diff --git a/company_linkage/README.md b/company_linkage/README.md index 6a585284..b9ededab 100644 --- a/company_linkage/README.md +++ b/company_linkage/README.md @@ -14,42 +14,7 @@ script to get top conference papers and total papers, these tests will work for This code is dependent on internal CSET BigQuery datasets; without access to these datasets, you will not be able to run some of this code as-is. -## Tasks to build visualization data - -1. [organizations.sql](sql/organizations.sql) -2. [ai_publications.sql](sql/ai_publications.sql) -3. [linked_ai_patents.sql](sql/linked_ai_patents.sql) -4. [top_conference_pubs.sql](sql/top_conference_pubs.sql) -5. [pubs_in_top_conferences.sql](sql/pubs_in_top_conferences.sql) -6. [all_publications.sql](sql/all_publications.sql) -7. `python3 aggregate_organizations.py aggregated_organizations.jsonl` -8. Replace `high_resolution_entities.aggregated_organizations` with the data from `aggregated_organizations.jsonl` using the [aggregated_organizations_schema](schemas/aggregated_organizations_schema.json) -9. `python3 get_ai_counts.py data/ai_company_papers.jsonl data/ai_company_patents.jsonl` -10. Upload `ai_company_papers.jsonl` to `ai_companies_visualization.ai_company_pubs` using the [ai_papers_schema](schemas/ai_papers_schema.json) -11. Upload `ai_company_patents.jsonl` to `ai_companies_visualization.ai_company_patents` using the [ai_patents_schema](schemas/ai_patents_schema.json) -12. `python3 top_papers.py top_paper_counts.jsonl` -13. Upload `top_paper_counts.jsonl` to `ai_companies_visualization.top_paper_counts` using the [top_papers_schema](schemas/top_papers_schema.json) -14. `python3 all_papers.py all_paper_counts.jsonl` -15. Upload `all_paper_counts.jsonl` to `ai_companies_visualization.total_paper_counts` using the [all_papers_schema](schemas/all_papers_schema.json) -16. [initial_visualization_data.sql](sql/initial_visualization_data.sql) -17. [visualization_data_with_by_year.sql](sql/visualization_data_with_by_year.sql) -18. [visualization_data_with_top_papers.sql](sql/visualization_data_with_top_papers.sql) -19. [visualization_data_with_all_papers.sql](sql/visualization_data_with_all_papers.sql) -20. [initial_patent_visualization_data.sql](sql/initial_patent_visualization_data.sql) -21. [patent_visualization_data_with_by_year.sql](sql/patent_visualization_data_with_by_year.sql) -22. [initial_paper_visualization_data.sql](sql/initial_paper_visualization_data.sql) -23. [paper_visualization_data_with_mag.sql](sql/paper_visualization_data_with_mag.sql) -24. [paper_visualization_data_with_clusters.sql](sql/paper_visualization_data_with_clusters.sql) -25. [paper_visualization_data_with_company_references.sql](sql/paper_visualization_data_with_company_references.sql) -26. [paper_visualization_data_with_tasks.sql](sql/paper_visualization_data_with_tasks.sql) -27. [paper_visualization_data_with_methods.sql](sql/paper_visualization_data_with_methods.sql) -28. [initial_workforce_visualization_data.sql](sql/initial_workforce_visualization_data.sql) -29. [workforce_visualization_data_with_ai_jobs.sql](sql/workforce_visualization_data_with_ai_jobs.sql) -30. [visualization_data_omit_by_rule.sql](sql/visualization_data_omit_by_rule.sql) -31. [visualization_data.sql](sql/visualization_data.sql) -32. [patent_visualization_data.sql](sql/patent_visualization_data.sql) -33. [paper_visualization_data.sql](sql/paper_visualization_data.sql) -34. [workforce_visualization_data.sql](sql/workforce_visualization_data.sql) +To view the order of tasks necessary to build visualization data, see the airflow DAG. # Deployment diff --git a/company_linkage/parat_data_dag.py