The Ideation Approach to the Search, Development, and Utilization
of Innovation Knowledge
Boris Zlotin and
The oft-quoted expression
"TRIZ is based on technology rather than psychology" is a direct
translation from the Russian. This declaration was made by Genrich
Altshuller to underscore the difference between TRIZ and the many other
creativity techniques, which were based on the thinking and/or behavioral
patterns of successful inventors. Altshuller was the first person who, as
early as the 1940s, refused to embrace an unreliable, unrepeatable, and
personality- dependent psychological approach to creativity. He instead
chose another way, one based on an analysis of the results of
creativity in technology – that is, inventions. This approach allowed
Altshuller to form his conclusions on the basis of information in patents
and other sources of technical information documenting the human
innovative experience. This accumulated knowledge of the most successful
inventive practices resulted in the following discoveries, which form the
cornerstones of TRIZ:
- Definition of an inventive problem
- Levels of invention
- Patterns of invention
- Patterns of technological evolution
In his examination of the patent fund,
Altshuller recognized that the same fundamental problem (i.e.,
contradiction) had been addressed by a number of inventions – but in
different areas of technology. He also observed that the same fundamental
solutions were used over and over again, often separated by many years.
Consider, for example, the following problems:
- Removing the stems and cores from bell
- Cleaning air filters
- Unpacking parts wrapped in protective
paper prior to assembly
- Splitting cracked diamonds along
In each case a similar solution was used:
some quantity of the product (peppers, diamonds, etc.) was placed in an
air-tight chamber, the pressure inside the chamber was increased slowly,
and then dropped abruptly. The sudden pressure drop creates a pressure
difference inside and outside the product, resulting in an
"explosion" that splits the product.
As mentioned previously, these inventions
occurred in different areas of technology and at different times. Yet the
fundamental problem that characterizes these inventions is the same, and
was solved in the same way. Clearly, if the latter inventors had known of
the earlier solutions, their tasks would have been much more
straightforward. Unfortunately, however, the inter-disciplinary barriers
made such an exchange of knowledge virtually impossible.
Altshuller reasoned that knowledge about
inventions could be extracted, compiled and generalized in such a way that
it was easily accessible by inventors in any area. Embarking on this work,
he gave birth to the first innovation knowledge base.
of Innovation Knowledge Bases
To be more precise, it must be acknowledged
that the first actual innovation knowledge base began with the first
documented invention or, even earlier, with the first trade
"know-how" transferred from father to son. To date, the world
patent library contains millions of patents categorized according to
patent classification. This library holds little value for inventors (or
potential inventors), however. In the above example, the likelihood that
an inventor trying to solve the diamond-splitting problem will find a
solution patented in the food industry is next to zero. Given this, we can
categorize the "innovation value" of this initial innovation
knowledge base at Level 0.
The first useful innovation knowledge base
began as a card file that contained descriptions of selected inventions.
The criteria for selection required that an invention be:
- Representative (i.e., similar inventions
existed in different areas of technology)
- Powerful (providing significant benefits
at low cost)
Given the fact that the bell pepper
invention corresponds to over several dozen similar inventions (analogs)
across many technological domains, and that it is sufficiently powerful,
it is considered an effective illustration.
Clearly, there are far fewer inventions
that meet the above criteria – perhaps numbering in the thousands versus
the millions of inventions contained in the "original"
innovation knowledge base. It is obvious as well that an individual
possessing such a card file can be much more productive, and thus it
represents the first innovation knowledge-base tool, with an
innovation value of Level 1. Following Altshuller, other TRIZ
practitioners and researchers began compiling their own invention card
files and exchanging among themselves the information they contained.
Despite the dramatic decrease in the number
of patents to search (and thus the relative speed with which patent could
be evaluated), the effectiveness of this first knowledge-base tool was
still limited as it lacked an adequate structure and/or search
"engine." The main challenge in utilizing the selected
inventions was in recognizing the analogy between problems that seemed
unrelated because they occurred in different industries and were described
using different terminology, yet were similar in a general sense.
