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Mining and Representing the Concept Space of Existing Ideas for Directed Ideation

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Abstract

Design innovation projects often generate large numbers of design ideas from designers, users and, increasingly, the crowd over the Internet. Such idea data are often used for selection and implementation, but in fact can also be used as sources of inspiration for further idea generation. In particular, the elementary concepts that underlie the original ideas can be recombined to generate new ideas. But it is not a trivial task to retrieve concepts from raw lists of ideas and data sources in a manner that can stimulate or generate new ideas. A significant difficulty lies in the fact that idea data are often expressed in unstructured natural languages. This paper develops a methodology that uses natural language processing to extract key words as elementary concepts embedded in massive idea descriptions and represents the elementary concept space in a core-periphery structure to direct the recombination of elementary concepts into new ideas. We apply the methodology to mine and represent the concept space underlying massive crowdsourced ideas and use it to generate new ideas for future transportation system designs in a real public-sector-sponsored project via humans and automated computer programs. Our analysis of the human and computer recombination processes and outcomes sheds light on future research directions for artificial intelligence in design ideation.

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... Recently, extensive research has established that crowdsourced design solution data collected from online ideation and task-orientated platforms (e.g., InnoCentive, Quirky, OpenIDEO, and Amazon Mechanical Turk) are useful for DbA research [97][98][99]. ...
... Both have been used in many data-driven DbA studies [5,15,64]. Although crowdsourced design data [97][98][99] and crowdfunded project data [100] have been explored in design research and are also potentially useful for DbA studies, they have not been fully exploited. In addition, most of the current data-driven DbA methods were designed to merely mine textual information as analogy candidates; ...
... In this paper, although we only focus on data-driven DbA methods and tools, AI and data science techniques can be also useful to augment other classical engineering design methods [132], such as TRIZ [65,66], design heuristics [133,134], design principles [96,135], design structure matrix [136,137], product family and platform design [138,139], first principles [140][141][142], C-K [143], blending [144] and combinational design [99,145]. We hope that researchers of these relevant fields may also find inspirations Schematic comparison between the traditional DbA process and future data-driven DbA system Table Caption List Table 1 Existing databases that have been adopted or developed for DbA Table 2 The applications of DbA tools and methods Table 3 Existing AI-based methods and algorithms that have been adopted or developed for DbA ...
Article
Full-text available
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.
... Specifically, patent documents contain rich engineering design information of the structures, functions, mechanisms, and principles that might be useful for the design inspiration process [5,13,15,40,72,94]. Recently, extensive research has established that crowdsourced design solution data collected from online ideation and task-orientated platforms (e.g., InnoCentive, Quirky, OpenIDEO, and Amazon Mechanical Turk) are useful for DbA research [97,98,99]. The crowdsourced solutions make it possible to mine the real-world ideation outputs of complex cognitive tasks generated by thousands of people across the world. ...
... Both have been used in many data-driven DbA studies [5,15,64]. Although crowdsourced design data [97,98,99] and crowdfunded project data [100] have been explored in design research and are also potentially useful for DbA studies, they have not been fully exploited. In addition, most of the current data-driven DbA methods were designed to merely mine textual information as analogy candidates; only a few researchers have focused on the data of other modalities [13,92]. ...
... In this paper, although we only focus on data-driven DbA methods and tools, AI and data science techniques can be also useful to augment other classical engineering design methods [132], such as TRIZ [65,66], design heuristics [133,134], design principles [94,135], design structure matrix [136,137], product family and platform design [138,139], first principles [140,141,142], C-K [143], blending [144] and combinational design [99,145]. We hope that researchers of these relevant fields may also find inspirations from this paper to advance the data-driven approaches for their design methodologies with latest AI and data science technologies. ...
Preprint
Full-text available
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.
... Recently, extensive research has established that crowdsourced design solution data collected from online ideation and taskorientated platforms (e.g., InnoCentive, Quirky, OpenIDEO, and Amazon Mechanical Turk) are useful for DbA research [97][98][99]. The crowdsourced solutions make it possible to mine the real-world ideation outputs of complex cognitive tasks generated by thousands of people across the world. ...
... Both have been used in many data-driven DbA studies [5,15,64]. Although crowdsourced design data [97][98][99] and crowdfunded project data [100] have been explored in design research and are also potentially useful for DbA studies, they have not been fully exploited. In addition, most of the current data-driven DbA methods were designed to merely mine textual information as analogy candidates; only a few researchers have focused on the data of other modalities [13,92]. ...
... In this paper, although we only focus on data-driven DbA methods and tools, AI and data science techniques can be also useful to augment other classical engineering design methods [132], such as TRIZ [65,66], design heuristics [133,134], design principles [96,135], design structure matrix [136,137], product family and platform design [138,139], first principles [140][141][142], C-K [143], blending [144], and combinational design [99,145]. We hope that researchers of these relevant fields may also find inspirations from this paper to advance the data-driven approaches for their design methodologies with latest AI and data science technologies. ...
Conference Paper
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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.
... They also indicated that designers searching broadly for knowledge may find more valuable invention opportunities. He et al. [72] extracted words as stimuli from a semantic network for design idea generation. They paid special attention to the words in the periphery of their concept network as the source of novelty. ...
... First, this study used a domain-specific data source to construct concept network, which was provided to designers as inspiration. Although Song and Luo [24], Song et al. [23,40], and He et al. [72] also used predefined domain-specific datasets for research, and these prior studies proved the practicability of domain-specific data, whereas smallscale data still have a few research limitations. Naturally, ConceptNet and TechNet contain a larger number of cross-domain terms than FSCN. ...
Article
The rapid growth of data and the requirement of designers to track massive data to obtain design stimuli have posed challenges to conceptual design, thereby promoting the development of data-driven design. Concept networks precisely capture design information from a large volume of unstructured and heterogeneous textual data and saliently decrease time and labor cost for designers to read texts, which creates new opportunities for developing a smart product design system. To advance data-driven design, this study proposes the novel function-structure concept network (FSCN) construction method, which combines sentence parsing and word/phrase extraction to integrate functional and structural information. Furthermore, a network analysis method is proposed to explore design information associations that contain both explicit and implicit associations together and thereby recommend them simultaneously to designers as inspirational stimuli to support design ideation. This approach can enhance designers' capabilities to build associations between design information, conceive new design ideas during conceptual design, and increase creativity for solving design problems. The proposed FSCN construction and analysis method can be used as an auxiliary tool to visualize associations among design information so as to inspire idea generation in the early stage of conceptual design. An illustrative example was used to validate the practicability of the proposed methodology. The code of the proposed method is available at https://github.com/KWflyer/FSCN.
... Jin and Dong [2020] extracted 10 design heuristics as stimuli from RedDot award-wining design concepts to help digital designers overcome design fixation. He et al. [2019] tested the use of word clouds as stimulators to inspire ideation. Meanwhile, some methods can be a guide and a simulator at the same time. ...
... During design activities, concepts can be represented in the forms of abstract diagram, verbal text, or spatial visualization. Guiding or stimulation-based methods can direct designers to generate concepts in either of the three forms, e.g., simple sketches [Shah et al., 2001, Goldschmidt andSmolkov, 2006], mind-mapping graph [Shih et al., 2009, Yagita et al., 2011, functional diagram [Stone et al., 2000], or textual description [He et al., 2019, Sarica et al., 2021. Some guides or stimulators may also lead to multiple forms of concept representation as designers may record their perception of ideas in different ways [Bonnardel and Didier, 2020, Ilevbare et al., 2013, Yilmaz et al., 2016. ...
Conference Paper
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Novel concepts are essential for design innovation and can be generated with the aid of data stimuli and computers. However, current generative design algorithms focus on diagrammatic or spatial concepts that are either too abstract to understand or too detailed for early phase design exploration. This paper explores the uses of generative pre-trained transformers (GPT) for natural language design concept generation. Our experiments involve the use of GPT-2 and GPT-3 for different creative reasonings in design tasks. Both show reasonably good performance for verbal design concept generation.
... To examine the effectiveness of crowdsourced stimuli, Goucher-Lambert and Cagan (2019) crowdsource concepts as three nouns and three verbs for 12 design problems and categorise these as near, far and medium stimuli based on the frequency and WordNet-based path similarity. He et al. (2019) crowdsource text descriptions of thousands of ideas to future transportation systems via Amazon Mechanical Turk. They (2019, pp. 3, 4) form a coword network of these ideas and use MINRES 22 to extract core ideas from the network. ...
