tag-patent-search-results-ip

Design and calibrate the Stage 3/4 tagging system for a patent-landscape program. Use after search-patents-ip and analyze-patent-search-results-ip to create a versioned four-column technology taxonomy, decision-relevant technical questions, evidence-backed patent groups, a reviewed tagging demonstration, and a complete empty-tag CSV for genuine human tagging at Stage 3.5; validate the returned tagged_pool.csv before routing to create-patent-search-report-ip.

Tag Patent Search Results

Role in the suite

Act as Stage 3/4 of create-patent-landscape-overview-ip.

Consume Stage 1 search/candidate artifacts and Stage 2 statistics/core/value evidence. Produce the tagging design, reviewed examples, and full-pool handoff. Pause for genuine human tagging at Stage 3.5. Validate the returned tagged pool, then route to create-patent-search-report-ip.

Do not tag the complete pool automatically. Do not require tagged_pool.csv before Stage 3; it is the output of the later human handoff.

Objectives

• Define a clear, versioned four-column technology decomposition.
• Calibrate it top-down from architecture and bottom-up from patent evidence.
• Form decision-relevant technical questions for Stage 4 evolution analysis.
• Select evidence-backed patent groups for deeper reading and reporting.
• Demonstrate the tag rules on a reviewed representative sample.
• Export the complete pool with genuinely empty human tag fields.
• Validate the human-returned dataset without inventing or repairing labels silently.

This is a classification-design and workflow stage, not a full legal review, a patent valuation, or a substitute for expert human tagging.

Required inputs

Artifact	Source	Use
candidate_pool.csv	Stage 1	Stable full or explicitly bounded record universe and branch hits
search_config.json	Stage 1	Scope, branch logic, preliminary concepts, query/version/provenance
panorama_stats.json	Stage 2	Population patterns, organizations, technology views, normalization decisions
patent_index.core.json	Stage 2	Reviewed branch evidence and technical-reading candidates
value_signals.json	Stage 2	Dated candidate-level proxies and evidence states

Optional:

• core_recall.csv for recall provenance;
• tech_taxonomy.txt as a preliminary Stage 1 hierarchy; and
• chart_data.json for aggregate cross-checks.

The preliminary hierarchy never overrides evidence or Stage 3 validation.

Authoritative outputs

Artifact	Content
tech_breakdown.json	Versioned four-column technology taxonomy, dictionary, validation, and rationale metadata
key_questions.json	Decision-relevant questions linked to validated nodes and evidence
patent_packages.csv	Evidence-backed reviewed patent groups and selection reasons
tagging_demo_sample.csv	Representative records with reviewed example tags and evidence
to_be_tagged.csv	Complete approved record set with empty human tag fields and handoff metadata referenced in the controlling JSON artifacts

Do not create taxonomy_proposal.md; store its substantive decomposition, question, selection, dictionary, and review rationale in the JSON metadata and handoff summary. Do not regenerate panorama_stats_report.html; Stage 2 already owns it.

Verified global MCP services

Reuse persisted upstream evidence first.

Deep Patent Mining — recommended

• Connector key: deep_patent_mining
• Marketplace: https://open.patsnap.com/marketplace/mcp-servers/patent-mining
• Official marketplace page: https://open.patsnap.com/marketplace/mcp-servers/patent-mining
• Use for bounded calibration of technical problem, means, effect, material, process, and application when the active schema supports it.

Patent Briefing — recommended

• Connector key: patent_briefing
• Marketplace: https://open.patsnap.com/marketplace/mcp-servers/patent-briefing
• Official marketplace page: https://open.patsnap.com/marketplace/mcp-servers/patent-briefing
• Use for ambiguous/selected claims, descriptions, family, status, translations, and images when deeper evidence is required.