Accordingly, the next step in the evolution of this knowledge base was
made by abstracting (generalizing) the "essence" of each
invention, omitting the details that related to a specific industry. For
example, all five of the inventions mentioned above may be described in
the following general manner: "Place a certain amount of the product
into an air-tight container; apply gradually-increasing pressure; then
quickly drop the pressure. The pressure difference inside and outside the
product results in a type of explosion that splits the product." In
this case, these five inventions can serve as illustrations of the more
general principle. This approach resulted in the creation of the
succeeding (Level 2) knowledge-base tools such as the 40 Innovation
Principles, 76 Standard Solutions, and collections of Effects and
The 40 Innovation Principles had no
structure. Rather, they were simply a list of recommendations in no
particular order. Moreover, they represented a mixture of at least three
different types of principles, as follows:
- Non-obvious recommendations such as
inversion or converting a harm into a benefit
- Recommendations for forcing a system’s
development according to the Patterns of Technological Evolution
discovered later (for example, segmentation, self-service, etc.)
- The most frequently applied physical
effects such as thermal expansion and utilization of films and
The collection of Effects and Phenomena
were structured, but the structure reflected the sciences from which they
were derived (physics, chemistry, etc.) and had nothing to do with the
needs of an inventor.
To make the knowledge-base tools useful for
invention purposes, each was supplied with its own search engine: the
Contradiction Table for the Principles, and a functional table for the
The 76 Standard Solutions was the first
tool to be structured according to an inventor’s needs (e.g., problem
type or desired improvement), although in a very general way. Also, the
first attempts to utilize a multi-step process ("chain") in
applying a knowledge base were introduced with this tool. For example,
those solutions called "Class 5" solutions contained
recommendations for increasing the ideality of an obtained solution via
the "smart" introduction of substances and/or fields required to
implement the solution.
The next logical step – to a Level 3
innovation knowledge-base (the Systems of Operators) – was skipped in
the evolution of knowledge-base tools within the classical TRIZ framework.
As will be shown later, the development of a complex, net-like structure
was hardly possible without computers, which were unavailable at that
time. Instead, in parallel with the development of Level 1 and 2 tools,
the most powerful (Level 4) knowledge-base tool started being developed,
namely, the Patterns of Technological Evolution.
System of Operators as a Level 3 Innovation Knowledge Base
The Operator as a creative
recommendation for system transformation
The definition of an Operator, along with
the main prerequisites and requirements for the development of the System
of Operators, were addressed in the paper "An Integrated Operational
Knowledge Base (System of Operators) and the Innovation Workbench™
System Software." This paper was originally prepared in 1992 for
publication in an issue of the Journal of TRIZ devoted to the
Kishinev School. It was pulled from publication, however, due to a related
patent pending. This article has been recently translated and is offered
here, together with this paper.
The objectives for the development of the
System of Operators were the following:
- Create an integrated knowledge-base tool
structured in a way that allows the user to quickly identify that
portion of the entire knowledge base relevant to the problem at hand.
- Elucidate and integrate the unique
experience accumulated by TRIZ practitioners in solving problems
utilizing TRIZ tools and approaches (the "associative chain"
In 1992, the name "Operator" was
chosen to avoid confusion with various elements of existing TRIZ
knowledge-base tools (Innovation Principle, Standard Solution, Separation
Principle, etc.). For the purposes of integration, an Operator denoted any
type of system transformation, including the 40 Principles and Standard
Solutions. Today, we have a better understanding of the nature of the
Operator as a means for creative (i.e., non-obvious) system transformation
versus one for direct knowledge transfer.
An Operator is considered creative if its
- Helps in overcoming psychological
inertia (Example: The Operator "inversion" is applied when
frozen sand is overcooled, rather than heated, to unload it from a
- Offers a different view of the problem
(Example: Facilitating the transportation of a heavy object via the
utilization of slippery pads rather than trying to reduce its weight.)
- Offers a solution that contains a
resolved typical potential contradiction or secondary problem before
it is even revealed (Example: Making a part asymmetrical helps reduce
its weight without the very likely result of sacrificing mechanical
- Offers a typical resource to solve a
problem (Example: The utilization of available substances suggests
making a corrosion test sample into a container for the acid in order
to eliminate the need for a testing chamber.)
- Suggests an evolutionary step (Example:
"Dynamization" makes the system more universal and
represents a new system generation.)