... To support ontology-based verification of requirements, Moitra et al. (2019, p. 347) propose that a requirement shall be expressed as follows: REQUIREMENT R (name); SYSTEM shall set x of X to x 1 (conclusion); when y ∈ Y (condition). Likewise, scholars have proposed templates for describing design concepts as well He et al. 2019;Luo, Sarica, and Wood 2021). While such a template-based approach works with a limited scope, it is necessary to implement text generation algorithms that are built out of RNNs, LSTM and Transformers. ...
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Full-text available
We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.
... Academic papers and patents usually represent original research outcomes or totally new inventions, which contain rich scientific and technological knowledge. Several attempts (Fu et al. 2013;He et al. 2019;McCaffrey and Spector 2018;Munoz and Tucker 2016;Sarica et al. 2020;Shi et al. 2017) have been made to apply the academic paper and patents to a design creativity task. However, one of the major limitations is that patents and scientific literature are restricted to only technological and scientific knowledge (Ernst 2003;Furukawa et al. 2015;Li et al. 2019;Shibata et al. 2008), while the nature of design tasks is of high diversity and complexity, with broad coverage of disciplines. ...
... Its statistical relationships are built on the co-occurrence between each pair of words in nearly one million engineering papers and one thousand design posts. He et al. (2019) created a semantic network with a coreperiphery structure according to the word clouds embedding co-occurrences information. In this way, the semantic network built the edges on a statistical level and could support engineering and technology creativity from a statistical perspective. ...
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Data-driven design is a process to reuse data sources and provide valuable information to provoke creative ideas in the stages of design. However, existing semantic networks for design creativity are built on data sources restricted to technological and scientific information. Existing studies build the edges of a semantic network on statistical or semantic relationships, which are less likely to make full use of the benefits from both types of relationships and discover implicit knowledge for design creativity. Therefore, to overcome the gaps, we constructed WikiLink, a semantic network based on Wikipedia, which is an integrated source of general knowledge and specific knowledge, with broad coverage of disciplines. The weight in WikiLink fuses both the statistic and semantic weights between concepts instead of simply one type of weight, and four algorithms are developed for inspiring new ideas. Evaluation experiments are undertaken, and the results show that the network is characterised by high coverage of terms, relationships and disciplines, which demonstrates and supports the network’s effectiveness and usefulness. A demonstration and case study results indicate that WikiLink can serve as an idea generation tool for creativity in conceptual design. The source code of WikiLink and the backend data are provided open-source for more users to explore and develop.
... The computational concept generation method, the Combinator, employed in the approach produces ideas in a random manner. Although randomness is an essential factor of creativity ( Carruthers, 2011), He et al. (2019) has indicated that computer-based random combinations often lead to meaningless outcomes through an empirical study. Controlled computer-based combinations as well as human combinations have higher probabilities in producing novel and feasible outcomes (He et al., 2019). ...
... Although randomness is an essential factor of creativity ( Carruthers, 2011), He et al. (2019) has indicated that computer-based random combinations often lead to meaningless outcomes through an empirical study. Controlled computer-based combinations as well as human combinations have higher probabilities in producing novel and feasible outcomes (He et al., 2019). Therefore, there is still a need to explore how computational approaches guide the idea combination processes. ...
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Conceptual design, as an early phase of the design process, is known to have the highest impact on determining the innovation level of design results. Although many tools exist to support designers in conceptual design, additional knowledge, especially knowledge related to emerging technologies, is still often needed. In this paper the authors aim to propose a data-driven creative concept generation and evaluation approach to support designers in incorporating emerging technologies in the new product early development stage. The approach is demonstrated by means of an illustrated example.
... Jin and Dong [2020] extracted 10 design heuristics as stimuli from RedDot award-wining design concepts to help digital designers overcome design fixation. He et al. [2019] tested the use of word clouds as stimulators to inspire ideation. Meanwhile, some methods can be a guide and a simulator at the same time. ...
... During design activities, concepts can be represented in the forms of abstract diagram, verbal text, or spatial visualization. Guiding or stimulation-based methods can direct designers to generate concepts in either of the three forms, e.g., simple sketches [Shah et al., 2001, Goldschmidt andSmolkov, 2006], mind-mapping graph [Shih et al., 2009, Yagita et al., 2011, functional diagram [Stone et al., 2000], or textual description [He et al., 2019, Sarica et al., 2021. Some guides or stimulators may also lead to multiple forms of concept representation as designers may record their perception of ideas in different ways [Bonnardel and Didier, 2020, Ilevbare et al., 2013, Yilmaz et al., 2016. ...
Preprint
Full-text available
Novel concepts are essential for design innovation and can be generated with the aid of data stimuli and computers. However, current generative design algorithms focus on diagrammatic or spatial concepts that are either too abstract to understand or too detailed for early phase design exploration. This paper explores the uses of generative pre-trained transformers (GPT) for natural language design concept generation. Our experiments involve the use of GPT-2 and GPT-3 for different creative reasonings in design tasks. Both show reasonably good performance for verbal design concept generation.
... First, in prior studies, the provision of design stimuli was at either the concept, document, or field level, but not all together. Concept terms may provide specific inspiration rapidly but lack details [24,68]. Patent documents may provide rich design details for systems, products, and processes but requires more time to read and efforts for comprehension that may cause fixation [13,69]. ...
Article
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.
... For example, network visualizations have been utilized to represent the whole technology space to support innovation and competitive intelligence [24,25,26], show the relations between components and subsystems to evalute designs [27,28,29] and inform design decisions [22,29,30], discover the patterns of design activities [31,32], reveal the structure of design document repositories to guide retrievals [4], and represent mind maps [33,34] and concept networks [21,35,36,37,38,39] for design ideation uses. On the other hand, a few studies explored other visualization methods such as word-clouds [40,41] based on design description texts. ...
Preprint
Full-text available
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.
... Knowledge-based stimulation approaches have been focusing on the retrieval and mapping of source knowledge into the target design domain [42]. He et al. [43] tested the use of word clouds as stimulators to inspire ideation. Luo et al. [9][10] introduced a computer-aided ideation tool InnoGPS to guide the provision of design stimuli. ...
Article
Generating novel and useful concepts is essential during the early design stage to explore a large variety of design opportunities, which usually requires advanced design thinking ability and a wide range of knowledge from designers. Growing works on computer-aided tools have explored the retrieval of knowledge and heuristics from design data. However, they only provide stimuli to inspire designers from limited aspects. This study explores the recent advance of the natural language generation (NLG) technique in the artificial intelligence (AI) field to automate the early-stage design concept generation. Specifically, a novel approach utilizing the generative pre-trained transformer (GPT) is proposed to leverage the knowledge and reasoning from textual data and transform them into new concepts in understandable language. Three concept generation tasks are defined to leverage different knowledge and reasoning: domain knowledge synthesis, problem-driven synthesis, and analogy-driven synthesis. The experiments with both human and data-driven evaluation show good performance in generating novel and useful concepts.
... For example, network visualizations have been utilized to represent the whole technology space to support innovation and competitive intelligence (Luo et al., , 2018Sarica, Yan, et al., 2020), show the relations between components and subsystems to evalute designs (He and Luo, 2017;Pasqual and De Weck, 2012;Sosa et al., 2007) and inform design decisions (Kim and Kim, 2012;Sosa et al., 2007), discover the patterns of design activities (Alstott et al., 2017;Cash et al., 2014;Cash and Štorga, 2015), reveal the structure of design document repositories to guide retrievals (Fu et al., 2013;Luo et al., 2021), and represent mind maps (Camburn, Arlitt, et al., 2020;Camburn, He, et al., 2020) and concept networks (Chen et al., 2019;Chen and Krishnamurthy, 2020;Liu et al., 2020;Sarica et al., 2019Sarica et al., , 2021Shi et al., 2017;Song, Evans, et al., 2020;Souili et al., 2015) for design ideation uses. On the other hand, a few studies explored other visualization methods such as word-clouds (He, Camburn, Liu, et al., 2019; based on design description texts. Although one can rigorously read and study a design document or description to discover all the design-related entities and comprehend their relations, such a human process is tedious, labourintensive, and limited by the domain-specific knowledge of the reader. ...
