Research Papers

The Meaning of “Near” and “Far”: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output

[+] Author and Article Information
Katherine Fu, Jonathan Cagan

Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213

Joel Chan, Christian Schunn

Department of Psychology,
University of Pittsburgh,
Pittsburgh, PA 15213

Kenneth Kotovsky

Department of Psychology,
Carnegie Mellon University,
Pittsburgh, PA 15213

Kristin Wood

Engineering and Product Development Pillar,
Singapore University of Technical Design,
Singapore 138682, Republic of Singapore

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 14, 2012; final manuscript received November 21, 2012; published online January 7, 2013. Assoc. Editor: Bernard Yannou.

J. Mech. Des 135(2), 021007 (Jan 07, 2013) (12 pages) Paper No: MD-12-1259; doi: 10.1115/1.4023158 History: Received May 14, 2012; Revised November 21, 2012

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.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.


Casakin, H., and Goldschmidt, G., 1999, “Expertise and the Use of Visual Analogy: Implications for Design Education,” Des. Stud., 20, pp. 13–175. [CrossRef]
Goel, A. K., 1997, “Design, Analogy, and Creativity,” IEEE Expert, 12, pp. 62–70. [CrossRef]
Christensen, B. T., and Schunn, C. D., 2007, “The Relationship of Analogical Distance to Analogical Function and Preinventive Structure: The Case of Engineering Design,” Mem. Cognit., 35, pp. 29–38. [CrossRef] [PubMed]
Linsey, J., Murphy, J., Laux, J., Markman, A. B., and Wood, K. L., 2009, “Supporting Innovation by Promoting Analogical Reasoning,” Tools for Innovation, A.Markman and K.Wood, eds., Oxford University Press, New York.
Fu, K., Cagan, J., Kotovsky, K., and Wood, K., 2011, “Discovering Structure in Design Databases Through Function and Surface Based Mapping,” Proceedings of the ASME IDETC, Washington, D.C.
Fu, K., Cagan, J., and Kotovsky, K., 2011, “A Methodology for Discovering Structure in Design Databases,” Proceedings of the 18th International Conference on Engineering Design, Copenhagen, Denmark.
Fu, K., 2012, “Discovering and Exploring Structure in Design Databases and Its Role in Stimulating Design by Analogy,” Ph.D. dissertation, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA.
Tseng, I., Moss, J., Cagan, J., and Kotovsky, K., 2008, “The Role of Timing and Analogical Similarity in the Stimulation of Idea Generation in Design,” Des. Stud., 29, pp. 203–221. [CrossRef]
Moss, J., Kotovksy, K., and Cagan, J., 2007, “The Influence of Open Goals in the Acquisition of Problem Relevant Information,” J. Exp. Psychol. Learn. Mem. Cogn., 33, pp. 876–891. [CrossRef] [PubMed]
Moss, J., Cagan, J., and Kotovsky, K., 2007, “Design Ideas and Impasses: The Role of Open Goals,” Proceedings of the 16th International Conference on Engineering Design.
Linsey, J. S., Wood, K. L., and Markman, A. B., 2008, “Modality and Representation in Analogy,” Artif. Intell. Eng. Des. Anal. Manuf., 22, pp. 85–100. [CrossRef]
Hey, J., Linsey, J., Agogino, A. M., and Wood, K. L., 2008, “Analogies and Metaphors in Creative Design,” Int. J. Eng. Educ., 24, pp. 283–294.
Vattam, S., Helms, M., and Goel, A., 2010, “A Content Account of Creative Analogies in Biologically Inspired Design,” Artif. Intell. Eng. Des. Anal. Manuf., 24, pp. 467–481. [CrossRef]
Taura, T., Nagai, Y., and Tanaka, S., 2005, “Design Space Blending—A Key For Creative Design,” Proceedings of the ICED, Melbourne, Australia.
Koile, K., 2004, “An Intelligent Assistant for Conceptual Design: Informed Search Using a Mapping of Abstract Qualities to Physical Form,” Proceedings of the Design Computing and Cognition.
Visser, W., 1996, “Two Functions of Analogical Reasoning in Design: A Cognitive-Psychology Approach,” Des. Stud., 17, pp. 417–434. [CrossRef]
