Janghyeok Yoon

Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks

Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2) citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries.

Detecting signals of new technological opportunities using semantic patent analysis and outlier detection

In the competitive business environment, early identification of technological opportunities is crucial for technology strategy formulation and research and development planning. There exist previous studies that identify technological directions or areas from a broad view for technological opportunities, while few studies have researched a way to detect distinctive patents that can act as new technological opportunities at the individual patent level. This paper proposes a method of detecting new technological opportunities by using subject–action–object (SAO)-based semantic patent analysis and outlier detection. SAO structures are syntactically ordered sentences that can be automatically extracted by natural language processing of patent text; they explicitly show the structural relationships among technological components in a patent, and thus encode key findings of inventions and the expertise of inventors. Therefore, the proposed method allows quantification of structural dissimilarities among patents. We use outlier detection to identify unusual or distinctive patents in a given technology area; some of these outlier patents may represent new technological opportunities. The proposed method is illustrated using patents related to organic photovoltaic cells. We expect that this method can be incorporated into the research and development process for early identification of technological opportunities.

TrendPerceptor: A property-function based technology intelligence system for identifying technology trends from patents

Abstract Technology intelligence systems are vital components for planning of technology development and formulation of technology strategies. Although such systems provide computation supports for technology analysis, much effort and intervention of experts, who may be expensive or unavailable, is required in gathering processes of information for analysis. As a remedy, this paper proposes TrendPerceptor, a system that uses a property–function based approach. The proposed system assists experts (1) to identify trends in invention concepts from patents, and (2) to perform evolution trend analysis of patents for technology forecasting. For this purpose, a module of the system uses grammatical analysis of textual information to automatically extract properties and functions, which show innovation directions in a given technology. Using the identified properties and functions, a module for invention concept analysis based on network analysis and a module for evolution trend analysis based on TRIZ (Russian acronym of the Theory of Inventive Problem Solving) trends are suggested. This paper describes the architecture of a system composed of these three modules, and illustrates two case studies using the system.

SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells

This paper suggests a method for Subject–Action–Object (SAO) network analysis of patents for technology trends identification by using the concept of function. The proposed method solves the shortcoming of the keyword-based approach to identification of technology trends, i.e., that it cannot represent how technologies are used or for what purpose. The concept of function provides information on how a technology is used and how it interacts with other technologies; the keyword-based approach does not provide such information. The proposed method uses an SAO model and represents “key concept” instead of “key word”. We present a procedure that formulates an SAO network by using SAO models extracted from patent documents, and a method that applies actor network theory to analyze technology implications of the SAO network. To demonstrate the effectiveness of the SAO network this paper presents a case study of patents related to Polymer Electrolyte Membrane technology in Proton Exchange Membrane Fuel Cells.

Identifying patent infringement using SAO based semantic technological similarities

Companies should investigate possible patent infringement and cope with potential risks because patent litigation may have a tremendous financial impact. An important factor to identify the possibility of patent infringement is the technological similarity among patents, so this paper considered technological similarity as a criterion for judging the possibility of infringement. Technological similarities can be measured by transforming patent documents into abstracted forms which contain specific technological key-findings and structural relationships among technological components in the invention. Although keyword-based technological similarity has been widely adopted for patent analysis related research, it is inadequate for identifying patent infringement because a keyword vector cannot reflect specific technological key-findings and structural relationships among technological components. As a remedy, this paper exploited a subject–action–object (SAO) based semantic technological similarity. An SAO structure explicitly describes the structural relationships among technological components in the patent, and the set of SAO structures is considered to be a detailed picture of the inventor’s expertise, which is the specific key-findings in the patent. Therefore, an SAO based semantic technological similarity can identify patent infringement. Semantic similarity between SAO structures is automatically measured using SAO based semantic similarity measurement method using WordNet, and the technological relationships among patents were mapped onto a 2-dimensional space using multidimensional scaling (MDS). Furthermore, a clustering algorithm is used to automatically suggest possible patent infringement cases, allowing large sets of patents to be handled with minimal effort by human experts. The proposed method will be verified by detecting real patent infringement in prostate cancer treatment technology, and we expect this method to relieve human experts’ work in identifying patent infringement.