How Operators can grow
Another important issue related to the
System of Operators was the categorization of all known Operators into
- Universal, i.e., applicable to any
problem. Examples are inversion and partial/excessive action.
- Semi-universal, or General (i.e.,
applicable to many situations). Examples are those Operators useful
for eliminating a class of harmful actions.
- Specific (i.e., specialized). Examples
are Operators that constitute methods for dispensing a substance.
This categorization turned out to be very
important, as it has shown the future direction of the growth of the
Operators. For example, it is almost impossible to discover new universal
Operators such as those mentioned above, however, it is relatively easy to
expand the area of specialized Operators. The normal way this expansion is
achieved is by adjusting universal or general Operators to specific needs.
For example, at the present time we are ready to introduce a group of
specialized Operators for eliminating various types of leakage (gas or
fluid). Several other groups of Operators are in the process of
Net-like structure and associative
Another important feature of the System of
Operators is its net-like structure. It is well-known that Genrich
Altshuller made his discoveries and developed numerous tools by analyzing
the wealth of the patent fund without using any particular methods and/or
tools. Basically, Classical TRIZ was founded on inventions that were made
without TRIZ and represented the elucidation of the best intuitive
By the early 1990s, when we began working
on the System of Operators, the situation had changed dramatically: there
were thousands of TRIZ users and hundreds of inventions that had resulted
from the utilization of TRIZ. We therefore had a unique opportunity to
take the second step: verbalizing the phenomenon called "TRIZ
intuition" or the "TRIZ way of thinking." By observing and
analyzing the process of solving problems with TRIZ, we realized that the
process is one of making a specific chain of associations. Consider, for
example, that one must find a way to protect an object from overheating.
An Operator recommends introducing a substance that will draw off the
excessive heat. At this point, one might decide that the solution has been
found. However, an experienced TRIZ practitioner will not be satisfied.
He/she will likely understand that this solution is not the ideal one,
since an additional substance must be introduced into the system,
increasing its complexity. To make it more ideal, one should consider
so-called "smart" ways of introducing a substance without actual
introducing it, or, to at least withdraw the substance as soon as it has
fulfilled its function. The next step will then be to consider the methods
of withdrawing a substance. One way to facilitate withdrawal is to
transform the substance into a mobile state: gaseous, fluid, granular,
etc. Let us assume the gaseous state sounds promising to our inventor. Now
he/she can consider ways to achieve this necessary transformation, such as
phase transformation (e.g., evaporation), combustion, chemical reaction,
etc. It would also be beneficial to facilitate the transition utilizing a
resource such as excessive heat. Summarizing these steps, we have the
- Introduce a substance to withdraw
- Withdraw the substance after it has
absorbed the heat
- . . . via substance transformation into
a mobile state
- . . . via evaporation
- . . . via the utilization of excessive
Now the solution is fairly clear: introduce
an easily evaporated substance that will disappear while protecting the
overheated object. It is obvious that such way of thinking allows one to
enhance the initial idea in the direction of higher ideality and
TRIZ practitioners know that it takes years
of experience to achieve their level of qualification. However, because
associative chains model the way of thinking of the best TRIZ
practitioners, the TRIZ novice can become as effective as the experienced
TRIZ practitioner if these chains are built ahead of time and incorporated
into a ready-to-use tool. The System of Operators is such tool, containing
thousands of links that help the user navigate through the system. These
links create a net-like structure whose development would be nearly
impossible without a computer.
is Not Necessarily Better, or, How to Increase the Value of an Innovation
All "value levels" for an
innovation knowledge base can be seen on the following chart:
||System of Operators
||40 Innovation Principles
76 Standard Solutions
||Patent libraries and
other sources of technical information
According to this chart, it is relatively
easy to increase the number of knowledge units on Level 1 (for example, by
simply including in the base any invention available on Level 0). This
doesn’t empower the knowledge base very much, however. Furthermore,
moving inventions from Level 0 to Level 1 or 2 without proper screening
for innovation usefulness creates informational "noise." For
example, including the effect "super fluidity of liquid helium"
into the innovation knowledge base makes little sense, for the following
- It requires very complex equipment
- There are few situations in general
engineering when this effect is applicable. However, in those special
situations where it can help engineers, they are usually aware of it
and thus the benefit of knowledge transfer is negligible.
