Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering

Technology opportunity discovery (TOD), customized to a firm's current technology capability, can be a good starting point to formulate a technology strategy for a firm that lacks technology information, experts, and/or facilities. Although patent-based studies have suggested systematic methods for customized TOD, these methods have limitations such as insufficient consideration of a target firm's technology portfolio and difficulty of method reproducibility due to expert intervention-based text mining. Therefore, this paper proposes an approach to determine application technology opportunities customized to a target firm by applying collaborative filtering to firms' technology portfolios, which are represented as a set of patent classification codes of the firm's patents. The proposed method involves 1) structuring technology portfolios as firm-international patent classification (IPC) distribution vectors using main group-level IPC codes of the applicants' patents, 2) recommending main group-level IPCs untapped by the target firm and with high preference scores by using collaborative filtering, and 3) classifying the recommended IPCs for the firm's strategic decision-making support using indexes of heterogeneity, growth rate, and competition level. To show the workings of this approach, we applied it to a high-tech firm with wireless communication technology, building on the analysis of large-scale patents and their applicants. This approach is expected to contribute to the systematic identification of application technology opportunities customized to firms and across various industries, and to become a basis for developing future technology intelligence systems.

[1]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[2]  John Riedl,et al.  An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms , 2002, Information Retrieval.

[3]  Ken-ichi Matsumoto,et al.  Accelerating cross-project knowledge collaboration using collaborative filtering and social networks , 2005, MSR.

[4]  Jani Suomalainen Smartphone assisted security pairings for the Internet of Things , 2014, 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE).

[5]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

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

[7]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[8]  Annie Chen,et al.  Context-Aware Collaborative Filtering System: Predicting the User's Preference in the Ubiquitous Computing Environment , 2005, LoCA.

[9]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[10]  Klaus Brockhoff,et al.  Instruments for patent data analyses in business firms , 1992 .

[11]  Yuen-Hsien Tseng,et al.  Text mining techniques for patent analysis , 2007, Inf. Process. Manag..

[12]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[13]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[14]  H. Ernst,et al.  Patent portfolio analysis as a useful tool for identifying R&D and business opportunities--an empirical application in the nutrition and health industry , 2006 .

[15]  Oh-Jin Kwon,et al.  Product opportunity identification based on internal capabilities using text mining and association rule mining , 2016 .

[16]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[17]  Richard R. Nelson,et al.  On the Sources and Significance of Interindustry Differences in Technological Opportunities , 1995 .

[18]  Myong Kee Jeong,et al.  Inter-cluster connectivity analysis for technology opportunity discovery , 2014, Scientometrics.

[19]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[20]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[21]  John Hannon,et al.  Recommending twitter users to follow using content and collaborative filtering approaches , 2010, RecSys '10.

[22]  Ismael Rafols,et al.  Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC) , 2012, Scientometrics.

[23]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[24]  Amy J. C. Trappey,et al.  Using patent data for technology forecasting: China RFID patent analysis , 2011, Adv. Eng. Informatics.

[25]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[26]  P. Savioz,et al.  Strategic forecast tool for SMEs: how the opportunity landscape interacts with business strategy to anticipate technological trends , 2002 .

[27]  Chung-Jen Chen,et al.  Patent portfolio diversity, technology strategy, and firm value , 2006, IEEE Trans. Engineering Management.

[28]  Dominic Bucerzan,et al.  SmartSteg: A New Android Based Steganography Application , 2013, Int. J. Comput. Commun. Control.

[29]  Yongtae Park,et al.  Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis , 2013, Scientometrics.

[30]  B. Hako Strategies for diversification , 1972 .

[31]  Ola Olsson,et al.  Technological Opportunity and Growth , 2005 .

[32]  Kwangsoo Kim,et al.  Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks , 2011, Scientometrics.

[33]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[34]  J. Galbreath,et al.  The Drivers of Climate Change Innovations: Evidence from the Australian Wine Industry , 2016 .

[35]  Holger Ernst,et al.  The Use of Patent Data for Technological Forecasting: The Diffusion of CNC-Technology in the Machine Tool Industry , 1997 .

[36]  Holger Ernst,et al.  Patent information for strategic technology management , 2003 .

[37]  S. Harris,et al.  Analysis of multilocus fingerprinting data sets containing missing data , 2006 .

[38]  S. Dou,et al.  One-pot aqueous synthesis of cysteine-capped CdTe/CdS core–shell nanowires , 2014, Journal of Nanoparticle Research.

[39]  Inseok Song,et al.  Identifying product opportunities using collaborative filtering-based patent analysis , 2017, Comput. Ind. Eng..

[40]  John R. Hauser,et al.  Metrics to value R&D : an annotated bibliography : special report , 2015 .

[41]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[42]  Tugrul U. Daim,et al.  Forecasting emerging technologies: Use of bibliometrics and patent analysis , 2006 .

[43]  Alfred Kleinknecht,et al.  Why do firms cooperate on R&D? an empirical study , 1992 .

[44]  Georg Groh,et al.  Recommendations in taste related domains: collaborative filtering vs. social filtering , 2007, GROUP.

[45]  Bart Selman,et al.  Referral Web: combining social networks and collaborative filtering , 1997, CACM.

[46]  Yongtae Park,et al.  A patent portfolio-based approach for assessing potential R&D partners: An application of the Shapley value , 2016 .

[47]  D. Popp,et al.  Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts , 2008 .

[48]  Chanwoo Cho,et al.  An Empirical Analysis on Purposes, Drivers and Activities of Technology Opportunity Discovery: The Case of Korean SMEs in the Manufacturing Sector , 2016 .

[49]  Jaewoo Kang,et al.  A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell , 2012, Scientometrics.

[50]  Alan L. Porter,et al.  Technology opportunities analysis , 1995 .

[51]  Inchae Park,et al.  Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining , 2014 .

[52]  Yongtae Park,et al.  Identification of technological knowledge intermediaries , 2010, Scientometrics.

[53]  J. Hazel,et al.  BINARY (PRESENCE-ABSENCE) SIMILARITY COEFFICIENTS , 1969 .

[54]  Janghyeok Yoon,et al.  Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: the case of Korean national R&D , 2013, Scientometrics.

[55]  Kwangsoo Kim,et al.  Identification of promising patents for technology transfers using TRIZ evolution trends , 2013, Expert Syst. Appl..

[56]  Holger Ernst,et al.  Patent portfolios for strategic R & D planning , 1998 .

[57]  Janghyeok Yoon,et al.  Tracing evolving trends in printed electronics using patent information , 2014, Journal of Nanoparticle Research.

[58]  Jonghwa Kim,et al.  Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework , 2015 .