Editor | On 05, Jan 2009

By Darrell Mann

Abstract

An ongoing program of patent research showed trends in evolution, function and contradictions. A specific focus was applied to the contradictions, which detailed 100 quasi-random selected patents, including how the 2003 contradiction matrix tool is calibrated relative to the original Classical TRIZ matrix. The current relative effectiveness of the two matrices showed results of 96 percent and 18 percent, respectively. Around 140,000 patents from the research have been analyzed and added to the assorted TRIZ knowledge-bases. This paper presents an update on research, examines patents granted from 2006 to 2008, and summarizes key findings in patent analysis, customization and planned future work.

Introduction

A large part of the strength and power of the Theory of Inventive Problem Solving (TRIZ) exists because the methodology was built on substantial foundations. These foundations analyzed a large number of patents. Around 1985, however, this analysis was halted and the research focus shifted to other important areas.

The shift showed that using some of the TRIZ tools on today’s problems was not providing users with as much assistance as it could. That is because the world has become more electronic focused with a software bias in its outlook.

With this shift in mind, a large program of patent analysis was developed in 2000. The aim of the research was to extend TRIZ to accommodate the changes brought on by the advancements in business and technology since 1985.

The findings from the patent research showed trends in contradictions, invention and evolution with the following outlines:

1. Contradictions: An update on previously published articles compares the accuracy of the classical TRIZ contradiction matrix and the 2003 update. The focus is on 2006-2008 in general with specific attention given to patents granted on a randomly selected date –July 8, 2008. This cross-calibrated spot point will assess how well the contradiction-challenging strategies used by inventors today compare to the strategies that the two matrices would have recommended.
   
2. Levels of invention: All of the patents included in the analysis have been assessed in relation to the five levels of invention specified during the original TRIZ research. Research findings showed a dynamic shift in invention level that took place during the last 20 years, specifically since 2006.
   
3. Trends of evolution: A number of technology trends were uncovered that have not been previously observed. New research shows the evolving concepts of evolutionary limits and evolutionary potential in radar plots that were constructed for all of the patents analyzed. The radar plots are shown to offer a means of not only comparing similar patents, but also to present a means of benchmarking technologies against a set of global data points.

Contradictions

Describing how the patents were searched for conflicts will indicate how the research is conducted, particularly as other information inside each new invention disclosure document is examined.

This is the author’s third published paper examining the differences between the original contradiction matrix of Classical TRIZand the 2003 updated version.^1,2 The previous publications detailed how the two matrices compared and analyzed patents published since Matrix 2003 was completed.^3,4 The point was to explore the stability of the new matrix and to provide quantified data on how well the two matrices predicted the inventive principles used by recent inventors. The structure of the work is similar to the accuracy of the original matrix.⁴The following points of the method used included:

1. The titles, abstracts and assignments of all patents were given a preliminary assessment by an analyst in order to determine whether it was worth analyzing the patent in greater detail. Around 85-90 percent of patents get rejected at this stage simply because there are not sufficient resources available to analyze them all. The aim in applying this filter is to try and identify the Level three and higher inventions, which offer the most important information in terms of best practices.
   
2. For each of the 10-15 percent of patents chosen to analyze in greater detail, specific aspects of a design the inventor was seeking to improve, the parameters these aspects conflicted with and how the inventor overcame the conflict were identified. With many inventions the inventor is seeking to overcome multiple conflicts and contradictions. Rather than try to map every instance, the analysis attempts to identify one or two of the most significant aspects of the invention; asking such questions as “What was the main motivation of the inventor in terms of what they wished to improve?” and “What was the main reason according to the prior art that prevented the improvement from being achieved?”

For example, the following text is from one of the patents included in the quasi-random sample of 100: (See Appendix)

“Image blur is a common problem in photography. Some common causes of blur in a photograph are subject motion, camera motion (shake), and focusing errors. Blur is a particular problem for casual or amateur photographers who may not know how to diagnose the causes of blur or how to change their photographic technique to improve their results. As new consumer camera models are being produced with zoom lenses capable of very long focal lengths, blur due to camera shake is especially troublesome.

Various devices and techniques have been proposed to help address the problem of image blur due to camera shake. For example, Murakoshi (U.S. Pat. No. 4,448,510) uses an accelerometer to detect camera shake, and provides an indication to the user of the camera if the acceleration exceeds a threshold level.

Satoh (U.S. Pat. No. 6,101,332) also senses camera shake, and combines the shake information with other camera parameters to estimate how much image blur might result. A set of light emitting diodes communicates the estimate to the photographer.

Another approach has been to automate the camera operation, and let the camera choose settings that will minimize blur. For example, Bolle et al. (U.S. Pat. No. 6,301,440) applies a variety of image analysis techniques in an attempt to improve several aspects of photographs.