As a result, adding the above effect would
only render the search for solutions longer, and without an eventual
It seems that working at the higher levels
requires the highest degree of TRIZ qualification and experience, and
results in the increased value of the knowledge base at a much higher
rate. These crucial factors encouraged our choice to develop the System of
Operators and extend the Patterns/Lines of Evolution. To date, over 400
Operators and 300 of Lines of Evolution have been developed.
Search as an Alternative to the System of Operators
Back in the 1940s, Genrich Altshuller
defined five levels of invention. Approximately 20 years later he
calculated the percentage of inventions existing at each level in the
patent fund, as shown below:
It is well known in TRIZ that
knowledge-base tools like the Innovation Principles and Standard Solutions
help users obtain inventions of level 2 and 3, respectively. Because these
tools are actually tools for knowledge transfer from one area of
technology to another, the reverse statement can be made: inventions of
level 1 to 3 (which constitute more than 90% of inventions, according to
Altshuller’s patent search) are transferable as well. In other words,
for any given problem, there is more than a 90% of chance that a similar
problem has already been addressed somewhere, at some time. The question
now becomes: how can the relevant patents or other appropriate information
The problem of searching invention
information is not much different from that of searching any other
information, therefore, known approaches can be used – for example,
using key words. Two serious problems should be mentioned, however:
- Only relatively recent patents are
available for electronic search
- Use of typical Internet browsers such as
Yahoo, Infoseek, etc. for complicated searches is an extensive job
that carries no guarantee of success.
Recently, development and utilization of
new types of intellectual (semantic) browsers has begun, offering the
- Identification, in the presented textual
material, of the most significant words and word combinations
describing the problem in the best possible way
- Utilization of special semantic
dictionaries that enable analogs and equivalents to be found for
selected expressions, and key word clusters (instead of key words) to
- Searches for relevant clusters in given
sources of information, and estimates as to the probability of
relevance of the obtained material.
Basically, a machine replaces the human’s
understanding of the meaning of the text with an analysis of word
combinations contained in the text. Let us consider a hypothetical
example. We describe a problem of cooling a large, underground
transformer. The analyzer, finding mention of the words
"transformer," "ground," "electrical
energy," and "cooling," might find that ground is
associated with ground water, that the Earth is a porous substance, and
thus that the water for cooling the equipment can be moved by way of an
electrical field: electro-osmosis. (As it happens, a patent exists for the
method just described – the example is still relevant, however.)
Although modern browsers are adequate for finding articles describing
things similar to what the user has requested, or finding patent
citations, they are not yet "intelligent" enough to provide this
level of performance when dealing with creative problems, due to the
- While it is very difficult to create a
detailed and accurate problem description, success depends almost
entirely on the accuracy and correctness of this description.
Moreover, to compile such a description one must accurately and
correctly formulate an inventive problem, which is often as difficult
as solving the problem itself.
- To create a useful problem description,
much depends on the individual’s linguistic and professional
capabilities. Further, a language barrier (the necessity of using a
second language rather than one’s native language) makes the
situation even worse. And lastly, a time factor (i.e., the situation
wherein search materials were written 10-20 years ago or more) can
complicate the situation as well.
- The effectiveness of a browser depends
on the volume and accuracy of its semantic dictionary. The federal
government and private companies have already spent millions of
dollars on research and development of semantic thesauruses, however,
the results are still far from satisfactory.
Two alternative systems for Innovation
Knowledge Management were described above: the System of Operators as an
internal (built-in) representation of knowledge based on the TRIZ analysis
of past and present worldwide innovations and TRIZ experience (knowledge
base); and a direct electronic search (external knowledge base). As usual,
each has its own advantages and disadvantages, as follows:
System of Operators:
- Provides a powerful TRIZ approach
that offers carefully selected, well-proven, and
"purified" innovation knowledge, independent of
- An easy and quick system for
exploring the knowledge base, organized according to the problem
solver’s needs, i.e., in the form of a menu system.
