Realising the affective potential of patents: a new model of database interpretation for user-centred design

ABSTRACT This research sets out a new interpretation of the patent database using affective design parameters. While this resource contains a vast quantity of technical information, its extraction and use in practical design settings is extremely challenging. Until now, all filing and subsequent landscaping or profiling of patents has been based on their technical characteristics. We set out an alternative approach that utilises crowdsourcing to first summarise patents and then applies text analysis tools to assess the summarising text in relation to three affective parameters: appearance, ease of use, and semantics. The results been used to create novel patent clusters that provide an alternative perspective on relevant technical data, and support user-centric engineering design. The workflow and tasks to effectively interface with the crowd are outlined, and the process for harvesting and processing responses using a combination of manual and computational analysis is reviewed. The process creates sets of descriptive words for each patent which differ significantly from those created using only functional requirements, and support a new paradigm for the use of big data in engineering design – one that utilises desirable affective qualities as the basis for scouring and presenting relevant functional patent information for concept generation and development.

[1]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL 2006.

[2]  Jonathan Corney,et al.  The analysis and presentation of patents to support engineering design , 2016 .

[3]  Angela Petit,et al.  Observing the User Experience: A Practitioner's Guide to User Research (Second Edition) [book review] , 2013, IEEE Trans. Prof. Commun..

[4]  Mitsuo Nagamachi,et al.  Validation of Kansei Engineering Adoption in E-commerce Web Design , 2009 .

[5]  Ted Pedersen,et al.  An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet , 2002, CICLing.

[6]  Rakesh Khurana,et al.  The curse of the superstar CEO. , 2002, Harvard business review.

[7]  Mike Kuniavsky,et al.  Observing the User Experience: A Practitioner's Guide to User Research (Morgan Kaufmann Series in Interactive Technologies) (The Morgan Kaufmann Series in Interactive Technologies) , 2003 .

[8]  Gregory D. Graff,et al.  Patent landscaping for life sciences innovation: toward consistent and transparent practices , 2013, Nature Biotechnology.

[9]  Simeon Keates,et al.  Inclusive Design: Design for the Whole Population , 2003 .

[10]  Hideyoshi Yanagisawa Kansei Quality in Product Design , 2011 .

[11]  Cathy Barnes,et al.  Decision support for the design of affective products , 2009 .

[12]  M Johns,et al.  Change by Design!? , 2018, Organization and Newness.

[13]  Kwangsoo Kim,et al.  Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis , 2012, Scientometrics.

[14]  Robert F. Eberle Developing Imagination Through Scamper. , 1972 .

[15]  Sarah Kettley,et al.  Wearable Health Technology Design: A Humanist Accessory Approach , 2017 .

[16]  Davide Russo,et al.  Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search , 2013, Adv. Eng. Informatics.

[17]  Panagiotis G. Ipeirotis Demographics of Mechanical Turk , 2010 .

[18]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[19]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[20]  SeungHee Lee Pleasure with Products : Design based on Kansei , 2000 .

[21]  Bill Moggridge,et al.  Designing interactions , 2006 .

[22]  T. Brown,et al.  Change by Design , 2011 .

[23]  Brian Henson,et al.  Beyond usability: designing for consumers' product experience using the Rasch model , 2015 .

[24]  Denis Cavallucci,et al.  TRIZ – The Theory of Inventive Problem Solving , 2017, Springer International Publishing.

[25]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[26]  J. Jacoby,et al.  Consumer Behavior , 2024 .

[27]  S. Nagasawa Present state of Kansei engineering in Japan , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[28]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[29]  E. Ozcan Vieira,et al.  Auditory and visual contributions to affective product quality , 2017 .

[30]  Andy Gibbs,et al.  Advanced document retrieval techniques for patent research , 2008 .

[31]  D. Norman Emotional design : why we love (or hate) everyday things , 2004 .

[32]  Nick Cramer,et al.  Automatic Keyword Extraction from Individual Documents , 2010 .

[33]  原 研哉,et al.  Designing design = デザインのデザイン , 2007 .

[34]  Benjamin S. Bloom,et al.  Taxonomy of Educational Objectives: The Classification of Educational Goals. , 1957 .

[35]  Gunter R. Ladewig,et al.  TRIZ: The Theory of Inventive Problem Solving , 2008 .

[36]  James R. Lewis,et al.  Usability: Lessons Learned … and Yet to Be Learned , 2014, Int. J. Hum. Comput. Interact..

[37]  Olga Kokshagina,et al.  Designing techniques for systemic impact: lessons from C-K theory and matroid structures , 2016, Research in Engineering Design.

[38]  Mitsuo Nagamachi,et al.  Kansei Engineering: A new ergonomic consumer-oriented technology for product development , 1995 .

[39]  Roger Jianxin Jiao,et al.  A Kansei mining system for affective design , 2006, Expert Syst. Appl..

[40]  Jonathan Chapman,et al.  Emotionally Durable Design: Objects, Experiences and Empathy , 2015 .

[41]  Olga Kokshagina,et al.  Should we manage the process of inventing? Designing for patentability , 2017 .

[42]  Bill Tomlinson,et al.  Who are the crowdworkers?: shifting demographics in mechanical turk , 2010, CHI Extended Abstracts.

[43]  Michael E. Lesk,et al.  Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone , 1986, SIGDOC '86.

[44]  Jonathan Corney,et al.  The generation of problem-focussed patent clusters: a comparative analysis of crowd intelligence with algorithmic and expert approaches , 2017, Design Science.

[45]  Martin C. Maguire,et al.  Methods to support human-centred design , 2001, Int. J. Hum. Comput. Stud..

[46]  Sari Kujala,et al.  Capturing users’ perceptions of valuable experience and meaning , 2009 .

[47]  Petra Moser Patents and Innovation: Evidence from Economic History , 2012 .