Datasets:

deep1003
/

PATSTAT-AI-Complete-Master-1950-2026

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README.md ADDED Viewed

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+---
+pretty_name: PATSTAT AI Complete Master, 1950-2026
+language:
+- en
+license: other
+task_categories:
+- text-classification
+tags:
+- patents
+- artificial-intelligence
+- patstat
+- bibliometrics
+- innovation
+- cpc
+- ipc
+size_categories:
+- 1M<n<10M
+configs:
+- config_name: all_periods
+  data_files:
+  - split: train
+    path: data/by_priority_period/*.parquet
+- config_name: 1950s
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_1950s.parquet
+- config_name: 1960s
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_1960s.parquet
+- config_name: 1970s
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_1970s.parquet
+- config_name: 1980s
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_1980s.parquet
+- config_name: 1990s
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_1990s.parquet
+- config_name: 2000s
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_2000s.parquet
+- config_name: 2010s
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_2010s.parquet
+- config_name: 2020_2026
+  data_files:
+  - split: train
+    path: data/by_priority_period/patstat_ai_2020_2026.parquet
+---
+# PATSTAT AI Complete Master, 1950-2026
+## Dataset Summary
+This private research dataset contains 2,330,553 unique patent applications and 175 columns related to artificial-intelligence patents. It integrates a legacy AI patent master with newly collected PATSTAT Online records. Duplicate resolution uses `app_id`, and the legacy record takes precedence when an application appears in both sources.
+The data are partitioned by `priority_year` to support selective loading. The `2020_2026` category includes priority years 2020 through 2026. Records outside the documented range, if any, are retained in explicit boundary or unknown-year categories.
+## Repository Structure
+```text
+data/by_priority_period/  Parquet data partitions by priority-year category
+documentation/            Technical report and dataset report
+metadata/                 Schema, manifests, descriptive statistics, and QA reports
+code/                     Reproducible integration and backfill scripts
+```
+## Data Sources and Version
+- Primary database: PATSTAT Online 2026 Spring.
+- Legacy component: 1,850,664 legacy-only applications.
+- Overlap component: 186,715 applications present in both sources; legacy values take precedence.
+- New component: 293,174 new-only applications.
+- Final key: 2,330,553 unique `app_id` values; no duplicate application IDs.
+## Categories
+The main configuration loads all Parquet files. Period-specific configurations load a single priority-year category. These are storage and access categories, not machine-learning labels.
+```python
+from datasets import load_dataset
+all_data = load_dataset("deep1003/PATSTAT-AI-Complete-Master-1950-2026", "all_periods")
+recent = load_dataset("deep1003/PATSTAT-AI-Complete-Master-1950-2026", "2020_2026")
+```
+Authentication is required while the repository remains private.
+## Core Variables
+Core identifiers and content fields include `app_id`, patent office, application number, priority year, title, abstract, applicant information, inventor information, applicant and inventor country codes, IPC codes, and CPC codes. The complete 175-column schema is available in `metadata/schema.json`; column-level completeness is available in `metadata/column_descriptive_statistics.csv`.
+IPC codes are present for 2,326,676 applications (99.8336%). CPC codes are present for 1,850,180 applications (79.3880%). Missing CPC values can reflect the absence of a PATSTAT CPC relation and should not automatically be interpreted as collection failure.
+## Country Completion
+Country fields include observed and rule-based completed values. The completion logic uses applicant, inventor, family, name, and patent-office evidence. Users must inspect the method and provenance fields before treating a completed country as directly observed. Patent-office country is not equivalent to inventor nationality or residence.
+## Intended Uses
+The dataset is intended for patent landscaping, bibliometric research, technology-trend analysis, innovation studies, and reproducible methodological evaluation. It is not intended for legal-status determination, individual profiling, automated decisions about people, or inference of nationality from names.
+## Limitations
+- Inventor and applicant country values are incomplete in the source data and may contain rule-based completion.
+- Multiple inventors or applicants may be represented as delimited values. Users must normalize these fields before person-level or country-level counting.
+- A patent office identifies the filing authority, not the inventor's country.
+- Recent priority years are subject to publication and database-update lags.
+- PATSTAT coverage and field definitions vary by office, jurisdiction, and year.
+- CPC is less complete than IPC in the integrated master.
+## Privacy and Responsible Use
+Patent records may contain names and address-related fields. Do not use this dataset to profile, contact, rank, or make consequential decisions about individuals. Apply data-minimization and applicable privacy rules. The dataset should remain private until redistribution rights, personal-data handling, and institutional release requirements have been reviewed.
+## License and Access
+No open-data license is asserted for the underlying PATSTAT-derived records. Access to and reuse of PATSTAT content remain subject to the applicable EPO/PATSTAT terms and the uploader's institutional permissions. This repository is created as private by default. Repository access does not transfer ownership or waive third-party rights.
+## Reproducibility and Verification
+The repository includes processing code, integration reports, file-level SHA-256 checksums, and a technical report. The packaging script creates Zstandard-compressed Parquet files and a manifest containing row counts, byte sizes, and checksums. Verify the manifest after downloading.
+## Citation
+If permitted to use this dataset, cite the repository version or commit hash, the accompanying technical report, and EPO PATSTAT Online 2026 Spring. Add project authorship and institutional citation details before external distribution.

code/apply_ipc_cpc_backfill.py ADDED Viewed

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+"""Apply verified 1950-2026 combined PATSTAT IPC/CPC downloads to blank final-master fields."""
+import csv,gzip,json,os
+from collections import defaultdict,Counter
+from pathlib import Path
+csv.field_size_limit(100_000_000)
+BASE=Path('/Users/deep1003/data3/webofscience_ai_global_export/bibtex/ai_policy_organized_20260619/patstat')
+RAW=BASE/'online_sql_exports/patstat_2026_spring_ai_expanded_v2_full_fields'
+OUT=BASE/'final_master_20260712';FINAL=OUT/'patstat_ai_complete_master_legacy_priority_20260712.csv.gz';TMP=OUT/'_ipc_cpc_backfill.tmp.csv.gz'
+vals=defaultdict(lambda:{'ipc':[],'cpc':[]})
+def add(a,k,v):
+ v=(v or '').strip()
+ if v and v not in vals[a][k]:vals[a][k].append(v)
+for p in sorted(RAW.glob('patstat_ai_classification_*_rows_*.csv')):
+  with p.open(encoding='utf-8-sig',newline='') as f:
+   for r in csv.DictReader(f,delimiter=';'):
+    for key,col in [('ipc','all_ipc_codes'),('cpc','all_cpc_codes')]:
+     for code in (r.get(col,'') or '').split(';'):add(r.get('app_id',''),key,code)
+filled=Counter();rows=0
+with gzip.open(FINAL,'rt',encoding='utf-8',newline='') as src,gzip.open(TMP,'wt',encoding='utf-8',newline='') as dst:
+ rd=csv.DictReader(src);w=csv.DictWriter(dst,fieldnames=rd.fieldnames);w.writeheader()
+ for r in rd:
+  rows+=1;x=vals.get(r['app_id'])
+  if x:
+   if not (r.get('all_ipc_codes')or'').strip() and x['ipc']:r['all_ipc_codes']='; '.join(x['ipc']);r['ipc_count']=str(len(x['ipc']));filled['ipc']+=1
+   if not (r.get('all_cpc_codes')or'').strip() and x['cpc']:r['all_cpc_codes']='; '.join(x['cpc']);r['cpc_count']=str(len(x['cpc']));filled['cpc']+=1
+  w.writerow(r)
+os.replace(TMP,FINAL)
+report={'state':'applied','years':[1950,2026],'rows':rows,'download_app_ids':len(vals),'filled_blank_rows':dict(filled),'rule':'existing nonblank values preserved; only blanks filled'}
+(OUT/'ipc_cpc_backfill_1950_2026_report.json').write_text(json.dumps(report,indent=2),encoding='utf-8')
+print(json.dumps(report,indent=2))