Conference Paper
Full-text available
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.
... These standard stopwords lists are also utilized in the text pre-processing steps of many engineering design studies focusing on tasks such as topic modelling [15][16][17], feature extraction [18,19], design information extraction [20,21], design representation [22][23][24][25], text a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 classification [26], semantic network and ontology construction [4,[27][28][29] and query completion [20,30]. ...
Article
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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.
... AskNature is a webbased tool [51], with more than 1,600 biological strategy cases in its repository, to provide biological inspirations to designers. Several recent studies have shown crowdsourced design idea data from online platforms, such as Amazon Mechanical Turk, can be mined and represented with machine learning techniques to be used as design inspiration stimuli [8,52]. ...
Article
Full-text available
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-by-analogy. 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-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.
... Furthermore, the knowledge contained in academic papers and patents is usually not up-to-the-minute, as it is time-consuming to publish papers and file patents.In recent years, there is an emerging interest in applying crowdsourcing approaches to create databases for supporting engineering design activities. For example, Goucher-Lambert and Cagan[49] and He et al.[34] used crowdsourced idea descriptions as sources of design stimulation for supporting idea generation; Forbes et al.[95] introduced a crowdsourcing approach to construct a knowledge base for product innovation; and Camburn et al.[96] employed crowdsourcing to gather actual industry design concepts. Crowdsourcing produces massive, diverse and up-to-the-minute knowledge in a cost-effective manner, which presents a promising choice for constructing semantic networks for engineering design. ...
Article
Full-text available
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.
... While Gopsill et al. (2015) has applied coword analysis to small text, such as engineering e-mail, enabling the evolution of topics to be monitored and associations with requirements scope creep to be developed. Small texts have also been of interest to He et al. (2019) who has applied co-word analysis to synthesise concept designs from crowd-sourced ideation exercises. It is not only the quantitative metrics afforded by this analytical technique but also the ability to aggregate and visualise a large corpus of information into more manageable forms for human interpretation and decision making (Figure 1). ...
Article
Full-text available
Design & Manufacture Knowledge Mapping is a critical activity in medium-to-large organisations supporting many organisational activities. However, techniques for effective mapping of knowledge often employ interviews, consultations and appraisals. Although invaluable in providing expert insight, the application of such methods is inherently intrusive and resource intensive. This paper presents word co-occurrence graphs as a means to automatically generate knowledge maps from technical documents and validates against expert generated knowledge maps.
... In engineering design, Linsey et al. [39] demonstrated the idea of design-by-analogy using WordTree and showed its power on design problem re-representation. Further, He et al. [40] proposed a core-periphery word cloud method to visualize textual concepts for the purpose of augmenting creative ideation in the early design stages. These works build the foundation of textual stimulation in design and elaborate on the effects of different structuring methods. ...
Article
Mind-mapping is useful for externalizing ideas and their relationships surrounding a central problem. However, balancing between the exploration of different aspects (breadth) of the problem with respect to the detailed exploration of each of its aspects (depth) can be challenging, especially for novices. The goal of this paper is to investigate the notion of “reflection-in-design” through a novel interactive digital mind-mapping workflow that we call “QCue”. The idea behind this workflow is to incorporate the notion of reflective thinking through two mechanisms: (1) offering suggestions to promote depth exploration through user's queries (Q), and (2) asking questions (Cue) to promote reflection for breadth exploration. This paper is an extension of our prior work where our focus was mainly on the algorithmic development and implementation of a cognitive support mechanism behind QCue enabled by ConceptNet (a graph-based rich ontology with “commonsense” knowledge). In this extended work, we first present a detailed summary of how QCue facilitated the breadth-depth balance in a mind-mapping task. Second, we present a comparison between QCue and conventional digital mind-mapping i.e. without our algorithm through a between-subjects user study. Third, we present new detailed analysis on the usage of different cognitive mechanisms provided by QCue. We further consolidate our prior quantitative analysis and build a connection with our observational analysis. Finally, we discuss in detail the different cognitive mechanisms provided by QCue to stimulate reflection in design.
... Early stage ideation is a critical step in the design process. Mind maps are a popular tool for generating and organizing high level concepts early in that process (Buzan and Buzan 1996;Marshall et al. 2016;Otto and Wood 2001;Anderson et al. 2017a, b;Jensen et al. 2018a, b;Zahedi and Heaton 2016;Iyer et al. 2009;Davies 2011;Camburn et al. 2020;He et al. 2019). A mind map generally consists of a hierarchical series of nodes containing categories and concepts, often either expressed as words, short phrases or sketches (Marshall et al. 2016). ...
Article
Full-text available
Early-stage ideation is a critical step in the design process. Mind maps are a popular tool for generating design concepts and in general for hierarchically organizing design insights. We explore an application for high-level concept synthesis in early stage design, which is typically difficult due to the broad space of options in early stages (e.g., as compared to parametric automation tools which are typically applicable in concept refinement stages or detail design). However, developing a useful mind map often demands a considerable time investment from a diverse design team. To facilitate the process of creating mind maps, we present an approach to crowdsourcing both concepts and binning of said concepts, using a mix of human evaluators and machine learning. The resulting computer-aided mind map has a significantly higher average concept novelty, and no significant difference in average feasibility (quantity can be set independently) as manually generated mind maps, includes distinct concepts, and reduces cost in terms of the designers’ time. This approach has the potential to make early-stage ideation faster, scalable and parallelizable, while creating alternative approaches to searching for a breadth and diversity of ideas. Emerging research explores the use of machine learning and other advanced computational techniques to amplify the mind mapping process. This work demonstrates the use of the both the EM-SVD, and HDBSCAN algorithms in an inferential clustering approach to reduce the number of one-to-one comparisons required in forming clusters of concepts. Crowdsourced human effort assists the process for both concept generation and clustering in the mind map. This process provides a viable approach to augment ideation methods, reduces the workload on a design team, and thus provides an efficient and useful machine learning based clustering approach.
... Currently, the largest knowledge graph is that of Googleincluding over 70 billion facts that aid queries using Google Search, Google Assistant, Google Home, and Google Developer API. 8 Some entity types in the Google Knowledge Graph include MusicAlbum, LocalBusiness, MovieSeries, EducationalOrganization, etc. Similar to Google's knowledge graph, Amazon Alexa is aided by Evi. 9 In the engineering literature, scholars have often relied on WordNet-a lexical database, 10 which provides both lexical (e.g., hypernym) and quantitative (e.g., Jiang-Conrath similarity [26]) relationships between common-sense terms [27][28][29][30][31][32]. Han et al. [14,33] utilize ConceptNet relationships to obtain analogies and combinations for a search entity. ...
Article
We propose a large scalable engineering knowledge base as an integrated knowledge graph, comprising sets of (entity, relationship, entity) triples that are real-world engineering ‘facts’ 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 entities and their associated relationships that are supposedly meaningful from an engineering design perspective. Such a knowledge base is expected to support inferencing, reasoning, recalling in various engineering design tasks. The knowledge base has a greater size and coverage in comparison with the previously used knowledge bases in the engineering design literature.
... Prior studies have pointed out that technological improvement or novelty arises from the recombination or synthesis of existing technologies (He and Luo 2017;He et al. 2019), which, in our cases, can be viewed as such mutations of the technological genotype. Following the analogy framework, the latest innovations in autonomous vehicles have changed the genotype of automobiles and increased the values of automobiles by fusing artificial intelligence to assist or automate driving and battery-powered electric powertrain to replace combustion engines. ...
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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.
... To support ontology-based verification of requirements, Moitra et al. [267, p. 347] propose that a requirement shall be expressed as follows: REQUIREMENT R (name); SYSTEM shall set of to 1 (conclusion); when ∈ (condition). Likewise, scholars have proposed syntax for described design concepts as well [103], [154], [284]. While such a template-based approach works with a limited scope, it is necessary to implement text generation algorithms that are built out of RNNs, LSTM, and Transformers. ...
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We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.
... Prior studies have pointed out that technological improvement or novelty arises from the recombination or synthesis of existing technologies (He and Luo 2017;He et al. 2019), which, in our cases, can be viewed as such mutations of the technological genotype. Following the analogy framework, the latest innovations in autonomous vehicles have changed the genotype of automobiles and increased the values of automobiles by fusing artificial intelligence to assist or automate driving and battery-powered electric powertrain to replace combustion engines. ...
