Skip to main content
SpringerLink
Log in

Less Is More? In Patents, Design Transformations that Add Occur More Often Than Those that Subtract

  • Conference paper
  • First Online:
Design Computing and Cognition’20

Abstract

This article examines how design transformations are described in one specific but important context: patents. Using text analytics, we examined term frequency and term frequency-inverse document frequency from 33,100 full patents from 2017 sourced from the US Patent and Trade Office. Using a corpus-based approach, we developed lexicons to capture two general types of design transformation: addition and subtraction. In patent data we collected and analyzed, addition design transformations were more common than subtraction design transformations (2.7:1). The ratio of addition to subtraction was higher than ratios in non-design texts (1:2.5). While patents represent one area of design, and the patent texts we analyzed were not necessarily written by designers themselves, something about the process that produces patents leads to far greater use of addition than subtraction. We discuss possible reasons for and implications of these findings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tseng Y-H, Lin C-J, Lin Y-I (2007) Text mining techniques for patent analysis. Inf Process Manag 43:1216–1247. https://doi.org/10.1016/j.ipm.2006.11.011

    Article  Google Scholar 

  2. Oxford Academic (2019) Global Collaborative Patents. The Economic Journal | Oxford Academic. https://academic.oup.com/ej/article/128/612/F235/5089453. Accessed 29 Nov 2019

  3. Zhang L, Li L, Li T (2015) Patent mining: a survey. ACM SIGKDD Explor Newsl 16:1–19. https://doi.org/10.1145/2783702.2783704

    Article  Google Scholar 

  4. Yoon B, Park Y (2004) A text-mining-based patent network: analytical tool for high-technology trend. J High Technol Manag Res 15:37–50. https://doi.org/10.1016/j.hitech.2003.09.003

    Article  Google Scholar 

  5. Lee S, Yoon B, Park Y (2009) An approach to discovering new technology opportunities: keyword-based patent map approach. Technovation 29:481–497. https://doi.org/10.1016/j.technovation.2008.10.006

    Article  Google Scholar 

  6. Murphy JT (2011) Patent-based analogy search tool for innovative concept generation. Thesis

    Google Scholar 

  7. Trappey AJC, Trappey CV, Wu C-Y, Lin C-W (2012) A patent quality analysis for innovative technology and product development. Adv Eng Inform 26:26–34. https://doi.org/10.1016/j.aei.2011.06.005

    Article  Google Scholar 

  8. He Y, Luo J (2017) Novelty, conventionality, and value of invention. In: JohnS G (ed) Design Computing and Cognition 2016. Springer, Cham, pp 23–38

    Google Scholar 

  9. Simon HA (1973) The structure of Ill structured problems. Artif Intell 21

    Google Scholar 

  10. Goldschmidt G (2011) Avoiding design fixation: transformation and abstraction in mapping from source to target. J Creat Behav 45:92–100. https://doi.org/10.1002/j.2162-6057.2011.tb01088.x

    Article  Google Scholar 

  11. Dong A (2017) Functional lock-in and the problem of design transformation. Res Eng Des 28:203–221. https://doi.org/10.1007/s00163-016-0234-3

    Article  Google Scholar 

  12. Singh V, Skiles SM, Krager JE et al (2009) Innovations in design through transformation: a fundamental study of transformation principles. J Mech Des 131. https://doi.org/10.1115/1.3125205

  13. Holyoak KJ (1984) Mental models in problem solving. In: Tutorials in learning and memory: essays in honor of gordon bower, pp 193–218

    Google Scholar 

  14. Daly SR, Christian JL, Yilmaz S et al (2012) Assessing design heuristics for idea generation in an introductory engineering course. Int J Eng Educ 28:463–473

    Google Scholar 

  15. Dym CL, Agogino AM, Eris O et al (2005) Engineering design thinking, teaching, and learning. J Eng Educ 94:103–120. https://doi.org/10.1002/j.2168-9830.2005.tb00832.x

    Article  Google Scholar 

  16. Cross N (2001) Design cognition: results from protocol and other empirical studies of design activity. In: Design knowing and learning: cognition in design education. Elsevier, pp 79–103

    Google Scholar 

  17. Schön DA (1992) Designing as reflective conversation with the materials of a design situation. Knowl-Based Syst 5:3–14. https://doi.org/10.1016/0950-7051(92)90020-G

    Article  Google Scholar 

  18. Dong A (2007) The enactment of design through language. Des Stud 28:5–21. https://doi.org/10.1016/j.destud.2006.07.001

    Article  Google Scholar 

  19. Suwa M, Purcell T, Gero J (1998) Macroscopic analysis of design processes based on a scheme for coding designers’ cognitive actions. Des Stud 19:455–483. https://doi.org/10.1016/S0142-694X(98)00016-7

    Article  Google Scholar 

  20. Poggenpohl S, Chayutsahakij P, Jeamsinkul C (2004) Language definition and its role in developing a design discourse. Des Stud 25:579–605. https://doi.org/10.1016/j.destud.2004.02.002

