Technology Opportunities Analysis Research Articles

Analyzing critical core components’ technology opportunities based on multilayer networks from a lifecycle perspective: A case study of offshore wind turbine foundation

Among renewable energy sources, offshore wind power has emerged as an important trend due to its more stable and powerful wind resources. Advances in the fundamental technology of offshore wind turbines are crucial for ensuring the long-term feasibility and reliability of wind power projects. Existing technology opportunity analysis (TOA) studies based on link prediction network analysis seldom consider the technical characteristics of the target component from different perspectives, such as lifecycle. In this study, we propose a framework to analyze technology opportunities for offshore wind turbine foundations. The method consists of four steps: 1) Collect patents related to the target component. 2) Use BERT to automatically layer patents into corresponding lifecycle stages. 3) Build a multilayer network centered on the target component. 4) Use link prediction to recommend technology opportunities throughout the lifecycle and at each stage of the target component. Using offshore wind power foundations as an example, the effectiveness of the method is verified. Our findings indicate that improvements in structural arrangements of foundations and anti-corrosion coatings are future technology opportunities for offshore wind power foundations. Multilayer network analysis based on link prediction from a lifecycle perspective provides a valuable reference for future TOA research and practice. Additionally, it offers enterprises insights into future technology opportunities at various lifecycle stages, presenting a new perspective for optimizing R&D resource allocation and market positioning based on these results.

Focused ultrasound therapy in movement disorders: management roadmap toward optimal pathway organization.

MRI-guided focused ultrasound (MRgFUS) lesioning is an innovative, safe and effective treatment which provides an innovative development in the field of minimally invasive stereotactic neurosurgery. Based on the application of focused ultrasound energy under full MR planning and thermal imaging control, unilateral lesioning of the thalamus, subthalamic nucleus, and globus pallidus is indicated for the treatment of movement disorders, including essential tremor, Parkinson's disease, and dystonia. We started to apply this technique in February 2019 for the treatment of patients with movement disorders. The authors developed a diagnostic therapeutic care pathway, which is herewith proposed and applied as an explication of standard clinical practice in use. The project was the result of the application of different methods such as Health Technology Assessment (HTA), Strengths, Weaknesses, Opportunities and Threats analysis (SWOT) and Demin -Plan, Do, Check, Act (PDCA) cycle. The aim of this project was to standardize the MRgFUS diagnostic-therapeutic pathway (DTP), describe its application and the appropriateness of different phases (patient selection, intervention phase and follow-up). Here, we described in detail our experience in the DTP application from 2019 up to now in 610 patients with movement disorders.

Mapping the field: A bibliometric literature review on technology mining

Technology mining (or tech mining, TM) is an emerging research field in science, technology, and innovation studies. However, due to the rapid increase and widespread application of TM research, accurately capturing research topics and emerging developments in TM has become a challenge for scholars. Therefore, this bibliometric literature review combines quantitative methods and content analysis to explore the research foundation and development frontiers of TM and distinguish emerging research topics from relatively mature ones, aiming to deepen the understanding. More specifically, it utilizes co-citation analysis and bibliographic coupling techniques to analyze the TM publication dataset. The results indicate that TM research is mainly based on four foundational areas, and there are five current frontier clusters. Emerging topic detection further shows that technology topic analysis, technology opportunity analysis, and technology management and decision support are currently emerging TM research topics.

Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis

Despite the substantial contributions of many studies on firm-specific technology opportunity analysis (TOA), there is a lack of understanding of the technology portfolios of organizations and actors of technology innovation activities. The study proposes a new firm-specific TOA approach using graph representation, rule-based machine learning, and index analysis. First, organizations’ technology portfolios are characterized by multiple graphs consisting of technological components based on their own patent information. Second, given an organization of interest for a TOA, its core technology, which is represented as links between technological components, is defined and significant association rules are identified through our rule-based machine learning pipeline. Third, new-to-firm technology opportunities are identified from a set of association rules and evaluated using quantitative metrics. Finally, we examine the evaluation metrics on which each organization focuses by tracking the patenting activities of the organizations after the analysis period. Consequently, we can enhance the understanding of organizational technology portfolios and provide firm-specific technology opportunities. Our empirical results for multiple organizations showed that the proposed approach is effective and valuable as a decision-supporting tool for TOA in practice.

