Identifying Technology Opportunities Research Articles

From technology opportunities to solutions generation via patent analysis: Application of machine learning-based link prediction

Technology convergence represents a significant mode of technological innovation that is widely prevalent across various industries. This innovative approach integrates multiple technologies to develop new integrated solutions, thereby fostering a competitive advantage for enterprises. Anticipating future potential technology convergence is of paramount importance for businesses. However, previous research has predominantly relied on the topological information of convergence networks, overlooking the nodal attributes and inter-nodal relationships that have an impact on the emergence of technology convergence. To enhance existing studies, this paper employs three types of features: node attributes and inter-node relationships based on the drivers of technology convergence, along with link prediction similarity indices. Additionally, we utilize Graph Convolutional Neural Network (GCN) for node embedding to leverage node attributes. Machine learning models are utilized for link prediction based on these features to identify potential technology opportunities. To guide research and development (R&D) efforts, we recommend high-value patents for each node using entropy weighting across five metrics that objectively quantify patent value, and transform patent abstracts into vectors using Doc2Vec. Patents with high similarity in abstract text between nodes are utilized to extract technical solutions and fuse ideas for technology convergence. A case study is conducted within the autonomous driving industry, leveraging comprehensive information including node attributes, inter-node relationships, and topology-based similarities to identify technology opportunities and guide the generation of R&D ideas through the convergence of technical solutions.

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.

Identifying firm-specific technology opportunities in a supply chain: Link prediction analysis in multilayer networks

Although technology opportunity discovery (TOD) for a target firm has considered the firm’s internal characteristics such as technological capabilities and R&D directions, the firm’s external environment, especially the supply chains it is in, is generally ignored. This paper proposes a novel approach to identifying firm-specific technology opportunities in each echelon of a multi-echelon supply chain, with the interactions among the technologies of different echelons considered. The proposed approach comprises four steps: 1) dividing the supply chain echelons and collecting patents for each echelon; 2) constructing a firm-centered International Patent Classification (IPC) multilayer network of the multi-echelon supply chain; 3) recommending IPC codes in each echelon of the supply chain for the target firm using a link prediction algorithm; and 4) evaluating technology opportunities to support the firm’s R&D decisions with the indicators of technology importance, technology maturity, and technology impact. Using the General Motors Company (GM) in the fuel cell vehicle (FCV) supply chain as a case study, it is verified that the method can identify technology opportunities for GM efficiently and effectively. This work proposes an innovative method that identifies firm-specific technology opportunities from a supply chain perspective, and pioneers to apply multilayer networks to TOD.

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.

A patent-based approach to identifying potential technology opportunities realizable from a firm’s internal capabilities

Technology opportunity identification leads to technological advances with future-oriented ideas by predicting the direction of change in technological trends. Many researchers have proposed several processes for identifying technology opportunities using various types of data sources. Among them, patents have been widely used as a dominant source in that they can serve as a reliable source of knowledge. Previous studies have built patent maps and explored outliers or vacant spaces on the map to discover new technology opportunities. They also have developed a morphological matrix and modified the shapes of its dimensions to produce new ideas. However, they have dealt with measuring generic potential values without taking account of a firm’s internal capabilities. To secure meaningful opportunities, it is necessary to examine how well the opportunities identified are suitable for the internal capabilities. Moreover, they have focused on the technology opportunities at the present time. Technology opportunities, however, have value in the future, not the present. Motivated to address these limitations, this study proposes a novel approach that facilitates a firm to identify potential technology opportunities realizable from its internal capabilities. This study is expected to improve R&D planning capabilities and contribute to the establishment of a next-generation R&D planning system.

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.

An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks

Technology opportunity discovery (TOD) based on firm's technology portfolio is categorized into text mining-based and patent classification-based approaches. Despite their apparent benefits, the former has reproducibility issues due to the experts’ subjectivity, whereas the latter lacks consideration of technical attributes that constitute individual technologies. The F-term, a multi-dimensional subject indexing system, provides patent classification codes representing technical attributes and structures that vary according to subject technology. The present study proposes a TOD model employing a link prediction analysis of F-terms. The proposed model based on F-terms comprises four steps: 1) constructing a universal F-term network using F-term co-occurrences; 2) generating a firm-centered F-term network highlighting a target firm's technology portfolio; 3) applying a proposed link prediction index to identify opportunity F-terms; and 4) assessing these opportunities in terms of technical attributes using a visual map with technology impact and heterogeneity indices. A case study is conducted on a Japanese firm to demonstrate the function and validity of this model, which aims to assist firms to identify technology opportunities with high practicality considering both their technology portfolios and the entire technology ecology. Moreover, this study represents a contribution to an early attempt to apply large-scale F-terms to the quantitative TOD.