code/build_complete_master.py ADDED Viewed

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+"""Build, country-fill, and merge PATSTAT 2023-2026 collection with legacy Stage3 master."""
+import csv, gzip, json, re
+from collections import Counter, defaultdict
+from pathlib import Path
+csv.field_size_limit(100_000_000)
+ROOT=Path('/Users/deep1003/data3/webofscience_ai_global_export/bibtex/ai_policy_organized_20260619/patstat')
+RAW=ROOT/'online_sql_exports/patstat_2026_spring_ai_expanded_v2_full_fields'
+OUT=ROOT/'final_master_20260712'; OUT.mkdir(parents=True,exist_ok=True)
+LEGACY=ROOT/'final_three_datasets_20260622/patstat_keyword_precise_ai_20260704_stage3_person_fields_joined_to_ai_full.csv.gz'
+YEARS=(2023,2024,2025,2026)
+BITS={'KR':1,'US':2,'CN':4}; HAS=8
+VALID=re.compile(r'^[A-Z]{2}$')
+TERMS={'KR':['SAMSUNG','LG','HYUNDAI','SK HYNIX','KIA','POSCO','ETRI','KAIST'],'US':['IBM','GOOGLE','MICROSOFT','INTEL','QUALCOMM','APPLE','AMAZON','META PLATFORMS','NVIDIA','GENERAL ELECTRIC'],'CN':['HUAWEI','TENCENT','ALIBABA','BAIDU','ZTE','XIAOMI','BYD','STATE GRID','PING AN']}
+PATS={c:[re.compile(rf'(?<![A-Z0-9]){re.escape(t)}(?![A-Z0-9])') for t in ts] for c,ts in TERMS.items()}
+def files(y,g): return sorted(RAW.glob(f'patstat_ai_{g}_{y}_{y}_rows_*.csv'))
+def uniq_add(d,k,v):
+ v=(v or '').strip()
+ if v and v not in d[k]: d[k].append(v)
+def mask(v):
+ s={x.strip().upper() for x in (v or '').split(';') if x.strip()}
+ s={x for x in s if VALID.fullmatch(x) and x not in {'NA','UNKNOWN'}}
+ return (HAS|sum(b for c,b in BITS.items() if c in s)) if s else 0
+def nmask(v):
+ v=(v or '').upper(); m=sum(BITS[c] for c,ps in PATS.items() if any(p.search(v) for p in ps))
+ return HAS|m if m else 0
+def integrate_year(y):
+ master={}; fields=[]
+ for p in files(y,'application'):
+  with p.open(encoding='utf-8-sig',newline='') as f:
+   for r in csv.DictReader(f,delimiter=';'):
+    if not fields: fields=list(r)
+    a=r['app_id']; master.setdefault(a,r)
+    for k,v in r.items():
+     if not master[a].get(k) and v: master[a][k]=v
+ agg=defaultdict(lambda:defaultdict(list))
+ for p in files(y,'person'):
+  with p.open(encoding='utf-8-sig',newline='') as f:
+   for r in csv.DictReader(f,delimiter=';'):
+    a=r.get('app_id');
+    if a not in master: continue
+    for pref,seq in [('applicant','applt_seq_nr'),('inventor','invt_seq_nr')]:
+     try: on=int(r.get(seq) or 0)>0
+     except: on=False
+     if on:
+      for out,src in [('person_ids','person_id'),('names','psn_name'),('countries','person_ctry_code'),('addresses','person_address'),('sectors','psn_sector')]: uniq_add(agg[a],f'{pref}_{out}',r.get(src) or (r.get('person_name') if out=='names' else ''))
+ specs={'ipc':[('all_ipc_codes','ipc_class_symbol')],'cpc':[('all_cpc_codes','cpc_class_symbol')],'publication':[('publication_ids','pat_publn_id'),('publication_numbers','publn_nr'),('publication_dates','publn_date')],'priority':[('priority_appln_ids','prior_appln_id')]}
+ for g,maps in specs.items():
+  for p in files(y,g):
+   with p.open(encoding='utf-8-sig',newline='') as f:
+    for r in csv.DictReader(f,delimiter=';'):
+     a=r.get('app_id');
+     if a in master:
+      for o,s in maps: uniq_add(agg[a],o,r.get(s))
+ added=['applicant_person_ids','applicant_names','applicant_countries','applicant_addresses','applicant_sectors','inventor_person_ids','inventor_names','inventor_countries','inventor_addresses','inventor_sectors','all_ipc_codes','all_cpc_codes','publication_ids','publication_numbers','publication_dates','priority_appln_ids']
+ out=OUT/f'patstat_ai_integrated_{y}.csv.gz'
+ with gzip.open(out,'wt',encoding='utf-8',newline='') as f:
+  w=csv.DictWriter(f,fieldnames=fields+added); w.writeheader()
+  for a in sorted(master,key=int):
+   r=master[a]
+   for k in added:r[k]='; '.join(agg[a].get(k,[]))
+   w.writerow(r)
+ print('integrated',y,len(master),flush=True); return out,len(master),fields+added
+def main():
+ integ=[]; collection={}; schemas=[]
+ for y in YEARS:
+  p,n,s=integrate_year(y); integ.append(p); schemas.append(s)
+  with gzip.open(p,'rt',encoding='utf-8',newline='') as f:
+   for r in csv.DictReader(f): collection[r['app_id']]=r
+ # family maps from direct applicant, else inventor
+ doc=defaultdict(int); inp=defaultdict(int)
+ before_country=Counter(); before_office=defaultdict(Counter)
+ for r in collection.values():
+  m=mask(r.get('applicant_countries')) or mask(r.get('inventor_countries'))
+  for c,b in BITS.items(): before_country[c]+=bool(m&b); before_office[r.get('patent_office','')][c]+=bool(m&b)
+  if m:
+   for k,d in [('docdb_family_id',doc),('inpadoc_family_id',inp)]:
+    fid=(r.get(k) or '').strip()
+    if fid and fid not in {'0','0.0'}: d[fid]|=m
+ methods=Counter(); after_country=Counter(); after_office=defaultdict(Counter)
+ for r in collection.values():
+  m=mask(r.get('applicant_countries')); method='applicant' if m else ''
+  if not m: m=mask(r.get('inventor_countries')); method='inventor' if m else ''
+  if not m:
+   fid=(r.get('docdb_family_id') or '').strip(); m=doc.get(fid,0) if fid not in {'','0','0.0'} else 0
+   if not m:
+    fid=(r.get('inpadoc_family_id') or '').strip(); m=inp.get(fid,0) if fid not in {'','0','0.0'} else 0
+   if m: method='family'
+  if not m: m=nmask(r.get('applicant_names')); method='name' if m else ''
+  if not m and r.get('patent_office') in BITS: m=HAS|BITS[r['patent_office']]; method='office'
+  if not m: method='none'
+  r['country_kr']=str(int(bool(m&1))); r['country_us']=str(int(bool(m&2))); r['country_cn']=str(int(bool(m&4))); r['country_fill_method']=method; r['patstat_release']='PATSTAT Online 2026 Spring'; r['record_source']='new_collection_2023_2026'
+  methods[method]+=1
+  for c,b in BITS.items(): after_country[c]+=bool(m&b); after_office[r.get('patent_office','')][c]+=bool(m&b)
+ new_schema=[]
+ for s in schemas:
+  for c in s+['country_kr','country_us','country_cn','country_fill_method','patstat_release','record_source']:
+   if c not in new_schema:new_schema.append(c)
+ newout=OUT/'patstat_ai_new_collection_2023_2026_integrated_country_filled.csv.gz'
+ with gzip.open(newout,'wt',encoding='utf-8',newline='') as f:
+  w=csv.DictWriter(f,fieldnames=new_schema);w.writeheader();w.writerows(collection.values())
+ # legacy priority: legacy values win on shared columns; new-only columns are appended and populated on matches
+ with gzip.open(LEGACY,'rt',encoding='utf-8-sig',newline='') as f: legacy_schema=next(csv.reader(f))
+ append=[c for c in new_schema if c not in legacy_schema]; final_schema=legacy_schema+append
+ final=OUT/'patstat_ai_complete_master_legacy_priority_20260712.csv.gz'; legacy_ids=set(); legacy_n=matched=0
+ with gzip.open(LEGACY,'rt',encoding='utf-8-sig',newline='') as src,gzip.open(final,'wt',encoding='utf-8',newline='') as dst:
+  rd=csv.DictReader(src);w=csv.DictWriter(dst,fieldnames=final_schema);w.writeheader()
+  for r in rd:
+   a=r['app_id'];legacy_ids.add(a);legacy_n+=1; nr=collection.get(a)
+   if nr: matched+=1
+   for c in append:r[c]=nr.get(c,'') if nr else ''
+   if 'record_source' in append:r['record_source']='legacy_priority_overlap' if nr else 'legacy_only'
+   w.writerow(r)
+  new_only=0
+  for a,nr in collection.items():
+   if a in legacy_ids:continue
+   row={c:'' for c in final_schema}
+   for c,v in nr.items():
+    if c in row:row[c]=v
+   row['record_source']='new_only';w.writerow(row);new_only+=1
+ report={'collection_status':'complete','years':list(YEARS),'new_integrated_rows':len(collection),'new_duplicate_app_ids_removed':sum(x[1] for x in [(0,0)]),'country_before':dict(before_country),'country_after':dict(after_country),'country_change':{c:after_country[c]-before_country[c] for c in BITS},'fill_methods':dict(methods),'office_before':{o:dict(v) for o,v in before_office.items()},'office_after':{o:dict(v) for o,v in after_office.items()},'legacy_rows':legacy_n,'overlap_rows_legacy_wins':matched,'new_only_rows':new_only,'final_rows':legacy_n+new_only,'legacy_columns':len(legacy_schema),'new_columns_added':append,'final_columns':len(final_schema),'outputs':{'new_integrated':str(newout),'complete_master':str(final)}}
+ (OUT/'integration_report.json').write_text(json.dumps(report,indent=2),encoding='utf-8')
+ print(json.dumps(report,indent=2),flush=True)
+if __name__=='__main__':main()

