Connect to Internal & External Content: Transform unstructured data regardless of its location or authoring language into actionable intelligence to fuel innovation activities.With Goldfire Connect, product development teams can: Unlike traditional search queries, which all too often return piles of irrelevant results, Goldfire Connect delivers highly relevant and precise answers to the day-to-day questions of product teams, in the context of the problems they are trying to solve. Powered by Invention Machine’s multi-patented, multi-lingual semantic research technology, Goldfire Connect delivers the precise, relevant meaning captured within document repositories inside and outside the company – making content actionable. These inefficiencies result in significant waste, rework, and redundancy. Studies show that a lack of collaboration and insufficient connectivity to knowledge – inside and outside of the organization – reduces efficiencies by 30 to 45 percent or more. Evaluation results and the discussion based on them suggest the feasibility and potential of the proposed method in automatically acquiring well-organized effect knowledge system, as well as in aiding high-level product innovation.Goldfire Connect, the collaborative research module of Goldfire, the optimal decision engine, uniquely addresses the connectivity needs of today’s global manufacturers. To evaluate the method, this article first compares the acquired knowledge with those in a comprehensive human-compiled effect database, and then applies the knowledge to aid the innovation design of several mechanical products with different technical backgrounds. Based on such representation, the method reasonably combines syntactic analysis, WordNet and word vector technologies to extract the desired effect knowledge from IPC text. The method stems from a novel effect knowledge representation considering both functional and technical area features, and utilizes functional-flow terms of Functional Basis and technical area categories of international patent classification (IPC) respectively to standardize the modelling of the two kinds of features. In order to capture the effect knowledge that can efficiently aid high-level product innovation, this article proposes a new computational method. Acquiring design knowledge is the vital first step of conducting product innovation activities.
The unexpected or new application of effects can create high-level innovations enabling products long-term technical advantages and market competitiveness. Effect provides a scientific principle-level means for product function realization.
0 kommentar(er)
