Japan TRIZ Symposium 2010 Paper


Computer-Aided (Systematic) Innovation:
New Tools and New Ways of Thinking
Darrell Mann (Systematic Innovation Ltd, UK),
Dr Paul Filmore and Mir Abubakr Shadad (University of Plymouth, UK)
The Sixth TRIZ Symposium in Japan, Held by Japan TRIZ Society on Sept. 9-11, 2010 at Kanagawa Institute of Technology, Atsugi, Kanagawa, Japan
Introduction by Toru Nakagawa (Osaka Gakuin Univ.), Apr. 2, 2011
[Posted on Sept 19, 2011] 

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Editor's Note (Toru Nakagawa, Sept. 17, 2011)

This paper was presented by Gaetano Cascini a year ago in an Oral session at the Sixth TRIZ Symposium in Japan, 2010.  Presentation slides have been posted in PDF both in English and in Japanese translation (by Yutaka Watanbe) in the Members-only page of Japan TRIZ Society's Official Site since last March.  For wider circulation, they are now posted here publicly under the permissions of the Authors. 

Last April I posted an introduction to this paper as a part of my Personal Report of the Symposium.  The excerpt is posted here again in English.  This paper reports a new software tool for supporting the users in inventive problem solving; the software is dialog based and seems flexible in the structure.  I wish my introduction may be of some help for you to understand this valuable presentation.

 

Top of this page Abstract Slides in PDF Slides in Japanese, PDF Nakagawa's Introduction Nakagawa's Personal Report of Japan TRIZ Symp. 2010 Japan TRIZ Symp. 2010 Japanese page

[1] Abstract

Computer-Aided (Systematic) Innovation:
New Tools and New Ways of Thinking

Darrell Mann (Systematic Innovation Ltd, UK),
Dr Paul Filmore and Mir Abubakr Shadad (University of Plymouth, UK)

Abstract

The paper discusses recent research to proceduralise and automate aspects of the TRIZ/Systematic Innovation process. Three particular areas are discussed:

1) The development of a toolkit (AEGIS) aimed at increasing the speed with which designers can evolve designs using TRIZ-based ‘intelligent mutation algorithms.
2) The development of a piece of software (ApolloSigma) aimed at speeding the process of identifying high potential patents from the global patent databases.
3) The development of a toolkit (iTrenDNA) aimed at helping engineers and designers to better understand unspoken consumer and market needs.

Each aspect of the work will be described in the context of a range of exemplar case study examples:

Extended Abstract

AEGIS

In theory, this is the part of the innovation story that is the easiest to automate. Thanks to several decades of TRIZ research, we now know that there are only a relatively small number of strategies (as found in the Inventive Principles, Trends, and Inventive Standards for example) that have the possibility to result in a ‘more ideal’ solution. In theory, then, if we are able to translate these strategies into appropriate mathematical algorithms, we ought to be able to configure a computerized design system. In practice, this is something that we have been experimenting with for some time now. Figure 1, for example, illustrates a screenshot from the prototype AEGIS semi-automated evolutionary design tool.

This software is described as ‘semi-automated’ since, although it is able to take a (currently 2D) design and apply mutations to it, the software has no understanding of context, and so delegates that responsibility back to the user. Thus, to take a few examples from the above packaging design mutations, although it is possible to inform the computer that ‘adding holes’, ‘adding curvature’, ‘add protrusions’, ‘change colour’ or ‘increasing asymmetry’ all act in the direction of ‘more ideal’, the algorithm has no idea where or how much of the instruction to apply to the design. Consequently, what the software is currently programmed to do is, starting from a ‘parent’ design, apply a number (presently 16) of intelligent mutations which then get presented back to the user as offspring. The user is able to select one of these offspring to become the new parent, and then to repeat the process by creating a new set of mutated offspring. In this way, by selecting the ‘best’ of the offspring after each round of mutations, the user is effectively supplying a ‘best’ context.

Figure 1: Semi-Automated Evolutionary Design of Simple Products

The only new thing about this kind of evolutionary design approach is that rather than relying on random mutation as the evolution engine, we can utilize TRIZ heuristics to provide a more intelligent mutation algorithm. One of the first evolutionary design approaches was a computer game version of Richard Dawkin’s ‘Blind Watchmaker’. What the Blind Watchmaker game was able to show was that, starting from a small set of very simple rules, it was possible to create some very sophisticated structures from a surprisingly small number (typically less than 40) of design iterations. Having now incorporated non-random mutations, early tests suggest that we can evolve to strong design solutions in a considerably smaller number of iterations.

