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Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named PatNet built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best-preferred model to associate homogeneous (e.g., patent-patent) and heterogeneous (e.g., inventor-assignee) pairs of entities.

Amphibious ground-aerial vehicles fuse flying and driving modes to enable more flexible air-land mobility and have received growing attention recently. By analyzing the existing amphibious vehicles, we highlight the autonomous fly-driving functionality for the effective uses of amphibious vehicles in complex three-dimensional urban transportation systems. We review and summarize the key enabling technologies for intelligent flying-driving in existing amphibious vehicle designs, identify major technological barriers and propose potential solutions for future research and innovation. This paper aims to serve as a guide for research and development of intelligent amphibious vehicles for urban transportation toward the future.

Patent retrieval influences several applications within engineering design research, education, and practice as well as applications that concern innovation, intellectual property, and knowledge management etc. In this article, we propose a method to retrieve patents relevant to an initial set of patents, by synthesizing state-of-the-art techniques among natural language processing and knowledge graph embedding. Our method involves a patent embedding that captures text, citation, and inventor information, which individually represent different facets of knowledge communicated through a patent document. We obtain text embeddings using Sentence-BERT applied to titles and abstracts. We obtain citation and inventor embeddings through TransE that is trained using the corresponding knowledge graphs. We identify using a classification task that the concatenation of text, citation, and inventor embeddings offers a plausible representation of a patent. While the proposed patent embedding could be used to associate a pair of patents, we observe using a recall task that multiple initial patents could be associated with a target patent using mean cosine similarity, which could then be utilized to rank all target patents and retrieve the most relevant ones. We apply the proposed patent retrieval method to a set of patents corresponding to a product family and an inventor's portfolio.

Data-driven conceptual design methods and tools aim to inspire human ideation for new design concepts by providing external inspirational stimuli. In prior studies, the stimuli have been limited in terms of coverage, granularity, and retrieval guidance. Here, we present a knowledge-based expert system that provides design stimuli across the semantic, document and field levels simultaneously from all fields of engineering and technology and that follows creativity theories to guide the retrieval and use of stimuli according to the knowledge distance. The system is centered on the use of a network of all technology fields in the patent classification system, to store and organize the world’s cumulative data on the technological knowledge, concepts and solutions in the total patent database according to statistically-estimated knowledge distance between technology fields. In turn, knowledge distance guides the network-based exploration and retrieval of inspirational stimuli for inferences across near and far fields to generate new design ideas by analogy and combination. With two case studies, we showcase the effectiveness of using the system to explore and retrieve multilevel inspirational stimuli and generate new design ideas for both problem solving and open-ended innovation. These case studies also demonstrate the computer-aided ideation process, which is data-driven, computationally augmented, theoretically grounded, visually inspiring, and rapid.

There are growing efforts to mine public and common-sense semantic network databases for engineering design ideation stimuli. However, there is still a lack of design ideation aids based on semantic network databases that are specialized in engineering or technology-based knowledge. In this study, we present a new methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design. The core of the methodology is to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas. We demonstrate the effectiveness in general, and use strategies and ideation outcome implications of the methodology via a case study of flying car design idea generation.

In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized and often confused with several other data-related phenomena. This paper defines and crystalizes "data-driven innovation" as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization (DDO), data-based innovation (DBI), and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, I elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process and present a process-based taxonomy of different data-driven innovation approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact data-driven innovation.

Design representation is a common task in the design process to facilitate learning, analysis, redesign, communication, and other design activities. Traditional representation techniques rely on human expertise and manual construction and are difficult to repeat and scale. Here, we propose a methodology that utilizes a pre-trained large-scale cross-domain design knowledge base to automatically generate design representation as a semantic network, i.e., a network of the entities and relations, based on design descriptions in texts or natural languages. Our methodology requires no ad hoc statistics. Based on a participatory study, we reveal the effectiveness and differences of the semantic network representations that are automatically generated with alternative knowledge bases. The findings illuminate future research directions to enhance design representation as semantic networks.

Technology positions of firms may determine their competitive advantages and innovation capabilities. While a tangible understanding of technology positions can inform competitive intelligence, the...

In complex systems, design changes of one component or subsystem may require the redesign of other components or be stimulated by the redesign of other components. Changes propagate through the design dependency paths among the components of a system and thus present challenges to the design and management of the system. Therefore, an assessment of the influence and susceptibility of components is useful for system design decisions such as which components to standardize, modularize, or embed flexibility to address future design changes. Finding the overall influence or susceptibility of individual components throughout a complex system is an infinite regress problem, as both the sources and targets of the influences are the same set of components. A component influences some other components, which influence other components, and so on. Such influences can be propagated back to the initiating components through cycles of design dependencies among components. In this paper, we model change propagation in complex systems as an infinite regress problem and derive eigenvector-based indicators of component influence and susceptibility. We demonstrate our method based on several case studies.