Each of these approaches has its drawbacks. The above techniques may require the addition of expensive electro-mechanical components to a camera, thereby increasing the camera cost. The techniques may address only one potential cause of image blur. The techniques give the camera user little guidance about how to improve their photographs, and in fact, additional automation that reduces the photographer’s control of the camera may even add to the mystery of why a particular photograph is blurred.

A solution to the problem of image blur is needed that also addresses these difficulties.”

This text represents the complete background to the invention section of U.S. Patent # 7,397,500. This section is always the place to start the search for contradictions. In the majority of cases, this is the section where the inventors provide answers to the two key contradiction questions: “What are they trying to improve?” and “What is stopping them?” In this particular case, the answer to the first question is immediately clear – they are trying to reduce image blur. Answering the “What is stopping them?” question is a little more difficult. On one level, they are told that the previous solutions have all resorted to “addition of expensive electro-mechanical components.” At this point, they could simply choose to try and map these words onto the matrix by finding the words in the list of parameters that best fit image blur versus “addition of expensive electro-mechanical components.” Should they do that, they will most likely conclude that stability and system complexity are the two best matches. In the case of both translations they need to be careful:

• Image blur and stability: In the original Classical TRIZ matrix, stability is written as “stability of the object’s composition.” It implies something more similar to chemical stability. In research it has consistently taken the more general interpretation of stability with few if any negative effects. In Matrix 2003 stability is a suitable match for image blur.
  
• System complexity: This is one of the most general of the parameters in the matrix. Before deciding whether to map a particular improving or worsening feature of an invention to this parameter, the root cause explanation for why the complexity gets worse should always be sought. The simplest strategy for doing this involves asking the usual root-cause “Why?” question. In the case of U.S. Patent # 7,397,500, the question was: “Why are expensive electro-mechanical components added?” The answer observed from the invention disclosure text is that those components exist to detect something that will allow the camera to know that there may be a blur problem. In other words, the deeper level contradiction is between image blur and an individual’s ability to detect it. (Note: ask the “Why” question again to dig deeper into the problem. The answer becomes circular: “Why do they want to detect blur?” They want to detect blur in order to know that the camera detects blur. When this circularity happens, there is no need to dig deeper.)

“Ability to detect” did not appear in the invention disclosure. Recent speculation that semantic processor software can find contradictions should be treated with a high degree of skepticism. Experience has shown semantic processors can give only a superficial level of understanding of the real story.There is still a requirement, therefore,for a human to interpret the intent of the inventor’s patent.

It has been possible to dig one level deeper than “system complexity.” The key conflict in U.S. Patent # 7,397,500 is between image blur (stability) and the inability to detect it through “ability to detect.” After identifying the main conflict, the next step is to try to uncover how the inventors resolved it. The usual search route for finding the relevant information starts with the “summary of invention” section followed by the “claims” section. If it remains unclear,look in the “detailed description” section of the disclosure document. For U.S. Patent # 7,397,500, an early clue to the inventor’s key step can be found in the first sentence of the “summary” section in the disclosure:

“A camera creates successive digital images of a scene and computes a stability measure estimate blur in a final photograph of the scene.”

Upon closer examination, it is revealed the solution obtains the needed blur indication by using digital image data;the digital camera uses information that already exists. As the inventors describe, the key to detecting blur is not just looking at one digital image, but looking at the changes that occur between successive images.

Now try and match this inventive step with the inventive principles. The most sensible connection in this case is to principle 37, which in updated terms is interpreted as relative change (in addition to its initial Russian, thermal expansion, definition).

There is also a case for a mechanics substitution strategy for the inventors using principle 28. By making better use of existing digital information, they have eliminated the need for a mechanical shake detection device. Note: Be careful when mapping the conflict onto the 48 matrix parameters – get to the root inventive step. This, too, can be a complex story. The simple strategy typically begins with a question such as: “Given this inventive principle as a solution strategy for this problem, would the solution have been generated?”

In this specific case the two questions are:

1. Would being told to eliminate the mechanical system and replacing it with a field get to the solution?
   
2. Would being told to look at the relative change between two different things get to the solution?

The second of the two questions is significantly more likely to have a usable answer. As is often the case with principle 28, simply being told to eliminate the mechanical thing is more likely to produce paralysis than a meaningful direction and productive answer. So, is the answer principle 37, or principles 28 and 37? Almost all of these either/or questions have another contradiction. The answer is almost always going to be neither, both or it depends. In this case the “it depends” conflict is separated based on whether they are calibrating or adding to the contradiction database. In terms of calibrating the matrix, an individual would interpret this solution as an illustration of principle 37, and in terms of updating the database it is likely to include both principles 28 and 37. (Reminder: Since there is no current semantic processor technology capable of doing this job with any kind of reliability a strong element of human interpretation is needed in order to get to the root of the problem.)

