# A Novel Method for Technology Roadmapping: Nanorobots
## 1 Introduction
Traditional technology roadmap development methods have favored Delphi surveys, relying on expert knowledge to identify technological, industrial, and product themes. They often encounter difficulties in analyzing the semantic content within technological texts. Some scholars have sought to enhance expert interactions with objective data, but they have often used a unidimensional data analysis to track technological evolution trends. Technological development is diverse, and the semantic information in multi-source text data contains more hidden information that is challenging to identify, such as how technology drives product development and how the market propels technological advancements. Analyzing the evolution of themes from a multi-dimensional perspective can provide a more comprehensive understanding of the direction of technological development in a field and enhance the quality of technology roadmaps. In the context of a multi-dimensional perspective topic analysis, the selection of an appropriate model is critical due to the need to handle diverse datasets and facilitate a detailed topic extraction and analysis. ALBERT, short for A Lite BERT and developed by Google in 2019, represents an advanced version of BERT that mitigates issues related to a large parameter size and a high computational demand. ALBERT introduces cross-layer parameter sharing and factorized embedding parameterization, which significantly reduce the model's parameter count, thereby enabling the processing of larger datasets within constrained computational limits [14]. These modifications not only enhance the efficiency of the model, but they also improve its regularization, leading to better generalization across tasks. This is particularly advantageous for analyzing multi-topic texts, especially in applications involving diverse and cross-domain textual datasets [15]. When compared to other models, such as the RoBERTa, DistilBERT, and GPT models, the ALBERT model stands out for its lightweight architecture and superior performance in the granularity of topic analyses. Consequently, the ALBERT model is selected for its robust performance in the analysis of multi-thematic texts. Regulatory, ethical, and safety issues associated with deploying nanorobots and technology roadmapping should be considered thoroughly in practice in the first place. This paper focuses on the technological developments of nanorobots using a novel technology roadmapping method. It introduces an innovative framework that utilizes the ALBERT method to refine and interpret nanorobot technology themes, thereby enhancing the understanding and strategic planning within this domain [16]. It employs text information mining and presents a nanorobot technology roadmap interrelation analysis framework based on the ALBERT method. This approach improves traditional technology roadmap construction methods in two ways: firstly, this paper utilizes automated phrase mining to refine key themes, substantially enhancing the depth of information extraction and the interpretability of nanorobot information, and thereby improving the efficiency and accuracy of technology roadmap analyses. Secondly, this paper examines interrelations in the nanorobot technology roadmap from multiple text information sources and depicts them more effectively using the ThemeRiver map method. As a result, this approach highlights the current development focus of nanorobot technology and predicts future technological application prospects.