TRIZ Future Conference 2004

TRIZ the Theory of Inventive Problem Solving is a living science and a practical methodology: millions of patents have been examined to look for principles of innovation and patterns of excellence. Large and small companies are using TRIZ to solve problems and to develop strategies for future technologies. The TRIZ Future Conference is the annual meeting of the European TRIZ Association, with contributions from everywhere in the world. The aims of the 2004 edition are the integration of TRIZ with other methodologies and the dissemination of systematic innovation practices even through SMEs: a broad spectrum of subjects in several fields debated with experts, practitioners and TRIZ newcomers

Design-by-analogy: experimental evaluation of a functional analogy search methodology for concept generation improvement

Design-by-analogy is a growing field of study and practice, due to its power to augment and extend traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. This paper presents the results of experimentally testing a new method for extracting functional analogies from general data sources, such as patent databases, to assist designers in systematically seeking and identifying analogies. In summary, the approach produces significantly improved results on the novelty of solutions generated and no significant change in the total quantity of solutions generated. Computationally, this design-by-analogy facilitation methodology uses a novel functional vector space representation to quantify the functional similarity between represented design problems and, in this case, patent descriptions of products. The mapping of the patents into the functional analogous words enables the generation of functionally relevant novel ideas that can be customized in various ways. Overall, this approach provides functionally relevant novel sources of design-by-analogy inspiration to designers and design teams.SUTD-MIT International Design Centre (IDC)National Science Foundation (U.S.) (Grant Numbers CMMI-0855326, CMMI-0855510, and CMMI-08552930

Practice-based methodology for effectively modeling and documenting search, protection and innovation

AbstractThis work relates to a methodology for effectively modeling an Action and Problem System and documenting a path built by means of patent databases. The aim of this work is to provide an improved method and operative tool for a quick and reliable patents investigation driven by Boolean algorithms. The method has been tested with several projects for companies of different industrial areas. Moreover in the last months the method has been used in case studies by students from the University of Bergamo with good results after a very few hours of training. Two specific case studies will be discussed in this paper in order to clarify the operative value of said method and to show the results obtained in terms of solutions found and of efforts requested

A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation

The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.Comment: 11 pages, 11 figure

Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification

Intelligence tools have been developed and applied widely in many different areas in engineering, business and management. Many commercialized tools for business intelligence are available in the market. However, no practically useful tools for technology intelligence are available at this time, and very little academic research in technology intelligence methods has been conducted to date. Patent databases are the most important data source for technology intelligence tools, but patents inherently contain unstructured data. Consequently, extracting text data from patent databases, converting that data to meaningful information and generating useful knowledge from this information become complex tasks. These tasks are currently being performed very ineffectively, inefficiently and unreliably by human experts. This deficiency is particularly vexing in product planning, where awareness of market needs and technological capabilities is critical for identifying opportunities for new products and services. Total nescience of the text of patents, as well as inadequate, unreliable and untimely knowledge derived from these patents, may consequently result in missed opportunities that could lead to severe competitive disadvantage and potentially catastrophic loss of revenue. The research performed in this dissertation tries to correct the abovementioned deficiency with an approach called patent mining. The research is conducted at Finex, an iron casting company that produces traditional kitchen skillets. To \u27mine\u27 pertinent patents, experts in new product development at Finex modeled one ontology for the required product features and another for the attributes of requisite metallurgical enabling technologies from which new product opportunities for skillets are identified by applying natural language processing, information retrieval, and machine learning (classification) to the text of patents in the USPTO database. Three main scenarios are examined in my research. Regular classification (RC) relies on keywords that are extracted directly from a group of USPTO patents. Ontological classification (OC) relies on keywords that result from an ontology developed by Finex experts, which is evaluated and improved by a panel of external experts. Ontological semantic classification (OSC) uses these ontological keywords and their synonyms, which are extracted from the WordNet database. For each scenario, I evaluate the performance of three classifiers: k-Nearest Neighbor (k-NN), random forest, and Support Vector Machine (SVM). My research shows that OSC is the best scenario and SVM is the best classifier for identifying product planning opportunities, because this combination yields the highest score in metrics that are generally used to measure classification performance in machine learning (e.g., ROC-AUC and F-score). My method also significantly outperforms current practice, because I demonstrate in an experiment that neither the experts at Finex nor the panel of external experts are able to search for and judge relevant patents with any degree of effectiveness, efficiency or reliability. This dissertation provides the rudiments of a theoretical foundation for patent mining, which has yielded a machine learning method that is deployed successfully in a new product planning setting (Finex). Further development of this method could make a significant contribution to management practice by identifying opportunities for new product development that have been missed by the approaches that have been deployed to date