- Represents the acquired human
innovation experience since the dawn of mankind
- Updates require the work of TRIZ
specialists to screen new patents and producing new Operators
- Due to the high level of
abstraction, additional creative work is required for
Direct electronic search:
- Very recent inventions are
available for search
- No special preliminary work on
Operators is required
- Recent search engines (browsers)
are dependent on terminology and language proficiency
- Only relatively recent inventions
(patents) are available for electronic search.
- Searches based on word clusters
are hundreds of times more complex and thus time consuming
The TRIZ approach to dealing with
alternative systems recommends that we consider integrating them,
targeting the elimination of negative features while conserving (or even
improving) positive ones. The results of the work in this direction
undertaken by the Ideation Research Group are described below.
The System of Operators and the other
knowledge-base tools mentioned above help in solving problems that have
been formulated in some manner (either right or wrong). When used with a
complex innovation situation with many inter-related problems, rather than
with a single problem, the efficiency of utilizing the System of Operators
can become close to zero. Although some Operators incorporate certain
changes into the problem statement (as mentioned in the section entitled
"The Operator as a creative recommendation for system
transformation"), they cannot address multi-faceted,
multi-hierarchical situations or systems in their entirety.
At the same time, it is widely known that a
well-formulated problem is a problem that is nearly solved. Often, by
reformulating the problem, the solution becomes obvious or is more easily
obtained than with the initial problem statement. Breaking up a complex
and unclear innovation situation into a set of individual, well-defined
problems is a key to a successful problem solving.
The fact that the same problem situation
may have multiple problem statements is rooted in Mr. Altshuller’s
multi-screen model of creative thinking or the so-called "systems
approach." According to this approach, any system has a hierarchical
structure that includes subordinate sub-systems and at least one
higher-level system to which it, in turn, serves as a sub-system. Very
often the links between the system, sub-systems and super-systems are
rigid enough to ensure that a change in one part of the system causes
substantial changes (either positive or negative) in adjacent systems and
sub-systems, in particular:
- A breakdown in one part of the system
can cause undesired consequences in other parts of the system, and in
the system as a whole
- An undesired situation in one part of
the system can be eliminated by changing a different part of the
As a result, one problem can be addressed
in different – and often very diverse – ways, that is, the assertion
can be made that every problem has more than one way by which it can be
Suppose we are faced with the problem of
how to increase the speed of an airplane. This problem can be approached
from various standpoints, such as: increasing engine power, improving the
aero-dynamics of the airplane body, etc. At the same time, we can
formulate this problem at a higher system level by addressing the purpose
we wish to achieve by increasing the airplane’s speed. Obviously, we
want to increase the speed so that the flight-time will be reduced. But at
the same time, a commercial airplane belongs to the super-system named
"transportation." In this case, we should consider the other
systems that contribute to the overall time spent in taking a trip,
including the time required to get to the airport, check in, wait for an
available gate, pick up luggage, etc.
Continuing in this manner, we can change
the problem statement to consider, for example, reducing the time spent on
the ground rather than in the air. This change seems even more reasonable
from the standpoint of resource availability for system improvement: it
may very well be that there is a physical limit to the increase in speed
that can be achieved, however, the ground service systems have much in the
way of resources by which improvements can be made.
In the course of their work, many TRIZ
specialists have been in situations where a customer has spent an enormous
amount of time trying to solve the wrong problem, and the TRIZ specialist
succeeds because he/she offers a different approach.
Refined and processed nickel is usually
supplied in granular form in the shape of small pellets. To produce these
pellets, molten nickel is dispersed into water by being dropped from a
substantial height. The drops of molten nickel are cooled by the air as
they fall towards the water, becoming somewhat hardened in the process.
Upon entering the water, they completely harden and solidify.
This approach works in principle but, in
practice, as multiple drops of nickel are released simultaneously, their
mutual proximity creates a localized thermal hot zone, which inhibits each
drop from cooling. As a result, the metal hits the water at a temperature
that is much higher than desired. Thermal shock results, which fractures
the metal, producing a significant quantity of unusable nickel powder.
To recover the powder, the manufacturers
attempted to introduce it into the furnace together with the nickel ore.
In this case, however, the nickel powder burns up before it reaches the
molten nickel surface, due to the high temperature and oxygen blasting. Te
problem was to find a way to protect this powder from burning.