Identifying Technology Opportunities for Electric Motors of Railway Vehicles with Patent Analysis

An electric motor is a device that changes electrical energy into mechanical energy for railway vehicles. When developing the electric motor, it used to be developed simply for structures or control methods of the motor itself without considering convergence with other devices or technologies. However, as the railway vehicles become more advanced, technology development through convergence with other devices or technologies is spreading. Therefore, based on patent data related to the electric motors applied to the railway vehicles, this research aims to carry out technical forecasting for establishing research and development (R and D) direction for new technologies by predicting vacant technologies from the point of view of technology convergence. In other words, we studied how to find the vacant technologies in a field of convergence technology for the electric motor of the railway vehicles by analyzing the patent data. More specifically, we search the patents data associated with the electric motor of the railway vehicle that contain multiple IPC codes, and use multiple IPC codes to determine the field of convergence technology. In addition, we extract keywords from the patents data related to each of the determined convergence technologies and define the vacant technologies by interpreting the field of convergence technology and the extracted keywords.

From stones to jewellery: Investigating technology opportunities from expired patents

This study focuses on the potential of expired patents, that is, patents freely available in the public domain, to become new technology opportunities. These technologies can create a new value, if they are well utilized and combined with other technologies, or go through further commercialization processes. Furthermore, such value can be obtained effectively, given their initial usability, proven by a patent system and high accessibility. Despite the numerous studies aimed at seeking technological opportunities from patents, however, little attention in academia has been given to those expired patents as a source of such opportunities; accordingly, the distinguishing features of expired patents as opportunities have been largely overlooked in the existing studies. To fill this research gap, we propose a novel approach to identify technology opportunities using the expired patents, where two types of opportunities are investigated: 1) opportunities within the target technological field named as promising technology; and 2) opportunities from other technological fields named as convergence technology. After key technological topics, either in the target field or in the reference field, are identified using LDA topic modelling, a target topic of interest is chosen, and its corresponding patents are evaluated from their patent quality and risk of implementation. Finally, the patents with high patent quality and low risk of implementation are identified as technology opportunities. The proposed approach is applied to biocosmetics products, and the research findings indicate that the expired patents can have as much importance as the valid patents as sources of new technology opportunities. Hence, by guiding the use of expired patents, the proposed approach is expected to reduce the costs and risks of adopting those patents for a firm's own business.

Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach

Technology opportunity discovery (TOD) is an important technique to help fuel cell vehicle (FCV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative TOD methods to effectively identify technology opportunities for FCV firms. This study proposes a structured TOD framework with a multi-level identification process. Based on technology portfolio analysis, it fully integrates the firm’s technology level analysis, technology potential analysis and patent novelty analysis. A series of techniques such as LDA (latent Dirichlet allocation), MDS (multidimensional scaling) and LOF (local outlier factor) are also applied in the framework. A total of 14,858 granted patent data of the FCV industry containing 798 patents of the target firm were extracted from the Derwent Innovation Index database as the input data of the empirical study. The result shows that the framework can provide a more profound analysis for identifying technology opportunities, which offer more appropriate insights in both strategic and operational level technological decisions for technology-oriented firms.

Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications

Technology opportunity discovery (TOD), have evolved over time from technology forecasting to an approach based on existing technology capabilities for increased practicality. Unfortunately, TOD studies are still lacking, in that they do not consider the unique direction of the target firm in technology development or the recent trends of technological fields. Consequently, this paper proposes an improved methodology for identifying technology opportunities with less uncertainty by focusing on the target firm's dynamic change of focus technology fields. The proposed approach is as follows: 1) generate a sequence database, containing firms' dynamic change of focus technology fields; 2) explore the frequent sequential patterns from a precedence enterprise (PE) sequence database using the PrefixSpan algorithm to identify the technology candidates from a PE sequence similar with that of the target firm; and 3) evaluate the candidates on technological similarity, business stability, and recency. The results of the proposed approach are expected to help firms discover appropriate technology opportunities by considering both their existing technological capacities and the dynamic change of their focus technology fields. Furthermore, the proposed approach can identify the most appropriate technology opportunities with less uncertainty in real-life business environments by evaluating technological similarity, business stability, and recency.