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., Accepted by JCISE

Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of design innovation and thus make breakthroughs. In this work, we construct a technology fitness landscape via deep neural embeddings of patent data. The landscape consists of 1,757 technology domains and their respective improvement rates. In the landscape, we found a high hill related to information and communication technologies (ICT) and a vast low plain of the remaining domains. The landscape presents a bird's eye view of the structure of the total technology space, providing a new way for innovators to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation., 10 pages, 7 figures

Design-by-Analogy (DbA) is a design methodology that draws inspiration from a source domain to a target domain to generate new solutions to problems or designs, which can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. Herein, we survey the prior data-driven DbA studies and categorize and analyze individual study according to the data, methods and applications in four categories including analogy encoding, retrieval, mapping, and evaluation. Based on such structured literature analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field.

There are increasing applications of natural language processing techniques for information retrieval, indexing, topic modelling and text classification in engineering contexts. A standard component of such tasks is the removal of stopwords, which are uninformative components of the data. While researchers use readily available stopwords lists that are derived from non-technical resources, the technical jargon of engineering fields contains their own highly frequent and uninformative words and there exists no standard stopwords list for technical language processing applications. Here we address this gap by rigorously identifying generic, insignificant, uninformative stopwords in engineering texts beyond the stopwords in general texts, based on the synthesis of alternative statistical measures such as term frequency, inverse document frequency, and entropy, and curating a stopwords dataset ready for technical language processing applications.

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., 11 pages, 11 figures

Textual idea data from online crowdsourcing contains rich information of the concepts that underlie the original ideas and can be recombined to generate new ideas. But representing such information in a way that can stimulate new ideas is not a trivial task, because crowdsourced data are often vast and in unstructured natural languages. This paper introduces a method that uses natural language processing to summarize a massive number of idea descriptions and represents the underlying concept space as word clouds with a core-periphery structure to inspire recombinations of such concepts into new ideas. We report the use of this method in a real public-sector-sponsored project to explore ideas for future transportation system design. Word clouds that represent the concept space underlying original crowdsourced ideas are used as ideation aids and stimulate many new ideas with varied novelty, usefulness and feasibility. The new ideas suggest that the proposed method helps expand the idea space. Our analysis of these ideas and a survey with the designers who generated them shed light on how people perceive and use the word clouds as ideation aids and suggest future research directions.

Patent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, time-consuming, and prone to human errors. In this paper, we propose a method to automate and augment the heuristic and iterative keyword discovery process. Specifically, we train a semantic engineering knowledge graph on the full patent database using natural language processing and semantic analysis, and use it as the basis to retrieve and rank the keywords contained in the retrieved patents. On this basis, searchers do not need to read patent texts but just select among the recommended keywords to expand their queries. The proposed method improves the completeness of the search keyword set and reduces the human effort for the same task.

A tangible understanding of the latent design knowledge base of a technology domain, i.e., the set of technologies and related design knowledge used to solve the specific problems of a domain, and how it evolves, can guide engineering design efforts in that domain. However, methods for extracting, analyzing and understanding the structure and evolutionary trajectories of a domain’s accumulated design knowledge base are still underdeveloped. This study introduces a network-based methodology for visualizing and analyzing the structure and expansion trajectories of the design knowledge base of a given technology domain. The methodology is centered on overlaying the total technology space, represented as a network of all known technologies based on patent data, with the specific knowledge positions and estimated expansion paths of a specific domain as a subgraph of the total network. We demonstrate the methodology via a case study of hybrid electric vehicles. The methodology may help designers understand the technology evolution trajectories of their domain and suggest next design opportunities or directions.

In this paper, we present a mobile crowdsensing approach to understand the daily lifestyle of the older population in Singapore. By implementing novel clustering, sensor fusion, and user profiling techniques to analyze the multisensor data (location, noise, and light) collected from a smartphone application, we identified the travel patterns at several points of interest (POI), the impact of travel frequency for certain POI, and three main user profiles. The results show that older adults mostly spend time at food courts and community centers in their home neighborhood, but they travel away from the neighborhood for healthcare and religious purposes. We found that POIs have more visits if they are easily accessible (in terms of travel time from home) regardless of the distance from home.

The patent database is often used by designers to search for inspirational stimuli for innovative design opportunities because of the large size, extensive variety, and the massive quantity of design information contained in patent documents. Growing work on design-by-analogy has adopted various vectorization approaches for associating design documents. However, they only focused on text analysis and ignored visual information. Research in engineering design and cognitive psychology has shown that visual stimuli may benefit design ideation. In this study, we focus on visual design stimuli and automatically derive the vector space and the design feature vectors representing design images. The automatic vectorization approach uses a novel convolutional neural network architecture named Dual-Visual Geometry Group (VGG) aiming to accomplish two tasks: visual material-type prediction and international patent classification (IPC) section-label predictions. The derived feature vectors that embed both visual characteristics and technology-related knowledge can be potentially utilized to guide the retrieval and use of near-field and far-field design stimuli according to their vector distances. We report the accuracy of the training tasks and also use a case study to demonstrate the advantages of design image retrievals based on our model.

