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

TeknoAssistant : a domain specific tech mining approach for technical problem-solving support

This paper presents TeknoAssistant, a domain-specific tech mining method for building a problem-solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naive Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either "problem", "solution" or "none" categories, and a problem-solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process

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

Generating Information Relation Matrix Using Semantic Patent Mining for Technology Planning: A Case of Nano-Sensor

For the purposes of technology planning and research and development strategy development, we present a semi-automated method that extracts text information from patent data, uses natural language processing to extract the key technical information of the patent, and then visualizes this information in a matrix form. We tried to support qualitative analysis of patent contents by extracting functions, components, and contexts, which are the most important information about inventions. We validated the method by applying it to patent data related to nanosensors. The matrix can emphasize technical information that have not been exploited in patents, and thereby identify development opportunities.111Ysciescopu

Extraction of Principle Knowledge from Process Patents for Manufacturing Process Innovation

Process patents contain substantial knowledge of the principles behind manufacturing process problems-solving; however, this knowledge is implicit in lengthy texts and cannot be directly reused in innovation design. To effectively support systematic manufacturing process innovation, this paper presents an approach to extracting principle innovation knowledge from process patents. The proposed approach consists of (1) classifying process patents by taking process method, manufacturing object and manufacturing feature as the references; (2) extracting generalized process contradiction parameters and the principles behind solving such process contradictions based on patent mining and technology abstraction of TRIZ (the theory of inventive problem solving); and (3) constructing a domain process contradiction matrix and mapping the relationship between the matrix and the corresponding process patents. Finally, a case study is presented to illustrate the applicability of the proposed approach

Applications of advanced transport aircraft in developing countries

Four representative market scenarios were studied to evaluate the relative performance of air-and surface-based transportation systems in meeting the needs of two developing contries, Brazil and Indonesia, which were selected for detailed case studies. The market scenarios were: remote mining, low-density transport, tropical forestry, and large cargo aircraft serving processing centers in resource-rich, remote areas. The long-term potential of various aircraft types, together with fleet requirements and necessary technology advances, is determined for each application

The combination of the disciplines of Techmining and semantic TRIZ for better and faster analyzing technology evolution

