flowleap-ai Recipe: Patent Landscape Analysis
Map the patent landscape for a technology area, identifying key players, trends,
and gaps. Each database uses its own query syntax — see flowleap-uspto for the
USPTO Lucene grammar.
Steps
Step 1: Define Search Scope
Write the scoping queries yourself (method in flowleap-patent): extract the
area's specific noun phrases, keep at least one discriminating term (a CPC
class alone maps a continent, not a landscape), and probe the count — a
landscape query legitimately lands broader than a novelty query, but a count
in the tens of thousands still means the scope is the technology area, not
the landscape:
# Count probe (read `total` from the payload), then adjust scope
flowleap --json tools run search_patents query='<self-written CQL>' range=1-1 details=false
Verify any CPC code against the official scheme before scoping on it —
flowleap patstat query on flowleap.cpc_scheme (see flowleap-patent);
group titles carry the specific technology, the 4-char class only the
headline.
Done when you have a probed EPO CQL query and a USPTO ODP query for the area.
Step 2: Broad Patent Search
flowleap --json patent search --query "<CQL from step 1>" --limit 50
flowleap --json uspto search --query "<ODP Lucene from step 1>" --limit 50
If one office answers provider_keys_required, its patent-data key is missing:
map the live office in full, label the landscape as covering that office only
because of a missing key — never as the shape of the field — and ask for the
free key at the end. flowleap patstat aggregates stay available keyless, but
they are twice-yearly snapshot counts, not a substitute for live search. See
flowleap-keys.
Done when both databases have returned their result sets, or the gated office is named as an open missing-key gap.
Step 3: Corpus Analytics
# Filing trends by year, country and CPC breakdowns, top assignees
flowleap --json analytics --keyword "<technology>" --date-from 2015-01-01
flowleap --json analytics --cpc <cpc-prefix> --country US --date-from 2020-01-01
Step 4: Identify Key Players
Scope the Step 1 query per applicant with pa= — wildcards catch name
variants (pa=GOOGLE* catches "Google LLC" and "Google Inc"), and
subsidiaries file separately (Google also files as Alphabet, DeepMind,
Waymo):
flowleap --json patent search --query 'pa=<ASSIGNEE>* AND <discriminating terms from step 1>' --limit 30
Step 5: Check Recent Activity
patent search returns relevance-ranked hits; ops search --cql adds CQL
date-range filtering (pd>=2024) for a time-sliced view the ranked search does
not expose:
flowleap ops search --cql "ti=<technology> AND pd>=2024" --start 1 --end 50
Step 6: Flag White Space
Cross the analytics CPC breakdown against the filing-year trend to flag subclasses that are sparse or declining while neighbours grow. Done when at least one sparse/declining CPC subclass (or a confirmed absence) is identified.
Output
A dataset segmented by database, applicant, and filing date, plus corpus-level
trend charts (filings per year, top assignees, CPC and country distributions).
When tallying players or counts from the search results, collapse to one entry
per patent family so multi-jurisdiction filings are not double-counted; the
corpus analytics figures are aggregate backend counts, reported as returned.
Visual deliverable
To turn Step 3's filing-trend numbers and Step 6's white-space finding into a
shareable HTML dashboard, follow this recipe's analysis through to the end,
then render it with recipe-custom-dashboard — its landscape white-space
template (CPC × year heatmap) is built for this recipe's final step, and its
filing-trends template covers Step 3's year-over-year counts. Analysis
logic stays here; the dashboard skill only owns presentation.