Paradigms for biologically inspired design

Biologically inspired design is attracting increasing interest since it offers access to a huge biological repository of well proven design principles that can be used for developing new and innovative products. Biological phenomena can inspire product innovation in as diverse areas as mechanical engineering, medical engineering, nanotechnology, photonics, environmental protection and agriculture. However, a major obstacle for the wider use of biologically inspired design is the knowledge barrier that exist between the application engineers that have insight into how to design suitable products and the biologists with detailed knowledge and experience in understanding how biological organisms function in their environment. The biologically inspired design process can therefore be approached using different design paradigms depending on the dominant opportunities, challenges and knowledge characteristics. Design paradigms are typically characterized as either problem-driven, solution-driven, sustainability driven, bioreplication or a combination of two or more of them. The design paradigms represent different ways of overcoming the knowledge barrier and the present paper presents a review of their characterization and application

Evidence-based design heuristics for idea generation

How do product designers create multiple concepts to consider? To address this question, we combine evidence from four empirical studies of design process and outcomes, including award-winning products, multiple concepts for a project by an experienced industrial designer, and concept sets from 48 industrial and engineering designers for a single design problem. This compilation of over 3450 design process outcomes is analyzed to extract concept variations evident across design problems and solutions. The resulting set of patterns, in the form of 77 Design Heuristics, catalog how designers appear to introduce intentional variation into conceptual product designs. These heuristics provide ‘cognitive shortcuts’ that can help designers generate more, and more varied, candidate concepts to consider in the early phases of design

Eco-innovation: Tools to facilitate early-stage workshops

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents research carried out into the use of creative tools at the early stages of eco-innovation. Eco-innovation is a practical approach aiming to develop new products and processes which significantly decrease our impact on the environment. Designers are trained to develop profitable products that increase production and consumption. Eco-innovation is a new discipline in which designers can radically reduce the environmental burdens of production and consumption through the innovation of new types of products and services. The main aim of this research was to develop an approach that would promote significant environmental improvements whilst remaining a practical, design-focused discipline. Problems and under-investigated aspects of eco-innovation were identified: • Creative approaches at early stages of eco-innovation were under-investigated and few tools had been developed for use at the early stages. • Empirical design research techniques had rarely been used to assess new eco-innovation tools or to inform their subsequent development. The focus of the research work was the development and testing of tools to facilitate workshops at the early stages of eco-innovation. Not only was the goal to facilitate the generation of radical ideas but also to ensure that these were developed into appropriate solutions having the potential to be taken up in industry. The development of the tools was based on literature research, worked examples and interviews. The tools were tested in controlled workshop experiments and the results were analysed using various empirical techniques. First, an idea-recording technique to improve the efficiency of generating and harvesting ideas in a team design process was developed. This novel tool was called the Product Ideas Tree (PIT) diagram. The tool was tested for its ability to facilitate design workshops. Secondly, a structured approach to innovation - the theory of inventive problem solving (TRIZ) - was investigated. Worked examples using some of the tools from TRIZ were presented and a limited number of tools were selected and simplified for testing in team design workshops. The PIT diagram and TRIZ tools experiments established which attributes of the tools and approaches were most beneficial. The development and testing of these specific tools provided the following general contributions to eco-innovation: • A model for eco-innovation that describes the factors influencing the discipline and the attributes of good practice. • A recommended process to transform radical ideas into appropriate solutions to improve their potential to be taken up in industry. • General insights into the use of tools in early-stage workshops such as: tool selection, integration into existing processes, system-level problem solving and providing thematic information. • Suggested improvements for testing tools in controlled workshop experiments.EPSR

Implementation of a distance learning program focused on continuing medical education with the support of patent-based data mining

Purpose – The purpose of this paper is to present the use of a free code computational tool, Patent2net, in the search of patents for the implementation of distance learning aimed at Continuing Medical Education. Design/methodology/approach – This technical report is based on the extraction, organization and availability, in the format of graphs and dynamic tables, and also based on information in other patents on the subject, made available in the Espacenet database. Findings – As a result, it was possible to identify a Chinese patent, free for reproduction in Brazil, which describes an e-learning system that simulates 3D scenarios for training nursing teams. Research limitations/implications – The paper has used one unique patent database, but containing more than 100m documents. Practical implications – The selected patent can contribute to the improvement of care and behavioral techniques of the health professionals. Social implications – The training of health professionals can improve the public and supplementary health systems. Originality/value – This is the first paper in that de technometric analisys of patents was used to solve a problem regarding the training of health professionals