code/coalesce_new_source_fields.py ADDED Viewed

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+"""Fill selected blank legacy fields from new PATSTAT source while preserving nonblank legacy values."""
+import csv,gzip,json,os
+from collections import defaultdict,Counter
+from pathlib import Path
+csv.field_size_limit(100_000_000)
+BASE=Path('/Users/deep1003/data3/webofscience_ai_global_export/bibtex/ai_policy_organized_20260619/patstat')
+OUT=BASE/'final_master_20260712'; RAW=BASE/'online_sql_exports/patstat_2026_spring_ai_expanded_v2_full_fields'
+FINAL=OUT/'patstat_ai_complete_master_legacy_priority_20260712.csv.gz';TMP=OUT/'_coalesce.tmp.csv.gz'
+NEW=OUT/'patstat_ai_new_collection_2023_2026_integrated_country_filled.csv.gz'
+YEARS=(2023,2024,2025,2026)
+def add(d,k,v):
+ v=(v or '').strip()
+ if v and v not in d[k]:d[k].append(v)
+def code_count(v):
+ return str(len({x.strip() for x in (v or '').split(';') if x.strip()})) if (v or '').strip() else ''
+new={}
+with gzip.open(NEW,'rt',encoding='utf-8',newline='') as f:
+ for r in csv.DictReader(f):new[r['app_id']]={'all_ipc_codes':r.get('all_ipc_codes',''),'all_cpc_codes':r.get('all_cpc_codes','')}
+people=defaultdict(lambda:defaultdict(list))
+for y in YEARS:
+ for p in sorted(RAW.glob(f'patstat_ai_person_{y}_{y}_rows_*.csv')):
+  with p.open(encoding='utf-8-sig',newline='') as f:
+   for r in csv.DictReader(f,delimiter=';'):
+    a=r.get('app_id')
+    if a not in new:continue
+    for pref,seq in [('applicant','applt_seq_nr'),('inventor','invt_seq_nr')]:
+     try:on=int(r.get(seq) or 0)>0
+     except:on=False
+     if not on:continue
+     add(people[a],f'{pref}_std_ids',r.get('doc_std_name_id'))
+     add(people[a],f'{pref}_countries',r.get('person_ctry_code'))
+     add(people[a],f'{pref}_addresses',r.get('person_address'))
+     name=r.get('doc_std_name') or r.get('psn_name') or r.get('person_name') or ''
+     info='|'.join([r.get('person_id','').strip(),name.strip(),r.get('person_ctry_code','').strip(),r.get('person_address','').strip()]).rstrip('|')
+     add(people[a],f'{pref}_info',info)
+for a,x in new.items():
+ p=people.get(a,{})
+ x.update({'applicant_info':'; '.join(p.get('applicant_info',[])),'applicant_std_name_ids':'; '.join(p.get('applicant_std_ids',[])),'applicant_ctry_codes':'; '.join(p.get('applicant_countries',[])),'inventor_info':'; '.join(p.get('inventor_info',[])),'inventor_ctry_codes':'; '.join(p.get('inventor_countries',[])),'inventor_addresses':'; '.join(p.get('inventor_addresses',[]))})
+targets=['all_ipc_codes','ipc_count','all_cpc_codes','cpc_count','applicant_info','applicant_std_name_ids','applicant_ctry_codes','inventor_info','inventor_ctry_codes','inventor_addresses']
+filled=Counter();before=Counter();after=Counter();rows=0
+with gzip.open(FINAL,'rt',encoding='utf-8',newline='') as src,gzip.open(TMP,'wt',encoding='utf-8',newline='') as dst:
+ rd=csv.DictReader(src);w=csv.DictWriter(dst,fieldnames=rd.fieldnames);w.writeheader()
+ for r in rd:
+  rows+=1;n=new.get(r['app_id'],{})
+  for c in targets:before[c]+=bool((r.get(c) or '').strip())
+  for c in ['all_ipc_codes','all_cpc_codes','applicant_info','applicant_std_name_ids','applicant_ctry_codes','inventor_info','inventor_ctry_codes','inventor_addresses']:
+   if not (r.get(c) or '').strip() and (n.get(c) or '').strip():r[c]=n[c];filled[c]+=1
+  if not (r.get('ipc_count') or '').strip() and (r.get('all_ipc_codes') or '').strip():r['ipc_count']=code_count(r['all_ipc_codes']);filled['ipc_count']+=1
+  if not (r.get('cpc_count') or '').strip() and (r.get('all_cpc_codes') or '').strip():r['cpc_count']=code_count(r['all_cpc_codes']);filled['cpc_count']+=1
+  for c in targets:after[c]+=bool((r.get(c) or '').strip())
+  w.writerow(r)
+os.replace(TMP,FINAL)
+report={'rows':rows,'rule':'preserve existing nonblank; fill blank from new PATSTAT source; derive counts from final code lists','targets':targets,'before_nonempty':dict(before),'filled_rows':dict(filled),'after_nonempty':dict(after),'remaining_missing':{c:rows-after[c] for c in targets}}
+(OUT/'coalesced_fields_report.json').write_text(json.dumps(report,indent=2),encoding='utf-8')
+print(json.dumps(report,indent=2))

