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Artificial Inventiveness

Learn algorithms for inventive design, support ideation, out-of-box thinking and creativity

About This Course

It is an online course for all interested in algorithms of systematic creativity. We will learn the algorithms for step-by-step heuristics, conceptual design, new patentable ideas. Course modules are modern TRIZ and other analytical tools that have proven their efficiency in industry.

Have you ever thought why it is hard to find an idea that can save a product, project or business? How to analyze the situation where you need an out of box solution? How to deliver systematically the list of patentable concepts to improve a product or a service? How to circumvent patents of competitors and develop IP strategy?

There are videos with theoretical part in the course, interactive quizzes and problems. There are 50+ examples of smart new product design, technology trouble shooting and inventive solutions, many of which are coming from success and failure stories of technological giants. Most cases originate from engineering domain, although basic knowledge like how car breaks work and what is inside a refrigerator...except food ;).

Through the course you will:

  • Learn via real cases
  • Take part in brain teasers
  • Challenge yourself in quizzes
  • Apply the tools for inventive design
  • Build your portfolio

Learning Outcomes

Upon successful completion of the course the learner is expected to be able to:

LO1. Distinguish conceptual design phase and instruments of it

LO2. Search and analyze patent landscape

LO3. Modulate ideation algorithms

LO4. Design a new product and concept of the service on demand

LO5. Identify the voice of the product and forecast technology evolution

LO6. Evaluate design concepts from managerial and production prospectives

Expected Prior Knowledge

Engineering background of at least BSc level would be the best fit for the course. But secondary school physics and mathematics can help in understanding most examples and cases. The main prerequisite is the interest and ability to systematic analysis. Basic computer and internet literacy is expected at the level of using internet-communication and documentation tools. Positive mind, readiness to work in international groups, curiosity help.

Course Instructors

Leonid Chechurin

Prof. Leonid Chechurin

Leonid Chechurin is the Professor of Industrial Management Department and Head for the System Engineering group at LUT University (Finland). He received his Doctor of Science Degree in 2010 with the dissertation on Mathematical Modeling and Analysis of Dynamic Systems. He has more than 40 publications in the fields of control and system theory and automation, mathematical modeling, creativity and innovations. He has been involved in the supervision of about 50 M.Sc. theses and dissertations.

Prof. Chechurin has the outstanding industrial experience, he was employed by leading innovating technology companies like Samsung Electronics or LG Electronics as a consultant for engineering design group (5 years in total). He has been consulting or teaching at General Electric Global Research Center (USA, Germany, India and Shanghai), Wrigley (USA), British American Tobacco (UK-USA), FMC (USA) and others (in total more than 50 seminars and consulting sessions and several research projects on inventive engineering design).

Iuliia Shnai

Iuliia Shnai

Iuliia Shnai is a doctoral candidate in Lappeenranta University of Technology (LUT, Finland) in department of Industrial Engineering and Management. She is interested in different aspects of e-learning from teacher and learner perspective. She is currently a project driver of Erasmus+ CEPHEI project. She has a technical background and obtained Bachelor and Master Degree majoring in Management of Innovation Technologies in Peter the Great St. Petersburg Polytechnic University (SPbSTU). In her research, she focuses on one of the learning designs, flipped classroom. Practically, she provides experiments with transition courses from traditional to blended and online format. And develop a systematic approach to course design for teachers support.

ECTS Credits


Course Content and Structure

The course includes in the following modules:

  1. Introduction
  2. Function modelling
  3. Function-oriented search
  4. Ideal final result
  5. Contradictions

Assessment Methods and Weighting Scheme

The course will be assessed through:

  1. Quizzes - 40%
  2. Problems - 60%

Language of Instruction