Mastering §103 Obviousness with AI

§103 obviousness can be difficult and time-consuming. Specifically, analyzing how multiple references can be combined to arrive at the claimed invention. This step often determines whether an argument is persuasive or falls apart under scrutiny.

Traditionally, building a defensible motivation to combine (MTC) arguments can require hours of manual analysis, iteration, and legal reasoning. Practitioners must test different combinations, evaluate compatibility between references, and construct a meaningful legal narrative.

AI is now transforming this process.

Patlytics introduces a structured, AI-driven approach to §103 analysis, enabling teams to generate defensible, citation-backed motivation to combine arguments in minutes.

Why §103 Is the Hardest Part of Invalidity Analysis

Unlike §102 novelty, §103 requires a more nuanced argument.

Practitioners must demonstrate:

• Why multiple references would be combined
• How those references complement one another
• That the references do not “teach away” from each other

This process is inherently iterative and often involves testing multiple combinations before identifying the strongest argument.

As a result, §103 analysis can be one of the most time-intensive aspects of patent litigation and post-grant proceedings.

Automating Combination Strategy with AI

A key bottleneck in §103 analysis is identifying the right combination of references.

This has generally required manual trial and error: pairing references, testing mappings, and evaluating whether the combination supports the claim.

Patlytics accelerates this process with its “Suggest Combination” feature.

The system:

• Reviews all discovered prior art references
• Evaluates possible combinations (one primary + up to three secondary references)
• Scores each combination based on how well it supports the claim

The highest-scoring combinations are surfaced visually, allowing practitioners to quickly identify the strongest candidates for further analysis.

This replaces hours of manual experimentation with a quick starting point for §103 strategy.

Generating Motivation to Combine

Identifying a strong combination is just the first step.

The next task is building a coherent legal narrative explaining why the combination makes sense.

Patlytics automates this through its Motivation to Combine (MTC) analysis.

Once a combination is selected, the platform:

• Analyzes how the references relate to one another
• Evaluates their complementary technical teachings
• Generates a high-level explanation of why a POSITA would combine them

This high-level reasoning is then translated into limitation-by-limitation motivations, embedded directly within the claim chart.

The result is a fully structured argument that connects:

• Claim limitations
• Prior art disclosures
• Legal reasoning under §103

Grounded in Legal Frameworks

A possible concern with AI-generated content is whether it reflects actual legal standards.

Patlytics addresses this by grounding its MTC analysis in established obviousness doctrine, including principles derived from KSR v. Teleflex.

The system evaluates combinations using a structured framework that considers:

• The problem test: whether the references address similar technical problems
• The field test: whether the references are within the same or analogous fields
• Teaching away: whether references discourage the proposed combination

By incorporating these factors, the platform ensures that generated arguments are not just technically plausible but legally relevant.

Iterating on §103 Strategy in Real Time

Obviousness arguments are rarely finalized on the first attempt.

Practitioners often need to:

• Test alternative combinations
• Adjust claim interpretations
• Refine motivations based on new insights

Patlytics enables this through rapid iteration within the same workspace.

Users can:

• Compare multiple combinations side by side
• Regenerate motivation to combine analyses
• Refine arguments without restarting the workflow

This allows teams to move from static analysis to dynamic, strategy-driven iteration.

Reducing Time While Improving Defensibility

The traditional §103 workflow can be both time-intensive and prone to inconsistency.

By automating:

• Combination selection
• Claim mapping
• Motivation to combine generation
• Legal framework evaluation

Patlytics reduces the mechanical burden of obviousness analysis while improving consistency.

This enables practitioners to focus on:

• Refining legal strategy
• Stress-testing arguments
• Preparing litigation-ready work product

rather than assembling arguments from scratch.

A New Approach to Obviousness Analysis

As patent litigation becomes more complex, the ability to build strong §103 arguments efficiently is increasingly important.

By combining:

• Data-driven reference selection
• Structured legal reasoning
• Integrated claim charting
• Rapid iteration capabilities

Patlytics transforms §103 analysis into a more scalable, consistent, and defensible workflow.

To learn more about Patlytics, book a demo today.