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Extraction of TRIZ Contradictions from Patents Using LLM


Core Concepts
Using Large Language Models (LLMs) like GPT-4, TRIZ contradictions can be efficiently extracted from patent texts, offering a promising alternative to existing methods.
Abstract
The content discusses the importance of patents in innovation, the limitations of traditional keyword-based patent retrieval methods, and the emergence of advanced techniques using neural AI Transformer language models like GPT-4 for extracting TRIZ contradictions. It introduces LangChain as a framework for LLM applications and presents a method to extract contradictions from patents based on Prompt Engineering. The paper evaluates the effectiveness of GPT-4 in contradiction extraction through case studies and quantitative analysis, showcasing its potential for innovative concept extraction. Structure: Introduction to Patents and Innovation Limitations of Keyword-Based Patent Retrieval Importance of Extracting Inventive Concepts from Patents Challenges in Extracting TRIZ Contradictions Introduction to Large Language Models (LLMs) Proposed Method using GPT-4 and LangChain Case Studies: Evaluation of LLM-based Extraction Quantitative Evaluation Comparing GPT-4 with Existing Methods Conclusion and Future Work
Stats
Recent advances focus on dense vectors based on neural AI Transformer language models like Google BERT. PaGAN dataset contains 3,200 English-language patents with expert-annotated TRIZ contradictions. F1-value of 0.93 achieved by comparing text similarity between GPT-4 extractions and PaGAN annotations.
Quotes
"TRIZ remains a classic approach promising technical solutions with higher innovativeness." "LLMs offer an alternative to smaller models like BERT for inventive concept extraction." "GPT-4 shows promising abilities in summarizing technical contradictions."

Key Insights Distilled From

by Stefan Trapp... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14258.pdf
LLM-based Extraction of Contradictions from Patents

Deeper Inquiries

How can open-source LLM alternatives enhance the reproducibility of results?

Open-source Large Language Model (LLM) alternatives can significantly enhance the reproducibility of results by providing transparency and accessibility to researchers. With open-source models, the underlying architecture, training data, and fine-tuning processes are often shared openly, allowing other researchers to replicate experiments easily. This transparency fosters trust in the results obtained from these models as they can be validated and verified by others in the scientific community. Moreover, open-source LLMs enable collaborative development and improvement efforts. Researchers can contribute to enhancing the model's performance, addressing biases, or adapting it for specific use cases through community-driven initiatives like GitHub repositories or research collaborations. This collective effort ensures that advancements in LLM technology are continuously refined and updated based on feedback from a diverse range of experts. By leveraging open-source LLM alternatives, researchers have access to a wider array of tools and resources for experimentation and validation. They can modify parameters, experiment with different architectures or hyperparameters, and customize prompts more flexibly according to their specific research needs. This flexibility allows for greater control over the modeling process and enables researchers to tailor their approaches for optimal performance in extracting inventive concepts from patents.

What are the implications of commercial black box LLMs on processing big data like patents?

The utilization of commercial black box Large Language Models (LLMs) for processing big data such as patents poses several implications related to transparency, cost-effectiveness, scalability, and ethical considerations: Transparency: Commercial black box LLMs may lack transparency regarding their internal workings due to proprietary algorithms or limited documentation provided by vendors. This opacity makes it challenging for researchers to understand how decisions are made within these models when processing patent data. Cost-Effectiveness: Using commercial black box LLMs for large-scale patent analysis could incur significant costs due to licensing fees or usage charges associated with proprietary software solutions. These expenses might limit access for smaller research teams or organizations with budget constraints. Scalability: Processing vast amounts of patent text using commercial black box LLMs may face scalability issues if there are limitations on computational resources or restrictions on concurrent processing capabilities imposed by vendors. Ethical Considerations: The use of commercial black box LLMs raises concerns about privacy violations if sensitive information contained within patents is not adequately protected during analysis processes conducted by third-party providers. Dependency Risk: Relying solely on commercial solutions without alternative options could create dependency risks where organizations become reliant on external vendors' technologies without developing in-house expertise or diversifying toolsets. To mitigate these implications when working with big patent datasets using commercial black box LLMs, researchers should carefully evaluate vendor agreements regarding data privacy policies, explore cost-effective licensing options tailored to their project requirements while ensuring compliance with ethical guidelines governing intellectual property rights protection.

How might Prompt Engineering further improve the efficiency of extracting inventive concepts?

Prompt Engineering offers a powerful approach that can enhance the efficiency of extracting inventive concepts from patents using Large Language Models (LLMs) like GPT-4: 1 - Customized Prompts: By crafting well-designed prompts tailored specifically towards identifying technical contradictions within patent texts based on TRIZ principles such as problems/solutions/contradictions/advantages/actions parameters etc., Prompt Engineering guides an AI model like GPT-4 towards relevant content extraction efficiently. 2 - Few-shot Learning: Leveraging Few-shot learning techniques through prompt engineering allows users to provide minimal examples along with prompts enabling rapid adaptation & learning even without extensive finetuning which boosts productivity especially when dealing with limited annotated datasets. 3 - In-context Learning: Through In-context learning facilitated via prompt engineering methodologies wherein contextual cues guide language model responses directly rather than modifying underlying parameters enhances adaptability & accuracy particularly useful when handling complex linguistic structures found in patent documents. 4 - Task-specific Instructions: Crafting task-specific instructions embedded within prompts directs AI models towards precise actions required such as sentence classification (problems/solutions), summarization (TRIZ contradictions), parameter identification/action assignment thereby streamlining concept extraction workflows effectively. 5 - Structured Output Formatting: Defining structured output formats within prompts ensures consistent presentation styles across extracted content aiding readability & comprehension making post-processing tasks easier while maintaining uniformity throughout result sets generated by AI systems. 6 - Iterative Refinement: Continual refinement cycles involving iterative testing & modification strategies applied at prompt level allow optimization opportunities leading towards improved performance outcomes over time resulting in enhanced accuracy levels during concept extraction phases.
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