ARTIFICIAL INTELLIGENCE RESEARCH
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.
Executive Impact
Our cutting-edge framework provides a robust, data-driven approach to identify and capitalize on emerging technological opportunities, ensuring strategic advantage in dynamic markets.
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Patents Analyzed
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Optimal Model Accuracy
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Key Opportunity Topics Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Leveraging AI for Strategic Advantage
This research demonstrates how a synergistic combination of deep learning and explainable AI (XAI) can revolutionize Technology Opportunity Analysis (TOA). By moving beyond traditional expert-driven methods, AI enables the systematic identification of emerging technological themes from vast datasets like patent documents. The integration of advanced NLP, such as Biterm Topic Modeling, with robust deep learning classifiers (DNN, CNN, RNN, LSTM) provides unparalleled accuracy in categorizing patent authorization outcomes. Furthermore, XAI, specifically SHAP analysis, offers critical interpretability, allowing enterprises to understand which specific technology themes drive innovation success, thereby transforming predictive models into actionable strategic intelligence.
70%
Accuracy Rate of Optimal Model (DNN) in Patent Categorization, Highlighting Predictive Efficacy for Opportunity Discovery
Enterprise Process Flow
Patent database
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Text info (title, abstract)
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Synonym merging
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Tech topics extraction (BTM)
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Sampling optimization (SMOTE)
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Deep learning classification (DNN, CNN, RNN, LSTM)
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Trained model (best performance)
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SHAP analysis
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Key features (tech opportunities)
| Metric | DNN | CNN | RNN | LSTM |
|---|---|---|---|---|
| Accuracy | 0.70 | 0.61 | 0.63 | 0.61 |
| Precision | 0.93 | 0.94 | 0.93 | 0.94 |
| Recall | 0.72 | 0.62 | 0.63 | 0.61 |
| F1 | 0.81 | 0.74 | 0.75 | 0.74 |
| AUC | 0.63 | 0.63 | 0.60 | 0.62 |
Lithium-Ion Battery Sector Innovation
The framework was empirically validated in the lithium-ion battery sector, a rapidly evolving domain. Analysis of 10,579 patent records (2021-2023) identified several high-potential technological opportunities, including:
- Silicon-coated porous carbon composite negative electrode preparation.
- Ceramic-coated composite polymer separator for battery surface enhancement.
- Graphene-oxide-coated silicon-carbon composite negative electrode preparation.
- Polymer-based solid electrolytes containing organic additives.
Estimate Your AI-Driven Innovation ROI
Quantify the potential time and cost savings by automating technology opportunity analysis within your R&D pipeline.
Annual Savings
$0
Hours Reclaimed Annually
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Your AI Implementation Roadmap
A clear, phased approach to integrating advanced AI for technology opportunity analysis within your organization.
Phase 1: Data Integration & Preprocessing
Establish data pipelines for patent databases, perform lexical enrichment, synonym merging, and prepare data for topic modeling.
Phase 2: Biterm Topic Modeling & Feature Engineering
Apply BTM to extract latent technological themes from short texts, optimizing input dimensions and generating discriminative feature sets.
Phase 3: Deep Learning Model Development & Validation
Train and validate hybrid deep learning classifiers (DNN, CNN, RNN, LSTM) for patent categorization, ensuring robust predictive performance.
Phase 4: Explainable AI & Opportunity Identification
Integrate SHAP analysis to interpret model predictions, identify critical features, and translate statistically significant technology themes into actionable opportunities.
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