After finding several solutions to this
problem, the problem statement was changed. It was clear that attempts to
improve the powder utilization process did not constitute ideal solutions
because the root cause of the problem was unresolved: i.e., the problem of
producing the powder in the first place. Moreover, an additional harmful
result of this root problem was that a certain number of the pellets
fractured not during production but later, as they were being transported
to the customer. This resulted in customer dissatisfaction. Focusing on
the nickel production process itself rather than on the utilization of
powder allowed a solution to be found that rendered the problem of powder
In this case, the problem statement was
changed due to experience, TRIZ intuition, etc. The challenge we faced,
then, was in transforming this intuition into a well-defined process that
can be followed by anyone.
The actual development of the Problem
Formulation process began around 1985. The following well-established
methods and techniques were taken into consideration (shown in historical
analysis developed by Larry Miles to describe a product/process in
terms of its hierarchical system of numerous useful functions.
diagram developed by Ishikawa Kaoru to describe a process in terms
of cause-effect relationships.
of ARIZ (in early versions) developed by G. Altshuller to identify
problems formulated on higher and/or lower levels of system
hierarchy, which might replace the initial (and sometimes
unsolvable) problem statement.
later versions of ARIZ devoted to changing and/or replacing the
initial problem statement with a more promising one(s) in those
situations where the initial problem statement can not be resolved.
model of creative thinking developed by G. Altshuller based on the
systems approach and which encourages the problem solver to consider
the whole system rather than focus on the sub-system associated with
concept of conflict, including its graphical representation.
experience accumulated by Boris Zlotin and other TRIZ specialists in
changing/replacing the initial problem statement by a more promising
The Problem Formulator™
is an analytical tool that encompasses the problem formulation process.
It can be used manually or with software support. The process includes two
steps: building a cause-effect (event) diagram; and the formulation
itself. Accordingly, the associated software tools include two main
modules. A brief history of the development of the Problem Formulator is
and Alla Zusman develop the first step-by-step process for analyzing
a given problem statement, restoring the initial innovation
situation and identifying potential directions for innovation.
offers an integrated graph of useful and harmful functions/
effects/events, formulating eight key questions for identifying the
links between useful, harmful and correcting functions/events,
identifying contradiction (key) nodes, and introducing standard
frames for problem statements that fit all possible problem
situations. This technique was included as a chapter in ARIZ –SMVA
Malkin's group began developing a software module for the automatic
generation of problem statements and building of graphical models.
They suggested introducing specific link words reflecting useful and
harmful relationships, in order to allow the software to identify
the function type and built a corresponding mathematical model.
of a Navigator – a system that assists one in building the
graphical model using a set of questions and a pre-determined work
scenario. Patent application filed.
practice with the Problem Formulator.
No. 5,581,663 issued.
of a Problem Formulator for Windows-95. New features introduced: an
additional link ("hinders"), extended lists of standard
problem statements including formulated contradictions, the ability
to edit graphical features.
The development of software capable of
formulating problems related to an innovation situation had always been a
formidable task, and represented a challenge similar to the classical
problem in the area of Artificial Intelligence (AI), where a machine must
be able to recognize a meaning presented in text form. However, we could
avoid solving this long-standing problem by utilizing some elementary
patterns found in structural linguistics. Thus is was discovered that
automated problem formulation can be provided via the following
- Divide all text elements into two types:
- Invariants, that is, elements that do
not change during the formulation process and thus do not need to be
"understood" by a machine. These elements contain specific
information (functions, actions, effects, events, and other
statements) related to the problem situation.
- A limited number of changeable,
standardized elements (link verbs) that describe the relationships
between the invariants and that can be recognized (and acted
accordingly upon) by a machine.
- Define the minimum amount of standard
link verbs that will allow any situation to be described (so far, four
such link verbs are sufficient, and further research is directed
toward improving the quality of the descriptions that can be made,
with the possibility of reducing the number from four).
- Visualize the relationships between
invariants with the help of graphical images of link verbs (various
types of arrows).
- Develop rules and algorithms for
transforming the graphical description of a problem/system into a set
of relevant problem statements.
- Adjust the available knowledge-base
tools according to the automatically formulated problem statements.
