Innovation through pertinent patents research based on physical phenomena involved

One can find innovative solutions to complex industrial problems by looking for knowledge in patents. Traditional search using keywords in databases of patents has been widely used. Currently, different computational methods that limit human intervention have been developed. We aim to define a method to improve the search for relevant patents in order to solve industrial problems and specifically to deduce evolution opportunities. The non-automatic, semi-automatic, and automatic search methods use keywords. For a detailed keyword search, we propose as a basis the functional decomposition and the analysis of the physical phenomena involved in the achievement of the function to fulfill. The search for solutions to design a bi-phasic separator in deep offshore shows the method presented in this paper

Using Patent Information for Identification of New Product Features with High Market Potential

AbstractThe current situation in planning and executing of innovation projects within industrial companies still contains considerable drawbacks such as the lack of the tools for systematic task definition for short to long term innovation or low reliability of market success prediction for new product concepts in the early stages of innovation process. Despite the best efforts to reduce risk of failures, the majority of all industrial innovation initiatives offers only incremental improvements compared to the products on the market. The attempts to incorporate customers into new product development require time-expensive customer interviews or extensive field research and seldom deliver significant competitive advantages. The paper discusses the possibilities of fast systematic identification of underserved customer needs, innovation tasks and new product features based on the internal competences of companies, on the analysis of the patent databases and the verification through market tests. The new approach focuses on identification and evaluation of customer needs, understood as solution-neutral benefits, which are expected by the customers. It proposes to enhance the function and contradiction analysis of technical systems and the analysis of the customer working process with the identification of customer benefits obtained from the patent databases. Similar customers’ working processes in various industrial sectors often have different levels of technical and technological evolution. This fact gives the opportunity to identify and to transfer customer needs known in one sector to another sector where these needs are still latent. Using patent information makes the customer needs transfer feasible and manageable

ARIZ85 and patent-driven knowledge support

AbstractThe growing complexity of technical solutions, which encompass knowledge from different scientific fields, makes necessary, also for multi-disciplinary working teams, the consultation of information sources. Indeed, tacit knowledge is essential, but often not sufficient to achieve a proficient problem solving process. Besides, the most comprehensive tool of the TRIZ body of knowledge, i.e. ARIZ, requires, more or less explicitly, the retrieval of new knowledge in order to entirely exploit its potential to drive towards valuable solutions.A multitude of contributions from the literature support various common tasks encountered when using TRIZ and requiring additional information; most of them hold the objective of speeding up the generation of inventive solutions thanks to the capabilities of text mining techniques. Nevertheless, no global study has been conducted to fully disclose the effective knowledge requirements of ARIZ. With respect to this deficiency, the present paper illustrates an analysis of the algorithm with the specific objective of identifying the different types of information needs that can be satisfied by patents. The results of the investigation lay bare the most significant gaps of the research in the field. Further on, an initial proposal is advanced to structure the retrieval of relevant information from patent sources currently not supported by existing methodologies and software applications, so as to exploit the vast amount of technical knowledge contained in there. An illustrative experiment sheds light on the relevance of control parameters as input terms for the definition of search queries aimed at retrieving patents sharing the same physical contradiction of the problem to be treated

Patent data driven innovation logic

Innovation research is conventionally conducted with creativity techniques such as TRIZ, Mind Mapping, Brainstorming, etc. (Dewulf, Baillie 1998). Patent research is typically used to research novelty or prior art, and legal studies. This thesis is at the intersection of creativity techniques, and patent data analysis. It describes how to utilise patent data for distilling Innovation Logic and conducting innovation research. Using the patent research tool PatentInspiration (© AULIVE Software NV), the 4 different stages of the Innovation Logic approach have been subjected to text analysis in patent literature. The specific text patterns were identified and documented on several case studies, with one case study across the whole thesis: the toothbrush. The opportunities and limitations of Patent Data Driven Innovation Research have been documented and discussed. This methodology has been demonstrated within a proposed structural approach to problem solving, technology marketing and innovation research. Furthermore, the potential of artificial idea generation and artificial creativity was examined and debated for the purpose of computer aided creativity. This thesis examines and confirms three claims: CLAIM 1: PROPERTIES AND FUNCTIONS CAN BE ADJECTIVES AND VERBS IN PATENT LITERATURE CLAIM 2: PATENT DATA ANALYSIS AUGMENTS THE FULL INNOVATION LOGIC PROCESS CLAIM 3: ARTIFICIAL INNOVATION METHODS CAN BE FUELED BY PATENT DATA Patent data can be text mined, acting as a global brain consisting of over 100 million invention documents. It is possible to use this existing data to reverse engineer thinking methodologies, allowing scientists and engineers to solve new problems, invent new products or processes, or find new markets for existing technologies. Patent Data Driven Innovation Logic will demonstrate a systematic innovation approach that combines the force of contemporary data mining methods on patent literature, with a structured innovation research methodology.Open Acces