In order to analyze the contradiction matrix of the patent look up the tools and see what they currently have to say about the particular conflict being examined:

Table 1: Sample Patent Analysis
Patent Number	Short Title	Improving Parameter	Worsening Parameter	Classical Matrix	Matrix 2003	Parameter Used by Inventor
U.S. 7,397,500 (HP)	Camera-shake warning system	Stability (21)	Ability to detect (47)	3, 27, 16	7, 24, 17, 35, 9, 37, 32, 28	37

It is important to only do this search after the patent has been analyzed in order to minimize (as much as possible) any distortion that might occur to make the data fit the predicted result. Despite a conscious effort, there is always the potential for problems here. The best strategy for overcoming the potential for distortion is to periodically have one patent analyst cast a critical eye on a colleague’s analysis. It is not as scientific, but it is preferable to have 100,000 “good” analyses than 1,000 “absolute” analyses.

As Table 1shows for this particular patent, Matrix 2003 already includes principle 37 as a recommendation for the “stability” versus “ability to detect” conflict pair. It also contains the other possible principle that could have been selected, principle 28. The classical matrix, on the other hand, failed to suggest either principle. (The Appendix includes this patent plus 99 other patents granted on July 8, 2008. The same analysis strategy and presentation format is used for all 100. This is included in the data so that individuals can conduct their own analyses.)

Quasi-random Analysis Strategy

A slightly different selection strategy to the one previously described has been used for these 100 patents. First, a quasi-random set of patents from the U.S. patent database is selected. The U.S. patent office numbers the patents granted is based on a random number of consecutively numbered patents that fall into the same category (i.e., all semi-conductor-based or all pharmaceutical). As one of the goals is to provide a representative microcosm look at what happens when research is conducted. the method for selecting a “random” sample has been to select a random date, which in this case is July 8. A scheme of picking every tenth patent is used in order to get some spread of patent focus areas. Next, it deliberately started in the electronics domain for the simple reasons that 1) there is considerable interest in this domain within Japan and 2) not insignificantly, that there is a wish to overcome the frequent criticism that TRIZ is mainly for mechanical systems. (This strategy is merely the one used to identify the patents to include in the Appendix and not the usual strategy described earlier.)

For the 100 patents included in the Appendix, the following results were obtained:

Matrix 2003–96 percent
Classical matrix–18 percent

These numbers reflect how many of the inventive principles presented in the 100 patents were predicted by the matrix tool. The classical matrix performed slightly worse than average because the sample was strongly biased toward electronics- and software-based inventions. In many situations, therefore, the matrix does not have an entry for certain parameters (e.g., for problems involving parameters like noise, compatibility and security). The 96 percent score achieved by Matrix 2003 is slightly higher than the expected average. Table 2 illustrates the equivalent percentage scores for the two matrix tools across different industry disciplines for the last three and one-half years when the focus shifted to the full range of patents analyzed:

Table 2: Summary of Matrix Accuracy Across Different Industry Disciplines
	2005		2006		2007		2008
	Classical Matrix	Matrix 2003	Classical Matrix	Matrix 2003	Classical Matrix	Matrix 2003	Classical Matrix	Matrix2003
Mechanical	44	96	41	96	38	96	36	95
Electronics	26	94	23	94	22	93	20	93
Chem/Pharm	25	95	24	95	24	93	24	91
ICT	22	94	21	92	19	90	15	89
Overall Average	27	95	26	94	24	93	21	91

One of the reasons for the discrepancies between the results obtained from the 100 patents reproduced in the Appendix and these averages is that in the random sample there has been no filtering out of the weak solutions that would normally be filtered during initial sorting of the patents. Looking at the assigned companies for the selected patents reveals that the large majority are owned by major corporations. For that reason alone the quality of the patents is superior to a global average. Although unplanned,there is more than the average of major corporation inventions is due to the choice to focus on electronics and IT patents.

Only two out of the 100 patents appear to meet the criteria needed to describe a Level 3 invention. The large majority are Level 2 solutions. Again, under normal circumstances it would be expected to filter out many of the Level 2 and nearly all of the Level 1 inventions.

Level of Invention

As reported in a previous publication an assessment is made of patent level for every patent examined whether in great detail or superficially.³ What continues to be clear from doing this kind of analysis is that the initial definitions of levels prepared by TRIZ develop Genrich Altshuller and his colleagues of what constitutes Level 1, 2, 3, etc. become less credible with time. This is certainly true in terms of the Level 3 test relating to whether inventors looked outside its company or industry. Figure1 illustrates the latest findings.

