TRIZ Principles & Theory • TRIZ with AI: A New Approach to Innovative Problem Solving - Workshop and training program combining TRIZ methodology with AI to solve real-world problems and design innovative products and services. Source: Six Sigma Institute
TRIZ AI & Technologies • TRIZ AI Prompts: Boost Innovation with Creativity Framework - Seven powerful AI prompts based on TRIZ, SCAMPER, and First Principles methodologies to enhance creative output and complex problem-solving. Source: Eq4C Innovation Tools
• TRIZ with AI - A New Approach to Problem Solving - AI-TRIZ combines TRIZ methodology with NLP, computer vision, and ChatGPT/Claude to automate innovative problem-solving and patent analysis. Source: Six Sigma Institute
• TRIZ as Cross-Disciplinary Innovation Methodology - TRIZ provides a structured pathway from problem identification through contradiction analysis to inventive solutions across disciplines. Source: MDPI
TRIZ AI & Technologies
• TRIZ 2026: From Soviet Logic to AI-Driven Patent Analysis - AI-TRIZ evolution automates patent logic analysis in seconds, transforming how organizations approach innovation from historical trends to automated solutions. Source: LinkedIn
TRIZ as Cross-Disciplinary Innovation Methodology - Recent research examines TRIZ (Theory of Inventive Problem Solving) as a powerful cross-disciplinary innovation methodology providing structured pathways for problem identification and contradiction resolution. Source: MDPI
Blue Ocean Strategy Meets TRIZ - TRIZ (Theory of Inventive Problem Solving) provides systematic methodologies specifically designed to resolve contradictions without compromise, complementing Blue Ocean Strategy approaches. Source: LinkedIn
Contradictions of Automotive NVH and TRIZ Tools - Research paper demonstrating TRIZ's application in resolving automotive engineering contradictions through innovative problem-solving rather than traditional trade-offs. Source: SAE Mobilus
Peter Fisk released a comprehensive guide featuring 33 essential innovation tools reordered by likely impact over the next decade. Includes TRIZ-based methodologies among emerging innovation approaches and frameworks. Source:Peter Fisk
Research combining IDeS (Innovative Design for Sustainability) method with TRIZ principles for polymer additive manufacturing (DfAM). Demonstrates systematic innovation approach to optimize component performance while minimizing cost and time. Source:MDPI Polymers Journal
TRIZ with AI – A New Approach to Innovative Problem Solving - Recent research demonstrates how TRIZ (Theory of Inventive Problem Solving) can be combined with artificial intelligence to enhance innovation methodologies. The integration creates systematic approaches to solving complex engineering and business problems by leveraging both human ingenuity and machine learning. Source: Six Sigma Institute
TRIZ AI & Automation
Oil Production Prediction Using TRIZ Conflict Resolution - Recent research published on ResearchGate demonstrates practical applications of TRIZ conflict resolution principles combined with neural networks for complex prediction problems. This shows TRIZ's evolving role in AI-powered automation. Source: ResearchGate
TRIZ Tools & Software
Integrated AHP-QFD-TRIZ Framework for Rescue Drone Innovation - Recent case studies show integration of TRIZ with other innovation methodologies like Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD) for designing innovative products, demonstrating TRIZ's continued relevance in engineering design. Source: JUY Unmanned Aerial Vehicles
Unlocking Innovation: Exploring TRIZ Principles With Real-World Examples TRIZ (Theory of Inventive Problem Solving) provides systematic patterns of innovation applicable across industries. Rather than relying on trial-and-error, TRIZ offers proven principles for resolving technical contradictions and accelerating innovation. Source: OreateAI
Research on the Systematic Innovation Methodology of TRIZ Comprehensive research tracing TRIZ development from the mid-20th century to modern applications. TRIZ originated from analyzing millions of patents to extract common problem-solving principles and principles now applicable to complex engineering challenges. Source: OreateAI
TRIZ AI & Automation
The Invisible Infrastructure of Innovation Genius TRIZ provides the systematic infrastructure for innovation, transforming it from an unpredictable art into a learnable discipline. AI is now being applied to automate TRIZ analysis and accelerate problem-solving across complex domains. Source: Logic of Innovation
World Conference of AI-Powered Innovation and TRIZ Methodology 2026 The 2026 conference brings together researchers and practitioners exploring the convergence of TRIZ methodology with AI technologies. Sessions cover autonomous systems, transhumanism, and technology complexity in modern innovation contexts. Source: Springer
TRIZ Applications & Case Studies
Integrated AHP-QFD-TRIZ Framework for Innovative Rescue Drone Design A case study demonstrating TRIZ's application in rescue drone design. By combining TRIZ with quality function deployment and analytic hierarchy process, researchers resolved technical contradictions to create more effective emergency response solutions. Source: JuYeuAV
Are patents brick walls or puzzles waiting to be solved?
As engineers and innovators, we often view Intellectual Property (IP) law as a minefield that stifles creativity. You come up with a brilliant idea, only to find a competitor has locked it down with a patent so broad it seems impossible to navigate.
