For solo patent attorneys and agents, prior art analysis is the most time-consuming bottleneck in drafting. The difference between a weak application and a strong one lies in how precisely you articulate novelty. Generic AI summaries miss the mark—they describe what a reference says, not why it fails to anticipate your client’s invention. The solution is a structured approach: teach the AI to extract specific, actionable distinctions.
Four Questions That Force Precision
To transform a generic summary into a novelty-focused analysis, your system prompt must demand answers to four targeted questions derived from proven drafting workflows:
- How does my invention’s point of novelty differ? The AI must compare the reference’s teaching against the claimed invention’s unique feature, not just paraphrase the abstract.
- What are the explicit limitations or gaps in the prior art? Identify what the reference fails to disclose—missing elements, unaddressed problems, or incomplete solutions.
- What is the core technical problem addressed by this reference? Distinguish the reference’s problem statement from your client’s problem to reveal divergent technical trajectories.
- What is the specific combination of elements that forms its solution? Map the reference’s structural or methodical combination, then contrast it with your novel arrangement.
These questions force the AI to move beyond surface-level description and into the analytical reasoning that underpins a robust novelty argument.
System Prompt Template in Action
Here is the exact system prompt template that operationalizes this approach:
“You are an expert patent analyst. For each prior art reference provided, analyze it using the following structure: (1) Identify the core technical problem the reference solves. (2) List the specific combination of elements that constitute its solution. (3) Identify explicit limitations or gaps—what does the reference not teach or suggest? (4) Compare the reference’s point of novelty with the invention described in the attached disclosure, highlighting key differences. Output in bullet-point format under each heading.”
When you feed a reference PDF or text into the AI with this prompt, the output becomes a structured, arguments-ready brief. You can copy the gaps and distinctions directly into a 112 rejection response or use them to frame the “Objects of the Invention” section in a draft application shell.
From Summary to Application Shell
Once the AI has extracted these distinctions, the next step is drafting the application shell. Use the identified gaps to define the invention’s scope: the limitations in the prior art become the problem your client’s invention solves. The combination of elements in the reference becomes the starting point for your “Background of the Invention,” and the differences become the foundation for the “Summary” and independent claims. This workflow cuts shell drafting time by 60–70% while improving the quality of your novelty positioning.
For solo practitioners, this structured AI summarization engine is not about replacing expertise—it is about amplifying it. By teaching the AI to ask the right questions, you turn every prior art reference into a building block for a stronger, more defensible application.
For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Solo Patent Attorneys/Agents: How to Automate Prior Art Search Summarization and Draft Application Shells.