Research Papers

Design-by-Analogy: Exploring for Analogical Inspiration With Behavior, Material, and Component-Based Structural Representation of Patent Databases

[+] Author and Article Information
Hyeonik Song

School of Mechanical Engineering,
Georgia Institute of Technology,
801 Ferst Drive. MRDC 3340,
Atlanta, GA 30332
e-mail: hyeoniksong@gatech.edu

Katherine Fu

School of Mechanical Engineering,
Georgia Institute of Technology,
801 Ferst Dr. MRDC 4508,
Atlanta, GA 30332
e-mail: katherine.fu@me.gatech.edu

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received August 9, 2018; final manuscript received March 13, 2019; published online April 26, 2019. Assoc. Editor: Matthew I. Campbell.

J. Comput. Inf. Sci. Eng 19(2), 021014 (Apr 26, 2019) (18 pages) Paper No: JCISE-18-1202; doi: 10.1115/1.4043364 History: Received August 09, 2018; Revised March 13, 2019

Design-by-analogy (DbA) is an important method for innovation that has gained much attention due to its history of leading to successful and novel design solutions. The method uses a repository of existing design solutions where designers can recognize and retrieve analogical inspirations. Yet, exploring for analogical inspiration has been a laborious task for designers. This work presents a computational methodology that is driven by a topic modeling technique called non-negative matrix factorization (NMF). NMF is widely used in the text mining field for its ability to discover topics within documents based on their semantic content. In the proposed methodology, NMF is performed iteratively to build hierarchical repositories of design solutions, with which designers can explore clusters of analogical stimuli. This methodology has been applied to a repository of mechanical design-related patents, processed to contain only component-, behavior-, or material-based content to test if unique and valuable attribute-based analogical inspiration can be discovered from the different representations of patent data. The hierarchical repositories have been visualized, and a case study has been conducted to test the effectiveness of the analogical retrieval process of the proposed methodology. Overall, this paper demonstrates that the exploration-based computational methodology may provide designers an enhanced control over design repositories to retrieve analogical inspiration for DbA practice.

