In the forefront of our AI research, we’ve pinpointed three pivotal roles that AI can fulfill. Each role carves out its own niche of benefits, poised to create ripples of change in the industrial landscape. Building on this foundation, our previous article’s focus has been on AI’s capacity as a virtual intern. We delved into how the AI model, in this capacity, can manage routine tasks, learn from interactions, and assist with a variety of entry-level duties. If you missed it, be sure to check out our deep dive into AI’s intern capabilities.

Today, we elevate the discussion to AI’s capabilities as an expert. Unlike interns who are in the early stages of their career journey, experts are seasoned professionals with a wealth of knowledge and experience in their field. Similarly, AI, when functioning as an expert, transcends beyond the basics to offer profound insights and advanced problem-solving skills.

AI Large Language Models (LLMs), such as the one you’re interacting with now, are not just repositories of vast information; they are sophisticated analytical engines capable of precision and recall—key metrics in information retrieval. Precision ensures the accuracy of the information provided, while recall measures the completeness of the responses generated. Together, these metrics enable AI LLMs to function as reliable experts, providing comprehensive and accurate information across various domains.

The true strength of AI LLMs lies in their ability to effectively recall the vast knowledge they were exposed to during training and apply sophisticated reasoning on top of that knowledge. This powerful combination enables AI LLMs to serve as ever-available experts across a wide range of use cases. Whether it’s answering complex queries, analyzing data, or providing insights, these language models can leverage their extensive knowledge base and reasoning capabilities to deliver valuable and reliable information.

AI Model Use-Cases: As an Expert

The capabilities of AI language models extend far beyond just recalling information. These advanced systems are being leveraged as expert tools across various domains, revolutionizing workflows and enhancing decision-making processes.

Use-Case 1: Automated Patent Proofreading

One notable application lies in the realm of patent proofreading. AI transforms this traditionally time-consuming process by employing advanced algorithms and Natural Language Processing techniques. These techniques detect a wide range of errors and inconsistencies in patent documents. Multiple AI-based tools can scan claims, specifications, and figures. They identify issues such as antecedent basis errors, incorrect dependencies, formatting problems, and inconsistencies in part numbering. This automation reduces the risk of weakened or invalidated patents due to drafting mistakes, allowing patent professionals to focus on substantive, strategic work that drives value for their clients.

Use-Case 2: AI Model as a PHOSITA (Person Having Ordinary Skill in the Art)

AI language models are proving invaluable in assisting patent attorneys in understanding the scope and nuances of patent claims, particularly when dealing with complex technical domains outside their area of expertise. Traditionally, attorneys have relied on technical experts to interpret claims, but AI simplifies this process. By providing the claim text and specifying the desired context or word count, attorneys can quickly obtain clear, concise summaries tailored to their needs.

For instance, if a patent attorney needs to understand a claim related to telecommunications but lacks proficiency in that field, they can prompt the AI. The AI can then break down the claim into simple language suitable for a non-expert. This AI-assisted approach, acting as an expert, streamlines the process of understanding complex technical concepts. It makes it more efficient and accessible for legal professionals.

Use-Case 3: Summarizing Multiple Patents

One compelling use case is the summarization and extraction of key concepts or common themes from multiple patents. Thanks to their massive context length, AI models from providers like OpenAI, Google, and Anthropic can analyze extensive data in real-time. By combining the full text or selected portions of numerous patents, and providing them to the AI with a contextual prompt, patent professionals can unlock valuable insights. This approach allows for the analysis of 10 or more patents simultaneously.

Prompts such as “Summarize common key concepts across the provided patents with a focus on V2X technology” or “Derive overall trends and problems solved in these patents related to CRISPR” can enable AI model to distill complex information into concise, actionable summaries. In this scenario, the AI operates independently. And, the professional only needs to provide the initial prompt, allowing the AI to handle the heavy lifting.

Use-Case 4: AI-assisted Video Explanation

Another powerful application involves the use of multimodal AI model for analyzing multiple data types, including text, images, and videos. For instance, the Google Gemini 1.5 Pro model supports multimodal analysis. This enables patent professionals to extract key concepts from videos quickly and efficiently.

By simply uploading a video to the AI and providing a contextual prompt, professionals can leverage the AI’s capabilities. This allows them to process complex multimedia content and generate concise, insightful summaries. Here, too, the AI handles the bulk of the work, freeing up professionals to focus on higher-level tasks.

As AI language models continue to advance, their ability to serve as expert assistants becomes evident. They are capable of understanding and distilling complex information, which is increasingly valuable across various industries, including the patent domain.

Precautions while Leveraging AI Model as an Expert

While the potential of AI language models as expert assistants is undeniable, it’s crucial to exercise caution and maintain a critical mindset when relying on their outputs. As with any powerful tool, there are important considerations to keep in mind.

1. High Dependency on Initial Input to the AI Model:
   The quality of an AI’s output is heavily dependent on the prompt or instructions provided by the user. Even slight variations in the initial prompt can lead to significant differences in the AI’s response. As such, patent professionals must develop a strong intuition for crafting prompts that effectively capture their intent and desired context. This skill can only be honed through extensive practice and experience working with AI models.
   
2. Hallucinations of the AI Model:
   Despite the remarkable advancements in AI language models, such as GPT-4 and Claude Opus 3, which have surpassed multiple quality benchmarks, the issue of hallucinations remains a persistent challenge. Hallucinations refer to instances where the AI generates plausible-sounding but factually incorrect or inconsistent information.
   These hallucinations can manifest in various forms, and significant research efforts are underway to mitigate this problem. However, in the current landscape of generative AI, hallucinations are still a reality that users must be mindful of. As such, it is imperative to verify the AI’s outputs rigorously and avoid blindly accepting them at face value.

By acknowledging these precautions and developing a nuanced understanding of AI’s capabilities and limitations, patent professionals can harness the power of these cutting-edge language models while mitigating potential risks and ensuring the highest standards of accuracy and reliability.

Final Thoughts

Characterizing AI language models as experts is justified by their remarkable ability to analyze vast amounts of data and extract insights. AI’s role as an expert is distinct from scenarios where it functions as an intern. As an expert, the patent professional initiates the AI workflow by providing instructions and input data. The input data can include patents, research papers, or videos.

The AI, then, operates independently, leveraging its knowledge to process the information and generate tailored outputs. This allows professionals to focus on complex Intellectual Property challenges while offloading tasks well-suited to AI’s capabilities. As AI continues evolving, its potential as an expert assistant will grow, enabling streamlined workflows and deeper insights.

However, a balanced approach is crucial, acknowledging AI’s strengths while remaining vigilant about limitations like hallucinations. Ultimately, AI should augment human expertise, not replace it, fostering a symbiotic relationship that drives innovation and progress in the patent industry.

Explore the alliance of AI and IP – Tap into our innovative AI Patent Solutions, formulated to support your IP requirements. Work alongside our AI experts to establish a dynamic, evolving strategy for your firm, to maintain a vanguard position in the innovation race. Additionally, keep an eye out for our forthcoming article that will explore AI’s role as a collaborative peer.

By: Dhananjay K. Das (ICT Business Development), Mayur Dhingra (COO Office) & Mitthatmeer Kaur (Content Creation & Strategy

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