code/finalize_legacy_country_flags.py ADDED Viewed

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+import csv,gzip,json,os
+from collections import Counter,defaultdict
+from pathlib import Path
+csv.field_size_limit(100_000_000)
+OUT=Path('/Users/deep1003/data3/webofscience_ai_global_export/bibtex/ai_policy_organized_20260619/patstat/final_master_20260712')
+LEG=Path('/Users/deep1003/data3/webofscience_ai_global_export/bibtex/ai_policy_organized_20260619/patstat/final_three_datasets_20260622/patstat_keyword_precise_ai_20260704_stage3_person_fields_joined_to_ai_full.csv.gz')
+FLAGS=Path('/Users/deep1003/data3/ai_stpi_presentation_202607/dispersion_analysis_20260712/data/patent_appid_country_flags.csv')
+NEW=OUT/'patstat_ai_new_collection_2023_2026_integrated_country_filled.csv.gz'
+FINAL=OUT/'patstat_ai_complete_master_legacy_priority_20260712.csv.gz'; TMP=OUT/'_complete_master.tmp.csv.gz'
+REP=OUT/'integration_report.json'
+new={}
+with gzip.open(NEW,'rt',encoding='utf-8',newline='') as f:
+ r=csv.DictReader(f); new_schema=r.fieldnames
+ for x in r:new[x['app_id']]=x
+with gzip.open(LEG,'rt',encoding='utf-8-sig',newline='') as f: legacy_schema=next(csv.reader(f))
+append=[c for c in new_schema if c not in legacy_schema]; schema=legacy_schema+append
+seen=set(); total=matched=0; countries=Counter(); offices=defaultdict(Counter); methods=Counter()
+with gzip.open(LEG,'rt',encoding='utf-8-sig',newline='') as lf, open(FLAGS,encoding='utf-8',newline='') as ff, gzip.open(TMP,'wt',encoding='utf-8',newline='') as of:
+ lr=csv.DictReader(lf); fr=csv.DictReader(ff); w=csv.DictWriter(of,fieldnames=schema);w.writeheader()
+ for row,flag in zip(lr,fr):
+  assert row['app_id']==flag['app_id']; a=row['app_id'];seen.add(a);total+=1;nr=new.get(a)
+  if nr:matched+=1
+  for c in append:row[c]=nr.get(c,'') if nr else ''
+  row['country_kr']=flag['kr'];row['country_us']=flag['us'];row['country_cn']=flag['cn'];row['country_fill_method']=flag['fill_method'];row['patstat_release']='PATSTAT Online 2026 Spring';row['record_source']='legacy_priority_overlap' if nr else 'legacy_only'
+  methods[row['country_fill_method']]+=1
+  for c,k in [('KR','country_kr'),('US','country_us'),('CN','country_cn')]:countries[c]+=int(row[k]);offices[row.get('patent_office','')][c]+=int(row[k])
+  w.writerow(row)
+ new_only=0
+ for a,row in new.items():
+  if a in seen:continue
+  out={c:'' for c in schema}
+  for c,v in row.items():
+   if c in out:out[c]=v
+  out['record_source']='new_only';w.writerow(out);new_only+=1;methods[out['country_fill_method']]+=1
+  for c,k in [('KR','country_kr'),('US','country_us'),('CN','country_cn')]:countries[c]+=int(out[k]);offices[out.get('patent_office','')][c]+=int(out[k])
+os.replace(TMP,FINAL)
+rep=json.load(open(REP));rep.update({'final_rows':total+new_only,'overlap_rows_legacy_wins':matched,'new_only_rows':new_only,'final_country_totals':dict(countries),'final_fill_method_counts':dict(methods),'final_office_country_totals':{o:dict(v) for o,v in offices.items()},'final_country_flags_note':'Legacy rows use legacy-derived country flags; new-only rows use new-collection-derived flags. Existing rows win on overlap.'})
+REP.write_text(json.dumps(rep,indent=2),encoding='utf-8')
+print(json.dumps({k:rep[k] for k in ['final_rows','overlap_rows_legacy_wins','new_only_rows','final_country_totals','final_fill_method_counts']},indent=2))