- Develop a navigator to direct the
process of building the graphical model by presenting the user with a
set of relevant questions.
The output of problem formulation is a set
of individual problem statements. Once these problems (i.e., problem
statements) are identified and elucidated, each of them usually represents
a distinctive direction towards a group of solutions. One of the most
surprising results of working with the Problem Formulator is the discovery
that the meticulous process of building the graphical model allows a
nearly exhaustive set of problem statements to be formulated. This in turn
can reveal quite promising approaches that might be non-obvious even to
experienced professionals. Often, once a new approach is spelled out, the
solution is straightforward.
Mapping and the Knowledge Wizard™
Analytical and knowledge-base tools
Any problem-solving process involves two
main components: the problem itself and the system in which the problem
exists. Typically, an inventor tries to eliminate the problem by changing
the system. But experienced inventors realize that when faced with a
difficult problem, it is helpful to reconsider the problem (i.e., change
the problem statement). In 1994, we suggested dividing all TRIZ tools into
two groups: analytical and knowledge-base, having in mind that
analytical tools help change the problem statement while knowledge-base
tools suggest ways for transforming the system.
It was also discovered that, in general,
while knowledge-base tools must be specific for addressing different types
of problems (e.g., specialized Operators developed for use in
technological situations will not work with business problems), analytical
tools are quite universal. Obviously, the Problem Formulator belongs in
the category of analytical tools and thus may be used to analyze any type
of situation, making it an effective tool for supporting the process of
Knowledge as a multi-dimensional net
A decision-making process is based on data,
information, and knowledge. Eliyahu Goldratt defines information as a
"portion of the data which impacts our actions, or if missing or not
available will impact our actions." Knowledge can be defined as a
collection of information, including data and the ways in which it can be
manipulated, capable of generating new information. Knowledge always
encompasses more than the information it is based upon. There are numerous
and complex logical or associative links between elements of information
(knowledge units) that comprise knowledge and transform it into a
multi-dimensional net. These links may change, making the whole
"alive" and capable of evolving and adapting to various specific
With this model as a base, we can build a
model of the creativity process as a "discharge" between
different elements of the knowledge net, and view the relevant
associations as the channels for this discharge. Consider, for example, an
individual focused on solving a problem related to the wearing of gear
teeth. An association based on the fact that the word "teeth"
may relate to biology as well as to technology might help him/her transfer
a solution known in biology, such as the growth or restoration of new
In other words, knowledge in the human
brain is capable of effectively transforming acquired information and
generating new information, converting knowledge into a valuable resource.
TRIZ technologies related to revealing and utilizing resources are, in
principle, applicable to the management of knowledge resources.
The acquisition, generation and transfer
The process of knowledge generation starts
with the collection and acquisition of various information via the
classical analytical method involving the splitting of complex systems
into elements and documenting the facts, parameters, relationships and
other information related to those elements. This process is always
conducted with the risk of losing important information related to the
system as a whole (rather than to its elements).
The process of transforming information
into knowledge is of an opposite nature. It is a synthetic process
resulting (consciously or otherwise) in the discovery of patterns and
mechanisms of system functioning, in the generation of missing information
in the form of hypothesis and theories, and eventually in the building of
a systemic, comprehensive knowledge net (or of appending to an existing
knowledge net). This process leads, in turn, to an understanding of the
system’s behavior, that is, to the ability to predict the actions and,
eventually, the evolution of a system.
The main problem of knowledge transfer is
accommodating it to the method of knowledge acquisition described above,
that is, to split it into elements arranged in consecutive chains and
which can be documented in text books, scientific papers or instructions.
This process is usually controlled by a knowledge "transmitter,"
however, systemic information can be lost as a result. The knowledge
"receiver" will replace the missing information on his/her own,
resulting in knowledge corruption, which causes communication problems and
There are certain known ways to address the
problem of knowledge transfer. These are based on an intuitive
understanding of the net-like knowledge structure and involve various ways
of visualizing knowledge in the form of tables, matrices, flowcharts,
structural and functional diagrams, etc. These methods, while definitely
useful, are insufficient.