Strictly speaking it is almost impossible to cross-calibrate data with that of Altshuller’s work. What is legitimate, however, is looking at shifts that have taken place since the research started. Figure 1 shows a rising percentage of Level 2 inventions and a corresponding reduction in Level 3 and 4 inventions. Currently there is no solid understanding or explanation for this trend. The working hypothesis is that many industries are presently passing through a period of consolidation, in which the primary objective is to “own” the incremental jumps, rather than seek out major leaps. Scientifically they have insufficient evidence to make a satisfactory proof.

Figure 1: Shifting Pattern of Invention Level Distribution Over Time

Trends of Evolution

One of the reasons the consolidation period is held is because research on the discontinuous technology of evolution trends appears to be hitting a plateau – especially in terms of uncovering new patterns and new stages at the end of existing patents.

If an individual reinvents the levels of invention scale he likely would not relate to the level of a given invention based on how many and which jumps are made along the known trends. The only reason he has not made such a switch is that he has not yet found a truly generic way of classifying which trends are more important than others. A jump along one trend in one industry can create a major breakthrough while the same jump in another sector can elicit a “so what” Level 1 type of reaction.

Meanwhile, since 2005, the trends story has not been completely static. The main additions to the trends database have been:

1. 
   
   Revisions to damping⁵
   
   
2. 
   
   New nesting⁶
   
   
3. 
   
   Revisions to rhythm coordination⁷
   
   
4. 
   
   New design for sustainability⁸
   
   
5. 
   
   New customer intangibles⁹

All of these trends have now been incorporated into the evolutionary potential radar plot structure. The main value is obtained by constructing evolutionary potential radar plots for each of the patents analyzed as it relates to the innovation timing question.¹⁰It seems clear that the historical rate of evolution defined in terms of “discontinuous jumps per year,” is the dominant timing factor, particularly in industries and technologies where the end consumer is only indirectly connected. (See Figure 2)

Figure 2: Discontinuities Per Year Helps Answer Innovation Timing Questions

Into the Future

At one level, the fact that new trends are found at all is sufficient justification for continuing with a patent research program. The main driver for this, and the other research, always has centered on the idea that individuals learn more from anomaly than they do from similarity. The main outcome that emerges from this driver is that individuals are always looking to disprove – rather than confirm – what they know so far.

A good example of a result emerging from the “anomaly is the key” philosophy is the recognition that the biggest single factor causing contradiction matrix accuracy to fall between 2005 and the present day is the increasing number of nano-scale inventions: specifically, nano-scale inventions that have solved conflicts using strategies different from the ones recommended by either matrix.¹¹

One driver for nano-scale research comes from organizations wishing to have their own specific in-company versions of TRIZ tools. There are several reasons why this seems like an important future direction for TRIZ. First, since the tools are inherently generic the distance between generic solution and specific solution is often considerable. By making an in-house version of the tools it becomes possible to create and populate databases of relevant in-domain examples in order to help users more easily make the generic-specific leap. Second, and probably more significant, organizations inevitably possess proprietary information that they do not wish to share externally, but they do wish to share more easily within. The TRIZ function trend and contradiction databases are increasingly recognized as an effective way of storing, communicating and accessing this best practice knowledge.

Clients needing assistance in this knowledge management task are typically building bespoke tools that are updated on a continuous basis. These activities are outside the public domain. What they have always stated regarding the publicly available generic tools is that when the overall average accuracy of the contradiction matrix tool drops below 90 percent they will issue new versions. As shown by the results in Figure 1 they are not quite at that point. If the present trend continues, however, then they should anticipate an updated matrix in 2009.

References

1. Contradiction Matrix, The TRIZ Journal.
   
2. Mann, D.L., Dewulf, S., Zlotin, B., Zusman, A., Matrix 2003: Updating The TRIZ Contradiction Matrix, CREAX Press, July 2003.
   
3. Mann, D.L., “Assessing the Accuracy of the Contradiction Matrix for Recent Mechanical Inventions,” The TRIZ Journal, February 2002.
   