code/prepare_huggingface_dataset.py ADDED Viewed

	@@ -0,0 +1,176 @@

+#!/usr/bin/env python3
+"""Prepare the PATSTAT AI master dataset for a private Hugging Face repository."""
+from __future__ import annotations
+import argparse
+import csv
+import gzip
+import hashlib
+import json
+import shutil
+from pathlib import Path
+import pyarrow as pa
+import pyarrow.csv as pacsv
+import pyarrow.parquet as pq
+PERIODS = (
+    (0, 1950, "before_1950"),
+    (1950, 1959, "1950s"),
+    (1960, 1969, "1960s"),
+    (1970, 1979, "1970s"),
+    (1980, 1989, "1980s"),
+    (1990, 1999, "1990s"),
+    (2000, 2009, "2000s"),
+    (2010, 2019, "2010s"),
+    (2020, 2026, "2020_2026"),
+)
+def period_label(year: int | None) -> str:
+    if year is None:
+        return "unknown_year"
+    for start, end, label in PERIODS:
+        if start <= year <= end:
+            return label
+    return "after_2026"
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(8 * 1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+def copy_supporting_files(source_dir: Path, output_dir: Path) -> None:
+    mappings = {
+        "HUGGINGFACE_README.md": "README.md",
+        "PATSTAT_AI_COMPLETE_MASTER_TECHNICAL_REPORT.pdf": "documentation/technical_report.pdf",
+        "PATSTAT_AI_COMPLETE_MASTER_TECHNICAL_REPORT.tex": "documentation/technical_report.tex",
+        "DATASET_REPORT.md": "documentation/dataset_report.md",
+        "final_master_column_descriptive_statistics.csv": "metadata/column_descriptive_statistics.csv",
+        "ipc_cpc_missingness_by_year.csv": "metadata/ipc_cpc_missingness_by_year.csv",
+        "integration_report.json": "metadata/integration_report.json",
+        "coalesced_fields_report.json": "metadata/coalesced_fields_report.json",
+        "ipc_cpc_backfill_1950_2026_report.json": "metadata/ipc_cpc_backfill_report.json",
+        "build_complete_master_20260712.py": "code/build_complete_master.py",
+        "finalize_legacy_country_flags.py": "code/finalize_legacy_country_flags.py",
+        "coalesce_new_source_fields.py": "code/coalesce_new_source_fields.py",
+        "apply_ipc_cpc_backfill_1990_2022.py": "code/apply_ipc_cpc_backfill.py",
+        "prepare_huggingface_dataset.py": "code/prepare_huggingface_dataset.py",
+    }
+    for source_name, target_name in mappings.items():
+        source = source_dir / source_name
+        if source.exists():
+            target = output_dir / target_name
+            target.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copy2(source, target)
+def build_dataset(source: Path, output_dir: Path) -> dict:
+    data_dir = output_dir / "data" / "by_priority_period"
+    if data_dir.exists():
+        shutil.rmtree(data_dir)
+    data_dir.mkdir(parents=True, exist_ok=True)
+    writers: dict[str, pq.ParquetWriter] = {}
+    counts: dict[str, int] = {}
+    schema: pa.Schema | None = None
+    with gzip.open(source, "rt", encoding="utf-8-sig", newline="") as stream:
+        columns = next(csv.reader(stream))
+    read_options = pacsv.ReadOptions(
+        block_size=64 * 1024 * 1024,
+        use_threads=True,
+        column_names=columns,
+        skip_rows=1,
+        encoding="utf8",
+    )
+    convert_options = pacsv.ConvertOptions(
+        column_types={column: pa.string() for column in columns},
+        strings_can_be_null=True,
+    )
+    reader = pacsv.open_csv(source, read_options=read_options, convert_options=convert_options)
+    try:
+        for batch in reader:
+            table = pa.Table.from_batches([batch])
+            if schema is None:
+                schema = table.schema
+            years = table.column("priority_year").to_pylist()
+            labels = [period_label(int(value)) if value is not None else "unknown_year" for value in years]
+            for label in sorted(set(labels)):
+                indices = pa.array([index for index, item in enumerate(labels) if item == label])
+                subset = table.take(indices)
+                target = data_dir / f"patstat_ai_{label}.parquet"
+                if label not in writers:
+                    writers[label] = pq.ParquetWriter(
+                        target,
+                        subset.schema,
+                        compression="zstd",
+                        compression_level=6,
+                        use_dictionary=True,
+                    )
+                writers[label].write_table(subset, row_group_size=50_000)
+                counts[label] = counts.get(label, 0) + subset.num_rows
+    finally:
+        for writer in writers.values():
+            writer.close()
+    if schema is None:
+        raise RuntimeError("The source dataset is empty.")
+    manifest_rows = []
+    for path in sorted(data_dir.glob("*.parquet")):
+        label = path.stem.removeprefix("patstat_ai_")
+        manifest_rows.append(
+            {
+                "category": label,
+                "rows": counts[label],
+                "bytes": path.stat().st_size,
+                "sha256": sha256(path),
+                "path": str(path.relative_to(output_dir)),
+            }
+        )
+    metadata_dir = output_dir / "metadata"
+    metadata_dir.mkdir(parents=True, exist_ok=True)
+    with (metadata_dir / "file_manifest.csv").open("w", encoding="utf-8", newline="") as stream:
+        writer = csv.DictWriter(stream, fieldnames=["category", "rows", "bytes", "sha256", "path"])
+        writer.writeheader()
+        writer.writerows(manifest_rows)
+    with (metadata_dir / "schema.json").open("w", encoding="utf-8") as stream:
+        json.dump(
+            [{"name": field.name, "type": str(field.type), "nullable": field.nullable} for field in schema],
+            stream,
+            indent=2,
+        )
+    return {
+        "source": str(source),
+        "source_sha256": sha256(source),
+        "rows": sum(counts.values()),
+        "columns": len(schema),
+        "categories": counts,
+        "files": manifest_rows,
+    }
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--source", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    args = parser.parse_args()
+    args.output.mkdir(parents=True, exist_ok=True)
+    result = build_dataset(args.source, args.output)
+    copy_supporting_files(args.source.parent, args.output)
+    with (args.output / "metadata" / "build_summary.json").open("w", encoding="utf-8") as stream:
+        json.dump(result, stream, indent=2)
+    print(json.dumps(result, indent=2))
+if __name__ == "__main__":
+    main()

data/by_priority_period/patstat_ai_1950s.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:d88690e6914c0fdcd8a1fb5cf8d30f6e469453b5a461f7c00e321785f1941458
+size 1085780

data/by_priority_period/patstat_ai_1960s.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:3bcea14c652cd4b1963d3bfef2e040e90851550fb80fc9a2722846a6f477f3e6
+size 2134226

data/by_priority_period/patstat_ai_1970s.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:911b5bfe50606830404ed32f19d5ee6cf1c883e411a41261a4b7cffd1e610bbf
+size 4132661

data/by_priority_period/patstat_ai_1980s.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:81b1b3ad6250aaf1e51d36e67f8fd9a00fb67741894d1a431cf884ccd6d15193
+size 12509045

data/by_priority_period/patstat_ai_1990s.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:6a72b312910bac3e73594402461baca4a022e19204d0cfd0040945c1904cd89a
+size 39589854

data/by_priority_period/patstat_ai_2000s.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:27d7b2797166d1ec415a47cfe278de85f58be8463cc2c9001145dea3f56ad6d8
+size 93546953

data/by_priority_period/patstat_ai_2010s.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:1eb2ce82a156c41eecc4cc51ebd27c24795394e26b876f5d57b84814ea434678
+size 341351099

data/by_priority_period/patstat_ai_2020_2026.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:e20112a7f4b7262266d4e4b36b8b78c2bdf103e9bc759e75e580851e1ab692fc
+size 438896400

data/by_priority_period/patstat_ai_before_1950.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:ff3a4fd57268b7e376d8f477016b68916f85b2e50bd1ca4244621a16ecbb5c20
+size 232760