An Automated Method for Identifying TRIZ Evolution Trends from Patents

Trend analysis of the Theory of Inventive Problem Solving (Russian acronym: TRIZ) identifies the evolutionary status of systems to seek directions for further improvement of technology by relating properties and functions obtained from patents to TRIZ trends. The property, which is a specific characteristic of a system, is usually described using adjectives; the function, which is an action that changes a feature of an object, is usually described using verbs. Methods exist to facilitate identification of TRIZ trends, but they rely heavily on human intervention to identify specific trends and trend phases. Therefore, this paper proposes a method that automates identification of TRIZ trends. The proposed method consists of (1) extracting binary relations of the 'adjective + noun' or 'verb + noun' forms from patents using natural language processing, (2) defining a 'reasons for jumps' rule base that arranges trend-specific binary relations for trend identification, and (3) determining specific trends and trend phases by measuring semantic sentence similarity between the binary relations from patents and the binary relations in the rule base. The final output of the method depicts the evolutionary potential as a normalized radar plot, which can be used as input for technology forecasting based on TRIZ trends. (C) 2011 Elsevier Ltd. All rights reserved.X111526sciescopu

Méthodologie d’aide à l’innovation par l’exploitation des brevets et des phénomènes physiques impliqués

The aim of this thesis work is the development of a methodology for knowledge extraction from patents to assist design engineers in the industrial problem-solving phase. The methodology is based on three pillars: definition, search / analysis and innovation. A comprehensive definition of the main function of the industrial system delimits the research field and allows the retrieval of initial keywords through a detailed analysis of what is currently available. The iterative patent search is based on functional decomposition and physical analysis. The analysis phase uses energy functional decomposition to identify energies, transmitted functional flows and physical phenomena involved in the energy conversion process in order to select potentially relevant physical effects. To delineate the exploration field we formulate search queries from a keywords database composed by initial, physical, and technological keywords. A discovery matrix based on the intersections between these keywords allows the classification of pertinent patents. The research for innovation opportunities exploits the discovery matrix in order to decipher the evolutionary trends followed by inventions. Opportunities are deduced from an analysis of the discovery matrix empty cells, an analysis of the evolution trends, and from changing the concept by energy converter substitution. We propose evolution trends constructed from the evolution laws of TRIZ theory, design heuristics, and rules of the art of the engineering. An application case concerning the study of the evolution and the proposal of innovative biphasic separation systems in deep offshore highlights the method.L’objectif de ce travail de thèse est de développer une méthodologie d’extraction de connaissances à partir de brevets pour aider les concepteurs dans la phase de résolution de problèmes industriels. La méthodologie est fondée sur trois piliers : la définition, la recherche / analyse et l’innovation. La définition exhaustive de la fonction principale du système industriel cible le champ de recherche et permet la récupération de mots clés initiaux grâce à une analyse approfondie de l’existant. La recherche itérative des brevets se base sur la décomposition fonctionnelle et sur l’analyse physique. L’analyse intègre la décomposition fonctionnelle énergétique pour déceler les énergies, les flux fonctionnels transmis et les phénomènes physiques impliqués dans le processus de conversion énergétique afin de sélectionner des effets physiques potentiellement pertinents. Pour délimiter le champ d’exploration nous formulons des requêtes de recherche à partir d’une base de données de mots clés constituée par des mots clés initiaux, des mots clés physiques et des mots clés technologiques. Une matrice des découvertes basée sur les croisements entre ces mots clés permet le classement des brevets pertinents. La recherche des opportunités d’innovation exploite la matrice des découvertes pour déceler les tendances évolutives suivies par les inventions. Les opportunités sont déduites à partir de l’analyse des cellules non pourvues de la matrice des découvertes, de l’analyse par tendances d’évolution et du changement de concept par la substitution du convertisseur énergétique. Nous proposons des tendances d’évolution construites à partir de lois d’évolution de la théorie TRIZ, d’heuristiques de conception et de règles de l’art de l’ingénieur. Un cas d’application concernant l’étude d’évolution et la proposition de nouveaux systèmes de séparation de mélanges bi-phasiques en offshore profond met en valeur la méthode