Combining SAO semantic analysis and morphology analysis to identify technology opportunities

Increasingly complex competitive environments drive corporations in almost all industries to conduct omnibearing innovation activities to enhance their technological innovation capability and international competitiveness. Against this background, we propose subject---action---object (SAO) based morphological analysis to identify technology opportunities by detecting prioritized combinations within the morphology matrix. SAO structures emphasize the key concepts with provision of diverse technology information based on semantic relationships. The combination of SAO semantic structures can support the establishment of matrix, which consists of two dimensions: compositions and properties of technology. Later, novel indicators are used to evaluate the subsequent technological feasibility of each new configuration under a customized analysis and prior combinations aided by a high score can be identified. We apply this method to the case of dye-sensitized solar cells (DSSCs) in patents documents. The approach holds promise to strengthen information support systems for commercial enterprises in technical innovation and market innovation activities. We believe the analysis can be adapted well to fit other technologies, especially in their emerging stage.

Identifying Technology Opportunities with Anomaly Detection Technique

Identifying Technology Opportunities with Anomaly Detection Technique

An empirical analysis on purposes, drivers and activities of technology opportunity discovery: the case of Korean SMEs in the manufacturing sector

Technology opportunity discovery (TOD) is becoming increasingly important for identifying technology opportunities so that they can be reflected in firms' strategy planning. However, understanding of TOD practices within firms is limited, and the diversity of TOD practices has not been fully reflected in previous studies, especially from the perspective of small and medium enterprises (SMEs), which typically lack diverse technology portfolios or plentiful innovation resources, so technology opportunities have seldom been defined and analyzed. This study aims to investigate SMEs' TOD practices and to identify their characteristics. Interviews were conducted with Korean SME executives to define a conceptual TOD model for SMEs, and 104 Korean SMEs' TOD drivers and activities were surveyed focusing business drivers, business models, and information gathering. The results were then analyzed to define six technology opportunity types and to derive similarities and differences among them, which are used to understand and further support SMEs' TOD practices. The research findings are expected to enhance understanding about TOD practices and to help establish SMEs' TOD strategies.

Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis

Small and medium enterprises (SMEs) have difficulties identifying appropriate technology opportunities under severe capability and resource constraints. To tackle this issue, we suggest a method for identifying technology opportunities that is customized to the existing technologies and technological capabilities of SMEs through two-stage patent analysis. An expert-based technological attribute---application table makes it possible to identify basic opportunities by multiple keyword matching. Also, non-traditional opportunities can be explored and identified by an iterative action---object analysis of patents. This two-stage patent analysis approach provides managers with a way of identifying specific technology opportunities in which their existing technologies can be utilized to the maximum extent, therefore helping them to develop technology strategies.

On the development of a technology intelligence tool for identifying technology opportunity

Technology intelligence tools have come to be regarded as vital components in planning for technology development and formulating technology strategies. However, most such tools currently focus on providing graphical frameworks and databases to support the process of technology analysis. Techpioneer, the proposed tool in this paper, aims to offer decisive information in order to identify technology opportunities. To this end, the system uses textual information from technological document databases and applies morphology analysis to derive promising alternatives and conjoint analysis to evaluate their priority. In addition, the method used in developing a technology dictionary is presented, employing clustering and network analysis. This system also has the ability to communicate with experts in order to estimate the value of existing patents, which is inevitable for the priority-setting of alternatives, construct a morphological matrix and so on. This paper presents the system architecture and functions of this tool and moreover, illustrates the prototype implementation and case study of the same.

A systematic approach for identifying technology opportunities: Keyword-based morphology analysis

Morphology analysis (MA), a representative qualitative technique in technology forecasting (TF), has been utilized to identify technology opportunities. However, conventional MA is subject to limitations in that there is no scientific or systematic way in establishing the morphology of technology, and it is difficult to prioritize the alternatives. As a remedy, we propose a keyword-based MA that is supported by a systematic procedure and quantitative data for concluding the morphology of technology. To this end, a technology dictionary is developed by factor analysis for keywords that are extracted from patent documents through text mining. Then, the morphology of patents is identified based on the technology dictionary. By listing the occupied configurations of collected patents, the unoccupied territory of configurations are suggested as technology opportunities. Moreover, the priority of alternatives is concluded, and similar and substitutive technologies can be analyzed for the purpose of extending morphology structure. Technical and managerial strategy for in-house R&D or cross-licensing can also be supported by examining the morphology portfolio of technologies. A thin film transistor–liquid crystal display (TFT-LCD) case is exemplified to illustrate the detailed procedure of this brand-new MA.