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Technology competition of human-AI collaboration on the film and animation creation

Anna Yu Ju Yen & Maria R. Lee

International Journal of Social and Humanistic Computing2024https://doi.org/10.1504/ijshc.2024.136701article
ABDC C
Weight
0.43

What the paper says

The proposed work aims to discover the international technology competition and development of human-artificial intelligence (AI) collaboration on content creation in the film and animation industries to support the strategic planning, decision-making of R&D, and soft innovation. The study demonstrates a hybrid approach that combines technology life cycle (TLC) and latent Dirichlet allocation (LDA) topic modelling. We analyse 1,982 patents of AI collaborating on creating film and animation in the primary patent application countries (i.e., patents applied to the intellectual property offices of the USA, China, Korea, Japan, and European Patent Office, EPO) from 2010 to 2020. The TLC results show growing trends in the international technology competition. The major topic trends corresponding to TLC phases denote strong potential or future stagnation signals in different countries. The study provides the future R&D signals and suggests stimulating soft innovation with human-AI collaboration to face growing competition.

3 citations

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https://doi.org/https://doi.org/10.1504/ijshc.2024.136701

Or copy a formatted citation

@article{anna2024,
  title        = {{Technology competition of human-AI collaboration on the film and animation creation}},
  author       = {Anna Yu Ju Yen & Maria R. Lee},
  journal      = {International Journal of Social and Humanistic Computing},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1504/ijshc.2024.136701},
}

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Technology competition of human-AI collaboration on the film and animation creation

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Evidence weight

0.43

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.31 × 0.4 = 0.12
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.