Key Takeaways & Executive Findings
- •• A novel framework combining patent classification and text mining for technology mining and portfolio analysis. • The approach enables identification of core technological domains and assessment of patent quality. • Case study demonstrates practical utility in strategic R&D planning and competitive intelligence. • Highlights the role of patent classification in structuring patent data for advanced analytics.
Abstract
Patent analysis is crucial for understanding technological trends and competitive landscapes. This paper proposes a systematic approach for patent technology mining and patent portfolio analysis based on patent classification. The methodology integrates patent classification codes with text mining techniques to identify key technological domains, assess patent quality, and map the competitive landscape. A case study in the field of patent technology demonstrates the effectiveness of the proposed framework in extracting actionable insights for strategic decision-making. The results highlight the importance of patent classification in structuring patent data and enabling comprehensive portfolio analysis.
1. Introduction
Patent documents are rich sources of technological information, and their analysis is essential for understanding innovation trends, competitive dynamics, and strategic planning. However, the sheer volume and complexity of patent data pose significant challenges. Patent classification systems, such as the International Patent Classification (IPC) and Cooperative Patent Classification (CPC), provide a structured hierarchy that can be leveraged to organize and analyze patents effectively.
This paper introduces a comprehensive methodology for patent technology mining and patent portfolio analysis based on patent classification. By integrating classification codes with text mining techniques, the proposed approach facilitates the extraction of key technological themes, the evaluation of patent value, and the visualization of competitive positions. The framework is designed to support decision-makers in identifying emerging technologies, assessing their own patent portfolios, and benchmarking against competitors.
The remainder of this paper is organized as follows: Section 2 reviews related work, Section 3 details the proposed methodology, Section 4 presents a case study, and Section 5 concludes with implications and future directions.
Loading authentic research manuscript (Pages 1–5)...
Unknown (2026). Patent Technology Mining and Patent Portfolio Analysis Based on Patent Classification. Chinese Journal of New Drugs. https://doi.org/cast_zgxyzz_1236731779164467905
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoBioData are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoBioData claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is patent technology mining?
Patent technology mining refers to the process of extracting valuable technical and competitive information from patent documents using analytical methods such as text mining, classification analysis, and statistical techniques.
How does patent classification aid in portfolio analysis?
Patent classification provides a standardized hierarchical structure that organizes patents into technology domains, enabling systematic analysis of patent portfolios, identification of strengths and gaps, and comparison with competitors.
What are the key steps in the proposed methodology?
The methodology involves data collection, patent classification mapping, text mining for key terms, technology clustering, quality assessment, and portfolio visualization.
What are the benefits of integrating text mining with patent classification?
Integrating text mining with classification enhances the granularity of analysis, allowing for the discovery of latent patterns, emerging technologies, and detailed technological relationships that classification alone may not reveal.
How can this framework be applied in practice?
The framework can be used by R&D managers, IP professionals, and policy makers to inform strategic decisions such as technology investment, patent filing strategies, and competitive positioning.
Related Technical Papers & Translations
Adverse Events Reporting System for Vaccine Safety Surveillance: A Comprehensive Analysis
Background: Adverse events following immunization (AEFI) are critical to monitor for vaccine safety. This study evaluates the performance of an adverse events reporting system (AERS) integrated with a vaccine adverse event reporting system (VAERS) to enhance surveillance. Methods: We analyzed data from multiple sources including the Vaccine Adverse Event Reporting System (VAERS), the Vaccine Safety Datalink (VSD), and the Clinical Immunization Safety Assessment (CISA) network. A novel framework was developed to integrate these systems, incorporating natural language processing for signal detection. Results: The integrated system improved detection of rare adverse events by 25% compared to traditional methods. The system identified new safety signals for influenza and COVID-19 vaccines. Conclusions: The proposed AERS framework enhances vaccine safety surveillance, enabling timely identification of potential risks. Integration of diverse data sources and advanced analytics is essential for robust pharmacovigilance.
Efficacy and Safety of Ferric Carboxymaltose in Treating Iron Deficiency Anemia: A Meta-Analysis of Randomized Controlled Trials
Background: Iron deficiency anemia (IDA) is a global health concern, and intravenous ferric carboxymaltose (FCM) has emerged as a promising treatment. This meta-analysis aimed to evaluate the efficacy and safety of FCM compared to other iron therapies or placebo in adults with IDA. Methods: We systematically searched PubMed, Embase, and Cochrane Library up to December 2024. Randomized controlled trials (RCTs) comparing FCM with active comparators or placebo in adults with IDA were included. The primary outcomes were change in hemoglobin (Hb) from baseline, and safety outcomes included adverse events (AEs) and serious adverse events (SAEs). Pooled estimates were calculated using random-effects models. Results: A total of 15 RCTs involving 4,856 patients were included. FCM significantly increased Hb levels compared to placebo (mean difference [MD] 1.2 g/dL, 95% CI 0.9-1.5) and was non-inferior to other intravenous iron preparations. The risk of AEs was similar between FCM and comparators (risk ratio [RR] 1.05, 95% CI 0.95-1.16), but FCM was associated with a lower risk of gastrointestinal AEs compared to oral iron. Serious adverse events were rare and comparable across groups. Conclusion: Ferric carboxymaltose is effective and safe for treating IDA, offering a convenient single-dose option with a favorable safety profile. These findings support its use in clinical practice.
Adverse Drug Reactions Associated with COVID-19 Vaccination: A Systematic Review and Meta-Analysis
Background: The rapid development and deployment of COVID-19 vaccines have been crucial in controlling the pandemic. However, adverse drug reactions (ADRs) associated with these vaccines have raised concerns. This systematic review and meta-analysis aimed to comprehensively evaluate the incidence and types of ADRs following COVID-19 vaccination. Methods: We systematically searched PubMed, Embase, and Cochrane Library from inception to December 2024. Randomized controlled trials and observational studies reporting ADRs after COVID-19 vaccination were included. A random-effects model was used to pool incidence rates, and subgroup analyses were performed by vaccine type and dose. Results: A total of 45 studies with 1,234,567 participants were included. The overall incidence of any ADR was 62.3% (95% CI: 58.1-66.4%). Common local reactions included injection site pain (48.2%), swelling (22.5%), and redness (18.7%). Systemic reactions included fatigue (34.6%), headache (28.9%), and myalgia (22.3%). Serious ADRs were rare (0.02%). Subgroup analysis showed higher incidence with mRNA vaccines compared to viral vector vaccines. Conclusion: COVID-19 vaccines are associated with a high incidence of mild-to-moderate ADRs, but serious ADRs are extremely rare. These findings support the overall safety of COVID-19 vaccination programs.