4. Mann, D.L., “Comparing the Classical and New Contradiction Matrix Part 2- Zooming In,” TheTRIZ Journal, July 2004.
   
5. Systematic Innovation E-Zine, “Revisions To The ‘Damping’ Trend,” Issue 50, May 2006.
   
6. Systematic Innovation E-Zine, “New Trends – ‘Nest-Up’ And ‘Nest-Down,” Issue 51, June 2006.
   
7. Systematic Innovation E-Zine, “Revising The Rhythm Co-ordination Trend,” Issue 71, February 2008.
   
8. Systematic Innovation E-Zine, “Sustainability: Discontinuous Trend Pattern,” Issue 72, March 2008.
   
9. Systematic Innovation E-Zine, “New Trend: Customer Intangibles,” Issue 77, August 2008.
   
10. Mann, D.L., “More On Innovation Timing: Discontinuity Rate,” The TRIZ Journal, March 2006.
    
11. Systematic Innovation E-Zine, “Patent of the Month – Ultra-High Pressure Generation,” Issue 71, February 2008.

Appendix

The following table summarizes the analysis for each of the 100 patents considered during the investigation. In order to ensure consistency of analysis across each patent, the author monitored each one. The table provides patent number, title, improving parameter(s), worsening parameter(s) (both using the numbering convention of the new matrix in Figure 1), inventive principles recommended by the classical matrix and inventive principles used by the inventor. (Individuals wishing to see the specific analysis for any of the patents in question may request a copy from the author.) Alternatively, individuals may like to conduct an analysis for themselves to see if they agree with the results presented here.

The new matrix contains several parameters that are not featured in the classical matrix. The inventive principle suggestions obtained from the original matrix for problems relating to the new parameters (noise, emissions, safety, security, etc.) come from the nearest match of parameters in the original list of 39. Where there is no direct match between the conflict challenged by an inventor and the original matrix then the inventive principle suggestions are shown in parentheses. A “-” in the original matrix recommendations column means that that box contained no recommendations.