Gentner, D., and Markman, A. B., 1997, “Structure Mapping in Analogy and Similarity,” Am. Psychol., 52, pp. 45–56. [CrossRef]
Dahl, D. W., and Moreau, P., 2002, “The Influence and Value of Analogical Thinking During New Product Ideation,” J. Mark. Res., 39, pp. 47–60. [CrossRef]
Wilson, J. O., Rosen, D., Nelson, B. A., and Yen, J., 2010, “The Effects of Biological Examples in Idea Generation,” Des. Stud., 31, pp. 169–186. [CrossRef]
Dyer, J. H., Gregersen, H. B., and Christensen, C. M., 2011, The Innovator's DNA: Mastering the Five Skills of Disruptive Innovators. Boston, Harvard Business Review Press, MA.
Dunbar, K., 1997, “How Scientists Think: On-Line Creativity and Conceptual Change in Science,” Creative Thought: An Investigation of Conceptual Structures and Processes, T. B.Ward, S. M.Smith, and J.Vaid, eds., American Psychological Association, Washington, D.C.
Weisberg, R. W., 2009, “On “Out-of-the-Box” Thinking in Creativity,” Tools for Innovation, A.Markman and K.Wood, eds., Oxford University Press, New York, pp. 23–47.
Gick, M. L., and Holyoak, K. J., 1980, “Analogical Problem Solving,” Cogn. Psychol., 12, pp. 306–355. [CrossRef]
Forbus, K. D., Gentner, D., and Law, K., 1994, “MAC/FAC: A Model of Similarity-Based Retrieval,” Cogn. Sci., 19, pp. 141–205. [CrossRef]
Gordon, W. J., 1961, Synectics: The Development of Creative Capacity, Harper and Brothers, New York.
French, M., 1988, Invention and Evolution: Design in Nature and Engineering, Cambridge University Press, Cambridge, United Kingdom.
Linsey, J., Markman, A. B., and Wood, K. L., 2012, “Design by Analogy: A Study of the WordTree Method for Problem Re-Representation,” J. Mech. Des., 134(4), p. 041009. [CrossRef]
Linsey, J., Markman, A. B., and Wood, K. L., 2008, “WordTrees: A Method for Design-by-Analogy,” Proceedings of the ASEE Annual Conference.
Linsey, J., Laux, J., Clauss, E. F., Wood, K., and Markman, A., 2007, “Increasing Innovation: A Trilogy of Experiments Towards a Design-by-Analogy Method,” Proceedings of the ASME Design Theory and Methodology Conference.
Hacco, E., and Shu, L. H., 2002, “Biomimetic Concept Generation Applied to Design for Remanufacture,” Proceedings of the ASME Design Engineering Technology Conference and Computers and Information in Engineering Conference.
Mcadams, D. A., and Wood, K. L., 2000, “Quantitative Measures for Design by Analogy,” Proceedings of the ASME Design Engineering Technology Conference.
Hirtz, J., Stone, R. B., Mcadams, D. A., Szykman, S., and Wood, K. L., 2002, “A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts,” NIST Technical Note 1447.
Altshuller, G. S., and Shapiro, R. B., 1956, “O (On the Psychology of Inventive Creation),” [ (Psychol. Issues), 6, 37–39 (1956) (in Russian)].
Zhang, R., Cha, J., and Lu, Y., 2007, “A Conceptual Design Model Using Axiomatic Design, Functional Basis and TRIZ,” Proceedings of the 2007 IEEE IEEM.
Chakrabarti, A. K., Dror, I., and Nopphdol, E., 1993, “Interorganizational Transfer of Knowledge: An Analysis of Patent Citations of a Defense Firm,” IEEE Trans. Eng. Manage., 40, pp. 217–233.
Indukuri, K. V., Ambekar, A. A., and Sureka, A., 2007, “Similarity Analysis of Patent Claims Using Natural Language Processing Techniques,” Proceedings of the International Conference on Computational Intelligence and Multimedia Applications.
Kasravi, C., and Risov, M., 2007, “Patent Mining—Discovery of Business Value From Patent Repositories,” Proceedings of the 40th Hawaii International Conference on System Sciences.
Bohm, M. R., Vucovich, J. P., and Stone, R. B., 2005, “Capturing Creativity: Using a Design Repository to Drive Concept Innovation,” Proceedings of DETC2005, DETC05/CIE-85105, Long Beach, CA.
Koch, S., Bosch, H., Giereth, M., and Ertl, T., 2009, “Iterative Integration of Visual Insights During Patent Search and Analysis,” Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, Atlantic City, NJ.