    Article  Google Scholar 

  21. Fu B-R, Hsu S-W, Liu C-H, Liu Y-C (2014) Statistical analysis of patent data relating to the organic Rankine cycle. Renew Sustain Energy Rev 39:986–994. https://doi.org/10.1016/j.rser.2014.07.070

    Article  Google Scholar 

  22. Sorce S, Malizia A, Gentile V et al (2019) Evaluation of a visual tool for early patent infringement detection during design. In: 7th international symposium on end-user development (IS-EUD 2019)

    Google Scholar 

  23. Eads D (2018) pypatent: Search and retrieve USPTO patent data

    Google Scholar 

  24. Shinmori A, Okumura M, Marukawa Y, Iwayama M (2003) Patent claim processing for readability. In: Proceedings of the ACL workshop on Patent corpus processing, vol 20, pp 56–65. https://doi.org/10.3115/1119303.1119310

  25. Bekkers R, Bongard R, Nuvolari A (2011) An empirical study on the determinants of essential patent claims in compatibility standards. Res Policy 40:1001–1015. https://doi.org/10.1016/j.respol.2011.05.004

    Article  Google Scholar 

  26. United States Patent and Trade Office (2020) Title of Invention. In: United State Patent and Trade Office. https://www.uspto.gov/web/offices/pac/mpep/s606.html. Accessed 17 Jan 2020

  27. United States Patent and Trade Office (2020) The Abstract: PCT Rule 8. In: United State Patent and Trade Office. https://www.uspto.gov/web/offices/pac/mpep/s1826.html. Accessed 17 Jan 2020

  28. Kasemsap K (2017) Text mining: current trends and applications. In: Web Data Mining and the Development of Knowledge-Based Decision Support Systems, pp 338–358. https://doi.org/10.4018/978-1-5225-1877-8.ch017

  29. Niemann H, Moehrle MG, Frischkorn J (2017) Use of a new patent text-mining and visualization method for identifying patenting patterns over time: concept, method and test application. Technol Forecast Soc Chang 115:210–220. https://doi.org/10.1016/j.techfore.2016.10.004

    Article  Google Scholar 

  30. Iordanskaja L, Kittredge R, Polguère A (1991) Lexical selection and paraphrase in a meaning-text generation model. In: Paris CL, Swartout WR, Mann WC (eds) Natural language generation in artificial intelligence and computational linguistics. Springer, Boston, pp 293–312

    Google Scholar 

  31. Mel’čuk I, Polguère A (2018) Theory and practice of lexicographic definition. J Cogn Sci 19:417–470. https://doi.org/10.17791/jcs.2018.19.4.417

  32. Cumming S (1986) The lexicon in text generation. In: Proceedings of the workshop on Strategic computing natural language - HLT 1986. Association for Computational Linguistics, Marina del Rey, California, p 242

    Google Scholar 

  33. Rice DR, Zorn C (undefined/ed) Corpus-based dictionaries for sentiment analysis of specialized vocabularies. Political Sci Res Methods 1–16. https://doi.org/10.1017/psrm.2019.10

  34. Riloff E, Shepherd J (1997) A Corpus-Based Approach for Building Semantic Lexicons. arXiv:cmp-lg/9706013

  35. Witherell P, Krishnamurty S, Grosse IR (2007) Ontologies for supporting engineering design optimization. J Comput Inf Sci Eng 7:141–150. https://doi.org/10.1115/1.2720882

    Article  Google Scholar 

  36. Pilehchian B, Staub-French S, Nepal MP (2015) A conceptual approach to track design changes within a multi-disciplinary building information modeling environment. Can J Civ Eng 42:139–152. https://doi.org/10.1139/cjce-2014-0078

    Article  Google Scholar 

  37. Riloff E, Shepherd J (1997) A Corpus-Based Approach for Building Semantic Lexicons 8

    Google Scholar 

  38. Khalaj J, Pedgley O (2019) A semantic discontinuity detection (SDD) method for comparing designers’ product expressions with users’ product impressions. Design Stud 62:36–67. https://doi.org/10.1016/j.destud.2019.02.002

    Article  Google Scholar 

Download references

Acknowledgements

The authors are indebted to many colleagues, collaborators, and commentators including Gabrielle Adams, Ben Converse, Andrew Hales, for their input and direction. The authors are also indebted to the following grants for facilitating the research and preparing the manuscript: National Science Foundation #1531041.

Author information

Authors and Affiliations

  1. University of Virginia, Charlottesville, USA

    Katelyn Stenger, Clara Na & Leidy Klotz

Authors
  1. Katelyn Stenger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Clara Na
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leidy Klotz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katelyn Stenger .

Editor information

Editors and Affiliations

  1. Department of Computer Science and School of Architecture, University of North Carolina, Charlotte, NC, USA

    John S. Gero

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stenger, K., Na, C., Klotz, L. (2022). Less Is More? In Patents, Design Transformations that Add Occur More Often Than Those that Subtract. In: Gero, J.S. (eds) Design Computing and Cognition’20. Springer, Cham. https://doi.org/10.1007/978-3-030-90625-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-90625-2_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90624-5

  • Online ISBN: 978-3-030-90625-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation