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In an era of rapidly expanding digital content, the number of e‐documents and the amount of knowledge frequently overwhelm the R&D teams and often impede intellectual property management. The purpose of this paper is to develop an automatic patent summarization method for accurate knowledge abstraction and effective R&D knowledge management.

This paper develops an integrated approach for automatic patent summary generation combining the concepts of key phrase recognition and significant information density. Significant information density is defined based on the domain‐specific key concepts/phrases, relevant phrases, title phrases, indicator phrases and topic sentences of a given patent document.

The document compression ratio and the knowledge retention ratio are used to measure both quantitative and qualitative outcomes of the new summarization methodology. Both measurements indicate the significant benefits and superior results of the method.

In order to implement the methodology with practical success, the accurate and efficient pre‐processing of identifying key concepts and relevant phrases of patent documents is required. The approach relies on a powerful text‐mining engine as the pre‐process module for key phrase extraction.

The methodology helps R&D companies consistently and automatically process, extract and summarize the core knowledge of related patent documents. This enabling technology is critical to R&D companies when they are competing to create new technologies and products for short life cycle marketplaces.

This research addresses a new perspective in R&D knowledge management, particularly in solving the knowledge‐overloading issue. The methodology helps R&D collaborative teams consistently to summarize the core knowledge of patent documents with efficiency. Efficient R&D knowledge management helps the firm to take advantage of IP positioning while avoiding patent conflict and infringement.

EXPRESS (a language used to define standard data models) is used to develop data models for products, categories, and chain stores allowing for ISO‐integrated product data management. By applying EXPRESS to the task of managing product and market information, retail data are integrated in a central database and are accessible in real time by members of the distribution channel Point of Sales (POS) systems at the store level provide the streams of data from retail stores. These data are collected and kept in a central database used for dynamic sales analysis and merchandise planning. The decision models for automated shelf layout, continuous sales analysis, and real‐time logistic management are incorporated into the marketing information system (MIS), which can be utilized by store managers through an easy‐accessed Web‐based interface. The WWW is the communicators’ medium used to link the retail headquarters with the distributed retail chain. The central object‐oriented database is based on the proposed EXPRESS data model and provides a means to manage large amounts of rapidly‐changing information. Stores that implement retail information systems of this nature can easily expand to suitable and profitable economies of scale without loss of information and control.

The purpose of this paper is to provide a clustering approach to segment supply chain partners in the automobile industry and prioritize services offered by third party logistics service (3PL) providers.

In total, 98 automobile and auto‐parts manufacturers are surveyed to identify service needs, preferences, and outsourcing commitments. By applying a two‐stage clustering approach combined with Ward's minimum‐variance method and the K‐means algorithm, the logistics companies prioritize their services to better satisfy groups of customers with specific preferences.

Four distinctive groups of manufacturers are identified using the two‐stage clustering approach. The clusters separate logistic preferences and outsourcing patterns of after market parts suppliers, original equipment service parts suppliers, original equipment manufacturer parts suppliers, and tier one car makers. The paper finds that distribution and delivery services hold the highest percentage of services outsourced among the manufacturers.

This paper models logistic services as customizable services and develops a data system methodology to define the profiles of automobile manufacturers and their preferred logistic services. Through the analysis of service preferences and clustering, the paper identifies the key logistic services that can be customized for members of the automobile supply chain. A case is provided which demonstrates how a logistics company can provide customized service designs for specific target markets and customers.

This paper aims to study the development of radio frequency identification (RFID) technology through an analysis of patents filed with and issued by the US Patent and Trademark Office. A close analysis of these clusters reveals the patent development strategies of two competing factions of RFID technology developers. This paper provides an analysis of the patents along with insights into the contents of the patents held by these two groups.