We propose a large, scalable engineering knowledge graph, comprising sets of real-world engineering “facts” as < entity, relationship, entity > triples that are found in the patent database. We apply a set of rules based on the syntactic and lexical properties of claims in a patent document to extract facts. We aggregate these facts within each patent document and integrate the aggregated sets of facts across the patent database to obtain an engineering knowledge graph. Such a knowledge graph is expected to support inference, reasoning, and recalling in various engineering tasks. The knowledge graph has a greater size and coverage in comparison with the previously used knowledge graphs and semantic networks in the engineering literature.

Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions., Comment: A Preprint Version

Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.

There have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice., 12 pages, 2 tables, conference

Online reward-based crowdfunding campaigns have emerged as an innovative approach for validating demands, discovering early adopters, and seeking learning and feedback in the design processes of innovative products. However, crowdfunding campaigns for innovative products are faced with a high degree of uncertainty and suffer meager rates of success to fulfill their values for design. To guide designers and innovators for crowdfunding campaigns, this paper presents a data-driven methodology to build a prediction model with critical factors for crowdfunding success, based on public online crowdfunding campaign data. Specifically, the methodology filters 26 candidate factors in the Real-Win-Worth framework and identifies the critical ones via step-wise regression to predict the amount of crowdfunding. We demonstrate the methodology via deriving prediction models and identifying essential factors from 3D printer and smartwatch campaign data on Kickstarter and Indiegogo. The critical factors can guide campaign developments, and the prediction model may evaluate crowdfunding potential of innovations in contexts, to increase the chance of crowdfunding success of innovative products., 12 pages, 3 figures, 7 tables, accepted by IEEE TEM

Despite extant research on how established firms manage interdependences and complementarities in an ecosystem, little is known regarding the ecosystem strategies that entrepreneurs can take to found and grow a new firm with resource constraints. Especially, there was no study on how startups may pursue specialization and ecosystem value co-creation when located in a region lacking co-specialized firms as potential ecosystem partners. Through a longitudinal study of Suntech from its founding and growth to the world’s largest solar PV producer, we identify two distinct ecosystem strategies of the founder-entrepreneur in the founding and growth stages: initially, engaging globally-dispersed ecosystem partners; and subsequently, cultivating new local ecosystem partners. Our analysis shed light on the entrepreneuri-al motivations and enablers for the staged ecosystem strategies, as well as the economic driver behind the shift of strategies across the founding and growth phases. Our findings contribute to the research on ecosystem strategy, by elucidating how entrepreneurs build ecosystems to build new ventures.

Prior studies have implied that the architecture of firms' participation in an innovation ecosystem may affect the evolvability of their own ecosystems, thus conditioning firm strategies and performance. However, specific influences are unknown. In this paper, we abstract and model an innovation ecosystem as a network of firms connected by their technological dependences and assess its evolvability in the framework of the NK model. Network simulations suggest that although firms' influence diversity promotes ecosystem evolvability, their influence density limits ecosystem evolvability. We also relate these findings to empirically observed differences in the architecture and evolvability of the automotive and electronics ecosystems. Implications from our findings may help firms either to better sense their ecosystems' evolution prospects and adjust their strategies accordingly or to design and manage their technological dependences and the architecture of their ecosystem participation to influence the evolvability of their ecosystem in favor of their strategic intents and capability advantages.

Contemporary products are usually designed and produced in large inter‐firm supply networks rather than by single firms. However, our understanding of such networks is still limited due to the lack of network‐wide empirical data as well as the complexity and nonlinearity of supply networks. Herein, we introduce a network formation model to extend and generalize the prior empirical studies that have revealed variable hierarchy topologies and firm‐level transaction specificities across the supply networks for automobiles and electronics. We call it the “hierarchy‐niche model.” With tuning the parameters for transaction specificity and transaction breadth, the model can generate a wide spectrum of stochastic networks that comply with the production hierarchy to varied degrees. Our simulation analyses show that the model‐generated stochastic networks capture hierarchical and cyclic topologies of real‐world automobile and electronics supply networks. The model, which relates firm‐level transaction patterns to network‐wide emergent topologies, can be further utilized to inform and guide firms’ transaction strategies concerning the overall supply network.

Traditionally, design opportunities and directions are conceived based on expertise, intuition, or time-consuming user studies and marketing research at the fuzzy front end of the design process. Herein, we propose the use of the total technology space map (TSM) as a visual ideation aid for rapidly conceiving high-level design opportunities. The map is comprised of various technology domains positioned according to knowledge proximity, which is measured based on a large quantity of patent data. It provides a systematic picture of the total technology space to enable stimulated ideation beyond the designer's knowledge. Designers can browse the map and navigate various technologies to conceive new design opportunities that relate different technologies across the space. We demonstrate the process of using TSM as a rapid ideation aid and then analyze its applications in two experiments to show its effectiveness and limitations. Furthermore, we have developed a cloud-based system for computer-aided ideation, that is, InnoGPS, to integrate interactive map browsing for conceiving high-level design opportunities with domain-specific patent retrieval for stimulating concrete technical concepts, and to potentially embed machine-learning and artificial intelligence in the map-aided ideation process.