal autonomous (autonomic) object. The 10 IVY-criteria for an ultimate ideal autonomous object could be regarded as an alphabet or the basic building blocks (DNA) for paradigms dealing with ultimate ideality, the zenith of which may be a holonic²⁷ web of ultimate ideal autonomous objects.

In line with object-oriented thinking, the 10 IVY-criteria of an ultimate ideal autonomous object could be applied to the following objects: computer hardware, software, and networks. Consequently, one could explore the concepts of �ultimate ideal autonomous (autonomic) hardware�; �ultimate ideal autonomous (autonomic) software�; �ultimate ideal autonomous (autonomic) networks.� These ideal objects could facilitate the innovation and design of products that satisfy both vendors and customers. No doubt, tools of TRIZ and the Theory of Ideal SuperSmart Learning would be valuable resources for strategically designing a holonic web of ultimate ideal objects.

Dr. Rodney K. King r.k.king@supersmartnetwork.com
Executive Coach, Consultant, and Trainer in
Versatile Product, Process, and Strategy Innovation
4 Keswick Drive, Hamilton ML3 7HN, Britain/(0)1698-421611

This article was originally published in March 2002 in the web site: http://www.supersmartnetwork.com

The author would like to thank Dr. Ellen Domb for her editorial comments and suggestions, which mainly formed the basis of this revised paper.

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Footnotes

1. The Theory of Ideal SuperSmart Learning could be downloaded from www.supersmartnetwork.com  or www.triz-journal.com/archives/2002/04/d/index.htm
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2. Pioneered by the Russian scientist, Genrich Altshuller, TRIZ was developed by examining patent databases and organizing methods by which inventors resolved �apparently impossible� technical contradictions. There are claims that over 1.5million patents have now been studied and analysed by TRIZ experts. The methods of TRIZ, however, have been largely applied to design problems in the manufacturing sector. TRIZ is increasingly being used in other domains as well as by major corporations and manufacturers all over the world. Although there are attempts to apply the methodology of TRIZ to the field of computing, applications are at an embryonic stage. Examples of applying TRIZ to computing could be found in the following articles: Rea (2001) and Retseptor (2002).
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3. �IVYality� is a concept that is introduced in the Theory of Ideal SuperSmart Learning to replace the relatively narrower concept of technical ideality as used in classic Theory of Inventive Problem Solving (TRIZ). In TRIZ, ideality is defined as the ratio of benefits to the sum of cost and harmful effects. IVYality focuses on advantages and disadvantages and has two operational criteria: level and degree of IVYality. The level of IVYality refers to the difference between advantages and disadvantages while the degree of IVYality refers to the ratio of advantages to disadvantages. Theoretically, advantages and disadvantages should be expressed in the same unit of measurement. When the advantages of a system are infinite and its disadvantages are zero, both the level and degree of IVYality are infinite. An important thesis of the IVY-paradigm, which is based on TRIZs ideality, states that technological and information systems generally move towards increasing IVYality. In this paper, IVYality is used synonymously with ideality.
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4. For more details on the application of (technical) ideality in inventive problem solving and technological forecasting, see the following literature: Mann (2002); Fey & Rivin (1999).
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5. Conditions of ideality are discussed in the (Internet) publication of the Theory of Ideal SuperSmart Learning.
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6. The word �automaticity� is preferred to automation, since the latter term connotes artefacts. Automaticity refers to autonomous systems in nature and automated systems in the world of artefacts.
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7. In practice, the criterion of no (additional) resource translates into obtaining the highest possible return or benefit on using an external resource.
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8. This criterion implies that practically ideal (computing) objects should be microscopic or molecular in size.
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9. These criteria could also be linked to TRIZs concept of �self�; see Mann (2002); Belski (2000).
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10. The square brackets indicate an indirect link.
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11. These hypotheses are presented with a view to establishing them as laws. The hypotheses are related to TRIZs concept of ideality. The author would like to collaborate with other researchers on testing the three hypotheses. External research on the hypotheses would also be welcomed.
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12. The degree of IVYality is similar to TRIZs operational definition of ideality, i.e.,
    ideality = benefits/(cost + harmful effects). In the �law� of infinite IVYality, benefits are subsumed under advantages while cost + harmful effects fall under disadvantages.
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13. The booklet, �The Theory of Ideal Supersmart Learning� (see http://www.supersmartnetwork.com), provides details on using table 1. What is described as �vertical profiling� in table 1 is similar to plotting a system�s evolutionary potential using multiple axes as in a radar chart; see Mann (2002). In vertical profiling, each variable for the given system is examined horizontally and rated in the cell that most describes its state. For instance, if a single personal computer is under review, then for item no. 1, its quantity would be rated as �one�; this falls under the general heading of �low.� Table 1 could also be used as a screen or �pane� in TRIZs multi (9)-window operator; see Mann (2002). In other words, vertical profiling could be carried out for a supersystem/system/subsystem in the past/present/future.
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14. See Rantanen & Domb (2002).
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15. See, for example, Salamotov, Y. (1999) TRIZ: The Right Solution at the Right Time. Hattem: Insytec B.V.; RRantanen, K. (1997) Levels of Solutions.
    http://www.triz-journal.com/archives/1997/12/d/index.htm
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16. An alternative format for exploration: �Find many and different ways to get rid of or exacerbate [problem type].�
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17. For a discussion on the characteristics of an inventive solution, see for example the book, Simplified TRIZ by K. Rantanen & E. Domb.
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18. See, in the TRIZ-Journal, K. Reas�s articles, TRIZ and Software as well as G. Retseptor�s article 40 Inventive Principles in Micro-electronics.
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19. For a list of thinking tools, see the versatile matrix in the Theory of Ideal Supersmart Learning; visit http://www.supersmartnetwork.com
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20. The originator of the Theory of Constraints is Dr. Eli Goldratt; see Goldratt, E.M. (1999) The Goal. Hampshire: Gower; Goldratt, E.M. (1994) Its Not Luck. Hampshire: Gower.
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21. Visit http://www.supersmartnetwork.com
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22. An alternative format for exploration: �Find many and different ways to get rid of or exacerbate [problem type].�
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23. For a description of the creativity technique of SCAMPER, see for example, Michalko, M. (1998) Thinkertoys. California: Ten Speed Press.
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24. Classic TRIZ uses a different approach, i.e., the contradiction matrix, to determine which inventive principles should be used for resolving technical contradictions. For �speed vs. reliability� contradiction, the contradiction matrix suggests the application of the following principles: (11): �Beforehand cushioning�; (35): �Parameter and property changes�; (27): �Inexpensive short-lived objects�; (28): �Mechanics substitution�. See Savransky, S.D. (2000) Engineering of Creativity: Introduction to TRIZ methodology of inventive problem solving. Florida: CRC Press.
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25. Object mapping is described and illustrated in King (2002). Object mapping is an integration of visual thinking techniques such as mind mapping and concept mapping.
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26. The creative web is described in more detail in King (2002).
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27. The term �holon� was coined by Arthur Koestler in his book, The Ghost in the Machine. According to Koestler, a holon refers to a node in a hierarchy which behaves �partly as wholes or wholly as parts.� In Pattern and Object Thinking (King, 2002), a holon could be described as �an object of an object of an object is��
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