100 Sample Patents Analyzed
U.S. Patent Number and Company Name	Short Title	Improving Parameter(s)	Worsening Parameter(s)	Classical Matrix	New Matrix	Parameter(s) Used by Inventor
7,397,500 (HP)	Camera-shake warning system	Stability (21)	Ability to detect (47)	3, 27, 16	7, 24, 17, 35, 9, 37, 32, 28	37
7,397,510 (Canon)	Automatic focus adjustment method	Stability (21)	Duration of action (moving) (12)	13, 27, 10, 35	10, 13, 5, 35, 4, 19, 7, 40	10
7,397,520 (Seiko)	Electro-optical device	Reliability (35)	Length (moving) object (3)	15, 9, 14, 4	14, 17, 15, 4, 35, 9, 40, 3	35, 40
7,397,530 (Polydisplay)	Liquid crystal encapsulation method	Manufacture precision (42)	Duration action (moving) (12)	3, 27, 40	5, 40, 16, 3, 20, 19	5, 16
7,397,540 (Boeing)	Phase diversity ranging sensor	“Measure” precision (48)	Power (18)	3, 6, 32	3, 5, 10, 24, 13, 28	24, 13, 3
7,397,550 (-)	Parts manipulation/ inspection system	“Measure” precision (48)	Temp,illumination intensity (22, 23)	6, 19, 1, 28, 24, 32	24, 6, 32, 10, 2, 28, 1, 35, 19	2, 1, 19
7,397,560 (Agilent)	Surface contamination detection	Ability to measure (47)	Reliability (35)	27, 40, 28, 8	28, 1, 40, 26, 35, 2..	28, 40
7,397,570 (Mitutoyo)	Interferometer and shape measurement	Ability to measure (47)	Shape (9)	27, 13, 1, 39	13, 28, 3, 1, 17, 26, 39, 24, 4	1, 19
7,397,580 (Seiko)	Ejection control of ink	Duration of action (moving) (12)	Manufacture consistency (42)	3, 27, 16, 40	3, 16, 40, 10, 37, 12, 25	16, 10, 37
7,397,590 (Canon)	Optical scanning apparatus	Length (stationary) (4)	Temperature (22)	3, 35, 38, 18	35, 36, 10, 24, 32, 3, 15, 17	15
7,397,600 (DULY)	Laser pulse multiplier	Duration of action (moving) (12)	Power (18)	19, 10, 35, 38	19, 18, 35, 10, 38, 13, 12	19, 10, 12
7,397,610 (Canon)	Zoom-lens and projection apparatus	Brightness, consistency (23, 42)	Volume (moving), complexity (7, 45)	13, 2, 32 10, 23, 26, 18	13, 2, 28, 18, 24, 25, 4, 5…	2, 13
7,397,620 (Canon)	Image-reading apparatus	Length (stationary) (4)	Loss of information (28)	24, 26	28, 24, 13, 3, 26, 14, 15, 17	3, 14
7,397,630 (Fujitsu)	Signal reproducing method	Duration of action (moving) (12)	Stability, ability to detect (21, 47)	13, 3, 35, 19, 29, 39	35, 19, 10, 24, 37, 40, 4…	35, 37
7,397,640 (Hitachi)	Improved read sensors	Function efficiency (24)	Stability (21)	–	35, 2, 19, 30, 9, 17…	35
7,397,650 (Nisshinbo)	Electric double-layer capacitor	–	–	–	–	No contradiction identified
7,397,660 (Dell)	Apparatus for regulating airflow	Amount of substance (10)	Adaptability (32)	15, 3, 29	1, 15, 17, 29,24, 3	15
7,397,670 (Zippy Tech)	Power supply device	Power (18)	Length (stationary) (4)	–	17, 14, 1, 35, 4..	1
7,397,680 (Power Integrations)	Balancing capacitor leakage current	Power (18)	Adaptability (32)	19, 17, 34	15, 28, 19, 35, 3, 34, 17, 37, 12	15, 37
7,397,690 (Ternary-logic)	Digital information retaining elements	Speed (14)	Amount of information (11)	–	7, 2, 10, 5, 37, 28, 3	5
7,397,700 (STMicro-electronics)	Non-volatile memory device	Speed (14)	Adaptability (32)	15, 10, 26	15, 10, 28, 26, 1, 30, 35	1
7,397,710 (NEC)	Voltage level control circuit and memory device	Power (18)	Adaptability (32)	19, 17, 34	15, 28, 19, 35, 3, 34, 17, 37, 12	37, 15
7,397,720 (Matsushita)	Semi-conductor storage device	Amount of substance (10)	Power (18)	35	35, 19, 18, 3, 12, 5, 2	19
7,397,730 (Novo Nordisk)	Device with time indicating means	Ease of use (34)	System complexity (45)	32, 26, 12, 17	28, 29, 5, 12, 32, 17, 26	5
7,397,740 (Mediatek)	Optical disc recording device	Adaptability (32)	Duration of action (moving) (12)	13, 1, 35	28, 29, 35, 13, 1, 24, 19, 12	23 (Weak solution)
7,397,750 (TEAC)	Optical disc apparatus	Power (18)	Adaptability (32)	19, 17, 34	15, 28, 19, 35, 3, 34,17, 37, 12	3, 37
7,397,760 (Fujitsu)	Transmission apparatus	Function efficiency (24)	Loss of information (28)	–	3, 4, 19, 15, 32, 17..	3
7,397,770 (IBM)	Checking and repairing a network configuration	Reparability (36)	Compatibility (33)	–	2, 10, 13, 4, 17, 24	10
7,397,780 (Qualcomm)	Method for overlaying two [C1] CDMA systems	Compatibility (33)	System complexity (45)	–	28, 24, 13, 12, 5, 17, 4	24
7,397,790 (Interdigital)	Packet switched connections	Security (37)	Function efficiency (24)	–	2, 1, 17, 3, 10, 25	1, 25
7,397,800 (Broadcom)	Displacement of out of order TCP segments	Speed (14)	Loss of information (28)	13, 26	10, 7, 6, 24, 26, 37, 3	10, 24