Murphy, J. T., 2011, “Patent-Based Analogy Search Tool for Innovative Concept Generation,” Ph.D. dissertation, Department of Mechanical Engineering, The University of Texas, Austin, TX.
Mukherjea, S., Bhuvan, B., and Kankar, P., 2005, “Information Retrieval and Knowledge Discovery Utilizing a BioMedical Patent Semantic Web,” IEEE Trans. Knowl. Data Eng., 17, pp. 1099–1110. [CrossRef]
Chakrabarti, S., Dom, B., Agrawal, R., and Raghavan, P., 1998, “Scalable Feature Selection, Classification and Signature Generation for Organizing Large Text Databases into Hierarchical Topic Taxonomies,” VLDB J., 7, pp. 163–178. [CrossRef]
Griffiths, T. L., Kemp, C., and Tenenbaum, J. B., “Bayesian Models of Cognition,” Cambridge Handbook of Computational Psychology, R.Sun, ed., Cambridge University Press, New York, pp. 59–100.
Kemp, C., and Tenenbaum, J. B., 2008, “The Discovery of Structural Form,” Proc. Natl. Acad. Sci. U. S. A., 105, p. 10687. [CrossRef] [PubMed]
Shepard, R. N., 1980, “Multidimensional Scaling, Tree-Fitting, and Clustering,” Science, 210, pp. 390–398. [CrossRef] [PubMed]
Inhelder, B., and Piaget, J., 1969, The Early Growth of Logic in the Child, W W Norton & Company, New York.
Anderson, J. R., 1991, “The Adaptive Nature of Human Categorization,” Psychol. Rev., 98, pp. 409–429. [CrossRef]
Huelsenbeck, J. P., and Ronquist, F., 2001, “MRBAYES: Bayesian Inference of Phylogenetic Trees,” Bioinformatics, 17, pp. 754–755. [CrossRef] [PubMed]
Fiske, A. P., 1992, “The Four Elementary Forms of Sociality: Framework for a Unified Theory of Social Relations,” Psychol. Rev., 99, pp. 689–723. [CrossRef] [PubMed]
Guttman, L., 1944, “A Basis for Scaling Qualitative Data,” Am. Sociol. Rev., 9, pp. 139–150. [CrossRef]
Bradley, R. A., and Terry, M. E., 1952, “Rank Analysis of Incomplete Block Designs. 1. The Method of Paired Comparisons,” Biometrika, 39, pp. 324–345.
Guttman, L., 1954, “A New Approach to Factor Analysis: The Radex,” Mathematical Thinking in the Social Sciences, P. F.Lazarsfeld, ed., Free Press, New York, pp. 258–348.
Wiggins, J. S., 1996, “An Informal History of the Interpersonal Circumplex Tradition,” J. Pers. Assess., 2, pp. 217–233. [CrossRef]
Sneath, P. H., and Sokal, R. R., 1973, Numerical Taxonomy: The Principles and Practice of Numerical Classification, Freeman, San Francisco, CA.
Collins, A. M., and Quillian, M. R., 1969, “Retrieval Time From Semantic Memory,” J. Verbal Learn. Verbal Behav., 8, pp. 240–247. [CrossRef]
Carroll, J. D., 1976, “Spatial, Nonspatial and Hybrid Models for Scaling,” Psychometrika, 41, pp. 439–463. [CrossRef]
Kohonen, T., 1997, Self-Organizing Maps, Springer, New York.
Kemp, C., and Tenenbaum, J., 2008, “The Discovery of Structural Form,” PNAS, Supporting Information Appendix 10.1073/pnas.080263110.
Landauer, T. K., and Dumais, S. T., 1997, “A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge,” Psychol. Rev., 1, pp. 211–240. [CrossRef]
Agogino, A. M., Song, S., and Hey, J., 2006, “Triangulation of Indicators of Successful Student Design Teams,” Int. J. Eng. Educ., 22, pp. 617–625.
Dong, A., and Agogino, A. M., 1997, “Text Analysis for Constructing Design Representations,” Artif. Intell. Eng., 11, pp. 65–75. [CrossRef]
Moss, J., Kotovsky, K., and Cagan, J., 2006, “The Role of Functionality in the Mental Representations of Engineering Students: Some Differences in the Early Stages of Expertise,” Cogn. Sci., 30, pp. 65–93. [CrossRef] [PubMed]
Deerwester, S., Dumais, S. T., Furnas, G. W., and Landauer, T. K., 1990, “Indexing by Latent Semantic Analysis,” J. Am. Soc. Inf. Sci., 41, pp. 391–407. [CrossRef]
Foltz, P. W., Kintsch, W., and Landauer, T. K., 1998, “The Measurement of Textual Coherence With Latent Semantic Analysis,” Discourse Process., 25, pp. 285–307. [CrossRef]