ApolloSigma

TRIZ research shows users that the future evolution of products, processes and components is highly predictable. Things like the Trends and Evolution Potential tools provide an objective means of calculating the likely ‘where’s and ‘when’s of an industry and the IP held within that industry. The calculation, however, still requires a deal of creative thought and involved analysis. A typical analysis for an IP family will take around 4-6 weeks to answer the key questions asked by business leaders (eg how much is my patent portfolio worth? What are the likely future risks? What are the likely future exploitation opportunities in other domains? Etc). The process is made possible thanks to having a database of three million radar plots and previous analyses, but it is not exactly an interactive analysis that permits live scenario planning activities to take place.

In order to solve that particular problem, we have built a number of fully automated IP value assessment algorithms built on the findings accrued from the three million datapoints. Because the measurement needs to be future-focused rather than historical, we have deliberately ignored the traditional measures of IP quality like citations and classifications. Instead, we have built search tools that take advantage of evolution trend information like for example the classic TRIZ ‘dynamization’ trend. By searching through the IP database looking for key words like ‘joint’, ‘flexible’, ‘pneumatic’, ‘field’, etc it is possible to rapidly assess the maturity and number of jumps that a current solution hasn’t made yet.

The output from the machine assessment measures IP against two important dimensions; the first looking at its current strength; the second looking at future potential:

Current Value Index – in this dimension we mine, for example, patent text looking for key-words that make the solution easy to circumvent. We have also identified a number of other correlating ‘strength’ factors such as number of independent Claims, length of Claim text, presence of quantified data, etc.

Future Value Index – this dimension very specifically uses the aforementioned trend keywords, but we also make a semantic search looking for function words in order that we can establish a hierarchical position of the IP under investigation relative to a universal hierarchy of functions.

The resulting output is typically plotted as shown in Figure 2. The plot divides the IP world into four distinct domains:

Figure 2: Sample Output From ApolloSigma Software

Examining Sony versus Samsung Patent ‘Quality’

The main purposes of the output, as shown in the Figure, is to first of all benchmark the IP of different players within an industry, or within a certain function. Looking within the portfolio for an organization, it is then aimed at providing portfolio management information – which are the things that can be dropped, ring-fenced or nurtured for example. Because the analysis is forward looking, its biggest value comes when used in conjunction with the trend information. In this role, it becomes possible for inventors and IP generators to assess the Future Value Index of a patent application before it is submitted. In this way, a piece of IP with a low score can be identified early and the inventor is able to look at the un-exploited trend jumps and determine which should then be incorporated into the invention disclosure.

TrenDNAi

Our recently published book, TrenDNA, is the outcome of a six year programme of research aimed at better understanding consumer and market behavior. The theory being that if we can better understand the unspoken words of the market, we can give our engineers and designers better problems to solve. TrenDNAi (Figure 3) is the recently developed software implementation of the TrenDNA process. Given the dynamic nature of consumer and market trends, it is expected that this on-line tool will allow users to access and make meaningful use of the most up-to-date trend information to better define what new products and services are likely to be successful in various marketplaces around the globe.

Figure 3: Sample Screenshot From TrenDNAi Software Tool

 


[2]  Presentation Slides in PDF

Presentation Slides in English in PDF (32 slides, 2.4 MB)

Presentation Slides in Japanese in PDF (32 slides, 2.5 MB) (Japanese translation by Yutaka Watanabe (SONY))

 


[3]  Introduction to the Presentation (by Nakagawa)

Excerpt from: 
Personal Report of The Sixth TRIZ Symposium in Japan, 2010, Part G
by Toru Nakagawa (Osaka Gakuin University),
Posted on Apr. 2, 2011 in "TRIZ Home Page in Japan"

 

Darrell Mann (Systematic Innovation Ltd, UK), Paul Filmore, and Mir Abubakr Shadad (University of Plymouth, UK) [E08, O-8] gave an Oral presentation with the title of "Computer-Aided (Systematic) Innovation: New Tools and New Ways of Thinking".  Paul Filmore was the presenter.  The Authors' Abstract is quoted here first.

