The strategic importance of monitoring changes in technology has been highlighted for achieving and maintaining firms’ competitive positions. In this respect, among others, patent citation analysis has been the most frequently adopted tool. However, it is subject to some drawbacks that stem from only consideration of citing-cited information and time lags between citing and cited patents. In response, we propose a modified formal concept analysis (FCA) approach to developing dynamic patent lattice that can analyze the complex relations among patents and evolutionary patterns of technological advances. The FCA is a mathematical tool for grouping objects with shared properties based on the lattice theory. The distinct strength of FCA, vis-á-vis other methods, lies in structuring and displaying the relations among objects in the amount of data. The FCA is modified to take time periods into account for the purpose of technology monitoring. Specifically, patents are first collected and transformed into structured data. Next, the dynamic patent lattice is developed by executing a modified FCA algorithm based on patent context. Finally, quantitative indexes are defined and gauged to conduct a more detailed analysis and obtain richer information. The proposed dynamic patent lattice can be effectively employed to aid decision making in technology monitoring
