tHow to discover new business opportunities and how to decide which ones are worth pursuing hasbecome today an increasingly common and crucial question for the survival of many start-ups and compa-nies that now have an established technology in a saturated market. The academic world has long soughtways to support these activities in a systematic way, even though there is no reference methodology yet. Inreality, there is even a lack of convergence in how to appeal to this activity, which appears under differentnames such as outward technology transfer, discovery of white space/technology/business opportunities.The only thing that the vast majority of searches have in common is that they all use patent databasesas a source of inspiration to search for new fields of application. In line with it, this paper suggests anovel patent based approach for automatize the extraction of a list of alternative technological oppor-tunities/products. It is based on a function-based approach embedded with syntactic parsers and othernatural language processing applications (NLP). They were also used to assess the different opportuni-ties and suggest which have the greatest potential for success in the market. More specifically, specificsemantic patterns have been developed to control a dependent semantic parser. Thanks to this tool wecan automatically analyses the content of a huge corpus of documents training the system to recognizemany technological features. This paper shows mechanisms for extracting all functions provided by agiven technology, the list of products that potentially can exploit such technology and a list of require-ments with which to compare products that differ in the technology used. As a result, a quantitativetransfer potential index for measuring how many possibilities a given technology has of replacing anexisting one is presented. Unlike many similar approaches applied to stimuli design, the method arrivesat precisely suggesting the final product into which to transfer the technology and not merely suggestinga functionally related area or compatible patent class. In addition, the assessment of the replacementindex is not based on distance criteria compared to the focal technology with a general purpose method,but on the contrary the judgement criteria change according to the application context. The automaticsearch for evaluation criteria from the specific patent content is an integral part of the method and helpsto limit subjectivity in evaluations by experts in the field. However, the goodness of the results obtainedis proportional to the level of knowledge of the user who interfaces to the system. Moreover, it can beuniversally applied to any technological context. An investigation of new application fields of heatingfabrics made of carbon fiber technology is presented as exemplary case.

(2020). Discovering new business opportunities with dependent semantic parsers [journal article - articolo]. In COMPUTERS IN INDUSTRY. Retrieved from http://hdl.handle.net/10446/167587

Discovering new business opportunities with dependent semantic parsers

Russo, Davide;Spreafico, Matteo;
2020-01-01

Abstract

tHow to discover new business opportunities and how to decide which ones are worth pursuing hasbecome today an increasingly common and crucial question for the survival of many start-ups and compa-nies that now have an established technology in a saturated market. The academic world has long soughtways to support these activities in a systematic way, even though there is no reference methodology yet. Inreality, there is even a lack of convergence in how to appeal to this activity, which appears under differentnames such as outward technology transfer, discovery of white space/technology/business opportunities.The only thing that the vast majority of searches have in common is that they all use patent databasesas a source of inspiration to search for new fields of application. In line with it, this paper suggests anovel patent based approach for automatize the extraction of a list of alternative technological oppor-tunities/products. It is based on a function-based approach embedded with syntactic parsers and othernatural language processing applications (NLP). They were also used to assess the different opportuni-ties and suggest which have the greatest potential for success in the market. More specifically, specificsemantic patterns have been developed to control a dependent semantic parser. Thanks to this tool wecan automatically analyses the content of a huge corpus of documents training the system to recognizemany technological features. This paper shows mechanisms for extracting all functions provided by agiven technology, the list of products that potentially can exploit such technology and a list of require-ments with which to compare products that differ in the technology used. As a result, a quantitativetransfer potential index for measuring how many possibilities a given technology has of replacing anexisting one is presented. Unlike many similar approaches applied to stimuli design, the method arrivesat precisely suggesting the final product into which to transfer the technology and not merely suggestinga functionally related area or compatible patent class. In addition, the assessment of the replacementindex is not based on distance criteria compared to the focal technology with a general purpose method,but on the contrary the judgement criteria change according to the application context. The automaticsearch for evaluation criteria from the specific patent content is an integral part of the method and helpsto limit subjectivity in evaluations by experts in the field. However, the goodness of the results obtainedis proportional to the level of knowledge of the user who interfaces to the system. Moreover, it can beuniversally applied to any technological context. An investigation of new application fields of heatingfabrics made of carbon fiber technology is presented as exemplary case.
articolo
2020
Russo, Davide; Spreafico, Matteo; Precorvi, Andrea
(2020). Discovering new business opportunities with dependent semantic parsers [journal article - articolo]. In COMPUTERS IN INDUSTRY. Retrieved from http://hdl.handle.net/10446/167587
File allegato/i alla scheda:
File Dimensione del file Formato  
1-s2.0-S0166361520305649-main.pdf

Solo gestori di archivio

Versione: publisher's version - versione editoriale
Licenza: Licenza default Aisberg
Dimensione del file 2.72 MB
Formato Adobe PDF
2.72 MB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/167587
Citazioni
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 11
social impact