Technology Opportunity Analysis Based on Deep Learning and Explainable Artificial Intelligence Model

This study introduces an integrated framework combining deep learning and explainable artificial intelligence (XAI) for systematic technology opportunity discovery. Technology Opportunity Analysis (TOA) is operationalized as a data-driven process that identifies emerging technological themes through multi-modal analysis of scientific artifacts. Our methodology employs lithium-ion battery patents filed over the past three years as empirical evidence, with Derwent patent titles being processed through Biterm Topic Modeling (BTM) to address short-text analytical challenges while optimizing input dimensions for subsequent classification tasks. A hybrid architecture incorporating four deep learning classifiers demonstrates patent categorization effectiveness, from which Shapley Additive Explanations (SHAP) analysis reveals critical decision-driving features—specifically those technology themes statistically significant for patent authorization outcomes. Empirical validation confirms the framework's capability in uncovering actionable technological opportunities within the lithium-ion battery sector.

• Publication year

  2025

• Venue

  Proceedings of the 2025 9th International Conference on Deep Learning Technologies

• Publication date

  2025-07-16

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1145/3760658.3760662
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models

  2025cited by this paper
• Identifying Promising Technologies Considering Technology Convergence: A Patent-Based Machine-Learning Approach

  2024cited by this paper
• Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis

  2023cited by this paper
• Combining machine learning with a pharmaceutical technology roadmap to analyze technological innovation opportunities

  2023cited by this paper
• Identifying technology opportunity using dual-attention model and technology-market concordance matrix

  2023cited by this paper
• An Efficient CSPK-FCM Explainable Artificial Intelligence Model on COVID-19 Data to Predict the Emotion Using Topic Modeling

  2023cited by this paper
• Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks

  2020cited by this paper
• On the Tractability of SHAP Explanations

  2020cited by this paper
• A novelty detection patent mining approach for analyzing technological opportunities

  2019cited by this paper
• Recommending untapped M&A opportunities: A combined approach using principal component analysis and collaborative filtering

  2019cited by this paper
• Advances and opportunities in machine learning for process data analytics

  2019influential reference
• Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications

  2019cited by this paper
• Seeded-BTM: Enabling Biterm Topic Model with Seeds for Product Aspect Mining

  2019cited by this paper
• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

  2019cited by this paper
• Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction

  2018cited by this paper
• Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network

  2018cited by this paper
• Discovering new technology opportunities based on patents: Text-mining and F-term analysis

  2017cited by this paper
• “Why Should I Trust You?”: Explaining the Predictions of Any Classifier

  2016cited by this paper
• A novel method of IP R&D using patent analysis and expert survey

  2015cited by this paper
• Data Science and its Relationship to Big Data and Data-Driven Decision Making

  2013cited by this paper
• A biterm topic model for short texts

  2013cited by this paper
• Empirical study of topic modeling in Twitter

  2010cited by this paper
• Latent Dirichlet Allocation

  2009cited by this paper
• THE EFFECTS OF INFORMATION TECHNOLOGY ON STRATEGIC DECISION MAKING

  1995cited by this paper

• No citing papers are available for this paper.