Article
Full-text available
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.
... Knowledge-based stimulation approaches have been focusing on the retrieval and mapping of source knowledge into the target design domain [42]. He et al. [43] tested the use of word clouds as stimulators to inspire ideation. Luo et al. [9][10] introduced a computer-aided ideation tool InnoGPS to guide the provision of design stimuli. ...
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Generating novel and useful concepts is essential during the early design stage to explore a large variety of design opportunities, which usually requires advanced design thinking ability and a wide range of knowledge from designers. Growing works on computer-aided tools have explored the retrieval of knowledge and heuristics from design data. However, they only provide stimuli to inspire designers from limited aspects. This study explores the recent advance of the natural language generation (NLG) technique in the artificial intelligence (AI) field to automate the early-stage design concept generation. Specifically, a novel approach utilizing the generative pre-trained transformer (GPT) is proposed to leverage the knowledge and reasoning from textual data and transform them into new concepts in understandable language. Three concept generation tasks are defined to leverage different knowledge and reasoning: domain knowledge synthesis, problem-driven synthesis, and analogy-driven synthesis. The experiments with both human and data-driven evaluation show good performance in generating novel and useful concepts.
... Meanwhile, sensor data from the usage phase is utilized to elicit potential user requirements by using a graphbased framework [34]. For conceptual design, the central task of applying data-driven methods is to facilitate designers' idea generation by extracting concepts and discovering their associations from data [35][36][37][38][39]. Besides, the evaluation of concepts is also important for pursuing rapid iteration of the design in a mass customization context. ...
Article
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Generative design provides a promising algorithmic solution for mass customization of products, improving both product variety and design efficiency. However, the current designer-driven generative design formulates the automated program in a manual manner and has insufficient ability to satisfy the diverse needs of individuals. In this work, we propose a data-driven generative design framework by integrating multiple types of data to improve the automation level and performance of detail design to boost design efficiency and improve user satisfaction. A computational workflow including automated shape synthesis and structure design methods is established. More specifically, existing designs selected based on user preferences are utilized in the shape synthesis for creating generative models. For structural design, user-product interaction data gathered by sensors are used as inputs for controlling the spatial distributions of heterogeneous lattice structures. Finally, the proposed concept and workflow are demonstrated with a bike saddle design with a personalized shape and inner structures to be manufactured with additive manufacturing.
... In design research, various statistical techniques have been utilized to extract, map and analyze the topics within a collection of design documents and discourses, e.g., keywords in papers from a conference or words or phrases spoken in a meeting (Chiarello et al., 2019;Dong et al., 2004;Dong, 2005;Dong and Agogino, 1996;He et al., 2019;Jiao and Qu, 2019;Siddharth et al., 2022a;Song et al., 2020a). However, analyzing a limited set of documents within a specific domain or context and employing local association rules might not lead to a proper representation of the true and universal design-related associations of the design entities occurring in a document or dataset. ...
Article
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 expertize and manual construction and are difficult to repeat and scale. Here, we present a methodology that utilizes a readily available large-scale multidisciplinary design knowledge base (KB) to automatically generate design representation as a semantic network, i.e., a network of the entities and relations, based on design descriptions in textual form. The methodology requires no ad hoc statistics, but a readily available KB. Thus, the KB has an essential impact on the usefulness and effectiveness of the methodology. Based on a participatory study, we observe the effectiveness and differences of the semantic network representations that are automatically generated with alternative KBs. Specifically, a KB that is trained on engineering-related data, TechNet, provides a more sensible representation of engineering design than commonsense KBs, WordNet and ConceptNet, to the participants who are engineers. We further discuss the implications of the findings and future research directions to enhance design representation as semantic networks.
... First, in prior studies, the provision of design stimuli was at either the concept, document, or field level, but not all together. Concept terms may provide specific inspiration rapidly but lack details [23,67]. Patent documents may provide rich design details for systems, products, and processes but requires more time to read and efforts for comprehension that may cause fixation [13,68]. ...
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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.
... They found that participants feel highly satisfied with Web 2.0 applications that have the following attributes: ease of use, effectiveness, controllability, interactivity, navigability, customizability, efficiency, information content coverage, understandability, and reliability. Recent work by He et al. [43] introduced a novel core-periphery structure representation of the design concept space and a recombination methodology to augment designers' ability to synthesize new ideas. Their proposed computer-based recombination model mitigates design fixation and enables faster exploitation of the given design concepts. ...
Article
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In this paper, we report on our investigation of human-AI collaboration for mind-mapping. We specifically focus on problem exploration in pre-conceptualization stages of early design. Our approach leverages the notion of query expansion — the process of refining a given search query for improving information retrieval. Assuming a mind-map as a network of nodes, we reformulate mind-mapping as a two-player game wherein both players (a human and an intelligent agent) take turns to add one node to the network at a time. Our contribution is the design, implementation, and evaluation of algorithm that powers the intelligent agent (AI). This paper is an extension of our prior work [1] wherein we developed this algorithm, dubbed Mini-Map, and implemented a web-based workflow enabled by ConceptNet (a large graph-based representation of “commonsense” knowledge). In this paper, we extend our prior work through a comprehensive comparison between human-AI collaboration and human-human collaboration for mind-mapping. We specifically extend our prior work by: (a) expanding on our previous quantitative analysis using established metrics and semantic studies, (b) presenting a new detailed video protocol analysis of the mind-mapping process, and (c) providing design implications for digital mind-mapping tools.
... The future of humanity is on a bearing of untold prosperity or suffering, 1 where the space of possible ideas we have access to will impact our potential to harness the promises of groundbreaking technologies or to mitigate many of the known and unknown existential risks ahead of us (Strumsky et al., 2010;Avin, 2019). The fundamental problem of ML algorithms that predict outcomes optimised on unseen data come from the loss of variance and increase of bias. 2 Even when efforts are made to re-design algorithms that account for the loss of these properties (He et al., 2019), loss of which introduces more homogeneity into the systems, 3 the points of convergence chosen by the algorithms are arbitrary and introduce other limiting aspects to this systems † anna@wisakanto.com 1 Status quo for humanity's current way of existence in the long term seems unlikely, see for example Baum et al., Long-Term Trajectories of Human Civilization (2018). ...
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This paper looks at philosophical questions that arise in the context of how humanity's space of ideas is transforming under machine learning (ML) systems. It sets three propositions relating to said space. First, that the impact of ML systems on humanity's collective thought is tangible and potentially limiting. Second, that ML systems converging arbitrarily at points affects the very space of ideas we are capable of spanning. Third, that truncation of space of ideas limits the cultivation of novel thought in the long term. As ML systems impact our thinking in interconnected and cumulative ways it becomes crucial to investigate how space of ideas transforms under different conditions to understand how we might be inadvertently limiting the space of ideas our human thought can span, and explore how ideas governed by automated systems might prove to be pervasive in society, cause advantageous cultural development to slow down, and potentially create an exclusive environment in which key ideas needed to assess existential risks will not persevere.
... Academic papers and patents are original research outcome or totally new inventions, which contain rich scientific and technological knowledge. Several attempts (Munoz and Tucker, 2016;Fu et al., 2013;He et al., 2019a;McCaffrey and Spector, 2018;Shi et al., 2017;Sarica et al., 2020) have been made to apply the academic paper and patents to a design innovation task. However, one of the major limitations is that patents and scientific literature are restricted to only technological and scientific knowledge (Shibata et al., 2008;Furukawa et al., 2015;Li et al., 2019;Ernst, 2003), while the nature of design tasks is of high diversity and complexity, with broad coverage of disciplines. ...
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Data-driven design and innovation is a process to reuse and provide valuable and useful information. However, existing semantic networks for design innovation is built on data source restricted to technological and scientific information. Besides, existing studies build the edges of a semantic network only on either statistical or semantic relationships, which is less likely to make full use of the benefits from both types of relationships and discover implicit knowledge for design innovation. Therefore, we constructed WikiLink, a semantic network based on Wikipedia. Combined weight which fuses both the statistic and semantic weights between concepts is introduced in WikiLink, and four algorithms are developed for inspiring new ideas. Evaluation experiments are undertaken and results show that the network is characterised by high coverage of terms, relationships and disciplines, which proves the network's effectiveness and usefulness. Then a demonstration and case study results indicate that WikiLink can serve as an idea generation tool for innovation in conceptual design. The source code of WikiLink and the backend data are provided open-source for more users to explore and build on.