The process of knowledge transfer can be
significantly improved through utilization of the Ideation/TRIZ tools and
processes, allowing information to be "packed" into available
"knowledge frames" such as the Patterns/Lines of Evolution,
typical contradictions, typical evolutionary models, etc. One of the most
promising directions we have found is that graphical models built with the
help of Problem Formulation techniques and tools are the best structures
to fit, reflect and map the net-like knowledge that resides in the human
Knowledge mapping with the help of the next
generation of Problem Formulator™, called the Knowledge
Wizard™, can facilitate all the processes related to
knowledge management mentioned above. For example, it is obvious that
the same subject or system might reflect different knowledge nets for
different people. Each knowledge net related to a specific subject is
personal, and depends on other knowledge possessed by an individual, on
his/her psychological profile, and on other parameters and circumstances.
Utilization of the Knowledge Wizard can reduce miscommunication caused by
these differences, and help with negotiations, decision making, education,
and personal interactions, and even serve as a tool for psychologists.
It was discovered that different
individuals build different function/event cause-effect diagrams related
to the same subject based on each individuals particular way of thinking.
Building two or more maps and analyzing the differences between them
allows the picture to be narrowed down without losing sight of the
A knowledge map (or graph) entered into a
computer allows knowledge to be transformed according to certain
algorithms, which take into consideration the following:
- Map structure presented through links
that connect knowledge units
- Information contained in knowledge units
Each type of knowledge unit may have its
own recommendations to be followed, additional questions to be asked,
explanations, typical problems associated with it, etc. For example, for
any unit of negative information, an event or statement included the
following typical problems can be automatically formulated:
- Find a way to prevent, reduce, or
eliminate the negative event.
- Find a way to benefit from the negative
The automatic transformation of knowledge
provides effective ways for the acquisition and utilization of that
knowledge. It is also found to be similar in many ways with the process of
translating text from one language to another. For example, knowledge
mapped in the Knowledge Wizard diagram reflect cause-effect relationships,
which can be "translated" into a new type of language called the
"problem description" (a set of related problems statements),
which in turn helps reduce psychological inertia and unveil new creative
approaches. As mentioned above, each type of description may have its own
knowledge base with further recommendations.
- The definitions of an innovation
knowledge base and its value levels were presented; these were used to
support the strategy chosen for development of the Ideation
knowledge-base tools, with the focus the on integrated System of
Operators and the Lines of Evolution.
- A new approach based on the
hybridization (combination) of two alternative approaches to the
development of an innovation knowledge base can result in a
breakthrough informational technology.
- Changing the problem statement is very
often a key to success. The problem formulation process and Problem
Formulator™ software tool allow the user to obtain a set
of nearly exhaustive problem statements, and thus help him/her unveil
promising, non-obvious approaches.
- A graphical model (functional graph,
event diagram, knowledge map) built with the help of the Problem
Formulator or Knowledge Wizard™ reflect the natural
structure of knowledge stored in the human brain, and serves as one of
the best ways to transfer and/or utilize knowledge for the creativity
- The system comprising the
"graphical model and the formulation module" provides the
"translation" from the functional or cause-effect
description of a situation into a new type of description – called
the problem description – allowing each problem statement to be
automatically connected, and thus its own knowledge base be obtained
for further consideration.
- 1. G.
S. Altshuller, Creativity as an Exact Science (Gordon and
Breach Science Publishers, 1984).
- Boris Zlotin and Alla Zusman, "An
Integrated Operational Knowledge Base (System of Operators) and the
Innovation WorkBench System Software,"
1992 (in Russian). See the English translation on the Ideation
International web site.
- G. S. Altshuller, Creativity as an
Exact Science (Gordon and Breach Science Publishers, 1984),
- John Terninko, Alla Zusman and Boris
Zlotin. Systematic Innovation; An Introduction to TRIZ (CRC St.
Lucie Press, 1998), 47-64.
- Boris Zlotin and Alla Zusman.
"Problems of ARIZ Enhancement," Journal of TRIZ 3,
no. 1 (1992), in Russian. See the English translation on the
scientific channel of our web site.
- Ideation Methodology educational
materials (Ideation International Inc. 1995).
- Eliyahu M. Goldratt. The Haystack
Syndrome (New York: North River Press, Inc., 1990).
- Development with significant
contribution of Len Kaplan and Sergey Malkin’s software team is