Optional gap resolution

• advanced_patent_search — https://open.patsnap.com/marketplace/mcp-servers/patent-search
• global_core_patent_database — https://open.patsnap.com/marketplace/mcp-servers/core-patents

Use optional connectors only for an approved material gap. Inspect the live schema and record operation/request/date/provenance. Do not copy source endpoint or operation aliases.

Step 0 — Validate and lock the Stage 3 scope

1. reconcile project/query version and data cutoff;
2. reconcile candidate row/family counts and stable identifiers;
3. reconcile Stage 2 branch IDs and organization normalization;
4. inspect preliminary taxonomy and branch-rule overlap;
5. record complete/capped/sample state;
6. define taxonomy purpose and downstream decisions;
7. define primary hierarchy versus cross-cutting tag dimensions;
8. define calibration evidence depth and bounded retrieval plan;
9. define human-review workload, authorized tool, privacy boundary, and return format;
10. preserve input versions/checksums and rollback state.

Stop if inputs come from different scopes or the human handoff cannot be authorized.

Step 1 — Draft the taxonomy top-down

Use system architecture, function, route, subsystem, process, material, product, and application knowledge to draft:

Column	Meaning
level_1	Major technology category or system area
level_2	Route, function, subsystem, or capability
level_3	Specific taggable method, mechanism, component, material, process, or technique
description	Inclusion, exclusion, ambiguity, and why the node matters

Use Stage 1 branches as anchors, not final taxonomy labels. Give every node a stable machine-readable ID and human-readable English display name.

Do not impose four to six Level-1 branches or forty Level-3 nodes. Choose a hierarchy that is discriminative, explainable, feasible to review, and useful to the decision.

Step 2 — Calibrate bottom-up

Select calibration evidence

Choose a reproducible sample across:

• every preliminary branch;
• high- and low-volume groups;
• ambiguous/overlap cases;
• organizations, years, languages, and jurisdictions;
• high-priority core records; and
• known near misses or unclassified records.

The source’s fifteen-to-twenty records per branch can be a planning starting point, not a universal limit. Define a bounded plan based on evidence complexity, connector limits, privacy, cost, context, and review risk.

Read evidence

Use:

• light: title and abstract for clear calibration/demo cases;
• standard: title, abstract, and selected claims for ambiguity/high-priority groups;
• deep: claims and description only for selected cases where the taxonomy boundary cannot otherwise be resolved.

Record evidence source, passage/field, translation state, and reviewer state.

Revise nodes

• add missing routes;
• merge indistinguishable nodes;
• split overloaded nodes;
• rename region-specific or opaque terminology;
• preserve ambiguous/unclassified states;
• define positive, negative, and near-miss examples; and
• version every material change and impact.

Stop calibration at the approved boundary and mark unresolved nodes. Do not fabricate technical problem, means, effect, IPC/CPC, or classification evidence.

Step 3 — Define label semantics

Primary hierarchy

Primary route siblings should be mutually clear enough for consistent classification. Do not assert strict mutual exclusivity where a patent genuinely combines routes.

Cross-cutting dimensions

Model these separately when useful:

• product/component;
• application context;
• technical problem;
• technical means;
• technical effect;
• material/process; and
• organization or review state.

Allow multi-label values when the domain requires them. Define delimiter, duplicate- count policy, maximum only if operationally justified, and how aggregate reports treat multi-label records.

Ambiguity rules

• A Level-3 label must belong to a valid Level-2 parent.
• Do not force a primary label when evidence is insufficient.
• Use needs_review or unclassified with reason.
• Preserve secondary/cross-cutting tags instead of hiding them in free text.
• Never treat Stage 1 search-rule hits as final labels.