Article
Ongoing work within the engineering design research community seeks to develop automated design methods and tools that enhance the natural capabilities of designers in developing highly innovative concepts. Central to this vision is the ability to first obtain a deep understanding of the underlying behavior and process dynamics that predict successful performance in early-stage concept generation. The objective of this research is to better understand the predictive factors that lead to improved performance during concept generation. In particular, this work focuses on the impact of idea fluency and timing of early-stage design concepts, and their effect on overall measures of ideation session success. To accomplish this, we leverage an existing large-scale dataset containing hundreds of early-stage design concepts; each concept contains detailed ratings regarding its overall feasibility, usefulness, and novelty, as well as when in the ideation session the idea was recorded. Surprisingly, results indicate that there is no effect of idea fluency or timing on the quality of the output when using a holistic evaluation mechanism, such as the innovation measure, instead of a single measure such as novelty. Thus, exceptional concepts can be achieved by all participant segments independent of idea fluency. Furthermore, in early-stage concept generation sessions, highest-rated concepts have an equal probability of occurring early and late in a session. Taken together, these findings can be used to improve performance in ideation by effectively determining when and which types of design interventions future design tools might suggest.
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We propose a large, scalable engineering knowledge graph, comprising sets of (entity, relationship, entity) triples that are real-world engineering facts 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 the 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.
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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.
Article
Data-driven conceptual design is rapidly emerging as a powerful approach to generate novel and meaningful ideas by leveraging external knowledge especially in the early design phase. Currently, most existing studies focus on the identification and exploration of design knowledge by either using common-sense or building specific-domain ontology databases and semantic networks. However, the overwhelming majority of engineering knowledge is published as highly unstructured and heterogeneous texts, which presents two main challenges for modern conceptual design: (a) how to capture the highly contextual and complex knowledge relationships, (b) how to efficiently retrieve of meaningful and valuable implicit knowledge associations. To this end, in this work, we propose a new data-driven conceptual design approach to represent and retrieve cross-domain knowledge concepts for enhancing design ideation. Specifically, this methodology is divided into three parts. Firstly, engineering design knowledge from the massive body of scientific literature is efficiently learned as information-dense word embeddings, which can encode complex and diverse engineering knowledge concepts into a common distributed vector space. Secondly, we develop a novel semantic association metric to effectively quantify the strength of both explicit and implicit knowledge associations, which further guides the construction of a novel large-scale design knowledge semantic network (DKSN). The resulting DKSN can structure cross-domain engineering knowledge concepts into a weighted directed graph with interconnected nodes. Thirdly, to automatically explore both explicit and implicit knowledge associations of design queries, we further establish an intelligent retrieval framework by applying pathfinding algorithms on the DKSN. Next, the validation results on three benchmarks MTURK-771, TTR and MDEH demonstrate that our constructed DKSN can represent and associate engineering knowledge concepts better than existing state-of-the-art semantic networks. Eventually, two case studies show the effectiveness and practicality of our proposed approach in the real-world engineering conceptual design.
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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.
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Eliciting user needs for individual components and features of a product or a service on a large scale is a key requirement for innovative design. Gathering and analyzing data as an initial discovery phase of a design process is usually accomplished with a small number of participants, employing qualitative research methods such as observations, focus groups, and interviews. This leaves an entire swath of pertinent user behavior, preferences, and opinions not captured. Sentiment analysis is a key enabler for large-scale need finding from online user reviews generated on a regular basis. A major limitation of current sentiment analysis approaches used in design sciences, however, is the need for laborious labeling and annotation of large review datasets for training, which in turn hinders their scalability and transferability across different domains. This article proposes an efficient and scalable methodology for automated and large-scale elicitation of attribute-level user needs. The methodology builds on the state-of-the-art pretrained deep language model, BERT (Bidirectional Encoder Representations from Transformers), with new convolutional net and named-entity recognition (NER) layers for extracting attribute, description, and sentiment words from online user review corpora. The machine translation algorithm BLEU (BiLingual Evaluation Understudy) is utilized to extract need expressions in the form of predefined part-of-speech combinations (e.g., adjective-noun, verb-noun). Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for apparel and footwear to demonstrate the performance, feasibility, and potentials of the developed methodology.
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This paper proposes a novel framework for building semantic networks from a seed design statement using Recursive Object Modeling (ROM), Word2Vec language modelling, and vector semantic-based method. Semantic Scholar API was used to retrieve abstracts of scientific papers to build ROM-based Semantic Networks to address the design problem implied in the seed design statement, following Environment Analysis from Environment-Based Design (EBD) methodology. The proposed framework was applied to construct the semantic network for a project to design aircraft braking systems, which demonstrates the framework's efficiency. The presented research makes two major contributions: a ROM-based phrase extractor and a domain-specific language model, which is trained on the automatically collected literature abstracts. Using a manually created and assessed truth set containing 100 pairs of abstract-key phrases, the phrase extractor was evaluated by benchmarking it with two existing off-the-shelf key phrase extraction algorithms: TextRank and Rake. The ROM-based phrase extractor extracted most key phrases from target domains and showed higher precision, recall, and F-1 scores than other methods. Meanwhile, the trained project-specific language model was evaluated using the NASA thesaurus. We randomly sampled 457 pairs of connected domain-specific terms related to aircraft braking and landing knowledge. Our Skip-gram model was compared with Google's pre-trained word2vec model and a baseline word2vec model. The results demonstrated that our language model could detect the most pairs of concepts from the NASA thesaurus. The generated semantic network can be applied to design information retrieval, computer-aided design idea generation, cross-domain communication support system, and designer training tool.
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Inspirational stimuli are known to be effective in supporting ideation during early-stage design. However, prior work has predominantly constrained designers to using text-only queries when searching for stimuli, which is not consistent with real-world design behavior where fluidity across modalities (e.g., visual, semantic, etc.) is standard practice. In the current work, we introduce a multi-modal search platform that retrieves inspirational stimuli in the form of 3D-model parts using text, appearance, and function-based search inputs. Computational methods leveraging a deep-learning approach are presented for designing and supporting this platform, which relies on deep-neural networks trained on a large dataset of 3D-model parts. This work further presents the results of a cognitive study ( n = 21) where the aforementioned search platform was used to find parts to inspire solutions to a design challenge. Participants engaged with three different search modalities: by keywords, 3D parts, and user-assembled 3D parts in their workspace. When searching by parts that are selected or in their workspace, participants had additional control over the similarity of appearance and function of results relative to the input. The results of this study demonstrate that the modality used impacts search behavior, such as in search frequency, how retrieved search results are engaged with, and how broadly the search space is covered. Specific results link interactions with the interface to search strategies participants may have used during the task. Findings suggest that when searching for inspirational stimuli, desired results can be achieved both by direct search inputs (e.g., by keyword) as well as by more randomly discovered examples, where a specific goal was not defined. Both search processes are found to be important to enable when designing search platforms for inspirational stimuli retrieval.
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This research aims to augment human cognition through the advancement and automation of mindmapping technologies, which could later support human creativity and virtual collaboration. Mindmapping is a visual brainstorming technique that allows problem solvers to utilize the human brain's ability to retrieve knowledge through similarity and association. While it is a powerful tool to generate concepts in any phase of problem-solving or design, the content of mindmaps is usually manually generated while listening or conversing and generating ideas, requiring a high cognitive load. This work introduces the development of a speech-driven automated mindmapping technology, called Speech2Mindmap. The specifics of the Speech2Mindmap algorithm are detailed, along with two case studies that serve to test its accuracy in comparison to human generated mindmaps, using audio recorded speech data as input. In the first case study, the Speech2Mindmap algorithm was evaluated on how well it represents manually generated human mindmapping output. The second case study evaluated the reliability of the Speech2Mindmap algorithm and examined the best performing methods and conditions to achieve the greatest similarity to human generated mindmaps. This research demonstrates that the Speech2Mindmap algorithm is capable of representing manually generated human mindmapping output, and found the best performing methods and conditions to generate a mindmap that is 80% similar, on average, to human generated mindmaps.