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Ullman, D. G. , 2003, The Mechanical Design Process, 3rd ed., McGraw-Hill, Boston, MA.
Fu, K. , 2012, “ Discovering and Exploring Structure in Design Databases and Its Role in Stimulating Design by Analogy,” Ph.D. thesis, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA.
Murphy, J. T. , 2011, “ Patent-Based Analogy Search Tool for Innovative Concept Generation,” Ph.D. thesis, The University of Texas at Austin, Austin, TX.
World Intellectual Property Organization, 2017, “World Intellectual Property Indicators 2017,” World Intellectual Property Organization, Geneva, Switzerland.
Casakin, H. , and Goldschmidt, G. , 1999, “ Expertise and the Use of Visual Analogy: Implications for Design Education,” Des. Stud., 20(2), pp. 153–175. [CrossRef]
Markman, A. B. , Wood, K. L. , Linsey, J. S. , Murphy, J. T. , and Laux, J. P. , 2009, Supporting Innovation by Promoting Analogical Reasoning, Oxford University Press, New York.
Tseng, I. , Moss, J. , Cagan, J. , and Kotovsky, K. , 2008, “ The Role of Timing and Analogical Similarity in the Stimulation of Idean Generation in Design,” Des. Stud., 29(3), pp. 203–221. [CrossRef]
Song, H. , Lopez, R. , Fu, K. , and Linsey, J. , 2017, “ Characterizing the Effects of Multiple Analogs and Extraneous Information for Novice Designers in Design-by-Analogy,” ASME J. Mech. Des., 140(3), p. 031101. [CrossRef]
Linsey, J. S. , Wood, K. L. , and Markman, A. B. , 2008, “ Modality and Representation in Analogy,” Artif. Intell. Eng. Des. Anal. Manuf., 22(02), pp. 85–100. [CrossRef]
Linsey, J. S. , Markman, A. B. , and Wood, K. L. , 2012, “ Design by Analogy: A Study of the WordTree Method for Problem Re-Representation,” ASME J. Mech. Des., 134(4), p. 041009. [CrossRef]
Cheong, H. , and Shu, L. H. , 2014, “ Retrieving Causally Related Functions From Natural-Language Text for Biomimetic Design,” ASME J. Mech. Des., 136(8), p. 081008. [CrossRef]
Lucero, B. , Turner, C. J. , and Linsey, J. , 2016, “ Design Repository & Analogy Computation Via Unit Language Analysis (DRACULA) Repository Development,” ASME Paper No. DETC2015-46640.
Sanaei, R. , Lu, W. , Blessing, L. T. M. , Otto, K. N. , and Wood, K. L. , 2017, “ Analogy Retrieval Through Textual Inference,” ASME Paper No. DETC2017-67943.
Montecchi, T. , Russo, D. , and Liu, Y. , 2013, “ Searching in Cooperative Patent Classification: Comparison Between Keyword and Concept-Based Search,” Adv. Eng. Inf., 27(3), pp. 335–345. [CrossRef]
Mukherjea, S. , Bamba, B. , and Kankar, P. , 2005, “ Information Retrieval and Knowledge Discovery Utilizing a Biomedical Patent Semantic Web,” IEEE Trans. Knowl. Data Eng., 17(8), pp. 1099–1110. [CrossRef]
Ahmed, S. , Wallace, K. M. , and Blessing, L. T. M. , 2003, “ Understanding the Differences Between How Novice and Experienced Designers Approach Design Tasks,” Res. Eng. Des.—Theory Appl. Concurrent Eng., 14(1), pp. 1–11.
Fricke, G. , 1996, “ Successful Individual Approaches in Engineering Design,” Res. Eng. Des.-Theory Appl. Concurrent Eng., 8(3), pp. 151–165.
Bjorklund, T. A. , 2013, “ Initial Mental Representations of Design Problems: Differences Between Experts and Novices,” Des. Stud., 34, pp. 135–160. [CrossRef]
Cross, N. , 2004, “ Expertise in Design: An Overview,” Des. Stud., 25(5), pp. 427–441. [CrossRef]
Razzouk, R. , and Shute, V. , 2012, “ What is Design Thinking and Why is it Important?,” Rev. Educ. Res., 82(3), pp. 330–483. [CrossRef]
Adams, R. S. , Turns, J. , and Atman, C. J. , 2003, “ What Could Design Learning Look Like?,” Design Thinking Research Symposium (DTRS), Sydney, Austrailia, Nov. 17–19. https://www.creativityandcognition.com/cc_conferences/cc03Design/papers/31AdamsDTRS6.pdf
Akin, Ö. , 1990, “ Necessary Conditions for Design Expertise and Creativity,” Des. Stud., 11(2), pp. 107–113. [CrossRef]
Atilola, O. , Tomko, M. , and Linsey, J. S. , 2016, “ The Effects of Representation on Idea Generation and Design Fixation: A Study Comparing Sketches and Function Trees,” Des. Stud., 42, pp. 110–136. [CrossRef]
Kang, I. S. , Na, S. H. , Kim, J. , and Lee, J. H. , 2007, “ Cluster-Based Patent Retrieval,” Inf. Process. Manage., 43, pp. 1173–1182. [CrossRef]
U.S. Patent and Trademark Office Electronic Information Products Division—Patent Technology Monitoring Team (PTMT), 2018, “ U.S. Patent Activity Calendar Years 1790 to the Present,” U.S. Patent and Trademark Office, Alexandria, VA, accessed Mar. 12, 2018, https://www.uspto.gov/web/offices/ac/ido/oeip/taf/h_counts.htm