Landauer, T. K., Foltz, P. W., and Laham, D., 1998, “An Introduction to Latent Semantic Analysis,” Discourse Process., 25, pp. 259–284. [CrossRef]
Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C., and Wood, K., 2012, “The Meaning of “Near” and “Far”: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output,” Proceedings of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Chicago, IL.
Chan, J., Fu, K., Schunn, C., Cagan, J., Wood, K., and Kotovsky, K., 2011, “On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples,” ASME J. Mech. Des., 133, p. 081004. [CrossRef]
Green, M., Dutson, A., Wood, K. L., Stone, R., and McAdams, D., 2002, “Integrating Service-Oriented Design Projects in the Engineering Curriculum,” Proceedings of the 2002 American Society for Engineering Education Annual Conference and Exposition.
Green, M., and Wood, K. L., 2004, “Service-Learning Approaches to International Humanitarian Design Projects: Assessment of Spiritual Impact,” Proceedings of the 2004 Christian Engineering Education Conference.
White, C., and Wood, K. L., 2010, “Influences and Interests in Humanitarian Engineering,” Proceedings of the 2010 ASEE Annual Conference, Global Colloqium on Engineering Education.
Markman, A. B., and Wood, K. L., 2009, Tools for Innovation, Oxford University Press, New York.
Boden, M. A., 2004, The Creative Mind: Myths and Mechanisms, Routledge, New York.
Shah, J. J., Vargas-Hernandez, N., and Smith, S. M., 2003, “Metrics for Measuring Ideation Effectiveness,” Des. Stud., 24, pp. 111–134. [CrossRef]
Girotra, K., Terwiesch, C., and Ulrich, K. T., 2010, “Idea Generation and the Quality of the Best Idea,” Manage. Sci., 56, pp. 591–605. [CrossRef]
Linsey, J., Tseng, I., Fu, K., Cagan, J., Wood, K., and Schunn, C., 2010, “A Study of Design Fixation, Its Mitigation and Perception in Engineering Design Faculty,” ASME J. Mech. Des., 132, p. 1041003. [CrossRef]
Invention Machine Goldfire: Unleashing the Power of Research, 2012, accessed 19 February 2012, http://inventionmachine.com/products-and-services/innovation-software/goldfire-Research/
Gentner, D., Brem, S., Ferguson, R. W., Wolff, P., Markman, A. B., and Forbus, K. D., 1997, “Analogy and Creativity in the Works of Johannes Kepler,” Creative Thought: An Investigation of Conceptual Structures and Processes, T. B.Ward, S. M.Smith, and J.Vaid, eds., American Psychological Association, Washington D.C., pp. 403–459.
Kurtz, K., Miao, C., and Gentner, D., 2001, “Learning by Analogical Bootstrapping,” J. Learn. Sci., 10, pp. 417–446. [CrossRef]
Kaplan, C., and Simon, H. A., 1990, “In Search of Insight,” Cogn. Psychol., 22, pp. 374–419. [CrossRef]
McCaffrey, T., 2012, “Innovation Relies on the Obscure: A Key to Overcoming the Classic Problem of Functional Fixedness,” Psychol. Sci., 23, pp. 215–218. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 1

Example high novelty (top) and low novelty (bottom) concepts

Grahic Jump Location
Fig. 2

Example high quality (top) and low quality (bottom) concepts

Grahic Jump Location
Fig. 3

Effect of distance of analogical stimuli on novelty, previous study, and current study results

Grahic Jump Location
Fig. 4

Effect of distance of analogical stimuli on quality, previous study, and current study results

Grahic Jump Location
Fig. 5

Participant judgment of relevance of analogical stimuli to design problem

Grahic Jump Location
Fig. 6

Original structure of 45 random patents used to choose stimuli sets in the current study

Grahic Jump Location
Fig. 7

Original structure of 45 random patents with 8 patents from previous work

Grahic Jump Location
Fig. 8

Original structure of 45 random patents with 8 patents from previous study and 100 additional random patents



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In