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The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework to augment designers' visual analogical thinking by stimulating them by providing relevant visual cues from a variety of categories. Two steps are taken to reach this goal: developing a flexible computational framework to explore various visual cues, i.e., shapes or sketches, based on the relevant datasets and conducting human-based behavioral studies to validate such visual cue exploration tools. This paper presents the results and insights obtained from the first step by addressing two research questions: how can the computational framework CAVAS be developed to provide designers in sketching with certain visual cues for stimulating their visual thinking process? and how can a computation tool learn a latent space which can capture the shape patterns of sketches. A visual cue exploration framework and a deep clustering model Cavas-DL are proposed to learn a latent space of sketches that reveal shape patterns for multiple sketch categories and simultaneously cluster the sketches to preserve and provide category information as part of visual cues. The distance- and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Performance evaluations of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The results have demonstrated the applicability of the Cavas-DL model as the basis for the human-based validation studies in the next step.
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To pursue innovation, design engineers need to continuously exploit the knowledge in their design domain and explore other relevant knowledge around the domain. While many methods and tools have been developed to retrieve knowledge within a given design domain, e.g., flying cars, knowledge discovery beyond the domain for innovation remains a challenge, and relevant methods are underdeveloped. Herein, we introduce a methodology to use a technology knowledge graph (TKG), which covers sematic-level knowledge in all technology fields defined in the international patent classification system, to retrieve the existing engineering knowledge in a domain and discover engineering concepts around the domain for future design considerations and innovation. We demonstrate the TKG-based methodology by applying it to explore the future designs of flying cars, an emerging domain with high uncertainty despite growing prospects.
Conference Paper
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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.
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A properly designed product-system platform seeks to reduce the cost and lead time for design and development of the product-system family. A key goal is to achieve a tradeoff between economy of scope from product variety and economy of scale from platform sharing. Traditionally, product platform planning uses heuristic and manual approaches and relies greatly on expertise and intuition. In this paper, we propose a data-driven method to draw the boundary of a platform-system, complementing other platform design approaches and assisting designers in the architecting process. The method generates a network of functions through relationships of their co-occurrences in prior designs of a product or systems domain and uses a network analysis algorithm to identify an optimal core-periphery structure. Functions identified in the network core co-occur cohesively and frequently with one another in prior designs, and thus are suggested for inclusion in the potential platform to be shared across a variety of product-systems with peripheral functions. We apply the method to identify the platform functions for the application domain of spherical rolling robots, based on patent data.
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Identifying relevant stimuli that help generate solutions of desired novelty and quality is challenging in analogical design. To quell this challenge, the multifaceted effects of using stimuli which are located at various analogical distances to the design problem on the novelty and quality of concepts generated using the stimuli are studied in this research. Data from a design project involving 105 student designers, individually generating 226 concepts of spherical rolling robots, are collected. From these data, 138 concepts generated with patents as stimuli and the patents used are analyzed. Analogical distance of a patent is measured in terms of knowledge similarity between technology classes constituting the patent and design problem domain of spherical rolling robots. The key observations are (a) technology classes in closer than farther distances from the design problem are used more frequently to generate concepts, (b) as analogical distance increases the novelty of concepts increases, and (c) as analogical distance decreases the quality of concepts increases.
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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.
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Identifying relevant stimuli that help generate solutions of desired novelty and quality is challenging in analogical design. To quell this challenge, the multifaceted effects of using stimuli which are located at various analogical distances to the design problem on the novelty and quality of concepts generated using the stimuli is studied in this research. Data from a design project involving 105 student designers, individually generating 226 concepts of spherical rolling robots is collected. From this data, 138 concepts generated with patents as stimuli and the patents used are analyzed. Analogical distance of a patent is measured in terms of knowledge similarity between technology classes constituting the patent and design problem domain of spherical rolling robots. The key observations are: (a) technology classes in closer than farther distances from the design problem are used more frequently to generate concepts, (b) as analogical distance increases the novelty of concepts increases, and (c) as analogical distance decreases the quality of concepts increases.
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Invention arises from novel combinations of prior technologies. However, prior studies of creativity have suggested that overly novel combinations may be harmful to invention. Apart from the factors of expertise, market, etc., there may be such a thing as ‘too much’ or ‘too little’ novelty that will determine an invention’s future value, but little empirical evidence exists in the literature. Using technical patents as the proxy of inventions, our analysis of 3.9 million patents identifies a clear ‘sweet spot’ in which the mix of novel combinations of prior technologies favors an invention’s eventual success. Specifically, we found that the invention categories with the highest mean values and hit rates have moderate novelty in the center of their combination space and high novelty in the extreme of their combination space. Too much or too little central novelty suppresses the positive contribution of extreme novelty in the invention. Furthermore, the combination of scientific and broader knowledge beyond patentable technologies creates additional value for invention and enlarges the advantage of the novelty sweet spot. These findings may further enable data-driven methods both for assessing invention novelty and for profiling inventors, and may inspire a new strand of data-driven design research and practice.
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Design is a ubiquitous human activity. Design is valued by individuals, teams, organizations, and cultures. There are patterns and recurrent phenomena across the diverse set of approaches to design and also variances. Designers can benefit from leveraging conceptual tools like process models, methods, and design principles to amplify design phenomena. There are many variant process models, methods, and principles for design. Likewise, usage of these conceptual tools differentiates in industrial contexts. We present an integrated process model, with exemplar methods and design principles that is synthesized from a review of several case studies in client based industrial design projects for product, service, and system development, professional education courses, and literature review. Concepts from several branches of design practice: (1) design thinking, (2) business design, (3) systems engineering, and (4) design engineering are integrated. A design process model, method set, and set of abstracted design principles are porposed.
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Computational tools and the methodologies used in the early phases of creation often have conflicting requirements related to structure and systematicity. This paper describes an exploration into the increasing role that computation could play in the early stage of the design process. Insights from case studies at the design consultancy IDEO identified several key design activities and process variables used throughout the projects. Breaking these down into the contributing knowledge and creative ‘functions’ allowed computational representations to be created and a creative prompt tool developed. Further considerations on the possible features for the next technologies developed for these design activities concluded that a ‘bricolage toolbox’ of small, flexible and personalisable modules will enable the more ad hoc approach that is present in the early phases of creation.
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We consider inventions as novel combinations of existing technological capabilities. Patent data allow us to explicitly identify such combinatorial processes in invention activities (Youn et al. in J R Soc Interface 12:20150272, 2015). Unconsidered in the previous research, not every new combination is novel to the same extent. Some combinations are naturally anticipated based on patent activities in the past or mere random choices, and some appear to deviate exceptionally from existing invention pathways. We calculate a relative likelihood that each pair of classification codes is put together at random, and a deviation from the empirical observation so as to assess the overall novelty (or conventionality) that the patent brings forth at each year. An invention is considered as unconventional if a pair of codes therein is unlikely to be used together given the statistics in the past. Temporal evolution of the distribution indicates that the patenting activities become more conventional with occasional cross-over combinations. Our analyses show that patents introducing novelty on top of the conventional units would receive higher citations, and hence have higher impact.
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Design-by-analogy is a powerful approach to augment traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. While the concept of design-by-analogy has been known for some time, few actual 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 functional analogies from data sources has been developed to provide this capability, here based on a functional basis rather than form or conflict descriptions. Building on past research, we utilize a functional vector space model (VSM) to quantify analogous similarity of an idea's functionality. We quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. We also develop document parsing algorithms to reduce text descriptions of the data sources down to the key functions, for use in the functional similarity analysis and functional vector space modeling. To do this, we apply Zipf's law on word count order reduction to reduce the words within the documents down to the applicable functionally critical terms, thus providing a mapping process for function based search. The reduction of a document into functional analogous words enables the matching to novel ideas that are functionally similar, which can be customized various ways. This approach thereby provides relevant sources of design-by-analogy inspiration. As a verification of the approach, two original design problem case studies illustrate the distance range of analogical solutions that can be extracted. This range extends from very near-field, literal solutions to far-field cross-domain analogies.