documentation/dataset_report.md ADDED Viewed

	@@ -0,0 +1,120 @@

+# PATSTAT AI Complete Master — Integration Report
+Generated on 2026-07-12 from PATSTAT Online 2026 Spring.
+## Technical summary
+The 2023–2026 PATSTAT Online collection is complete for all six source groups: application, person, IPC, CPC, publication, and priority. The downloads contained 492,655 application rows. Deduplication at `app_id` grain removed 12,766 repeated application rows, producing 479,889 unique newly collected applications.
+The new collection overlaps the legacy Stage3 AI master on 186,715 `app_id` values. Following the required legacy-first rule, those rows retain all legacy values for shared columns. The 293,174 new-only applications were appended. The complete master contains 2,330,553 unique applications and 175 columns, with no duplicate `app_id` values.
+## Collection completion and annual integration
+| Year | Downloaded application rows | Integrated unique applications | Repeated rows removed |
+|---|---:|---:|---:|
+| 2023 | 212,379 | 199,613 | 12,766 |
+| 2024 | 192,234 | 192,234 | 0 |
+| 2025 | 88,033 | 88,033 | 0 |
+| 2026 | 9 | 9 | 0 |
+| **Total** | **492,655** | **479,889** | **12,766** |
+The yearly completion log records every year as `complete`, with all six groups present. The 2026 partition contains only nine applications in the current release and is therefore a small, incomplete calendar-year observation rather than a full-year total.
+## Country completion on the new collection
+Country participation was assigned in this order: applicant country, inventor country, DOCDB/INPADOC family country, standardized applicant-name match, patent-office fallback for KR/US/CN, then `none`. A patent can participate in more than one country, so country totals overlap.
+| Country | Before fill | After fill | Change |
+|---|---:|---:|---:|
+| KR | 25,926 | 26,961 | +1,035 |
+| US | 14,628 | 14,724 | +96 |
+| CN | 4,817 | 407,565 | +402,748 |
+Fill-method counts for the 479,889 newly collected applications are: applicant 65,117; inventor 136; family 2,015; name 13,842; office 388,549; none 10,230.
+### Selected patent-office changes
+| Patent office | Participation flag | Before | After | Change |
+|---|---|---:|---:|---:|
+| CN | CN | 0 | 402,632 | +402,632 |
+| CN | KR | 0 | 50 | +50 |
+| CN | US | 0 | 56 | +56 |
+| KR | KR | 24,708 | 25,550 | +842 |
+| JP | KR | 0 | 142 | +142 |
+| JP | US | 0 | 36 | +36 |
+| JP | CN | 0 | 114 | +114 |
+| US | US | 13,683 | 13,683 | 0 |
+| EP | US | 155 | 155 | 0 |
+The large CN change is primarily the stated office fallback. It is an assigned participation flag under the requested hierarchy, not newly observed inventor-country evidence.
+## Legacy-first complete master
+| Component | Rows |
+|---|---:|
+| Legacy-only | 1,850,664 |
+| Legacy/new overlap, legacy wins | 186,715 |
+| New-only appended | 293,174 |
+| **Final unique applications** | **2,330,553** |
+The legacy schema had 156 columns. Nineteen fields absent from the legacy schema were added, yielding 175 columns. Added fields include source-level applicant/inventor identifiers, names, countries and addresses; publication and priority lists; country flags and fill method; PATSTAT release; and record provenance.
+For overlapping applications, shared legacy columns were preserved exactly. Newly added columns were populated from the new collection where available. Country flags on legacy rows use the previously generated legacy-country result; country flags on new-only rows use the new-collection result.
+Final participation totals are KR 170,936, US 481,680, and CN 1,105,478. These totals overlap because one patent can carry multiple country flags. All 2,330,553 rows have a non-empty `country_fill_method`; 126,774 are explicitly marked `none`.
+## Quality checks
+- Final row count: 2,330,553
+- Final column count: 175
+- Unique `app_id`: 2,330,553
+- Duplicate `app_id`: 0
+- Blank `country_fill_method`: 0
+- Grain: one row per `app_id`
+- Legacy-overlap precedence: legacy values retained for shared columns
+- Source provenance values: `legacy_only`, `legacy_priority_overlap`, `new_only`
+## Blank-field coalescing from the new source
+For selected shared columns, the final merge applies a coalescing rule: preserve every existing nonblank value and use the new PATSTAT source only when the existing value is blank. Code-count fields are derived from the resulting distinct semicolon-separated code lists.
+| Column | Nonblank before | Filled from new/derived | Nonblank after | Remaining missing |
+|---|---:|---:|---:|---:|
+| `all_ipc_codes` | 293,094 | 186,592 | 479,686 | 1,850,867 |
+| `ipc_count` | 0 | 479,686 | 479,686 | 1,850,867 |
+| `all_cpc_codes` | 1,847,475 | 2,567 | 1,850,042 | 480,511 |
+| `cpc_count` | 0 | 1,850,042 | 1,850,042 | 480,511 |
+| `applicant_info` | 0 | 466,084 | 466,084 | 1,864,469 |
+| `applicant_std_name_ids` | 0 | 466,084 | 466,084 | 1,864,469 |
+| `applicant_ctry_codes` | 0 | 65,117 | 65,117 | 2,265,436 |
+| `inventor_info` | 0 | 464,579 | 464,579 | 1,865,974 |
+| `inventor_ctry_codes` | 0 | 47,432 | 47,432 | 2,283,121 |
+| `inventor_addresses` | 0 | 0 | 0 | 2,330,553 |
+`inventor_addresses` remains empty because the newly collected PATSTAT person rows contain no nonblank inventor address values. No address was inferred or substituted from another field.
+## Files and reproducibility
+- `patstat_ai_complete_master_legacy_priority_20260712.csv.gz`: final complete master
+- `patstat_ai_new_collection_2023_2026_integrated_country_filled.csv.gz`: new collection only
+- `patstat_ai_integrated_2023.csv.gz` through `patstat_ai_integrated_2026.csv.gz`: annual source integrations
+- `integration_report.json`: machine-readable counts and office/country cross-tabs
+- `build_complete_master_20260712.py`: annual integration, new-country fill, and initial merge
+- `finalize_legacy_country_flags.py`: legacy-first country-flag completion and final output
+- `coalesce_new_source_fields.py`: blank-only enrichment of selected shared fields
+- `coalesced_fields_report.json`: machine-readable before/after field completeness
+## Limitations
+Applicant and inventor country fields remain sparse for some patent offices. Family and name stages are inferred from related records or standardized organization names. Office fallback assigns KR/US/CN according to filing authority only after earlier evidence is absent; it does not establish inventor nationality or residence. Recent-year totals, especially 2026, are affected by release timing and publication/indexing lag.
+## Full IPC/CPC backfill from PATSTAT Online 2026 Spring
+A combined annual query was executed for every priority year from 1950 through 2026. Each result row contains `app_id`, `priority_year`, `all_ipc_codes`, and `all_cpc_codes`. All 77 years completed successfully. Existing nonblank code values were preserved and only blank fields were filled.
+| Field | Before backfill | Added | Final nonblank | Final missing | Final coverage |
+|---|---:|---:|---:|---:|---:|
+| `all_ipc_codes` | 479,686 | 1,846,990 | 2,326,676 | 3,877 | 99.83% |
+| `all_cpc_codes` | 1,850,042 | 138 | 1,850,180 | 480,373 | 79.39% |
+`ipc_count` and `cpc_count` were recalculated from distinct semicolon-separated codes. The full-file validation found zero count mismatches, zero duplicate `app_id` values, and a valid GZIP stream. The limited CPC increase reflects applications without a corresponding CPC relation in the PATSTAT source rather than an incomplete download.

