Browsing by Author "Trappey, Charles, V"

Now showing 1 - 5 of 5

A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
(2019-04-01)
Trappey, Amy J. C.
;
Chen, Paul P. J.
;
Trappey, Charles, V
;
Ma, Lin
;
管理科學系
Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade (January 2008 to June 2018). First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. Non-supervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify sub-technology clusters and their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain. By adopting unsupervised learning approaches for literature and patent mining, this research develops a novel technology e-discovery methodology and presents the detailed reviews and analyses of the solar power technology using the proposed e-discovery workflow. The insights of global solar technology development, based on both comprehensive literature and patent reviews and cross-analyses, helps energy companies select advanced technologies related to their key technical R&D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development.
Deriving Competitive Foresight Using an Ontology-Based Patent Roadmap and Valuation Analysis
(2019-04-01)
Trappey, Amy J. C.
;
Trappey, Charles, V
;
Chang, Ai-Che
;
Li, Jason X. K.
;
交大名義發表
An ontology-based patent roadmap and valuation analysis approach provides competitive foresights for a corporation's associated patent portfolios and the underlying business processes. To demonstrate the methodology and computer supported analysis system, the patent portfolios of two global smart e-retailers are compared. The patents in each company's portfolio are text mined and categorized based on the smart retailing ontology schema. Both cases' text mining results are plotted as patent portfolio roadmaps, linking patents to innovation categories, commercial applications, and business process models. The three-dimensional (3-D) patent valuation analysis method provides foresights of the companies' competitive advantages related to "patent scope" (PS), "patent importance" (PI), and "patent innovation" (PIN). The research implements a computer supported system to provide evidence how companies utilize patent portfolios as key strategies for protecting business related intellectual properties (IPs) while implementing sustainable and competitive business processes.
Intelligent collaborative patent mining using excessive topic generation
(2019-10-01)
Govindarajan, Usharani Hareesh
;
Trappey, Amy J. C.
;
Trappey, Charles, V
;
管理科學系
;
Department of Management Science
An inevitable consequence of the technology-driven economy has led to the increased importance of intellectual property protection through patents. Recent global pro-patenting shifts have further resulted in high technology overlaps. Technology components are now spread across a huge corpus of patent documents making its interpretation a knowledge-intensive engineering activity. Intelligent collaborative patent mining facilitates the integration of inputs from patented technology components held by diverse stakeholders. Topic generative models are powerful natural language tools used to decompose data corpus topics and associated word bag distributions. This research develops and validates a superior text mining methodology, called Excessive Topic Generation (ETG), as a preprocessing framework for topic analysis and visualization. The presented ETG methodology adapts the topic generation characteristics from Latent Dirichlet Allocation (LDA) with added capability to generate word distance relationships among key terms. The novel ETG approach is used as the core process for intelligent collaborative patent mining. A case study of 741 global Industrial Immersive Technology (ITT) patents covering inventive and novel concepts of Virtual Reality (VR), Augmented Reality (AR), and Brain Machine Interface (BMI) are systematically processed and analyzed using the proposed methodology. Based on the discovered topics of the IIT patents, patent classification (IPC/CPC) predictions are analyzed to validate the superior ETG results.
Intelligent trademark similarity analysis of image, spelling, and phonetic features using machine learning methodologies
(2020-08-01)
Trappey, Charles, V
;
Trappey, Amy J. C.
;
Lin, Sam C-C
;
管理科學系
;
Department of Management Science
The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater due diligence to protect intangible assets such as brands and corporate logos which can easily be copied or distributed through grey channels and internet sales sites. Trademarks (TMs) are government registered intellectual property rights (IPRs) used to legally protect a companies' identities and brand equity. The rapid growth of global trademark (TM) registrations and the number of TM infringement cases pose a great challenge for TM owners to detect infringement and take action to protect TMs, consumer trust, and market share. This research develops advanced TM similarity assessment models using machine learning (ML) approaches. Litigation principles over similarity follow US TM laws which are consistent with global TM protection convention under the World Intellectual Property Organization (WIPO). This research covers the similarity analysis of TM spelling, pronunciation, and images, which are most likely to cause TM confusion among customers. The research focuses on deploying machine learning for natural language (spelling and phonetic features) and image similarity analyses. The vector space modeling algorithms are trained and verified for the similarity analysis of TM wordings in both spelling and pronunciation. The convolutional neural network and Siamese neural network models are trained and verified for TM image similarity comparison. The training and testing sets consist of 250,000 and 20,000 different image pairs respectively. This research provides a significant contribution toward implementing intelligent and automated IPR protection. The system solution supports users (companies, TM attorneys, or IP officers) to identify similar registered TMs before registering new TMs ensuring uniqueness to avoid infringement disputes. The solution also supports automatic screening of online content to detect potential infringement of TM images and wording for effective global IPR protection.
Web Mining Customer Perceptions to Define Product Positions and Design Preferences
(2020-04-01)
Chang, Ai-Che
;
Trappey, Charles, V
;
Trappey, Amy J. C.
;
Chen, Luna W. L.
;
交大名義發表
E-commerce provides a global platform supporting product transactions through the consumer purchase lifecycle including communications of perceived satisfaction and dissatisfaction. The customer feedback functions and social networks of many e-commerce websites allow for the creation of extremely large databases that can be mined to model the customers' perceptions toward online purchases. This research uses online customer reviews as the business intelligence corpus to help companies redesign products that better satisfy consumer preferences and differentiate their product offerings. After identifying the specific webpages of customer reviews, a web crawler collects review text. Computer-supported text mining, cluster analysis, and perceptual mapping are combined as a systematic analytic approach to compare products in a given domain. The study assists phone manufacturers to understand the positive and negative perceptions of customers related to their post-purchase experiences. The customer-preferred product functions, features, and price positions provide valuable strategic intelligence for new product designs and market differentiation.