The analysis is based on Intermec Technologies and the RFID Patent Pool, the two major players in this domain. The comparison of Intermec Technologies and RFID Patent Pool is conducted using meta‐data analysis and patent content clustering. The methodology and approach includes data pre‐processing, key phrase extraction using term frequency‐inverse document frequency, ontology construction, key phrase correlation measurement, patent technology clustering and patent document clustering. Clusters are derived using the K‐means approach and a prototype Legal Knowledge Management Platform.

The findings support a strong link between intellectual property and competitive advantage – specifically Intermec Technologies, which have not joined the RFID Patent Pool. The patent search results show that Intermec Technologies hold basic RFID patents in the early stages of technology development, which has placed the company in a dominant position.

The features of each cluster clearly depict the niches and specialties of companies and provide a historical framework of RFID technology development.

The RFID patent analysis shows that if a company holds crucial patents in the early stages of a developing technology which relate to the fundamental key aspects of the technology, then the company will be more likely to maintain a leading and dominant position in that industry segment (i.e. RFID in this study).

This research uses patent content cluster analysis to explain the rationale behind an alliance strategy decision.

The purpose of this research is to develop a prototype of agent‐based intelligent workflow system for product design collaboration in a distributed network environment.

This research separates the collaborative workflow enactment mechanisms from the collaborative workflow building tools for flexible workflow management. Applying the XML/RDF (resource description framework) ontology schema, workflow logic is described in a standard representation. Lastly, a case study in collaborative system‐on‐chip (SoC) design is depicted to demonstrate the agent‐based workflow system for the design collaboration on the web.

Agent technology can overcome the difficulty of interoperability in cross‐platform, distributed environment with standard RDF data schema. Control and update of workflow functions become flexible and versatile by simply modifying agent reasoning and behaviors.

When business partners want to collaborate, how to integrate agents in different workflows becomes a critical issues.

Agent technology can facilitate design cooperation and teamwork communication in a collaborative, transparent product development environment.

This research establishes generalized flow logic RDF models and an agent‐based intelligent workflow management system, called AWfMS, based on the RDF schema of workflow definition. AWfMS minimizes barriers in the distributed design process and hence increases design cooperations among partners.

With the growing trend toward the use of international supply chain and e‐commerce, logistic service providers for product warehousing, transportation and delivery are placing great emphasis on information technology (IT) to be competitive globally. Realizing that the current service tracking system merely supports order status tracking within a service provider, applies mobile agent technology for online order tracking across the global logistic alliances. Utilizes a three‐tier architecture for mobile agent technology and develops a prototype system for global logistic service tracking. Demonstrates the concept and technology proposed. The online service tracking services enable customers to monitor the real‐time status of their service requests and therefore becomes key tool for modern enterprises to compete successfully in a global marketplace.

The Greater China region (China, Taiwan and Hong Kong) has more than 1.2 billion people, about one fifth of the world’s total population. This incredibly large market continues to modernize rapidly, and over the last five years, the region has maintained a very high economic growth rate in comparison to the rest of the world. The combination of market size and economic growth makes Greater China the most promising place in the world for Internet products and services. China, Taiwan and Hong Kong recognize the opportunities and via public and private initiatives are investing in the development of information technology (IT) and the Internet infrastructure. This paper outlines the key electronic commerce (EC) trends and events in the region. Further, the research analyzes the current impediments to Internet commerce in China, Taiwan and Hong Kong and provides strategy and directions for the region’s EC development.

Information technology and its wide range of applications have begun to make their presence in a new generation of logistic and distribution service industry. A more flexible breed of application packages is emerging by the application of fourth generation language (4GL) technologies, which are able to provide foundations for true enterprise resource planning (ERP). There are many good reasons for adopting enterprise‐wide resource planning systems. This research, however, focuses on the development of a human resource assignment module (HR module), usually considered as an essential part of an ERP system. This module provides crucial human resource data and supports decisions in human resource utilization in distribution center operations. We detail the crucial algorithm for the HR module, which provides efficient and effective manpower management for key logistic/distribution center operations.

Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.

First, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.

Experiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.

A meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.