documentation/technical_report.pdf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:30eead55b769e7592b38d62a7ccc303ffdd293eebaa59906c64e31aa9aabd732
+size 166013

documentation/technical_report.tex ADDED Viewed

	@@ -0,0 +1,257 @@

+\documentclass[11pt,a4paper]{article}
+\usepackage[T1]{fontenc}
+\usepackage{lmodern}
+\usepackage[margin=24mm]{geometry}
+\usepackage{microtype}
+\usepackage{booktabs,longtable,tabularx,array}
+\usepackage{xcolor,graphicx,float}
+\usepackage[hidelinks]{hyperref}
+\usepackage{xurl}
+\usepackage{enumitem}
+\definecolor{navy}{HTML}{17365D}
+\definecolor{lightblue}{HTML}{EAF2F8}
+\definecolor{darkgray}{HTML}{4A4A4A}
+\hypersetup{pdftitle={PATSTAT AI Complete Master Technical Report},pdfauthor={STPI AI Patent Data Project}}
+\setlength{\parindent}{0pt}
+\setlength{\parskip}{5pt}
+\renewcommand{\arraystretch}{1.12}
+\setlength{\emergencystretch}{2em}
+\newcommand{\pct}{\%}
+\begin{document}
+\begin{titlepage}
+\centering
+\vspace*{28mm}
+{\color{navy}\rule{\textwidth}{1.2pt}}\\[12mm]
+{\Huge\bfseries PATSTAT AI Complete Master\\[3mm]Technical Report\par}
+\vspace{8mm}
+{\Large Legacy-priority integration, country completion, and IPC/CPC backfill\par}
+\vspace{16mm}
+{\large Dataset snapshot: 12 July 2026\par}
+{\large Source release: PATSTAT Online 2026 Spring\par}
+\vfill
+\begin{tabular}{rl}
+Observation grain: & One row per PATSTAT application identifier (\texttt{app\_id})\\
+Rows: & 2,330,553\\
+Columns: & 175\\
+Coverage years: & 1950--2026\\
+\end{tabular}
+\vfill
+{\color{navy}\rule{\textwidth}{1.2pt}}
+\end{titlepage}
+\pagenumbering{roman}
+\tableofcontents
+\newpage
+\pagenumbering{arabic}
+\section{Technical summary}
+The final PATSTAT AI Complete Master contains \textbf{2,330,553 unique patent applications and 175 columns}. Full-file validation found exactly 2,330,553 distinct \texttt{app\_id} values, zero duplicated application identifiers, and a valid GZIP stream. The intended analytical grain is one row per PATSTAT application.
+The master combines a 2,037,379-row legacy Stage3 AI dataset with 479,889 unique applications collected from PATSTAT Online 2026 Spring for 2023--2026. A total of 186,715 newly collected applications overlapped the legacy dataset; shared nonblank legacy values were retained. The integration appended 293,174 new-only applications and added 19 fields that were absent from the legacy schema.
+A subsequent combined PATSTAT classification query collected the application identifier, priority year, and aggregated IPC and CPC code lists for every year from 1950 through 2026. IPC coverage increased to 2,326,676 applications (99.83\%). CPC coverage is 1,850,180 applications (79.39\%); the remaining CPC gaps reflect applications without an observed PATSTAT CPC relation rather than incomplete annual downloads.
+Country participation flags are complete as stored variables, but their interpretation depends on the ordered completion rule. Direct applicant and inventor countries take precedence, followed by patent-family evidence, organization-name matching, and finally KR/US/CN filing-office fallback. Filing-office fallback is an operational participation assignment and does not establish inventor nationality or residence.
+\section{Dataset scope and lineage}
+\subsection{Input datasets}
+\begin{tabularx}{\textwidth}{>{\raggedright\arraybackslash}Xrr>{\raggedright\arraybackslash}X}
+\toprule
+Input & Rows & Columns & Role \\
+\midrule
+Legacy Stage3 AI master & 2,037,379 & 156 & Existing classified AI corpus and standardized person fields \\
+New PATSTAT 2023--2026 collection & 479,889 & 51 & Bibliographic, person, IPC, CPC, publication, and priority enrichment \\
+PATSTAT annual classification backfill & 6,186,011 app IDs observed & 4 & IPC/CPC completion for 1950--2026 \\
+\bottomrule
+\end{tabularx}
+The annual backfill population is larger than the final AI master because the PATSTAT query returns the full AI-filtered candidate population for each release-year interval. Only matching final-master \texttt{app\_id} values are applied.
+\subsection{Final row composition}
+\begin{table}[H]
+\centering
+\begin{tabular}{lr}
+\toprule
+Record provenance & Rows \\
+\midrule
+Legacy only & 1,850,664 \\
+Legacy/new overlap, legacy priority & 186,715 \\
+New only & 293,174 \\
+\midrule
+\textbf{Final unique applications} & \textbf{2,330,553} \\
+\bottomrule
+\end{tabular}
+\end{table}
+\section{Collection and integration methodology}
+\subsection{Application-level integration}
+PATSTAT relation tables were first aggregated to application grain. Applicant and inventor records were linked through PATSTAT person-application relations. IPC, CPC, publication, and priority relations were deduplicated within each application and stored as semicolon-separated lists. The final integration uses \texttt{app\_id} as the primary key.
+The merge rule is:
+\begin{enumerate}[leftmargin=*]
+\item Retain each legacy row and every nonblank legacy value.
+\item For an overlapping \texttt{app\_id}, fill a selected shared field only when the legacy value is blank.
+\item Add columns present only in the new collection.
+\item Append new-only \texttt{app\_id} values.
+\item Revalidate unique application grain after every material rewrite.
+\end{enumerate}
+\subsection{Combined IPC/CPC backfill}
+The final classification query returns one row per application:
+\begin{verbatim}
+app_id | priority_year | all_ipc_codes | all_cpc_codes
+\end{verbatim}
+IPC and CPC are aggregated independently with \texttt{STRING\_AGG} in correlated subqueries. This avoids an IPC-by-CPC Cartesian product. Annual queries were executed for all 77 years from 1950 through 2026. A total of 661 validated CSV download parts were produced. Each part was checked for required headers and expected row count before being accepted.
+\section{Country participation completion}
+Country flags follow this deterministic hierarchy:
+\begin{enumerate}[leftmargin=*]
+\item standardized applicant-country codes;
+\item standardized inventor-country codes when applicant country is unavailable;
+\item union of DOCDB family countries, then INPADOC family countries;
+\item boundary-aware standardized organization-name matching;
+\item KR, US, or CN filing-office fallback;
+\item explicit \texttt{none} when no rule applies.
+\end{enumerate}
+\begin{table}[H]
+\centering
+\begin{tabular}{lr}
+\toprule
+Final fill method & Applications \\
+\midrule
+Applicant & 982,860 \\
+Inventor & 6,498 \\
+Family & 169,352 \\
+Organization name & 64,529 \\
+Filing office & 980,540 \\
+None & 126,774 \\
+\bottomrule
+\end{tabular}
+\end{table}
+Final country-participation totals are KR 170,936, US 481,680, and CN 1,105,478. These totals overlap because one application can carry more than one country flag.
+\section{Column descriptive statistics}
+\subsection{Completeness distribution}
+\begin{table}[H]
+\centering
+\begin{tabular}{lr}
+\toprule
+Completeness interval & Number of columns \\
+\midrule
+100\% & 31 \\
+95\% to $<$100\% & 5 \\
+80\% to $<$95\% & 98 \\
+50\% to $<$80\% & 6 \\
+1\% to $<$50\% & 30 \\
+$>$0\% to $<$1\% & 4 \\
+0\% & 1 \\
+\midrule
+Total & 175 \\
+\bottomrule
+\end{tabular}
+\end{table}
+\subsection{Core bibliographic and classification fields}
+\begin{longtable}{p{0.38\textwidth}rrr}
+\toprule
+Column & Nonblank & Missing & Coverage \\
+\midrule
+\endhead
+\texttt{app\_id} & 2,330,553 & 0 & 100.00\% \\
+\texttt{patent\_office} & 2,330,553 & 0 & 100.00\% \\
+\texttt{priority\_year} & 2,330,553 & 0 & 100.00\% \\
+\texttt{title} & 2,325,259 & 5,294 & 99.77\% \\
+\texttt{abstract} & 2,259,040 & 71,513 & 96.93\% \\
+\texttt{all\_ipc\_codes} & 2,326,676 & 3,877 & 99.83\% \\
+\texttt{ipc\_count} & 2,326,676 & 3,877 & 99.83\% \\
+\texttt{all\_cpc\_codes} & 1,850,180 & 480,373 & 79.39\% \\
+\texttt{cpc\_count} & 1,850,180 & 480,373 & 79.39\% \\
+\bottomrule
+\end{longtable}
+\subsection{Applicant and inventor fields}
+\begin{longtable}{p{0.42\textwidth}rrr}
+\toprule
+Column & Nonblank & Missing & Coverage \\
+\midrule
+\endhead
+\texttt{stage3\_applicant\_names\_std} & 1,992,673 & 337,880 & 85.50\% \\
+\texttt{stage3\_applicant\_countries} & 1,992,674 & 337,879 & 85.50\% \\
+\texttt{stage3\_applicant\_affiliation} & 693,818 & 1,636,735 & 29.77\% \\
+\texttt{stage3\_inventor\_names\_std} & 1,982,429 & 348,124 & 85.06\% \\
+\texttt{stage3\_inventor\_countries} & 1,982,429 & 348,124 & 85.06\% \\
+\texttt{stage3\_inventor\_affiliation} & 1,473,896 & 856,657 & 63.24\% \\
+\texttt{applicant\_info} & 466,084 & 1,864,469 & 20.00\% \\
+\texttt{inventor\_info} & 464,579 & 1,865,974 & 19.93\% \\
+\bottomrule
+\end{longtable}
+The nonblank rate for country strings is not the same as the valid ISO-country rate because strings can contain \texttt{Unknown}. Country-level inference must use parsed valid codes and explicitly report the denominator.
+\section{Quality assurance results}
+\begin{table}[H]
+\centering
+\begin{tabularx}{\textwidth}{Xr}
+\toprule
+Check & Result \\
+\midrule
+Final row count & 2,330,553 \\
+Final column count & 175 \\
+Distinct \texttt{app\_id} & 2,330,553 \\
+Duplicate \texttt{app\_id} rows & 0 \\
+Blank \texttt{country\_fill\_method} & 0 \\
+IPC count/list mismatches & 0 \\
+CPC count/list mismatches & 0 \\
+Annual classification years completed & 77 of 77 \\
+Classification download parts & 661 \\
+GZIP integrity & Passed \\
+\bottomrule
+\end{tabularx}
+\end{table}
+The only entirely blank column is \texttt{inventor\_addresses}. It remains blank because the newly collected PATSTAT person rows did not contain nonblank inventor-address values; no address was inferred from another field.
+\section{Limitations and interpretation}
+\begin{itemize}[leftmargin=*]
+\item Filing office and inventor country are different concepts. Office fallback is retained as a documented operational rule, not a nationality measure.
+\item Multinational applications contribute to every represented country; country totals therefore overlap.
+\item Person-country coverage differs substantially across patent offices, so inventor-country comparisons describe observed records rather than a complete population.
+\item CPC coverage is lower than IPC coverage because not every PATSTAT application has an observed CPC relation.
+\item The 2026 partition contains only nine applications in the current collection and is not a complete calendar-year total.
+\item AI taxonomy fields are populated primarily for the legacy 2,037,379-row corpus. Newly appended applications can have blank historical AI-category columns even though they were selected by the PATSTAT AI query policy.
+\end{itemize}
+\section{Reproducibility and deliverables}
+Primary data file:
+\begin{quote}\small
+\path{patstat_ai_complete_master_legacy_priority_20260712.csv.gz}
+\end{quote}
+Supporting artifacts include the machine-readable integration and backfill reports, full 175-column descriptive-statistics CSV, annual collection status, SHA-256 checksum, and the Python scripts used for integration, country completion, blank-field coalescing, and IPC/CPC application.
+The validated data-file SHA-256 at report generation time is:
+\begin{quote}\ttfamily\small
+00f48da33ab6064f0fad4c891e8bc6aecc86f6ccde860767e1927777ee9fe43b
+\end{quote}
+\end{document}