Step 4 — Write tech_breakdown.json

Use this semantic contract:

text

nodes[]:
  node_id
  parent_id
  level_1
  level_2
  level_3
  description
  include_rules[]
  exclude_rules[]
  ambiguous_case_rules[]
  positive_examples[]
  negative_examples[]
  evidence_fields[]
  multi_label_policy
  validation_status
  example_publications[]
meta:
  taxonomy_id
  taxonomy_version
  purpose
  source_query_version
  decomposition_rationale
  calibration_method
  node_counts_by_level
  overlap_metrics
  unclassified_rate
  coverage_metrics
  change_log[]
  human_review_plan

Validate unique IDs, parent paths, definitions, allowed values, cycles, duplicate labels, unresolved nodes, and version metadata.

Step 5 — Write key_questions.json

Create open technical questions that seed Stage 4 evolution analysis.

Each question contains:

text

question_id
level_1_or_cross_branch_scope
question
decision_relevance
rationale
seed_node_ids[]
expected_evidence[]
counterevidence[]
review_status
uncertainty

Distribute questions by decision importance and branch complexity. Do not force ten questions or equal branch counts. A question must map to valid nodes and be answerable through patent evidence without presuming an evolution conclusion.

Step 6 — Write patent_packages.csv

Group logic

Organize groups by a priority node, route, technical question, product/application, organization comparison, or technical problem. Declare the logic.

Selection dimensions

Preserve the source’s six prompts as optional analyst hypotheses:

• materially different/disruptive technology;
• new application context;
• possible unaddressed user/system need;
• substantial reported performance change;
• new function; and
• new interaction mode.

These prompts do not establish legal novelty, inventiveness, technical superiority, demand, adoption, value, or licensing suitability.

Also consider technical relevance, route representativeness, evidence depth, family/ citation/status proxies, organization diversity, missing data, and review workload.

Do not require ten groups or three families each. Preserve sparse groups and state why.

Columns

text

package_id,package_basis,sub_domain,node_ids,family_id,representative_publication,
title,normalized_assignee,recommendation_reason,rubric_dimensions,value_signal,
evidence_ids,selection_limitations,review_status,next_review_action

Use review states such as abstract_based, claim_assisted, description_reviewed, and needs_review. Keep family/status/citation data dated and source-labeled.

Step 7 — Write tagging_demo_sample.csv

Choose enough records to demonstrate:

• each major primary branch;
• cross-cutting and multi-label behavior;
• boundary/near-miss cases;
• unclassified/needs_review states;
• different evidence depths; and
• common tagging errors.

Do not force twenty to thirty rows. Use a sample that teaches the taxonomy and remains reviewable.

Use columns:

text

record_id,publication_number,title,abstract,normalized_assignee,
tech_level_1,tech_level_2,tech_level_3,technical_problem,technical_means,
technical_effect,recommendation_level,evidence_text,evidence_source,
taxonomy_version,review_status,review_notes

Every filled tag must exist in the controlled dictionary and have traceable evidence.

Step 8 — Write to_be_tagged.csv

Export the complete approved candidate set using deterministic local file processing. Do not load every row into model context.

Use the same core column layout as the demo, plus any required traceability fields. Keep human tag fields empty:

• tech_level_1;
• tech_level_2;
• tech_level_3;
• technical_problem;
• technical_means;
• technical_effect;
• human recommendation/review fields.

Do not prefill them from branch-rule hits or machine suggestions. Machine suggestions, if explicitly requested, must use separate columns and must not masquerade as human tags.

CSV safety

• Use UTF-8 and a documented delimiter/quote/newline policy.
• Preserve stable record and family IDs and source order.
• Neutralize formula-leading values for spreadsheet/tool safety.
• Reject or escape embedded control characters and delimiter conflicts.
• Do not include secrets, unnecessary personal data, or unauthorized confidential text.
• Reconcile row count and identifier set against the approved candidate pool.

Record taxonomy/schema version, required/optional columns, allowed values, multi-label delimiter, row count, identifier checksum, file checksum, generation date, privacy boundary, and return instructions in tech_breakdown.json.meta and the handoff summary.

