Systematic innovation through patent based product aspect analysis

Duflou, Joost R.; Verhaegen, Paul-Armand

doi:10.1016/j.cirp.2011.03.007

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…For example, to identify that a contraption for extracting a cork stuck in a bottle could serve as relevant inspiration for easing childbirth, an automated system would need to figure out that a vacuum-based mechanism can serve the purpose of extraction of physical objects, and match this function to the problem of extracting babies stuck in the birth canal. At the same time, most structured knowledge bases that do provide richer, more structured representations (e.g., [6,62]) are hand-crafted and small, and previous efforts to scale-up have been limited in expressivity [17,22]. General-purpose knowledge bases (e.g., Cyc [44], NELL [53], DBpedia [20]) largely encode categorical knowledge (e.g., is-a, has-a) and rarely functional knowledge (e.g., used-for), and can also suffer from poor coverage [29].…”

Section: Introductionmentioning

confidence: 99%

Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

Hope¹,

Tamari²,

Kang³

et al. 2021

Preprint

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Web-scale repositories of products, patents and scientific papers offer an opportunity for building automated systems that scour millions of existing ideas and assist users in discovering novel inspirations and solutions to problems. Yet the current way ideas in such repositories are represented is largely in the form of unstructured text, which is not amenable to the kind of user interactions required for creative innovation. Prior work has pointed to the importance of functional representations -capturing the mechanisms and purposes of inventions -for allowing users to discover structural connections across ideas and creatively adapt existing technologies. However, previous work exploring the use of functional representations was either very coarse-grained and limited in expressivity, or dependent on manually curated knowledge bases with poor coverage and significant manual effort from users.To help bridge this gap and unlock the potential of large-scale idea mining, we propose a novel computational representation that automatically breaks up products into fine-grained functional facets. We train a model to extract these facets from a challenging real-world corpus of invention descriptions, and represent each product as a set of facet embeddings. We design similarity metrics that support granular matching between functional facets across ideas, and use them to build a novel functional search capability that enables expressive queries for mechanisms and purposes. We construct a graph capturing hierarchical relations between purposes and mechanisms across an entire corpus of products, and use the graph to help problem-solvers explore the design space around a focal problem and view related problem perspectives. In empirical user studies, our approach leads to a significant boost in search accuracy and in the quality of creative inspirations, outperforming strong baselines and state-of-art representations of product texts by 50-60%.

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Section: Introductionmentioning

confidence: 99%

Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

Hope¹,

Tamari²,

Kang³

et al. 2021

Preprint

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“…Some work has examined the value of automated sensemaking outputs for complex tasks like ideation [20,24], intelligence analysis [57], and conducting scientific literature reviews [13]. However, relatively little work has compared the value of automated sensemaking outputs to human-produced outputs for complex tasks in general.…”

Section: Automated Sensemaking Strategiesmentioning

confidence: 99%

Comparing Different Sensemaking Approaches for Large-Scale Ideation

Chan

Dang

Dow

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Large-scale idea generation platforms often expose ideators to previous ideas. However, research suggests people generate better ideas if they see abstracted solution paths (e.g., descriptions of solution approaches generated through human sensemaking) rather than being inundated with all prior ideas. Automated and semi-automated methods can also offer interpretations of earlier ideas. To benefit from sensemaking in practice with limited resources, ideation platform developers need to weigh the cost-quality tradeoffs of different methods for surfacing solution paths. To explore this, we conducted an online study where 245 participants generated ideas for two problems in one of five conditions: 1) no stimuli, 2) exposure to all prior ideas, or solution paths extracted from prior ideas using 3) a fully automated workflow, 4) a hybrid human-machine approach, and 5) a fully manual approach. Contrary to expectations, human-generated paths did not improve ideation (as measured by fluency and breadth of ideation) over simply showing all ideas. Machine-generated paths sometimes significantly improved fluency and breadth of ideation over no ideas (although at some cost to idea quality). These findings suggest that automated sensemaking can improve idea generation, but we need more research to understand the value of human sensemaking for crowd ideation.

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“…Given the importance of technical knowledge for the company's competitiveness, the protection of property rights is essential to increase the share of profits resulting from technological efforts in highly competitive markets (Pereira and Vasconcellos, 2014). Thus, it is noted that the patent occupying a prominent position is one of the different forms and tools used to protect property rights (Duflou and Verhaegen, 2011). However, using the industrial property system as a competitive intelligence that promotes a business environment conducive to innovation is a complex task and requires integration among all stakeholders of companies to leverage the technological development of their products (Barroso et al, 2009).…”

mentioning

confidence: 99%

Strategy for the development of agricultural machinery: importance of patent analysis

Silveira

Machado

Romano

et al. 2018

BJO&PM

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During the product development, patents help identify new project opportunities and, if well analyzed, can reveal technical strategies in terms of how countries, companies, and universities link patents and product development. In the context of the agricultural machinery sector there is a complexity in the integration of data from patents with the stages of development conceptions. In this context, the article aims to present how the information obtained from a patent bank can be used for the development of agricultural machinery. As a methodology, the Systematic Bibliographic Review was selected to identify and relate strategies that add value throughout the agricultural machinery project. As a result, research has shown that the use of patent information can assist in the development of agricultural machinery from technical data. In addition, patents are characterized as a valuable source of technological information, as it is inexpensive and capable of feeding a company in its own research activity and in the development of new products. It is concluded that the information from patents, suggested as strategies in the model of agricultural machinery development, allows for managerial and technical decisions aimed at improving products. Finally, the research contributed to fill the information gap between patents and the development of agricultural machinery, a diffusion of data that, if well connected, can promote a competitive differential in companies.

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Systematic innovation through patent based product aspect analysis

Cited by 8 publications

References 10 publications

Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

Comparing Different Sensemaking Approaches for Large-Scale Ideation

Strategy for the development of agricultural machinery: importance of patent analysis

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