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

Patent Data for Engineering Design: A Critical Review and Future Directions

Patent data have long been used for engineering design research because of its large and expanding size, and widely varying massive amount of design information contained in patents. Recent advances in artificial intelligence and data science present unprecedented opportunities to develop data-driven design methods and tools, as well as advance design science, using the patent database. Herein, we survey and categorize the patent-for-design literature based on its contributions to design theories, methods, tools, and strategies, as well as the types of patent data and data-driven methods used in respective studies. Our review highlights promising future research directions in patent data-driven design research and practice.Comment: Accepted by JCIS

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

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

Identifying patent conflicts: TRIZ-Led patent mapping

This paper presents a novel method of patent mapping for visualising conflicts between patent claims that incorporates the Theory of Inventive Problem Solving (TRIZ). The method uses TRIZ engineering parameters as the criteria for evaluating dissimilarities between patent claims, producing a visualisation based on Multi-Dimensional Scaling (MDS) that can be compared with legal judgments. The advantages of the method are that it (a) reduces evaluation complexity by transforming claim-to-claim comparisons into claim-to-criteria comparisons, and (b) provides a means of comparing judgment standards between different legal authorities in mechanical engineering terms. Reliability and validity of the method are tested through focus groups using a case study on aircraft seats. The scope of the method is limited to the field of mechanical inventions

Assessment of BioPattern in Novel Idea Generation for Bio-Inspired Design

BioPattern is a novel ideation tool for Bio-Inspired Design, built based on TRIZ, SAPPhIRE, and pattern language. It consists of an ontology, known as pattern-based ontology, and a sustainability evaluation, known as Ideal Windows. However, this framework has not been tested yet. Therefore, this article is to present the results and analysis of the case study conducted to assess this biomimicry framework. Two different groups of students, Creative & Innovation class (controlled group) and Integrated Engineering Design class (experimental group), are asked to generate innovative ideas where the experimental group employed BioPattern as the ideation tool. It is found that the level of innovation for the inventive ideas generated by the experimental group is much higher compared to that of the controlled group. Based on the inventive ideas produced by the experimental group, BioPattern is found to be efficient in ideation, able to generate effective solution, the problem-solution pairs of the ontology are adequate, and the biological solutions suggested are transferable as technological solutions. It can be concluded that BioPattern is able to bridge the biology-engineering gap

Automated functional analysis of patents for producing design insight

Patent analysis is a popular topic of research. However, designers do not engage with patents in the early design stage, as patents are time-consuming to read and understand due to their intricate structure and the legal terminologies used. Manually produced graphical representations of patent working principles for improving designers’ awareness of prior art have been demonstrated in previous research. In this paper, an automated approach is presented, utilising Natural Language Processing (NLP) techniques to identify the invention working principle from the patent independent claims and produce a visualisation. The outcomes of this automated approach are compared with previous manually produced examples. The results indicate over 40% match between the automatic and manual approach, which is a good basis for further development. The comparison suggests that the automated approach works well for features and relationships that are expressed explicitly and consistently but begin to lose accuracy when applied to complex sentences. The comparison also suggests that the accuracy of the proposed automated approach can be improved by using a trained part-of-speech (POS) tagger, improved parsing grammar and an ontology

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