AI-POWERED INSIGHTS
Unlock Deeper Insights into Public R&D Value with AI
This study introduces a novel framework for evaluating the social value of public R&D outputs using advanced natural language processing (NLP) and deep learning. Focusing on Artificial Intelligence (AI) R&D in South Korea, the framework integrates patent data and external news sources to classify public value across six categories: industrial advancements, safe society, sustainable environment, job creation, human health, and convenience of life. Leveraging GPT-3.5 for text parsing and classification, the study demonstrates a data-driven approach to assess the societal impacts of R&D programs, offering richer insights for policymakers beyond traditional quantitative metrics. The findings show varying public value orientations across different R&D programs, emphasizing the importance of a nuanced evaluation system.
Key Impact Metrics
A snapshot of the core data driving our analysis.
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R&D Budget Reduction (South Korea, 2023)
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AI-related Patents Analyzed
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Text Paragraphs from Patents
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News Opinion Columns Analyzed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Industrial Advancements
Research Framework
Public Value Categories (AI Technology)
Impact of GPT on Public Value Extraction
96%
of AI-related patent texts classified as Industrial Advancement
Enterprise Process Flow
Data Collection (Public R&D Outputs & External Sources)
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PV Extraction (NLP & LLM Classifier)
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R&D Policy Evaluation (PVs from Output to Program)
Comparison of public value categories for AI technology derived from National and international R&D planning and evaluation documents.
| Public Values | AI Technology Assessment (Europe) | AI Technology Assessment (South Korea) | Basic Plan for Science and Technology (South Korea) |
|---|---|---|---|
| Industrial Advancements | Advancement of AI technology, development of high-performance and real-time AI models, healthy ecosystem of firms | Industrial development, productivity and quality improvement, and service industry-added value creation via manufacturing intelligence and service industry intelligence | Fostering new industries and responding to industrial changes |
| Safe Society | Hazardous work replacement, privacy protection, protection of other people's rights, social protection, equality | Disaster/crisis safety management and response system, national defense and security | |
| Sustainable Environment | Leading carbon neutrality, sustainable environment, responding to environmental issues | ||
| Job Creation | Transferable skills, new career roles | Creating new forms of employment and new occupations | Creating science and technology-based jobs |
| Human Health | AI-based healthcare applications | Development of welfare services | Improving the medical/welfare system, preventive care |
| Convenience of Life | Efficiency or convenience, human wellbeing | Improving the convenience and efficiency of life, improving access to knowledge, and increasing convenience | Convenient life (smart city, ICT-based convergence technology services, etc.) |
GPT's Role in Enhancing R&D Evaluation
The study highlights that GPT-3.5-turbo was utilized for public value extraction, demonstrating high practical performance in text classification and semantic understanding. This approach efficiently handles costly manual data labeling and extracts public value information from key sentences in patents. The use of GPT not only advances the field of public R&D analysis but also provides a flexible framework that can be adapted to various domains, including healthcare, environmental policy, and defense. This significantly improves the accuracy and scalability of R&D evaluation systems.
Calculate Your Potential AI-driven ROI
Estimate the efficiency gains and cost savings your enterprise could achieve with an AI-driven R&D evaluation system.
Estimated Annual Savings
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Estimated Annual Hours Reclaimed
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Your AI Transformation Roadmap
A phased approach to integrating AI for public value assessment in R&D.
Phase 1: Data Integration & Baseline Assessment
Consolidate R&D output data (patents, publications) with external sources (news, reports). Establish baseline public value metrics.
Phase 2: NLP & LLM Model Deployment
Implement the GPT-based framework for automated public value extraction and classification.
Phase 3: Impact Analysis & Policy Recommendation
Analyze extracted public values at program level and formulate evidence-based policy recommendations.
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