But what if you could use those same patents as a blueprint for something even better?
I recently watched a fascinating breakdown from IdeaMechanics titled "How to Defeat 'Dragon Patents' & Invisible Components," and it completely flips the script on traditional engineering strategy. This isn't about sneaky copying; it’s about a sophisticated methodology called Design for Patentability (DFP).
Here is my review of the key takeaways from this must-watch video for any technical founder or R&D engineer.
1. Slaying the "Dragon Patent"
The video introduces the concept of a "Dragon Patent"—a patent written with such broad, generic language that it feels like a hydra. You cut off one head (design around one claim), and two more grow back (you infringe on another part of the description).
The Case Study: Honda held a patent for a rear-seat airbag that required a "means of support." This vague phrasing boxed competitors in—any support structure added would technically infringe.
The Solution: Instead of adding a support (which would infringe), Hyundai engineers looked at what was already there. They redesigned the airbag to wedge itself between the existing headrests. They didn't add a "means of support"; they utilized the environment. The dragon was slain not by fighting it, but by changing the battlefield.
2. Hunting "Ghost Components"
This was my favorite concept from the video. A "Ghost Component" is a part that isn't explicitly named in a patent but is physically required for the invention to work.
The Case Study: A Philip Morris patent described an e-cigarette with "independently controllable heating regions." While the word "controller" wasn't used, you clearly can't have independent control without a chip or circuit. That chip is the "Ghost."
The Workaround: To bypass this, engineers dusted off 19th-century "electric candle" technology. They created a heating element that burns like a fuse, moving a hot spot automatically without any digital control. They didn't just remove the component; they designed the ghost right out of the system.
3. The "Inventive Step" & Synergy
The video does a great job distinguishing between a simple mash-up and true innovation. Gluing wings to a laptop isn't patentable. But if those wings also function as a heat sink to cool the processor? That is Synergy.
The video argues that to defeat a patent, your solution shouldn't just be different; it should provide a "synergistic result"—a new, unexpected function that occurs when parts combine.
Why You Should Watch This Video
The most powerful takeaway is the shift from offense to defense. The video encourages you to wear the "black hat" and hack your own inventions. By hunting for "Dragon words" and "Ghost components" in your own designs before you file, you can build unhackable patents that force competitors to innovate around you.
It turns the dry world of IP law into an engineering challenge, and frankly, it makes the design process sound like a strategy game.
Verdict: Highly Recommended. Whether you are a startup founder or a lead engineer, this mindset shift could be the difference between a blocked product and a market-leading innovation.
The video introduces a provocative yet ethical mindset. Instead of hitting a wall when you find a competitor's patent, you use the DFP methodology to design around it. The video draws a crucial line between patentability (Is my idea new?) and infringement (Does my product use every piece of their claim?). DFP lives in the sweet spot where you satisfy the former while avoiding the latter [01:40].
The Trimming Framework: A 3-Step Process
The highlight of the video is the Trimming method—a systematic approach that feels like "patent surgery" [02:14]. Here is the breakdown:
Function Analysis: Deconstruct the existing patent into every component and define exactly what each piece does [02:34].
Identify the Trimmable: Look for the most expensive, complex, or redundant part [02:39].
Redistribute the Function: This is the "genius" step. You don't just delete the part; you reassign its job to other components already in the system [02:50].
Real-World Case Studies
The video provides three excellent examples that illustrate this technical "magic":
Painted Chocolate: By removing the edible paper step, engineers learned to print directly onto cooling chocolate—resulting in a simpler, non-infringing process [03:11].
The Air Filter: A complex "dead volume" box used to smooth airflow was deleted. The function was redistributed to the filter's existing empty space [03:42].
The Mouse Trap: A high-tech trap with solenoids and batteries was trimmed down to a purely mechanical gravity-fed device [04:13].
The Pro Strategy: Protect Your Own Inventions
My favorite takeaway is the "reverse" application: Trim your own designs before you file. By being your own toughest critic and trimming your design to its core, you create a "lean" patent that is significantly harder for competitors to hack or circumvent [05:25].
Final Verdict
Whether you are a startup founder, an R&D engineer, or a product designer, this video is a must-watch. It shifts the perspective from "How do I build this?" to "What can I remove to make this better and legally untouchable?"
Is the evolution of programming languages random, or does it follow a distinct, calculable law?
As developers, we often feel like we are riding a chaotic wave of new frameworks, languages, and paradigms. One day it's Object-Oriented Programming (OOP), the next it's Functional, and suddenly we are wrestling with Reactive streams. But what if I told you that this chaos isn't random?
I recently came across a fascinating paper titled "TRIZ-evolution of Programming Systems" by Victor Berdonosov, A. Zhivotova, and T. Sycheva. It attempts to do something audacious: apply the engineering laws of TRIZ (Theory of Inventive Problem Solving) to the history and future of software development.
If you are a fan of "big picture" computer science or just want to know what you might be coding in ten years, this paper is a hidden gem. Here is my review and why you should add it to your reading list.
What is TRIZ?