Technology opportunity analysis using hierarchical semantic networks and dual link prediction

Technology opportunity analysis using network analysis and link prediction has attracted the interest of both academia and industry. However, there are several unresolved issues with existing research, such as a lack of semantic relationships between nodes in a single-layer network, analyzing current technology trends rather than predicting future technology developments based on existing edges in a semantic network, and ignoring evaluation criteria for technology opportunity identification based on a single link prediction. This study proposes a new systematic methodology to address these issues and identify technology opportunities using a hierarchical semantic network and dual link prediction. The proposed methodology consists of three modules: 1) constructing the hierarchical semantic network based on SAO structures extracted from patents; 2) identifying technology opportunities in this semantic network through probabilistic-based link prediction; and 3) evaluating these opportunities via similarity-based link prediction. The viability and usefulness of the proposed methodology is proved by empirical analysis of the exploitation technology in the coal seam gas (CSG) industry. The results show that the hierarchical semantic network, including semantic and co-word relationships, can improve prediction accuracy. The dual link prediction can not only automatically identify technology opportunities with semantics, but also evaluate them to narrow down the problem-solving according to the novelty criteria. This study represents a contribution to existing research on technology opportunity analysis by integrating the hierarchical semantic network and dual link prediction.

Obtaining advantages from knowledge base: mapping the potential to develop new technologies

ABSTRACT It is necessary for the company to find suitable new growth points when growth hits the ceiling. Technology opportunity discovery is one kind of knowledge based risk mitigation strategy (TOD). Computer-based TOD has the advantage of fast and repeatable analysis, which can be used as a complement and reference to expert decision methods. This research shows an approach to help firms to discover technology opportunities based on their knowledge foundation to obtain a competitive advantage. The proposed method involves (1) identifying the critical technologies by text analysis, measuring the importance of technology in the network and the time of bursts, (2) evaluating the relative technological advantage (RTA) of the firms as knowledge base, (3) Calculating the relatedness between technologies, assessing the development potential of non-RTA technologies from the firm’s current knowledge base and the relatedness between technologies, (4) proposing a technology opportunity analysis framework that integrates the potential and importance for technological development. This article chooses Self-Driving Vehicles (SDV) as a case study to further explain and validate the approach. This approach helps firms understand their current state and growth potential. Such recommendations are based on the knowledge foundation of different firms and therefore are individualised.

From technology opportunities to ideas generation via cross-cutting patent analysis: Application of generative topographic mapping and link prediction

Technology opportunity analysis (TOA) with ideas generation has been recognized an important activity to remain competitive and lead the industry in the future. However, there are several issues with existing TOA, such as an unclear path from technology opportunities to ideas generation, a fuzzy integration between automated TOA techniques and expert-based methods, and a lack of detailed schemes for technology opportunities. This study proposes a new systematic approach to show the way from technology opportunities to ideas generation via cross-cutting patent analysis. The proposed approach is comprised of three stages: 1) establishing a cross-cutting relationship between the target and reference technologies through the results of F-terms; 2) collecting and processing patents to construct patent-keyword vector matrices of the target and reference technologies, respectively; and 3) migrating corresponding ideas via cosine similarity and link prediction for the target technology opportunities that are discovered based on generative topographic mapping (GTM). The feasibility and effectiveness of the proposed approach is demonstrated by empirical research on the exploitation technology in both the natural gas hydrate (NGH) and the coal bed methane (CBM) fields. This study represents a contribution to expand the existing TOA research into generating creative ideas by providing more detailed schemes for technology opportunities.

Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ

Technology opportunity analysis (TOA) has been the subject of many prior studies, most of which have focused on deconstructing and restructuring the original knowledge structure in a single domain. This study suggests a method by extending technology elements with BERT and TRIZ that endeavors to address these issues. First, patents collected from the Derwent database were used as data sources. Second, BERT was employed to construct a technology landscape as a vector space model where similar technology elements are classified into the same technology topic. Meanwhile, TEMPEST was employed to cluster technology topics and elements according to different functions and other dimensions. Third, technology elements were extended by function-oriented search (FOS), which is a useful method of TRIZ. It includes extracting new technology elements from newly retrieved patents about implementing a specific function in other domains. Fourth, technology opportunities were identified by recombining original and new technology elements and then verifying their feasibility. Finally, the proposed approach was employed in empirical analysis for unmanned ships and 10 technology opportunities generated through knowledge migration. The process designed in this study combines quantitative modeling and qualitative analysis, which realizes accurate search and efficient innovation among different domains.

Technology Opportunity Analysis Based on Machine Learning

The sustainable growth of a company requires a differentiated research and development strategy through the discovery of technology opportunities. However, previous studies fell short of the need for utilizing outlier keywords, based on approaches from various perspectives, to discover technology opportunities. In this study, a technology opportunity discovery method utilizing outlier keywords is proposed. First, the collected patent data are divided into several subsets, and outlier keywords are derived using the W2V and LOF. The derived keywords are clustered through the K-means algorithm. Finally, the similarity between the clusters is evaluated to determine the cluster with the most similarity as a potential technology. In this study, 5679 cases of unmanned aerial vehicle (UAV) patent data were utilized, from which three technology opportunities were derived: UAV defense technology, UAV charging station technology, and UAV measurement precision improvement technology. The proposed method will contribute to discovering differentiated technology fields in advance using technologies with semantic differences and outlier keywords, in which the meaning of words is considered through W2V application.

Research and demonstration of technology opportunity identification model based on text classification and core patents

The development opportunities of enterprises come from their customers, so it is very important to identify technology opportunities by analyzing of customer needs. This paper proposes a technology opportunity analysis model based on text classification and patent analysis, and gives a solution to find the key points of technology improvement from the perspective of customer needs. The model mainly includes three steps: the first step is to make clear the customer demand information, use text classification method to classify the technical fields, and determine the key research fields; The second step is to use the patent analysis method to find out the related patents in the key research areas, and use the TOPSIS entropy method to calculate the relative fitness of patents, to find out the core patents with the best comprehensive quality; The third step is to extract the specific technical solutions based on the core patents to provide a theoretical basis for enterprises to improve technology and improve customer satisfaction. Bearing steel is selected as the empirical research object in this study, and the research proves the validity of the technology opportunity analysis model.

Exploring Technological Solutions for Onboard Hydrogen Storage Systems Through a Heterogeneous Knowledge Network: From Current State to Future Research Opportunities

With the imminent threat of the energy crises, innovation in energy technologies is happening world-wide. The aim is to reduce our reliance on fossil fuels. Electric vehicles with fuel-cells that use hydrogen as an energy carrier are touted to be one of the most important potential replacements of the gasoline vehicle in both future transportation scenarios and emerging smart energy grids. However, hydrogen storage is a major technical barrier that lies between where we are now and the mass application of hydrogen energy. Further exploration of onboard hydrogen storage systems (OHSS) is urgently needed and, in this regard, a comprehensive technology opportunity analysis will help. Hence, with this research, we drew on scientific papers and patents related to OHSS and developed a novel methodology for investigating the past, present, and future development trends in OHSS. Specifically, we constructed a heterogeneous knowledge network using a unique multi-component structure with three core components: hydrogen carriers, hydrogen storage materials, and fuel cells. From this network, we extracted both the developed and underdeveloped technological solutions in the field and applied a well-designed evaluation system and prediction model to score the future development potential of these technological solutions. What emerged was the most promising directions of research in the short, medium, and long term. The results show that our methodology can effectively identify technology opportunities in OHSS, along with providing valuable decision support to researchers and enterprise managers associated with the development and application of OHSS.

Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease

Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease

Discovering technology opportunities based on the linkage between technology and business areas: matching patents and trademarks

ABSTRACT Technology opportunity analysis (TOA) is of great help to technology innovation and R&D strategy of enterprises. Most previous studies of TOA only focused on scientific research or technology development phases, seldom linking technology opportunities to business applications. Using both patent and trademark data, we focused on the linkage of technology and business areas and discovered technology opportunities based on firms’ existing technology base. First, we extracted common product keywords from two data sources to construct document-keyword matrices. Then, we matched patents and trademarks with common keywords to build the relevancy network between technology and the business areas. Finally, we discovered technology opportunities from potential undeveloped business areas, taking into account technology-business relevancy and firms’ technology base. We conducted a case study on 3D printing to test our method.

Technology Opportunity Analysis: Combining SAO Networks and Link Prediction

Detecting the first signs of change in one's technological surroundings is a critical factor in the success of an enterprise, and technology opportunity analysis can be a crucial process in identifying those signs. However, the common keyword-based methods of analysis do not fully express the relationships between technologies. Subject-action-object (SAO) analysis offers a solution to this problem but, currently, these methods only consider the relationships that already exist. Yet, intuitively, technology opportunities are most likely to reside in potential connections. To test this notion, in this article we conduct a case study on malignant melanoma of the skin. First, we construct an SAO network of the titles and abstracts of medical documents, then use a link prediction algorithm to identify probable future links between unconnected nodes. These possible new technology combinations are further analyzed with a backtracking algorithm to reveal the most promising technology opportunities. Further analysis of the results combined with medical knowledge confirms the effectiveness of our method.

Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies

With the advancement of science and the emergence of new technologies, technology opportunities analysis has attracted increasing attention from both society and academia. This study proposes a hybrid approach to integrate topic modeling, semantic SAO analysis, machine learning, and expert judgment, identifying technological topics and potential development opportunities. The systematical methodology is applied to analyze a set of 9,883 Derwent Innovation Index (DII) patents related to the dye-sensitized solar cell to present its potential contribution of technical intelligence for R&D management. Also, how the approach is validated and optimized is illustrated. The main contributions of this paper are two-fold. First, an optimized topic extraction model with high accuracy is constructed, considering both the patent classification codes and term location. Second, we integrate the topic modeling, SAO technique, and machine learning to explore semantic relationships among technological topics represented as a suite of terms. The methodology overcomes some drawbacks of the current studies. It can be used as a powerful tool for technological opportunities analysis.

Exploring technology opportunities and evolution of IoT-related logistics services with text mining

Many IoT technologies have been applied in the logistics industry in recent years, and they have had a substantial impact on many sectors such as shipping, air freight, warehousing, inventory, etc. Exploring technology opportunities and carrying out technological trend analysis are essential for IoT’s evolution, and there are many techniques or methods for doing so. In this paper, data analysis and text mining techniques, technology opportunity analysis (TOA) and technology-service evolution analysis (TSEA) have been applied to analyze and observe IoT technologies’ and services’ evolution. Academic journals, market reports, and patents have been collected and reviewed on the topic of IoT in the logistics field in this paper. Moreover, by using TOA, technology opportunities have been analyzed to explore IoT-related logistics services. The results of TOA, for example, show that cloud technology is essential to develop smart logistics services, and communication RFID technologies are key to developing information logistics services. Finally, TSEA enables the observation of IoT technology and logistics service evolution by combining unstructured and semi-structured data from text documents. Observing the results of TSEA, the evolution of IoT in logistics is identified, and the results of TSEA also confirm those of TOA using unstructured or semi-structured text data from documents only. The results of this paper are discussed and compared with those of some previous review studies. In summary, the results of this paper provide methodological guidelines on this topic for a comprehensive understanding of IoT-related logistics services.

Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database

Technology opportunity analysis has been the subject of many prior studies, although most of them have focused on discovering new technology ideas in a single narrow domain. This study proposes a product landscape analysis to identify product areas (i.e., potential technology opportunities) across multiple domains that firms can enter based on the technological capabilities embodied in their existing products. First, text mining is used to construct an integrated patent-product database from the United States patent and trademark database. Second, word2vec is employed to construct a product landscape as a vector space model where products with similar technological bases are located close to each other while maintaining the technological relationships. Third, given a product of interest, potential technology opportunities are identified via (1) automatic opportunity analysis that identifies product areas with technological bases similar to those of the product; and (2) interactive opportunity analysis that finds product areas based on experts’ queries modifying the technological bases of the product (i.e., addition and subtraction). Finally, ten quantitative indexes are developed to explore the implications of the potential technology opportunities identified. The case study covering 3,016,315 patents and 160,832 products confirms that the proposed approach is valuable as a creativity support tool for technology opportunity analysis in the era of convergence.

Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation

This research responds to the need for the use of quantitative data and scientific methods for technology opportunity analysis by focusing on idea generation. Interpreting innovation as a process of recombinant search, we propose a patent landscape analysis to generate ideas which are likely to have more novelty and value than others. For this, first, a patent landscape is constructed from patent classification information as a vector space model, where each position represents a configuration of technological components and corresponds to an idea and, if they exist, relevant patented inventions. Second, the novelty of ideas is assessed via the modified local outlier factor based on the distribution of existing patented inventions on the landscape. Finally, the value of ideas is estimated via naive Bayes models based on the forward citations of existing patented inventions. In addition, this study also investigates the recombinant synergies between different technological components and the relationships between novelty and value of ideas. A case study of pharmaceutical technology shows that our approach can guide organisations towards setting up effective search strategies for new technology development.

Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction

The shortening lifetime of technology requires companies to make intensive efforts to continuously explore new technology. Although many researchers have proposed visualization methods to find technology opportunities, little attention has been paid to present detailed directions of technology development with specified characteristics of technology. Thus, this research aims to suggest a systematic approach to conducting technology opportunity analysis by visualizing patent information, such as patent documents and citation relationships. First, keywords that explain core concepts, functions, and so on are extracted from collected patent documents by text mining. Second, patents are visualized in a two-dimensional space, and vacant cells are identified with their estimated keyword vectors by generative topographic mapping (GTM). Third, since many vacant cells will be potential candidates for developing new technologies, link prediction tools can choose promising vacant cells to connect existing cells with potential, but not yet existent, cells. Finally, the results of prediction are tested by comparing the predicted cells with the actual developed cells. The research reported in this paper is based in three technologies that have emerging, stable, and declining patterns, in order to illustrate the proposed approach, and investigate in which types it is relevant. It is found that the proposed approach provided a good prediction performance in the case of a technology that has a stable pattern. In addition, among link prediction methods, a semantic similarity-based approach showed better prediction results than a machine learning technique due to modest data availability for training. Thus, the results of this research can help R&D managers plan and evaluate R&D projects for technology development.

A Function-Based Knowledge Base for Technology Intelligence

The development of a practical technology intelligence system requires a knowledge base that structures the core information and its relationship distilled from large volumes of technical data. Previous studies have mainly focused on the methodological approaches for technology opportunities, while little attention has been paid to constructing a practical knowledge base. Therefore, this study proposes a procedure to construct a function-based knowledge base for technology intelligence. We define the product-function-technology relationship and subsequently present the detailed steps for the knowledge base construction. The knowledge base, which is constructed analyzing 1110582 patents between 2009 and 2013 from the United States Patent and Trademark Office database, contains the functional knowledge of products and technologies and the relationship between products and technologies. This study is the first attempt to develop a large-scale knowledge base using the concept of function and has the ability to serve as a basis not only for furthering technology opportunity analysis methods but also for developing practical technology intelligence systems.