A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field

This paper presents a relatively simple, objective and repeatable method for selecting sets of patents that are representative of a specific technological domain. The methodology consists of using search terms to locate the most representative international and US patent classes and determines the overlap of those classes to arrive at the final set of patents. Five different technological fields (computed tomography, solar photovoltaics, wind turbines, electric capacitors, electrochemical batteries) are used to test and demonstrate the proposed method. Comparison against traditional keyword searches and individual patent class searches shows that the method presented in this paper can find a set of patents with more relevance and completeness and no more effort than the other two methods. Follow on procedures to potentially improve the relevancy and completeness for specific domains are also defined and demonstrated. The method is compared to an expertly selected set of patents for an economic domain, and is shown to not be a suitable replacement for that particular use case. The paper also considers potential uses for this methodology and the underlying techniques as well as limitations of the methodology.SUTD-MIT International Design Cente

Tracing technological development trajectories: A genetic knowledge persistence-based main path approach

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method overcomes the aforementioned drawbacks defining main paths that are almost 10x less complex while containing more of the relevant important knowledge than the main path networks defined by the existing method.Comment: 20 pages, 7 figure

Tracing technological development trajectories: A genetic knowledge persistence-based main path approach

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.This work was supported by Hanyang University (Grant number: HY-2016, http://www.hanyang.ac.kr) and Singapore University of Technology and Design (Grant number: 6921538, http://www.sutd.edu.sg). We would like to thank Hanyang University and SUTD/MIT International Design Center for supporting the research

A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation

The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.Comment: 11 pages, 11 figure

Technology Structural Implications from the Extension of a Patent Search Method

Many areas of academic and industrial work make use of the notion of a ‘technology’. This paper attempts to reduce the ambiguity around the definition of what constitutes a ‘technology’ by extension of a method described previously that finds highly relevant patent sets for specified technological fields. The method relies on a less ambiguous definition that includes both a functional component and a component consisting of the underlying knowledge in a technological field to form a two-component definition. These two components form a useful definition of a technology that allows for objective, repeatable and thus comparable analysis of specific technologies. 28 technological domains are investigated: the extension of an earlier technique is shown to be capable of finding highly relevant and complete patent sets for each of the technologies. Overall, about 500,000 patents from 1976 to 2012 are classified into these 28 domains. The patents in each of these sets are not only highly relevant to the domain of interest but there are relatively low numbers of patents classified into any two of these domains (total patents classified in 2 domains are 2.9% of the total patents and the great majority of patent class pairs have zero overlap with a few of the 378 patent class pairs containing the bulk of the doubly listed patents). On the other hand, the patents within a given domain cite patents in other domains about 90% of the time. These results suggest that technology can be usefully decomposed to distinct units but that the inventions in these relatively tightly contained units depend upon widely spread additional knowledge

Facilitating Design-by-Analogy: Development of a Complete Functional Vocabulary and Functional Vector Approach to Analogical Search

Design-by-analogy is an effective approach to innovative concept generation, but can be elusive at times due to the fact that few methods and tools exist to assist designers in systematically seeking and identifying analogies from general data sources, databases, or repositories, such as patent databases. A new method for extracting analogies from data sources has been developed to provide this capability. Building on past research, we utilize a functional vector space model to quantify analogous similarity between a design problem and the data source of potential analogies. We quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. We develop a complete functional vocabulary to map the patent database to applicable functionally critical terms, using document parsing algorithms to reduce text descriptions of the data sources down to the key functions, and applying Zipf’s law on word count order reduction to reduce the words within the documents. The reduction of a document (in this case a patent) into functional analogous words enables the matching to novel ideas that are functionally similar, which can be customized in various ways. This approach thereby provides relevant sources of design-by-analogy inspiration. Although our implementation of the technique focuses on functional descriptions of patents and the mapping of these functions to those of the design problem, resulting in a set of analogies, we believe that this technique is applicable to other analogy data sources as well. As a verification of the approach, an original design problem for an automated window washer illustrates the distance range of analogical solutions that can be extracted, extending from very near-field, literal solutions to far-field cross-domain analogies. Finally, a comparison with a current patent search tool is performed to draw a contrast to the status quo and evaluate the effectiveness of this work.National Science Foundation (U.S.) (grant number CMMI-0855510)National Science Foundation (U.S.) (grant number CMMI-0855326)National Science Foundation (U.S.) (grant number CMMI-0855293)SUTD-MIT International Design Centre (IDC