Invention property-function network analysis of patents: a case of silicon-based thin film solar cells

Technology analysis is a process which uses textual analysis to detect trends in technological innovation. Co-word analysis (CWA), a popular method for technology analysis, encompasses (1) defining a set of keyword or key phrase patterns which are represented in technology-dependent terms, (2) generating a network that codifies the relations between occurrences of keywords or key phrases, and (3) identifying specific trends from the network. However, defining the set of keyword or key phrase patterns heavily relies on effort of experts, who may be expensive or unavailable. Furthermore defining keyword or key phrase patterns of new or emerging technology areas may be a difficult task even for experts. To solve the limitation in CWA, this research adopts a property-function based approach. The property is a specific characteristic of a product, and is usually described using adjectives; the function is a useful action of a product, and is usually described using verbs. Properties and functions represent the innovation concepts of a system, so they show innovation directions in a given technology. The proposed methodology automatically extracts properties and functions from patents using natural language processing. Using properties and functions as nodes, and co-occurrences as links, an invention property-function network (IPFN) can be generated. Using social network analysis, the methodology analyzes technological implications of indicators in the IPFN. Therefore, without predefining keyword or key phrase patterns, the methodology assists experts to more concentrate on their knowledge services that identify trends in technological innovation from patents. The methodology is illustrated using a case study of patents related to silicon-based thin film solar cells.

An automated method for identifying TRIZ evolution trends from patents

Trend analysis of the Theory of Inventive Problem Solving (Russian acronym: TRIZ) identifies the evolutionary status of systems to seek directions for further improvement of technology by relating properties and functions obtained from patents to TRIZ trends. The property, which is a specific characteristic of a system, is usually described using adjectives; the function, which is an action that changes a feature of an object, is usually described using verbs. Methods exist to facilitate identification of TRIZ trends, but they rely heavily on human intervention to identify specific trends and trend phases. Therefore, this paper proposes a method that automates identification of TRIZ trends. The proposed method consists of (1) extracting binary relations of the adjective+noun or verb+noun forms from patents using natural language processing, (2) defining a reasons for jumps rule base that arranges trend-specific binary relations for trend identification, and (3) determining specific trends and trend phases by measuring semantic sentence similarity between the binary relations from patents and the binary relations in the rule base. The final output of the method depicts the evolutionary potential as a normalized radar plot, which can be used as input for technology forecasting based on TRIZ trends.

Ontological functional modeling of technology for reusability

Technology reuse is important in that it dramatically reduces lead-time, efforts and costs in R&D activities coping with market drivers. In this perspective, understanding technologies from a functional viewpoint helps us search reusable technologies from various technology domains and reuse them. Function is the concept to abstract intention and ways of a technology from a technological standpoint, and reusable function as the core function is representative of the technology. Functional model which formalizes function is represented as an affecting action and one or more affected objects. If technologies and their reusable functional models are stored together in a knowledge base, it is possible to develop a foundation of the environment that allows us to find out reusable technologies by function-based search without regard to technology domains. In this environment, therefore, identifying reusable functionalities from a technology and representing their meanings clearly are the basis to stimulate technology reuse. As a part of constructing the environment for technology reuse, this paper suggests an ontological functional modeling methodology of technology which is composed of a procedure for extracting reusable functionalities and a WordNet-based representation to define ontological functional models.

A state-driven modeling approach to human interactions for knowledge intensive services

Knowledge intensive service activities have become to play a fundamentally important role in various industrial fields. Human workers generally undertake complex operations relying heavily on professional knowledge in service processes to develop and deliver the knowledge intensive services. That means the ability of humans to create, disseminate, or utilize the knowledge is the dominant factor in the processes. Therefore, the processes should be managed in a human-oriented way. In order to help humans work together, a strong representation of processes should be provided to facilitate them to clearly understand who they should interact with, which resources are exchanged, and what activities need to be performed. Human Interaction Management (HIM) has been suggested to comprehensively support the human-oriented processes, but it cannot provide a way to structure and visualize the interaction works although the interaction is the most basic nature of human works. Therefore, this paper presents a state-driven approach to modeling human interactions which clearly visualizes the interactions so that human workers can be guided through it. However, it cannot be expected for human workers to follow the guidelines completely. They continuously and dynamically redefine their processes towards the way that they want throughout the life of the processes. To support the dynamic human work behavior, this paper also presents a hybrid modeling methodology that consists of the top-down specification of interaction models for guideline modeling and the bottom-up evolution of the models for flexible enactment. The suggested methodology for human interactions based on the state-driven modeling approach provides a way to effectively manage the complex interactions in a human-oriented way.

Technology Opportunity Discovery Based on Firms' Technologies and Products

Department of Industrial Engineering, Konkuk UniversityTechnology opportunity discovery (TOD) based on technological capability is a process which identifies new product and technology items that can be developed by utilizing or improving a firm’s existing products or technologies. By taking into consideration the investment risk of R&D and its practicality, developing technological capability-based TOD methodology is considered to be important for both business and research. To this end, we propose a technological capability-based TOD method and its system using TOD knowledge base. The method can support four types of TOD cases, which are based on a firm’s existing technologies and products, and TOD knowledge base is developed by using function information extracted from patent documents. In this paper, we introduce the overall framework of the method and provide application examples on the four TOD cases using the prototype system.