The paper discusses recent research to proceduralise and automate aspects of the TRIZ/Systematic Innovation process. Three particular areas are discussed:
1) The development of a toolkit (AEGIS) aimed at increasing the speed with which designers can evolve designs using TRIZ-based ‘intelligent mutation algorithms.
2) The development of a piece of software (ApolloSigma) aimed at speeding the process of identifying high potential patents from the global patent databases.
3) The development of a toolkit (iTrenDNA) aimed at helping engineers and designers to better understand unspoken consumer and market needs.
Each aspect of the work will be described in the context of a range of exemplar case study examples:

[*** I missed to attend at this presentation due to the double track agenda.  So I am writing this introduction without seeing their demonstration of software tools.] 

(1) The first software tool is named 'Accelerated Evolutionary Graphics Interface System (AEGIS)'.  The slide (center) shows its interface, for Version 6.  Given a parent design (show in the top-left image) and options of mutation algorithms, the software generates number of modified images as shown in other cells (in the left part).  Version 6 has the new feature of multiple layers for constructing the images.  The mutation algorithms are partly random and partly oriented with TRIZ-based knowledge of Trends and Principles, shown in the slide (right).  Selecting one of the new images and set it as a next parent, the mutation can be calculated repeatedly.  Thus the design work can be carried out quickly by testing a lot of random variations under some control. 

(2) On the second topic, the Authors discuss to re-think the valuation of Intellectual Properties (IP).  Evaluation of specific IP's is of course demanded as shown in the slide (below-left).  There are needs of evaluating (a group of) IP's in a larger scope, as shown in the slide (below-right).  However, "IP valuation today delivers the wrong information too late" and hence "IP valuation is divorced from business strategy", the Authors say. 

  

For overcoming this situation, the Authors have proposed four different indexes for calculating the IP values. (See the slides (below).)  (a) An index to show the current value, which is calculated with the keywords detected by semantic analysis.  (b) An index of future value, which is evaluate with the untapped evolution potential and rate of change in the concepts of Trends of Evolution.  (c) Second index of future value reciprocally related to the number of steps from the Ideal Final Results (IFR) of Main Useful Function (MUF).  (d) 'Good' words/'Bad' words in relation to various Trends of Evolution.

 

  

The slide (below-left) shows the interface of their new software tool, named 'ApolloSigma'.  A patent (using its Patent Number ) or any text may be input to this piece of software, then its evaluation is output as an orange circle in the right window.  The window is a two-dimensional (x, y) space composed of the Current Value Index (x) and the Future Value Index (y).  The Authors suggest, in slide (below-right), to use this evaluation software for the purpose of evaluating the solution prior to filing a patent and of improving the solution by use of the recommendations based on bad and good words. 

  

(3)  The Authors further go on to discuss how to understand the customers/market's needs.  The slide (below-left) shows that Innovation happens when 'Voice of the System' matches with 'Voice of the Customers'.  And, they say, TRIZ is very good at the job of finding and meeting with the 'Voice of the System'. For example, as shown in the slide (below-center), concerning to 'What is the Perfect Shirt?', TRIZ can show us various Ideals, e.g. 'Big AND small', 'Thick AND thin', and SELF-cleaning, SELF-ironing, etc.  And TRIZ can guide us in finding such Ideals.  However, from here on which directions should we pursue?, the Authors pose.  At this position we need to find the 'Voice of the Customers' and follow that direction, the Authors suggest.

   

For finding the 'Voice of the Customers', the Authors group have published the textbook 'trenDNA' (slide (below-left).  It shows a large number of big trends (in the global/country scale) and their enhancing/conflicting relationships, and suggests a procedure for finding opportunities in the resolution of conflicting big trends. In the present paper the Authors have shown a prototype of their new software tool, named 'iTrenDNA', as shown in the slide (below-right). 

 

The slide (right) shows the Authors' Conclusions/Future work.  Here they state their position of 'Systematic Innovation (SI)' in contrast to TRIZ.  In place of Technical areas for TRIZ, they try to cope with Technical + Business areas for SI.  In place of Tangible knowledge for TRIZ, they are going to handle Tangible + Intangible knowledge for SI.  In place of Complicated problems for TRIZ, Complex problems for SI. These are the directions for SI beyond TRIZ, the Authors state.

[*** This is a presentation full of insights and background research. We should keep watching and follow their progress with interest.]

[Note: We have now posted the original presentation slides in the present Web site in English and in Japanese . (Sept. 19, 2011)]

Top of this page Abstract Slides in PDF Slides in Japanese, PDF Nakagawa's Introduction Nakagawa's Personal Report of Japan TRIZ Symp. 2010 Japan TRIZ Symp. 2010 Japanese page

 

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Last updated on Sept. 19, 2011.     Access point:  Editor: nakagawa@ogu.ac.jp