Step 9 — Validate Stage 3 outputs

Taxonomy

• IDs and parents are valid and versioned.
• Every node has definition/include/exclude/ambiguity guidance.
• Primary overlaps and cross-cutting multi-label policies are explicit.
• Coverage, overlap, and unclassified metrics are reported.
• No unsupported node or technical fact is fabricated.

Questions and groups

• Every question maps to valid seed nodes and has uncertainty/counterevidence.
• Every selected family has traceable reason/evidence and review state.
• Sparse evidence is visible; no quota drives invented groups or records.
• Analyst rubric prompts are not represented as legal or market conclusions.

Demo and handoff

• Demo values exist in the dictionary and cover material boundary cases.
• Full handoff contains every approved record exactly as intended.
• Human tag columns are empty.
• CSV encoding, quoting, delimiter, formula safety, IDs, row count, and checksums pass.
• Output schemas and taxonomy versions reconcile.

Stage 3.5 — Genuine human tagging boundary

After all five Stage 3 outputs validate:

1. provide to_be_tagged.csv, taxonomy dictionary/version, demonstration sample, and instructions to the authorized user/reviewer;
2. the user imports them into an approved tagging/data tool under their privacy and access controls;
3. authorized human reviewers classify the full approved pool;
4. the user returns the result as tagged_pool.csv; and
5. do not proceed to Stage 4 until the return validates.

Do not require a specific regional tagging product. Do not upload or send the file without explicit authorization.

Validate tagged_pool.csv

Check:

• expected filename, encoding, delimiter, columns, and taxonomy/schema version;
• file/identifier checksum and row-count reconciliation;
• stable record/family ID uniqueness and coverage;
• missing, duplicate, unexpected, and reordered records;
• allowed values and parent-child consistency;
• multi-label delimiter and duplicate behavior;
• evidence/review fields and unresolved states;
• formula/control-character safety;
• unexpected machine-filled versus human-reviewed fields; and
• privacy/security boundary.

Produce a validation summary with accepted/rejected row counts and issue categories. Do not silently coerce invalid labels, fill missing tags, drop rows, or change taxonomy version. Return failures to the authorized human reviewer.

Handoff to Stage 4

When the return validates, provide:

text

Stage 3 complete: taxonomy [id/version], [node counts], [question count],
[package/family counts], demo [row count].
Stage 3.5 accepted: tagged_pool.csv [row/family counts], [unresolved count],
schema/taxonomy [versions], checksum [recorded].
Route to create-patent-search-report-ip with all Stage 1–3.5 artifacts.

Do not create the Stage 4 report here.

Quality gate

• All required Stage 1/2 artifacts reconcile.
• No preexisting tagged_pool.csv is required to begin Stage 3.
• Exactly five authoritative Stage 3 outputs are created.
• Taxonomy rationale is preserved in JSON metadata, not an extra package/runtime file.
• Level counts and question/package/demo sizes are evidence-driven rather than quotas.
• Primary and cross-cutting/multi-label dimensions are clearly separated.
• Every demonstration/package tag and reason is traceable.
• Full-pool processing is deterministic and does not load every row into context.
• Human tag fields remain genuinely empty before Stage 3.5.
• Returned-pool validation passes before Stage 4.
• No Stage 2 snapshot or Stage 4 report is regenerated.

Stop conditions

Stop or narrow when:

• upstream artifacts conflict or cannot be parsed;
• taxonomy purpose or human-review capacity is undefined;
• calibration evidence cannot distinguish key nodes;
• connector limits or confidential-data rules prevent necessary evidence retrieval;
• a package/question/demo would require fabricated evidence;
• full-pool export cannot preserve safe stable identifiers;
• no authorized human tagging workflow exists;
• returned rows/versions/checksums/tags fail reconciliation; or
• the user requests automated full-pool tagging as if it were human validation.

Return completed artifacts, failed checks, affected rows/nodes, residual risk, and the exact next action. Do not bypass the Stage 3.5 boundary.