Patent Claim Mapper

Use when mapping patent claims to products, analyzing patent infringement, or preparing freedom-to-operate analyses. Compares patent claims against product features for biotech and pharmaceutical IP assessment.

Map patent claims to product features for infringement analysis, freedom-to-operate assessments, and competitive intelligence in biotech/pharma.

When to Use

• Use this skill when the task needs Use when mapping patent claims to products, analyzing patent infringement, or preparing freedom-to-operate analyses. Compares patent claims against product features for biotech and pharmaceutical IP assessment.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Use when mapping patent claims to products, analyzing patent infringement, or preparing freedom-to-operate analyses. Compares patent claims against product features for biotech and pharmaceutical IP assessment.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• dataclasses: unspecified. Declared in requirements.txt.

Example Usage

bash

cd "20260318/scientific-skills/Evidence Insight/patent-claim-mapper"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

bash

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

bash

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Quick Start

python

from scripts.claim_mapper import ClaimMapper

mapper = ClaimMapper()

# Map claims to product
mapping = mapper.analyze(
    patent_claims="patent_claims.txt",
    product_description="product_spec.txt"
)

Core Capabilities

1. Claim Parsing

python

claims = mapper.parse_claims(
    patent_file="US10123456B2.pdf",
    independent_only=False
)

2. Feature Mapping

python

mapping = mapper.map_to_product(
    claim="A monoclonal antibody that binds to PD-1...",
    product_features=product_specs
)

Mapping Results:

• Fully covered: Product implements claim
• Partially covered: Some elements present
• Not covered: Claim element missing
• Questionable: Requires legal review

3. Gap Analysis

python

gaps = mapper.identify_gaps(
    mapping_results,
    strategy="design_around"
)

CLI Usage

text

python scripts/claim_mapper.py \
  --patent US10123456B2 \
  --product product_spec.txt \
  --output mapping_report.pdf

---

Skill ID: 213 | Version: 1.0 | License: MIT

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of patent-claim-mapper and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

patent-claim-mapper only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

• references/audit-reference.md - Supported scope, audit commands, and fallback boundaries

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.