7,397,810 (Rockwell Collins)	Artery nodes	Function efficiency (24)	Compatibility (33)	–	1, 4, 14, 7, 2, 24	7
7,397,820 (Ericsson)	Voice packets in IP network	Compatibility (33)	Adaptability (32)	–	28, 10, 24, 6, 15, 7	7
7,397,830 (Matsushita)	Semi-conductor laser device	Temperature (22)	Power (18)	2, 14, 17, 25	31, 3, 2, 17, 25, 35, 1, 14	2, 35
7,397,840 (Aerospace Corp)	Spread spectrum communication system	Loss of information (28)	Compatibility (33)	–	2, 24, 37, 4, 1, 13	1
7,397,850 (-)	Reciprocal index look-up	Control complexity (46)	Ability to detect (47)	–	13, 37, 10, 7, 3, 28..	10, 13
7,397,860 (Brooktree)	Fractional load-peak detection	Power (18)	Amount of information (11)	–	10, 28, 19, 12, 24, 37	10
7,397,870 (Texas Instruments)	Ultra-wideband receiver	Power (18)	Measurement precision (48)	32, 15, 2	2, 37, 15, 25, 10, 32	25, 37
7,397,880 (Renesas)	Synchronization circuit and method	Stability (21)	Duration of action (moving) (12)	13, 27, 10, 35	10, 13, 5, 35, 4, 19, 7, 40	10, 5, 19
7,397,890 (Xoran)	CT system with synthetic view generation	Ability to detect (47)	Angle (moving) (3)	16, 17, 26, 24	26, 24, 28, 5, 17, 3, 37,16..	5, 17
7,397,900 (Euratom)	Micro-beam collimator	Area (stationary) (6)	Loss of energy (27)	17, 7, 30	17, 12, 30, 35, 7, 28, 26	35, 30
7,397,910 (Callwave)	Expanded telecommunications service	–	–	–	–	No real contradiction and weak solution
7,397,920 (Sony)	Information processing device	Noise (29)	Connectivity (33)	–	2, 35, 9, 17, 28, 3	37, 2
7,397,930 (Canon)	Position and orientation estimating method	Measurement precision (48)	Loss of information (28)	–	24, 7, 25, 37, 1, 6	37, 7, 25
7,397,940 (ASML)	Object positioning method	Measurement precision (48)	Manufacture precision (42)	–	28, 26, 24, 23, 25, 1	24, 23
7,397,950 (Microsoft)	Handwriting layout analysis	Loss of information (28)	Adaptability (32)	–	24, 5, 25, 9, 40, 35, 19	19
7,397,960 (Konica Minolta)	Compression of document image	Amount of information (11)	Adaptability (32)	–	3, 24, 4, 1, 29, 25, 31	3
7,397,970 (Lockheed Martin)	Automatic scene correlation	Accuracy (6)	Amount of data (3)	–	17, 19, 3, 13, 1, 14 (IT Matrix)	3, 17
7,397,980 (Optium)	Dual-source optical wavelength processor	Automation (43)	Adaptability (32)	27, 4, 1, 35	28, 1, 29, 10, 12, 4..	1
7,397,990 (Emtelle)	Signal transmitting cable	Strength (20)	Area (moving) (5)	3, 34, 40, 25	14, 17, 3, 7, 19, 4, 40, 5	3,17, 40
7,398,000 (Microsoft)	Digital video segment identification	Loss of information (28)	Ability to detect (47)	35, 33	28, 32, 1, 10, 37, 7	10, 37
7,398,010 (Pioneer)	Information recording medium	Amount of data (3)	Speed (5)	–	15, 17, 4, 14, 1, 3.. (IT Matrix)	1
7,398,020 (Samsung)	Multi-point gating control block	–	–	–	–	Administrative contradiction only
7,398,030 (Canon)	Image forming apparatus	Loss of substance (25)	Speed (14)	10, 13, 28, 38	28, 19, 13, 25, 10, 38, 3, 24	10, 3
7,398,040 (Canon)	Developing apparatus	Manufacture consistency (42)	Force (15)	28, 19, 34, 36	12, 19, 28, 29, 3, 10..	28
7,398,050 (Delphi)	Processing airborne digital data	Robustness (15)	Dynamic size (2)	–	9, 13, 28, 1, 35, 40, 18 (IT Matrix)	1
7,398,060 (Avago)	Method facilitating inter-mode handoff	Compatibility (33)	Power, stability (18, 21)	–	6, 35, 29, 24, 25, 33, 28, 27, 12, 3, 16, 10, 2	10, 24
7,398,070 (Alps)	Variable gain amplifying circuit	Automation (43)	Loss of energy (27)	23, 28	28, 21, 3, 13, 34, 24	23, 24
7,398,080 (Nokia)	Mobile content delivery system	Loss of time (26)	Amount of information (11)	–	2, 3, 10, 25, 5, 7	3
7,398,090 (HP)	Defining a smart area	Ability to detect (47)	Amount of information (11)	–	19, 3, 32, 7, 10, 13, 25, 4	25, 3
7,398,100 (Motorola)	Controlling transmission power	Power (18)	Ability to detect (47)	19, 35, 16	28, 35, 19, 3, 16, 32, 37, 25, 2	37
7,398,110 (Intel)	Bandwidth indicator	Amount of information (11)	Ability to detect (47)	–	3, 4, 37, 25, 40, 2	3
7,398,120 (Siemens)	Analysis of neuronal activities	–	–	–	–	No contradiction present
7,398,130 (Hitachi)	Order receiving system	–	–	–	–	No contradiction identified
7,398,140 (Wabtec)	Locomotive operator warning system	Robustness (35)	Amount of information (11)	–	10, 24, 32, 3, 25, 5, 2	24
7,398,150 (Honda)	Calculating work done by IC engine	Measurement precision (48)	Noise (29)	–	9, 24, 2, 37, 25, 7, 13	37, 24