Tesis por compendioThe purpose of the present thesis is to explore and to demonstrate how the combination of two methodological approaches, text mining plus the systemic vision of TRIZ empowered by semantics, can bring a larger and more comprehensive analysis of the evolution of a technology. Both approaches had been not combined before the first of the four papers constituents of the present thesis based in a compendium of publications. However, this combination applied to the evolution of technologies is increasingly being published in the scientific literature. Such combination shows a second benefit in the form of an improvement in accessing and connecting knowledge from disparate scientific literatures in a systematic manner. The common element in all these papers is the use of the technology mining approach, 'techmining', the application of text mining techniques based on technology management knowledge, combined with the use of semantic TRIZ, the advantage of syntactic applied to the systemic vision of TRIZ. These papers show that a better analysis of evolving technologies, e.g. by profiling technologies from a systemic point of view or, a better access to knowledge, e.g. by semantically connecting concepts with meaning, can be achieved. The research on applying the combination of these approaches to scientific and technological information analysis explores the advantages and new possibilities for technology trends assessment as well as the semantic connection of concepts which represents a change in the way information research can be done. The different applications of the aforementioned combination are explored by means of the here presented articles. The structure followed in this research is the collection of three papers published in international academic journals indexed in the most prestigious databases and one chapter in a proceedings book of an international congress. The attached articles show the research undertaken to demonstrate the aforementioned benefits of the proposed combination. Despite it can be found many methods and approaches about the assessment of the evolution of technologies, distributed across the literature, there is still a need to better understand which technologies may emerge, which may evolve faster and at what pace can they reach the market. The combination of the techmining approach and the semantic TRIZ approaches allows understanding the trends enriched with a systemic vision of the links, functions, and influences of constituent and enabling elements of a technology. Such systemic link of elements with its components and ecosystem also allows for a multi-dimensional view of a technology and further reduces the uncertainty to preview the progress of a technology. The papers presented in this dissertation are based on the combination of the TRIZ methodology, the techmining approach and the semantic TRIZ approach, applied to different technologies in different domains, to proof the advantages and implications of the combination. The articles try the different interactions of the combined approaches, applied to the assessment of different technologies, such as lithium batteries for the electric car, a medical case linked to a disease known as Meniére's Disease, the prognosis of prostate cancer, and the usage of probiotics as substitutes of antibiotics in the animal health. The wide range of technologies was selected to show the clear benefits of either combining the two approaches or applying predominantly one of them in the case of the Meniére's disease article. That difference in the nature of technologies also helped to better understand the systemic point of view of the technology, exploring new applications based on the general system theory from Bertalanffy as well as other related approaches about technologies.El propósito de la presente tesis es la exploración y la demostración de la combinación de dos enfoques metodológicos, la minería de textos y la visión sistémica de TRIZ reforzada con la semántica, pueden aportar un mayor y mas exhaustivo análisis de la evolución de una tecnología. Ambos enfoques no habían sido combinados antes del primero de los cuatro artículos que representan esta tesis por compendio de publicaciones, aunque dicha combinación ha sido crecientemente publicada en la literatura científica, para multiples propósitos desde entonces. Un segundo aporte proporcionado por esta combinación es la mejora de la capacidad de acceso al conocimiento y cómo ello supone un avance para el descubrimiento a través de literaturas no relacionadas "disparate literature discovery" de una forma metódica y científica. El elemento común en los artículos aquí presentados es el aprovechamiento de techmining, esto es, la minería de textos con base en la gestión tecnológica, por ejemplo mediante el perfilado de tecnologías, junto al enfoque de la metodología TRIZ potenciada por el análisis sintáctico y semántico, esto es, mediante la conexión semántica de conceptos, para un análisis más completo de la evolución tecnológica, proporcionando al mismo tiempo un acceso más racional al conocimiento. La investigación sobre la aplicación de la citada combinación al análisis de información científica y tecnológica explora las ventajas y nuevas posibilidades en la evaluación del avance de la tecnología, así como la conexión semántica de conceptos que representa nuevas posibilidades en la forma en que la investigación textual puede hacerse. La estructura de la investigación aquí presentada se muestra a través de los artículos publicados en revistas internacionales de alto impacto y el capítulo de los 'proceedings' de un congreso internacional. Dichos artículos muestran la investigación llevada a cabo para demostrar los beneficios mencionados de la combinación propuesta. A pesar de la gran actividad de