A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance

Measuring similarity between patents is an essential step to ensure novelty of innovation. However, a large number of methods of measuring the similarity between patents still rely on manual classification of patents by experts. Another body of research has proposed automated methods; nevertheless, most of it solely focuses on the semantic similarity of patents. In order to tackle these limitations, we propose a hybrid method for automatically measuring the similarity between patents, considering both semantic and technological similarities. We measure the semantic similarity based on patent texts using BERT, calculate the technological similarity with IPC codes using Jaccard similarity, and perform hybridization by assigning weights to the two similarity methods. Our evaluation result demonstrates that the proposed method outperforms the baseline that considers the semantic similarity only

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

Undergraduate Research Journal, Volume 15

Table of Contents: The Effects of Snorkel-Based Tourism on the Behavior of Reef Fishes / by Savannah Clapp, Lauren Rowsey, and Jordan Grant (p.1-18) -- People Broken Into Pieces Trying to Join: Byzantine Erotica and the Provocative Paradox / by Kendall DeBoer (p.19-28) -- The Legacy of 1830 Land Cover on Present-Day Massachusetts Forest Composition / by Sofie McComb (p.29-58) -- The Role of Labels in Making Inferences about Group Categorization / by Subhashini Madhavan (p.59-76) -- Transcending Autobiography: Simultaneity and Meaning in Elisabet Ney’s Lady Macbeth / by Kendall DeBoer (p.77-92) -- Comparison of Microbial Diversity in Local Wasps and Plant Surfaces / by Dylan Fall and Jo-anne Holley (p.93-104) -- Ambiguity in Biotechnology Patent Policy: Lessons from Association for Molecular Pathology v. Myriad Genetics, Inc. / by Chari Noddings (p.105-126) -- About the uncountability of the number of irrational powers of irrational numbers evaluated as rationals and solutions’ estimation for xx=y and xx =y / by Anca Andrei (p.127-133)Senate of College Council

The analysis and presentation of patents to support engineering design

This paper explores the role of patents in engineering design, and how the extraction and presentation of patent data could be improved for designers. We propose the use of crowdsourcing as a means to post tasks online for a crowd of people to participate and complete. The is-sues of assessment, searching, clustering and knowledge transfer are evaluated with respect to the literature. Opportunities for potential crowd intervention are then discussed, before the presentation of two initial studies. These related to the categorization and interpretation of patents respectively using an online platform. The initial results establish basic crowd capabilities in understanding patent text and interpreting patent drawings. This has shown that reasonable results can be achieved if tasks of appropriate duration and complexity are set, and if test questions are incorporated to ensure a basic level of understanding exists in the workers

Criteria-based patent mapping for assessing potential conflicts between patent claims

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Evaluating claim conflicts between patents is a crucial issue in patent applications and validity allegations. Existing patent informatics tools do not relate well to the legal requirements of identifying claim conflicts; innovation theory does not address patent evaluations; and the current legal approach has weaknesses in the repeatability between cases. Therefore, a need emerges to design a scientific method for evaluating conflicts between patent claims. This thesis presents research on the topic of identifying, evaluating, and visualising patent conflicts. ‘Conflict’ is used to have the same meaning as obviousness, which is an essential legal term under the UK Patents Act 1977. Building on existing methods, this research provides a novel method called Criteria-Based Patent Mapping, for assessing claim conflicts between patents. ‘Criteria-Based’ means that this assessment uses evaluation criteria that clarify the inventive step of the patent. The source of these criteria is the well-known Theory of Inventive Problem Solving (TRIZ), which is incorporated into a statistical method of 'Patent Mapping' for evaluating and visualising differences between patent claims. The application of the new method to four case studies shows that there are differences in judging standards between the legal authorities; and also shows an average value of 52% agreement in predicting potential conflicts between patent claims. Based upon these results, the original 39 TRIZ parameters can usually be refined to about 12 criteria. The scope of this method is restricted to patents in mechanical engineering due to the relevancy of TRIZ parameters. This research transforms difficult claim-to-claim evaluations into simpler claim-to-criteria comparisons that lead to more efficient and transparent patent evaluations. Such improvements will be useful for better decision-making in patent strategy

Design for Invention: A framework for identifying emerging design-prior art conflict

The increasing complexity of patented mechanical designs means that their novelty and inventive steps increasingly rely on interacting geometric features and how they contribute to device functions. These features and interactions are normally incorporated in patents through clear patent claims. However patents can be difficult to interpret and understand for designers due to their legal terminologies. This suggests there is a need for greater awareness of relevant prior art amongst designers in terms of avoiding potential conflict. This paper presents a framework that helps designers obtain insight on relevant prior art and enables emerging design-prior art comparison. The framework mainly contains development of a patent graphical functional representation, a domain-specific ontology and a semantic database. The graphical representation presenting the functional reasoning of patents in terms of interacting geometric features. A domain-specific ontology enables knowledge sharing and conceptualisation, providing a standardised vocabulary for describing patented designs. By formulating patent data into a semantic database, commonality of working principles between an emerging design and prior art can be identified. This enables early identification of potential conflict and thereby could help designers steer their emerging designs away from protected solutions. A computer tool being developed based on this approach is also described

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

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