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+}

metadata/ipc_cpc_backfill_report.json ADDED Viewed

	@@ -0,0 +1,14 @@

+{
+  "state": "applied",
+  "years": [
+    1950,
+    2026
+  ],
+  "rows": 2330553,
+  "download_app_ids": 6186011,
+  "filled_blank_rows": {
+    "ipc": 1846990,
+    "cpc": 138
+  },
+  "rule": "existing nonblank values preserved; only blanks filled"
+}

metadata/ipc_cpc_missingness_by_year.csv ADDED Viewed

	@@ -0,0 +1,79 @@

+year,rows,ipc_present,ipc_missing,ipc_missing_pct,cpc_present,cpc_missing,cpc_missing_pct,both_present,both_missing,ipc_missing_cpc_present,ipc_present_cpc_missing
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metadata/schema.json ADDED Viewed

	@@ -0,0 +1,877 @@

+[
+  {
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+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "patent_office",
+    "type": "string",
+    "nullable": true
+  },
+  {
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+    "type": "string",
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+  },
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+    "nullable": true
+  },
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+  },
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+    "type": "string",
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+  },
+  {
+    "name": "publication_count",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "first_publn_date",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "first_grant_publn_date",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "publication_info",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "all_cpc_codes",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "stage2_enrichment_available",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "agentic_ai_before_aggressive",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "agentic_ai_aggressive_keyword_strong",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "agentic_ai_aggressive_keyword_contextual",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "agentic_ai_aggressive_code_strong",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "agentic_ai_aggressive_code_contextual",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "agentic_ai_aggressive_newly_added",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "agentic_ai_aggressive_policy",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "deep_learning_before_aggressive",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "deep_learning_aggressive_keyword_strong",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "deep_learning_aggressive_keyword_contextual",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "deep_learning_aggressive_code_strong",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "deep_learning_aggressive_g06n3_context",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "deep_learning_aggressive_newly_added",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "deep_learning_aggressive_policy",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "in_ai_infra_set",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "ai_infra_partition",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "ai_infra_subcategory_final",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "canonical_generative_ai_before_aggressive",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "generative_ai_aggressive_strict_pattern",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "generative_ai_aggressive_contextual_pattern",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "generative_ai_aggressive_ai_context",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "generative_ai_aggressive_contextual_match",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "generative_ai_aggressive_newly_added",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "generative_ai_aggressive_policy",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "appln_filing_year_stage3",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "stage3_applicant_names_std",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "stage3_applicant_countries",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "stage3_applicant_affiliation",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "stage3_inventor_names_std",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "stage3_inventor_countries",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "stage3_inventor_affiliation",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "docdb_family_size",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "applicant_person_ids",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "applicant_names",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "applicant_countries",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "applicant_addresses",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "inventor_person_ids",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "inventor_names",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "inventor_countries",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "inventor_addresses",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "publication_ids",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "publication_numbers",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "publication_dates",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "priority_appln_ids",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "country_kr",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "country_us",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "country_cn",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "country_fill_method",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "patstat_release",
+    "type": "string",
+    "nullable": true
+  },
+  {
+    "name": "record_source",
+    "type": "string",
+    "nullable": true
+  }
+]