investigación y de la existencia de varios enfoques para la prospectiva y la previsión tecnológica presentes en la literatura científica, existe aún la necesidad de entender qué tecnologías pueden emerger, pueden evolucionar más rápido y a qué velocidad pueden llegar al mercado. La combinación de los enfoques de minería tecnológica o techmining y TRIZ semántico permite entender las tendencias de una tecnología dada, enriquecida con una visión de su sistémica, y teniendo en cuenta las conexiones de sus elementos y las influencias de sus elementos constituyentes. Tal conexión entre los components y su entorno permite una vision multidimensional de la tecnología reduciendo más aún la incertidumbre en la previsión de la evolución de una tecnología. Los artículos presentados en esta tesis son aplicaciones y exploraciones de la combinación de mencionada, a diferentes tecnologías de diversos ámbitos muy dispares entre sí, con el fin de demostrar sus ventajas e implicaciones. Los artículos tratan las diferentes interacciones entre ambos enfoques de trabajo, aplicados a tecnologías como baterías de litio para los vehículos eléctricos, un caso médico ligado a una dolencia como el síndrome de Méniere, a la prognosis del cáncer de próstata y al uso de probióticos en la alimentación animal como sustitución de los antibióticos. Este amplio rango de tecnologías han sido seleccionados para mostrar las ventajas, de forma más objetiva, de la combinación de ambos enfoques o con predominancia de alguno en particular, como es el caso del artículo explorando el síndrome de Méniere. Estas exploraciones permiten también entender mejor el punto de vista sistémico de una tecnología, descubriendo nuevas aplicaciones basadas en la teoría general de sistemas de Bertalanffy así como en otros enfoques relacionados.El propòsit de la present tesi és l'exploració i la demostració de la combinació de dos enfocaments metodològics, la minería de textes i la visió sistémica de TRIZ, reforçada amb la sintáctica i la semántica, mostrant que poden oferir un abast més gran i més holístic en l'enteniment de l'evolució d'una tecnología. Tots dos enfocaments no habían estat combinats abans del primer article dels quatre que composen aquesta tesi, però creixentment combinat dins la literatura científica per a múltiples propostes des de la primera publicació. Una segona aportació proporcionada per aquesta combinació és la millora de la capacitat d'accés al coneixement, i de com això suposa un avanç en l'àrea de recerca a traves de literatures no relacionades "disparate literature discovery" d'una forma metòdica i científica. L'element comú en els articles presentats en aquesta tesi és l'aprofitament de la mineria de textos amb base en la gestió tecnològica, 'techmining', per exemple mitjançant el perfilat de tecnologies, al costat de l'enfocament de la metodologia TRIZ potenciada per l'anàlisi sintàctica i semàntica, mitjançant la conexión semántica de conceptes, per assolir un anàlisi més complet de l'evolució tecnològica, així com per a garantir un accés més racional al coneixement. La investigació de l'aplicació de la combinació dels dos enfocaments a l'anàlisi d'informació científica i tecnològica realizat, exploren els avantatges i noves possibilitats en l'avaluació de l'avanç de tecnologies, així com la conexión de conceptes uqe representa noves possibilitats en la forma en què la investigació textual pot fer-se. L'estructura de la investigació ací presentada es mostra a través dels articles publicats i el capítol dels 'proceedings' d'un congrés internacional. Aquests articles mostren la investigació duta a terme per demostrar els beneficis esmentats. Tot i la gran activitat de recerca i enfocaments per a la prospectiva i la previsió tecnològica existents a la literatura científica, existeix encara la necessitat d'entendre quines tecnologies poden emergir, poden evolucionar més ràpid i a quina velocitat poden arribar al mercat. La combinació dels enfocaments de mineria tecnològica o 'techmining' i TRIZ semàntic permet entendre les tendències d'una tecnologia donada, amb una visió del seu sistema, les connexions dels seus elements i les influències dels elements constituents. Els articles presentats en aquesta tesi són aplicacions i exploracions de la combinació de la metodologia TRIZ, la seva potenciació mitjançant la semàntica i el techmining a diferents tecnologies de diversos àmbits, alguns molt dispars entre si, per tal de demostrar les seves avantatges i implicacions. Els articles tracten les diferents interaccions entre els dos enfocaments de treball, aplicats a tecnologies com bateries de liti per als vehicles elèctrics, un cas mèdic lligat a una malaltia com la síndrome de Ménière, a la prognosi del càncer de pròstata i en alimentació, a l'ús de probiòtics en l'alimentació animal com a substitució dels antibiòtics. Aquest ampli rang de tecnologies han estat seleccionats per mostrar els avantatges de forma més objectiva, de la combinació de tots dos enfocaments o amb predominança d'algun en particular, com és el cas de l'article explorant la síndrome de Ménière. Aquestes exploracions permeten també entendre millor el punt de vista sistèmic d'una tecnologia, descobrint noves aplicacions amb base en la teoria general de sistemes de Bertalanffy així com altres treballs relacionats.Vicente Gomila, JM. (2017). The combination of the disciplines of Techmining and semantic TRIZ for better and faster analyzing technology evolution [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/89088TESISCompendi

Metric Learning for Patent Similarity

TesisRealiza el uso de técnicas de aprendizaje de métricas para mejorar las medidas de similaridad textual de patentes.Consejo Nacional de Desarrollo Científico y Tecnológico - CNPqTesi

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