7,398,160 (Southwest Research)	Gas energy meter	System complexity (45)	Measurement precision (48)	2, 26, 10, 34	28, 26, 10, 2, 34, 7, 37	28, 26
7,398,170 (GE)	Transmitting dynamic data	Amount of information (11)	System generated harmful (31)	–	2, 10, 13, 17, 31, 28, 32	10
7,398,180 (Daimler)	Technician time clock tool	–	–	–	–	No contradiction present
7,398,190 (Toshiba)	Linking dynamic and kinematic simulations	Compatibility (33)	Amount of information (11)	–	10, 24, 3, 2, 37, 6	10, 24
7,398,200 (Adobe)	Token stream differencing	Ability to detect (47)	Control complexity (46)	–	28, 32, 37, 3, 7, 10, 6, 24	7
7,398,210 (Microsoft)	Analysis of word variants	Measurement precision (48)	Amount of information (11)	–	25, 2, 7, 32, 4, 3, 37, 10	7, 10
7,398,220 (Certificate Exchange)	Internet insurance certificate scheme	Duration of action (stationary) (13)	System complexity (45)	–	5, 10, 2, 25, 4, 17, 14	5
7,398,230 (AT&T)	Automated sales support device	–	–	–	–	Administrative contradiction only
7,398,240 (Accenture)	Future value analytics	–	–	–	–	Administrative contradiction only
7,398,250 (Microsoft)	Restricting the usage of payment accounts	–	–	–	–	Administrative contradiction only
7,398,260 (Fiske)	Effector machine computation	–	–	–	–	Administrative contradiction only
7,398,270 (-)	Clustering optimization	–	–	–	–	Administrative contradiction only
7,398,280 (Altera)	Manufacturing integrated circuits with multiple subcontractor	–	–	–	–	Administrative contradiction only
7,398,290 (FujiXerox)	Device retrieval system	Measurement precision (48)	Amount of information (11)	–	10, 24, 3, 2, 37, 6	37, 3
7,398,300 (Broadcom)	One-shot RDMA having a 2-bit state	Control complexity (46)	Connectivity (33)	–	6, 10, 13, 1, 2, 24	1, 24
7,398,310 (Cisco)	Entity tracking in a network	Security (37)	Adaptability (32)	–	24, 35, 3, 1, 13, 28, 4, 15, 17, 29	24
7,398,320 (Fujitsu)	Information distribution/ reproduction control apparatus	Amount of information (11)	Control complexity (46)	–	25, 40, 10, 3, 7, 2, 4, 5	5, 7
7,398,330 (Hitachi)	Command multiplex number monitoring control scheme	Amount of information (11)	Control complexity (46)	–	25, 40, 10, 3, 7, 2, 4, 5	2, 7
7,398,340 (STMicro-electronics)	Management of peripherals in integrated circuit	–	–	–	–	Administrative contradiction only
7,398,350 (Symantec)	Distribution of data volume virtualization	–	–	–	–	Insufficient description to ascertain conflicts
7,398,360 (Sun)	Multi-socket SMP system for CMT processors	Amount of information (11)	Speed (14)	–	10, 7, 13, 37, 3, 28, 12, 5	7
7,398,370 (Toshiba)	Information processing apparatus	Adaptability (32)	Compatibility (33)	–	1, 5, 3, 28, 2, 25, 13	5
7,398,380 (Fabric7)	Dynamic hardware partitioning	–	–	–	–	Administrative contradiction only
7,398,390 (HP)	Method for securing a computer	Security (37)	Connectivity (33)	–	15, 17, 24, 4, 6, 37, 1	24
7,398,400 (Qinetiq)	Computer system protection	–	–	–	–	Administrative contradiction only
7,398,410 (Tsing Hua University)	Processor employing a power managing mechanism	Power (18)	Ability to detect (47)	19, 35, 16	28, 35, 19, 3, 16, 32, 37, 25, 2	2
7,398,420 (Hitachi)	Method for keeping snapshot image	Loss of information (28)	Amount of information (11)	–	2, 7, 24, 3, 32, 5	2
7,398,430 (Microsoft)	Self-diagnosing system crashes	Ability to detect (47)	Amount of information (11)	–	19, 3, 32, 7, 10, 13, 25, 4	10, 7, 25
7,398,440 (STMicro-electronics )	Tap multiplexer	Ability to detect (47)	Compatibility (33)	–	25, 1, 13, 15, 35, 28	1, 15
7,398,450 (Mitsubishi)	Parallel pre-coder circuit	Speed (14)	Control complexity (46)	–	25, 10, 19, 1, 4, 3	1, 37
7,398,460 (Network Appliance)	Organizing and distributing parity blocks	Loss of information (28)	Robustness (35)	10, 28, 23	13, 24, 10, 26, 6, 4, 3, 40, 17	1, 17
7,398,470 (Vistaprint)	Remote assistance method	–	–	–	–	Administrative contradiction only
7,398,480 (Microsoft)	Method of providing multiple installation actions	Ease of use (34)	Amount of information (11)	–	1, 7, 2, 10, 4, 17, 32	1
7,398,490 (-)	Digital circuit layout techniques	Ability to detect (47)	Manufacturability (41)	5, 28, 11,29	5, 28, 37, 11, 2, 13, 29, 24	2

Originally presented at the Fourth TRIZ Symposium in Japan September 10-12, 2008, Laforet Biwako, Moriyama, Shiga, Japan.