Song, B. Y. , and Luo, J. X. , 2017, “ Mining Patent Precedents for Data-Driven Design: The Case of Spherical Rolling Robots,” ASME J. Mech. Des., 139(11), p. 111420. [CrossRef]
Kemp, C. , and Tenenbaum, J. B. , 2008, “ The Discovery of Structural Form,” Proc. Natl. Acad. Sci. U. S. A., 105(31), pp. 10687–10692. [CrossRef] [PubMed]
Paatero, P. , and Tapper, U. , 1994, “ Positive Matrix Factorization—A Nonnegative Factor Model With Optimal Utilization of Error-Estimates of Data Values,” Environmetrics, 5(2), pp. 111–126. [CrossRef]
Lee, D. D. , and Seung, H. S. , 1999, “ Learning the Parts of Objects by Non-Negative Matrix Factorization,” Nature, 401(6755), pp. 788–791. [CrossRef] [PubMed]
Pauca, V. P. , Shahnaz, F. , Berry, M. W. , and Plemmons, R. J. , 2004, “ Text Mining Using Non-Negative Matrix Factorizations,” Fourth SIAM International Conference on Data Mining, Lake Buena Vista, FL, Apr. 22–24, pp. 452–456.
Xu, W. , Liu, X. , and Gong, Y. , 2003, “ Document Clustering Based on Non-Negative Matrix Factorization,” 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR '03), Toronto, ON, Canada, July 28–Aug. 1, pp. 267–273.
Choo, J. , Lee, C. , Reddy, C. K. , and Park, H. , 2013, “ UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization,” IEEE Trans. Vis. Comput. Graph, 19, pp. 1992–2001. [CrossRef] [PubMed]
Dulebohn, J. H. , and Hoch, J. E. , 2017, “ Virtual Teams in Organizations,” Human Res. Manag. Rev., 27(4), pp. 569–574.
Kim, J. , and Park, H. , 2008, “ Toward Faster Nonnegative Matrix Factorization: A New Algorithm and Comparisons,” Eighth IEEE International Conference on Data Mining (ICDM), Pisa, Italy, Dec. 15–19, pp. 353–362.
Kim, J. , and Park, H. , 2011, “ Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons,” SIAM J. Sci. Comput., 33(6), pp. 3261–3281. [CrossRef]
Cichocki, A. , and Phan, A. H. , 2009, “ Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations,” IEICE Trans. Fundamentals Electron. Commun. Comput. Sci., E92a(3), pp. 708–721. [CrossRef]
Lim, W. , Du, R. , and Park, H. , 2018, “ CoDiNMF: Co-Clustering of Directed Graphs Via NMF,” Conference on Artificial Intelligence (AAAI18), New Orleans, LA, Feb. 2–7, pp. 3611–3618.
Fu, K. , Cagan, J. , Kotovsky, K. , and Wood, K. , 2013, “ Discovering Structure in Design Databases Through Functional and Surface Based Mapping,” ASME J. Mech. Des., 135(3), p. 031006. [CrossRef]
Greene, D. , O'Callaghan, D. , and Cunningham, P. , 2014, “ How Many Topics? Stability Analysis for Topic Models,” Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014, Springer, Berlin, pp. 498–513.
Kuang, D. , and Park, H. , 2013, “ Fast Rank-2 Nonnegative Matrix Factorization for Hierarchical Document Clustering,” 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, Aug. 11–14, pp. 739–747.


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Fig. 1

Illustration of NMF

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Fig. 2

Illustration of rank-2 NMF iterations

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Fig. 3

Transformation of 3D visualization to 2D visualization

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Fig. 4

Comparison of component, behavior, and material results

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Fig. 5

Number of patents in different CPC categories

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Fig. 6

Concepts generated using method 1

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Fig. 7

Patents (bolded) retrieved using suggested terms (underlined)

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Fig. 8

Concepts generated using method 2

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Fig. 9

Histogram of component result

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Fig. 10

Histogram of behavior result

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Fig. 11

Histogram of material result

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Fig. 12

Three-Dimensional and Two-Dimensional Visualizations for Component Representation of Patent Data

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Fig. 13

Three-Dimensional and Two-Dimensional Visualizations for Behavior Representation of Patent Data

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Fig. 14

Three-Dimensional and Two-Dimensional Visualizations for Material Representation of Patent Data



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