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This work lends insight into the meaning and impact of "near" and "far" analogies. A cognitive engineering design study is presented that examines the effect of the distance of analogical design stimuli on design solution generation, and places those findings in context of results from the literature. The work ultimately sheds new light on the impact of analogies in the design process and the significance of their distance from a design problem. In this work, the design repository from which analogical stimuli are chosen is the U.S. patent database, a natural choice, as it is one of the largest and easily accessed catalogued databases of inventions. The "near" and "far" analogical stimuli for this study were chosen based on a structure of patents, created using a combination of latent semantic analysis and a Bayesian based algorithm for discovering structural form, resulting in clusters of patents connected by their relative similarity. The findings of this engineering design study are juxtaposed with the findings of a previous study by the authors in design by analogy, which appear to be contradictory when viewed independently. However, by mapping the analogical stimuli used in the earlier work into similar structures along with the patents used in the current study, a relationship between all of the stimuli and their relative distance from the design problem is discovered. The results confirm that "near" and "far" are relative terms, and depend on the characteristics of the potential stimuli. Further, although the literature has shown that "far" analogical stimuli are more likely to lead to the generation of innovative solutions with novel characteristics, there is such a thing as too far. That is, if the stimuli are too distant, they then can become harmful to the design process. Importantly, as well, the data mapping approach to identify analogies works, and is able to impact the effectiveness of the design process. This work has implications not only in the area of finding inspirational designs to use for design by analogy processes in practice, but also for synthesis, or perhaps even unification, of future studies in the field of design by analogy. [DOI: 10.1115/1.4023158]
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Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
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A common but informal notion in social network analysis and other fields is the concept of a core/periphery structure. The intuitive conception entails a dense, cohesive core and a sparse, unconnected periphery. This paper seeks to formalize the intuitive notion of a core/periphery structure and suggests algorithms for detecting this structure, along with statistical tests for testing a priori hypotheses. Different models are presented for different kinds of graphs (directed and undirected, valued and nonvalued). In addition, the close relation of the continuous models developed to certain centrality measures is discussed.
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The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language. This paper reports on the simplified toolkit and explains how it is used in teaching NLP.
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Driven by pressure to reduce product development times, industry has started looking for new ways to exploit stores of engineering artifact knowledge. Engineers are increasingly turning to design repositories as knowledge banks to help them represent, capture, share, and reuse corporate design knowledge.
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Inspirational stimuli, such as analogies, are a prominent mechanism used to support designers. However, generating relevant inspirational stimuli remains challenging. This work explores the potential of using an untrained crowd workforce to generate stimuli for trained designers. Crowd workers developed solutions for twelve open-ended design problems from the literature. Solutions were text-mined to extract words along a frequency domain, which, along with computationally derived semantic distances, partitioned stimuli into closer or further distance categories for each problem. The utility of these stimuli was tested in a human subjects experiment (N = 96). Results indicate crowdsourcing holds potential to gather impactful inspirational stimuli for open-ended design problems. Near stimuli improve the feasibility and usefulness of designs solutions, while distant stimuli improved their uniqueness.
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The biological domain has the potential to offer a rich source of analogies to solve engineering design problems. However, due to the complexity embedded in biological systems, adding to the lack of structured, detailed, and searchable knowledge bases, engineering designers find it hard to access the knowledge in the biological domain, which therefore poses challenges in understanding the biological concepts in order to apply these concepts to engineering design problems. In order to assist the engineering designers in problem-solving, we report, in this paper, a web-based tool called Idea-Inspire 4.0 that supports analogical design using two broad features. First, the tool provides access to a number of biological systems using a searchable knowledge base. Second, it explains each one of these biological systems using a multi-modal representation: that is, using function decomposition model, text, function model, image, video, and audio. In this paper, we report two experiments that test how well the multi-modal representation in Idea-Inspire 4.0 supports understanding and application of biological concepts in engineering design problems. In one experiment, we use Bloom's method to test “ analysis ” and “ synthesis ” levels of understanding of a biological system. In the next experiment, we provide an engineering design problem along with a biological-analogous system and examine the novelty and requirement-satisfaction (two major indicators of creativity) of resulting design solutions. In both the experiments, the biological system (analogue) was provided using Idea-Inspire 4.0 as well as using a conventional text-image representation so that the efficacy of Idea-Inspire 4.0 is tested using a benchmark.
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Textual information is becoming available in abundance on the web, arising the requirement of techniques and tools to extract the meaningful information. One of such an important information extraction task is Named Entity Recognition and Classification. It is the problem of finding the members of various predetermined classes, such as person, organization, location, date/time, quantities, numbers etc. The concept of named entity extraction was first proposed in Sixth Message Understanding Conference in 1996. Since then, a number of techniques have been developed by many researchers for extracting diversity of entities from different languages and genres of text. Still, there is a growing interest among research community to develop more new approaches to extract diverse named entities which are helpful in various natural language applications. Here we present a survey of developments and progresses made in Named Entity Recognition and Classification research.
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This article explores the reported use of conceptual combination in Stephen R. Donaldson's development of the idea for his award-winning fantasy series, The Chronicles of Thomas Covenant the Unbeliever. Donaldson's (1991) own account is used to illustrate the general principles of a creative cognition approach to understanding creativity as well as the more specific role of the basic process of conceptual combination. The links between Donaldson's and others' anecdotal accounts of creativity and laboratory investigations are assessed. The article concludes with an argument for a "convergence" approach in which information from anecdotal accounts and laboratory studies is combined to provide a more complete picture of creative functioning than either approach alone can offer.
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Engineers and technology firms must continually explore new design opportunities and directions to sustain or thrive in technology competition. However, the related decisions are normally based on personal gut feeling or experiences. Although the analysis of user preferences and market trends may shed light on some design opportunities from a demand perspective, design opportunities are always conditioned or enabled by the technological capabilities of designers. Herein, we present a data-driven methodology for designers to analyze and identify what technologies they can design for the next, based on the principle-what a designer can currently design condition or enable what it can design next. The methodology is centered on an empirically built network map of all known technologies, whose distances are quantified using more than 5 million patent records, and various network analytics to position a designer according to the technologies that they can design, navigate technologies in the neighborhood, and identify feasible paths to far fields for novel opportunities. Furthermore, we have integrated the technology space map, and various map-based functions for designer positioning, neighborhood search, path finding, and knowledge discovery and learning, into a data-driven visual analytic system named InnoGPS. InnoGPS is a global position system (GPS) for finding innovation positions and directions in the technology space, and conceived by analogy from the GPS that we use for positioning, neighborhood search, and direction finding in the physical space.
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Crowdsourcing design has been applied in various areas of graphic design, software design, and product design. This paper draws on those experiences and research in diversity, creativity and motivation to present a process model for crowdsourcing experience design. Crowdsourcing experience design for volunteer online communities serves two purposes: to increase the motivation of participants by making them stakeholders in the success of the project, and to increase the creativity of the design by increasing the diversity of expertise beyond experts in experience design. Our process model for crowdsourcing design extends the meta-design architecture, where for online communities is designed to be iteratively re-designed by its users. We describe how our model has been deployed and adapted to a citizen science project where nature preserve visitors can participate in the design of a system called NatureNet. The major contribution of this paper is a model for crowdsourcing experience design and a case study of how we have deployed it for the design and development of NatureNet.
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The largely erroneous perception that break-throughs are impossible to predict arises from the tendency to focus on just the breakthroughs while ignoring the iterative process of invention and its distribution of outcomes. When all inventions are considered, they demonstrate a highly skewed distribution in which almost all inventions are useless, a few are of moderate value and only a very, very few are breakthroughs. Those breakthroughs constitute the "long tail" of innovation. If managers wish to understand how those break-throughs arise, they cannot ignore the process that generates the entire distribution. In particular, they need to keep in mind the following three measures of inventive success: shots on goal (the total number of inventions a company generates), average score (the mean value of those inventions) and maximum scores (the breakthrough inventions). Various factors can affect a company's inventive output, including the presence of inventors who work alone, the type of collaboration among those inventors who work in teams, the amount of team diversity and the degree to which inventors apply science in the innovation process. Greater team diversity, for instance, will help generate more shots on goal although, on average, those shots will be less successful. But diversity also will increase the variance of the outcome, such that failures as well as breakthroughs are more likely. Thus companies first need to identify how they want to improve their innovation process and then take the appropriate measures to address any deficiencies. Only then can they improve their capacity to innovate in ways that make the best sense for the organization as a whole.