First, a quick primer. TRIZ (a Russian acronym for Teoriya Resheniya Izobretatelskikh Zadach) was developed by Genrich Altshuller in the 1940s. He analyzed thousands of patents and discovered that technical systems evolve not randomly, but by overcoming specific contradictions.
For example, in a car engine, you want more power (good) but that usually adds weight (bad). Innovation happens when you solve this contradiction without a compromise. The authors of this paper argue that programming systems are also artificial systems and therefore follow these same immutable laws of evolution.
The Core Insight: Code Evolves by Conflict
The paper posits that every major shift in programming—from machine code to Assembly, to C, to Java, and beyond—was triggered by a specific systemic contradiction.
The authors map out a "Tree of Evolution" for programming paradigms. Instead of just listing history, they identify the "driving force" behind each jump. For instance, the transition to Object-Oriented Programming wasn't just a stylistic choice; it was a necessary resolution to the contradiction between the growing complexity of software systems and the human limit of manageability.
Why You Should Read It
Here is why this academic paper deserves a spot on a technical blogger's radar:
It turns "Hype" into "Science": We often chase trends because they are popular. This paper provides a framework to evaluate why a technology is winning. Is it solving a fundamental contradiction (e.g., Speed vs. Memory), or is it just noise?
The "Evolutionary Map": The paper presents an evolutionary map of programming languages. It essentially treats languages like biological species that adapt to survive. Seeing C++ or Python on this map changes how you view your daily tools.
Forecasting the Future: The most exciting part of the TRIZ methodology is that it is predictive. By identifying which contradictions in current languages are still unresolved, the authors (and you, the reader) can hypothesize what the next generation of languages must look like.
Key Takeaway
The authors suggest that we are not at the end of the road. Current paradigms still have "forgotten" contradictions that are waiting to be solved. The system that solves them will be the next big thing.
If you want to stop reacting to the future and start understanding it, give this paper a read. It’s a dense but rewarding look at the DNA of the code we write every day.
💡Blog Post: Don't Let Patents Stop You—Use Them as a Map for Innovation
Review of:"Finding the 'White Spots': How to See What Competitors Missed - Module II: The Analytical Toolkit"
We have all felt that sinking feeling in our gut: you have a brilliant idea, you start researching, and—bam—you hit a brick wall. A competitor has already patented it.
Most engineers and product designers see this as a dead end. But in the recent video breakdown of Design for Patentability (DFP), we learn that this "wall" is actually a map. By applying a rigorous engineering discipline rather than just trying to dodge infringement, you can use existing patents to engineer superior, non-infringing solutions.
Here is a review of the Analytical Toolkit presented in the video, which transforms intellectual property from a legal minefield into an innovation playground.
The Core Philosophy: Offense, Not Defense
The video makes a crucial distinction early on: Infringement is about stepping on toes, but Patentability is about standing on your own ground. The goal of DFP is to hit the "sweet spot"—creating a product that is both free to operate and novel enough to protect with your own IP.
To do this, the video introduces three powerful analytical tools:
1. Function Analysis: The Art of "Trimming"
This is the most fundamental tool in the box. Instead of looking at what a component is, you look at what it does (its function).
The Concept: The video introduces the "Rule of Contact," reminding us that for a part to work, it must physically interact with the recipient of the action.
The Case Study: A company held a monopoly on printing pictures onto chocolate using an edible paper transfer system. By mapping the functions, analysts realized 80% of the process was just handling this paper—a "providing function" rather than a productive one.
The Breakthrough: They "trimmed" the paper entirely and printed directly on the chocolate. The result? A cheaper, faster process that bypassed the competitor's patent completely.
2. The Interaction Matrix: X-Raying the Invention
When you are staring at a complex assembly, it’s hard to see what’s essential. The Interaction Matrix is a grid that maps how every component "talks" to every other component.
The Case Study: An automotive engineering team needed to smooth out air turbulence for a mass airflow sensor. The competitor’s patented solution added a clumsy "dead volume" box to settle the air.
The Breakthrough: The matrix revealed that the empty space inside the existing filter could perform the exact same function. They redesigned the outlet to use that space, eliminating the extra part. They didn't just avoid the patent; they built a better engine layout.
3. S-Curve Analysis: Finding the "White Spots"
While the first two tools zoom in, this tool zooms way out. It maps the lifecycle of a technology to tell you where the "White Spots"—the open territories for innovation—are hiding.
The Insight: Mature technologies (like mechanical circuit breakers) are at the top of their S-Curve; they are crowded and patent-heavy. Emerging tech (like light-actuated switching) is at the bottom of the curve.
The Strategy: Don’t fight for scraps in a crowded room. Use S-Curve analysis to pivot your R&D toward emerging technologies where patents are scarce and the potential for impact is massive.
Final Thoughts
This video module is a refreshing take on IP strategy. It moves away from the fear of being sued and toward the excitement of out-engineering the competition. It challenges us to stop treating patents as barriers and start treating them as blueprints for the next big breakthrough.
If you are stuck in a "patent deadlock," this toolkit might just be the key to breaking free.
