Artificial neural network approach for technology life cycle construction on patent data

Abstract

Technology development and its future trends are essential for decision making consideration to decide on the appropriate technology investment. Constructing Technology Life Cycle (TLC) allows stakeholders to identify technology trends in the future. Therefore, the forecasting approaches play a key role to obtain right decision making since there is need forecasting result with high accuracy and less computational time required. This study aims to construct TLC approach based on artificial intelligence to overcome these problems. This approach was developed through Artificial Neural Network (ANN) that has intelligent capability to define the technology performance towards patent analysis for forecasting and fitting the TLC phases. This study also employed the analysis of numerical gradient to strengthen the forecasting result. The potential of the proposed approach has been demonstrated by cordless telephone patent data as the worldwide tools to obtain up-to-date technology information. The findings can contribute to a better understanding of constructing TLC through intelligence approach that supported by numerical gradient analysis. The implication of this study is possibility that the model can be applied to the diverse technologies for facilitating to make proper technology investment decision.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.