Book
The concept generation process seems like an intuitional thought: difficult to capture and perform, although everyone is capable of it. It is not an analytical process but a synthetic process which has yet to be clarified. Furthermore, new research methods for investigating the concept generation process-a very difficult task since the concept generation process is driven by inner feelings deeply etched in the mind-are necessary to establish its theory and methodology. Concept Generation for Design Creativity - A Systematized Theory and Methodology presents the concept generation process both theoretically and methodologically. Theoretically, the concept generation process is discussed by comparing metaphor, abduction, and General Design Theory from the notions of similarities and dissimilarities. Analogy, blending, and integration by thematic relation have been explained methodologically. So far, these theories and methods have been discussed independently, and the relations among them have not been clarified. Two newly developed research methods to investigate the concept generation process are clearly explained: the explanation-based protocol analysis and constructive simulation. By reading Concept Generation for Design Creativity - A Systematized Theory and Methodology, students, researchers and lecturers in design disciplines (including engineering design, industrial design, software design, CHI, design education, and cognitive science ) can obtain a clear picture of the advanced research findings and the outline of the theories and methods for concept generation. Furthermore, readers are expected to achieve the competence to generate new concepts.
Chapter
It is commonly believed that creative thinking-the cognitive processes that bring about novel ideas and objects-is based on thinking "outside of the box." Creativity is assumed to require that we break away from our knowledge, and use some sort of extraordinary thought process to leap into the unknown. This chapter proposes, in contrast, that "inside-the-box thinking" is the basis for creativity: innovation is based on extensive knowledge in the area in question and moves beyond what is known in increments-small steps-based on ordinary cognitive processes, such as retrieval of knowledge from memory, analogical thinking, and logical reasoning. Examination of historical case studies of seminal innovations-Watson and Watson and Crick's discovery of the double helix; the Wright brothers' invention of the airplane; Edison's invention of the kinetoscope (the first moving pictures); Picasso's creation of his great painting Guernica; and a case study of innovation in industry, IDEO's development of a new shopping cart-supports the idea that creative thinking was based first on a deep knowledge of the area. The thinkers moved beyond what was known in increments, rather than leaps, and they built on the past, rather than rejecting it. The idea that creativity is based on inside-the-box thinking and ordinary cognitive processes has implications for corporate innovation, several of which are discussed. © 2009 by Arthur B. Markman and Kristin L. Wood. All rights reserved.
Article
This paper describes how a design repository can be used as a concept generation tool by drawing upon archived function-based design knowledge. Modern design methodologies include several types of activities to formally generate design concepts. Typical concept generation methods range from open-ended creative brainstorming activities to quantitative function-component analysis. A combination of two such methods - the chi-matrix and morphological matrix techniques - is the basis for this work. Building on existing functionality of the design repository, desired product functions can be specified in a search of stored design knowledge, returning a morphological matrix of artifacts solving the specified functions. Such a search is termed a morphological search. The repository morphological search feature is evaluated against concepts generated in a previous original design project. Results of the morphological search return are then compared to ten of the original concept variants generated during the design project. This comparison shows that 89% of the specified subfunctions return results and that, on average, 77% of the components used in the hand-generated concepts can be derived by using the morphological search feature
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Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.
Book
How cognitive psychology explains human creativity Conventional wisdom holds that creativity is a mysterious quality present in a select few individuals. The rest of us, the common view goes, can only stand in awe of great creative achievements: we could never paint Guernica or devise the structure of the DNA molecule because we lack access to the rarified thoughts and inspirations that bless geniuses like Picasso or Watson and Crick. Presented with this view, today's cognitive psychologists largely differ finding instead that "ordinary" people employ the same creative thought processes as the greats. Though used and developed differently by different people, creativity can and should be studied as a positive psychological feature shared by all humans. Creativity: Understanding Innovation in Problem Solving, Science, Invention, and the Arts presents the major psychological theories of creativity and illustrates important concepts with vibrant and detailed case studies that exemplify how to study creative acts with scientific rigor. Creativity includes: Two in-depth case studies—Watson and Crick's modeling of the DNA structure and Picasso's painting of Guernica— serve as examples throughout the text Methods used by psychologists to study the multiple facets of creativity The "ordinary thinking" or cognitive view of creativity and its challengers How problem–solving and experience relate to creative thinking Genius and madness and the relationship between creativity and psychopathology The possible role of the unconscious in creativity Psychometrics—testing for creativity and how personality factors affect creativity Confluence theories that use cognitive, personality, environmental, and other components to describe creativity Clearly and engagingly written by noted creativity expert Robert Weisberg, Creativity: Understanding Innovation in Problem Solving, Science, Invention, and the Arts takes both students and lay readers on an in-depth journey through contemporary cognitive psychology, showing how the discipline understands one of the most fundamental and fascinating human abilities. "This book will be a hit. It fills a large gap in the literature. It is a well-written, scholarly, balanced, and engaging book that will be enjoyed by students and faculty alike." —David Goldstein, University of Toronto
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Analogy and similarity are often assumed to be distinct psychological processes. In contrast to this position, the authors suggest that both similarity and analogy involve a process of structural alignment and mapping, that is, that similarity is like analogy. In this article, the authors first describe the structure-mapping process as it has been worked out for analogy. Then, this view is extended to similarity, where it is used to generate new predictions. Finally, the authors explore broader implications of structural alignment for psychological processing. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Research on innovation often highlights analogies from sources outside the current problem domain as a major source of novel concepts; however, the mechanisms underlying this relationship are not well understood. We analyzed the temporal interplay between far analogy use and creative concept generation in a professional design team's brainstorming conversations, investigating the hypothesis that far analogies lead directly to very novel concepts via large steps in conceptual spaces (jumps). Surprisingly, we found that concepts were more similar to their preceding concepts after far analogy use compared to baseline situations (i.e., without far analogy use). Yet far analogies increased the team's concept generation rate compared to baseline conditions. Overall, these results challenge the view that far analogies primarily lead to novel concepts via jumps in conceptual spaces and suggest alternative pathways from far analogies to novel concepts (e.g., iterative, deep exploration within a functional space).
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This paper presents a novel approach, referred to as the WordTree design-by-analogy method, for identifying distant-domain analogies as part of the ideation process. The WordTree method derives its effectiveness through a design team's knowledge and readily available information sources (e. g., patent databases, Google) and does not require specialized computational knowledge bases. A controlled cognitive experiment and an evaluation of the method with redesign projects illustrate the method's influence in assisting engineers in design-by-analogy. Individuals using the WordTree method identified significantly more analogies and searched outside the problem domain as compared to the control group. The team redesign projects demonstrate the WordTree method's effectiveness in longer-term, more realistic, higher validity team projects and with a variety of different design problems. Teams successfully identified effective analogies, analogous domains, and analogous patents. Unexpected and unique solutions are identified using the method. For example, one of the teams identified a dump truck and panning for gold as effective analogies for the design of a self-cleaning cat litter box. In the controlled experiment, a cherry pitter was identified and implemented as a solution for designing a machine to shell peanuts. The experimental results also highlight potential improvements for the method and areas for future research in engineering design theory. [DOI: 10.1115/1.4006145]
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Public involvement is a central concern for urban planners, but the challenge for planners is how best to implement such programs, given many difficulties inherent in the typical public involvement process. The medium of the Web enables us to harness collective intellect among a population in ways face-to-face planning meetings cannot. This article argues that the crowdsourcing model, a successful, Web-based, distributed problem solving and production model for business, is an appropriate model for enabling the citizen participation process in public planning projects. This article begins with an exploration of the challenges of public participation in urban planning projects, particularly in the harnessing of creative solutions. An explanation of the theories of collective intelligence and crowd wisdom follows, arguing for the medium of the Web as an appropriate technology for harnessing far-flung genius. An exploration of crowdsourcing in a hypothetical neighborhood planning example, along with a consideration of the challenges of implementing crowdsourcing, concludes the article.
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How does man know anything and, in particular, how can we account for creative thought? Campbell posits 2 major conditions: mechanisms which produce wide and frequent variation (an inductive, trial and error, fluency of ideas) and criteria for the selection of the inductive given (the critical function). The ramifications of this perspective are explored in terms of organic evolution and human history, and in terms of psychology and epistemology. This exposition is offered as a pretheoretical model.
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Darwinism provides not only a theory of biological evolution but also supplies a