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

Deriving Technology Intelligence from Patents: Preposition-based Semantic Analysis

Patents are one of the most reliable sources of technology intelligence, and the true value of patent analysis stems from its capability of describing the content of technology based on the relationships between keywords. To date a number of techniques for analyzing the information contained in patent documents that focus on the relationships between keywords have been suggested. However, a drawback of the existing keyword approaches is that they cannot yet determine the types of relationships between the keywords. This study proposes a novel approach based on preposition semantic analysis network which overcomes the limitations of the existing keywords-based network analysis and demonstrates its potential through an application. A preposition is a word that defines the relationship between two neighboring words, and, in the case of patents, prepositions aid in revealing the relationships between keywords related to technologies. To demonstrate the approach, patents regarding an electric vehicle were employed. 13 prepositions were identified which could be used to define 5 relationships between neighboring technological terms: “inclusion (utilization),” “objective (purpose),” “effect,” “process,” and “likeness.” The proposed approach is expected to improve the usability of keyword-based patent analyses and support more elaborate studies on patent documents

Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification

Innovation and entrepreneurship in organization studies

This work, following the conceptual recombination theory, by which ideas and concepts are mentally merged to transform and extend knowledge, explores and summarizes the current sources of academic literature, simultaneously engaging research in the fields of entrepreneurship and creativity. A bibliometric co-citation analysis was applied to identify the invisible colleges and the latent relationships among the most significant papers. Multivariate analyses including cluster, latent class, multidimensional scaling, and Pathfinder were combined to map the nodes positioning the literature. The goal of this study is (1) to increase the awareness of scholars by detecting and visualizing the intellectual structure of the shared ground among both sets of literature; (2) to identify the connected schools of thought, methods, constructs, and theories to problematize or literature gaps to be filled; (3) to reveal the network structure the central, bridge, and peripheral nodes and to hypothesize trailblazing trends, sidings, or forgotten contributions; and (4) to generate, thanks to a creativity grant, new insights to enable entrepreneurs to explore new frontiers. Using creativity techniques and a panel of experts in support, 26 keywords were generated, extracted, assessed and exploited to identify the research unit of 1533 articles. Following a further evaluation process, 73 major co-cited items were finally selected. Given the transverse nature of the creativity domain and of the search for academic interdisciplinary comminglings, data were collected from Science (SCI), Social Sciences (SSCI) and Arts and Humanities (A&HCI) citation indices for the 1991-2013 period

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