b/company_linkage/parat_data_dag.py index 4cfcd805..07058eb6 100644 --- a/company_linkage/parat_data_dag.py +++ b/company_linkage/parat_data_dag.py @@ -153,7 +153,8 @@ cmds=["/bin/bash"], arguments=["-c", (f"echo 'getting AI counts!' ; rm -r ai || true ; " f"mkdir -p ai && " - f"python3 get_ai_counts.py ai/ai_company_papers.jsonl ai/ai_company_patents.jsonl && " + f"python3 get_ai_counts.py ai/ai_company_papers.jsonl ai/ai_company_patents.jsonl " + f"ai/ai_company_patent_grants.jsonl && " f"gsutil -m cp -r ai gs://{DATA_BUCKET}/{tmp_dir}/ ")], namespace="default", image=f"us.gcr.io/{PROJECT_ID}/parat", @@ -199,6 +200,17 @@ write_disposition="WRITE_TRUNCATE" ) + load_ai_patent_grants = GCSToBigQueryOperator( + task_id=f"load_ai_company_patent_grants", + bucket=DATA_BUCKET, + source_objects=[f"{tmp_dir}/ai/ai_company_patent_grants.jsonl"], + schema_object=f"{schema_dir}/ai_patents_schema.json", + destination_project_dataset_table=f"{staging_dataset}.ai_company_patent_grants", + source_format="NEWLINE_DELIMITED_JSON", + create_disposition="CREATE_IF_NEEDED", + write_disposition="WRITE_TRUNCATE" + ) + run_papers = [] for paper_type in ["top", "all"]: @@ -350,6 +362,7 @@ >> run_get_ai_counts >> load_ai_papers >> load_ai_patents + >> load_ai_patent_grants >> run_papers >> load_top_papers >> load_all_papers diff --git a/company_linkage/parat_scripts/get_ai_counts.py b/company_linkage/parat_scripts/get_ai_counts.py index 681f704d..5df57e13 100644 --- a/company_linkage/parat_scripts/get_ai_counts.py +++ b/company_linkage/parat_scripts/get_ai_counts.py @@ -191,7 +191,7 @@ def run_query_id_papers(self, table_name: str, test: bool = False) -> list: "nlp": element["nlp"], "robotics": element["robotics"]}) return company_rows - def run_query_id_patents(self): + def run_query_id_patents(self, table_name: str): """ Get patent counts one by one using CSET_ids. :return: @@ -240,7 +240,7 @@ def run_query_id_patents(self): Machine_Learning, Search_Methods FROM - staging_ai_companies_visualization.linked_ai_patents + staging_ai_companies_visualization.{table_name} WHERE regexp_contains(assignee, r'(?i){regexes[0]}') """ # if we have more than one regex for an org, include all of them if len(regexes) > 1: @@ -274,8 +274,12 @@ def write_output(self, row_list: list, output_file) -> None: def main() -> None: parser = argparse.ArgumentParser() - parser.add_argument("output_papers", type=str, help="A jsonl file for writing output paper data to create new tables") - parser.add_argument("output_patents", type=str, help="A jsonl file for writing output patent data to create new tables") + parser.add_argument("output_papers", type=str, + help="A jsonl file for writing output paper data to create new tables") + parser.add_argument("output_patents", type=str, + help="A jsonl file for writing output patent data to create new tables") + parser.add_argument("output_patent_grants", type=str, + help="A jsonl file for writing output patent grants data to create new tables") args = parser.parse_args() if "jsonl" not in args.output_papers or "jsonl" not in args.output_patents: parser.print_help() @@ -287,10 +291,14 @@ def main() -> None: company_rows = count_getter.run_query_id_papers(table_name) print("Writing results") count_getter.write_output(company_rows, args.output_papers) - print("Fetching patent data") - patent_companies = count_getter.run_query_id_patents() + print("Fetching patent applications data") + patent_companies = count_getter.run_query_id_patents("linked_ai_patents") print("Writing results") count_getter.write_output(patent_companies, args.output_patents) + print("Fetching patent grants data") + patent_grant_companies = count_getter.run_query_id_patents("linked_ai_patents_grants") + print("Writing results") + count_getter.write_output(patent_grant_companies, args.output_patent_grants) if __name__ == "__main__": main() \ No newline at end of file diff --git a/company_linkage/sequences/initial_data.csv b/company_linkage/sequences/initial_data.csv index c1a10432..b7aa753e 100644 --- a/company_linkage/sequences/initial_data.csv +++ b/company_linkage/sequences/initial_data.csv @@ -1,6 +1,7 @@ high_resolution_entities,organizations staging_ai_companies_visualization,ai_publications staging_ai_companies_visualization,linked_ai_patents +staging_ai_companies_visualization,linked_ai_patents_grants staging_ai_companies_visualization,top_conference_pubs staging_ai_companies_visualization,pubs_in_top_conferences staging_ai_companies_visualization,all_publications \ No newline at end of file diff --git a/company_linkage/sequences/visualization_data.csv b/company_linkage/sequences/visualization_data.csv index 09ae1630..30a711cf 100644 --- a/company_linkage/sequences/visualization_data.csv +++ b/company_linkage/sequences/visualization_data.csv @@ -4,6 +4,8 @@ staging_ai_companies_visualization,visualization_data_with_top_papers staging_ai_companies_visualization,visualization_data_with_all_papers staging_ai_companies_visualization,initial_patent_visualization_data staging_ai_companies_visualization,patent_visualization_data_with_by_year +staging_ai_companies_visualization,patent_visualization_data_with_grants +staging_ai_companies_visualization,patent_visualization_data_with_grants_by_year staging_ai_companies_visualization,initial_paper_visualization_data staging_ai_companies_visualization,paper_visualization_data_with_mag staging_ai_companies_visualization,paper_visualization_data_with_clusters diff --git a/company_linkage/sql/linked_ai_patents.sql b/company_linkage/sql/linked_ai_patents.sql index ee7360b2..fd15d8de 100644 --- a/company_linkage/sql/linked_ai_patents.sql +++ b/company_linkage/sql/linked_ai_patents.sql @@ -1,9 +1,9 @@ --- Pulling every AI-associated patent family id linked to every grid id of any assignee for that patent, and all the assignee names +-- Pulling every AI-associated patent family id linked to every ror id of any assignee for that patent, and all the assignee names -- We also pull in the AI subcategories and the years -- We also attempt to add in "fake" families for the patents that are missing patent families with patents_orig as ( SELECT - -- Pulling in the current assignee ror ids from dimensions + -- Pulling in the current assignee ror ids patent_id, family_id, assignee, diff --git a/company_linkage/sql/linked_ai_patents_grants.sql b/company_linkage/sql/linked_ai_patents_grants.sql new file mode 100644 index 00000000..c8948d14 --- /dev/null +++ b/company_linkage/sql/linked_ai_patents_grants.sql @@ -0,0 +1,121 @@ +-- Pulling every AI-associated patent family id linked to every grid id of any assignee for that patent, and all the assignee names +-- We also pull in the AI subcategories and the years +-- We also attempt to add in "fake" families for the patents that are missing patent families +with patents_orig as ( +SELECT + -- Pulling in the current assignee ror ids from dimensions + patent_id, + assignees_normalized.family_id, + assignee, + ror_id, + granted +FROM + unified_patents.assignees_normalized + LEFT JOIN + unified_patents.metadata + USING + (patent_id)), +all_ai as ( + -- Selecting all the family ids and patent IDs to get AI patents + -- Also select the year so we can get counts by year + SELECT + patent_id, + Physical_Sciences_and_Engineering, + Life_Sciences, + Security__eg_cybersecurity, + Transportation, + Industrial_and_Manufacturing, + Education, + Document_Mgt_and_Publishing, + Military, + Agricultural, + Computing_in_Government, + Personal_Devices_and_Computing, + Banking_and_Finance, + Telecommunications, + Networks__eg_social_IOT_etc, + Business, + Energy_Management, + Entertainment, + Nanotechnology, + Semiconductors, + Language_Processing, + Speech_Processing, + Knowledge_Representation, + Planning_and_Scheduling, + Control, Distributed_AI, + Robotics, + Computer_Vision, + Analytics_and_Algorithms, + Measuring_and_Testing, + Logic_Programming, + Fuzzy_Logic, + Probabilistic_Reasoning, + Ontology_Engineering, + Machine_Learning, + Search_Methods + FROM + unified_patents.ai_patents), + patent_years as ( + SELECT + patent_id, + EXTRACT(year FROM first_priority_date) as priority_year + FROM + unified_patents.dates + ) + SELECT + DISTINCT + -- If the family id is null we can't group by family id so we create a fake family id using the patent id + -- Since we can't group by family id there should only be one patent id in these cases + -- We're just doing this so our counts aren't blank + COALESCE(family_id, "X-" || patent_id) as family_id, + assignee, + ror_id, + MIN(priority_year) as priority_year, + LOGICAL_OR(granted) as granted, + LOGICAL_OR(Physical_Sciences_and_Engineering) as Physical_Sciences_and_Engineering, + LOGICAL_OR(Life_Sciences) as Life_Sciences, + LOGICAL_OR(Security__eg_cybersecurity) as Security__eg_cybersecurity, + LOGICAL_OR(Transportation) as Transportation, + LOGICAL_OR(Industrial_and_Manufacturing) as Industrial_and_Manufacturing, + LOGICAL_OR(Education) as Education, + LOGICAL_OR(Document_Mgt_and_Publishing) as Document_Mgt_and_Publishing, + LOGICAL_OR(Military) as Military, + LOGICAL_OR(Agricultural) as Agricultural, + LOGICAL_OR(Computing_in_Government) as Computing_in_Government, + LOGICAL_OR(Personal_Devices_and_Computing) as Personal_Devices_and_Computing, + LOGICAL_OR(Banking_and_Finance) as Banking_and_Finance, + LOGICAL_OR(Telecommunications) as Telecommunications, + LOGICAL_OR(Networks__eg_social_IOT_etc) as Networks__eg_social_IOT_etc, + LOGICAL_OR(Business) as Business, + LOGICAL_OR(Energy_Management) as Energy_Management, + LOGICAL_OR(Entertainment) as Entertainment, + LOGICAL_OR(Nanotechnology) as Nanotechnology, + LOGICAL_OR(Semiconductors) as Semiconductors, + LOGICAL_OR(Language_Processing) as Language_Processing, + LOGICAL_OR(Speech_Processing) as Speech_Processing, + LOGICAL_OR(Knowledge_Representation) as Knowledge_Representation, + LOGICAL_OR(Planning_and_Scheduling) as Planning_and_Scheduling, + LOGICAL_OR(Control) as Control, + LOGICAL_OR(Distributed_AI) as Distributed_AI, + LOGICAL_OR(Robotics) as Robotics, + LOGICAL_OR(Computer_Vision) as Computer_Vision, + LOGICAL_OR(Analytics_and_Algorithms) as Analytics_and_Algorithms, + LOGICAL_OR(Measuring_and_Testing) as Measuring_and_Testing, + LOGICAL_OR(Logic_Programming) as Logic_Programming, + LOGICAL_OR(Fuzzy_Logic) as Fuzzy_Logic, + LOGICAL_OR(Probabilistic_Reasoning) as Probabilistic_Reasoning, + LOGICAL_OR(Ontology_Engineering) as Ontology_Engineering, + LOGICAL_OR(Machine_Learning) as Machine_Learning, + LOGICAL_OR(Search_Methods) as Search_Methods + -- Only including patents if their ids are in or AI patent set, ensuring we have AI patents + FROM ( all_ai + LEFT JOIN patents_orig + USING (patent_id) + LEFT JOIN patent_years + USING (patent_id)) + WHERE priority_year IS NOT NULL and granted is true + GROUP BY + ror_id, + assignee, + family_id \ No newline at end of file diff --git a/company_linkage/sql/patent_visualization_data.sql b/company_linkage/sql/patent_visualization_data.sql index 8047506a..475c5460 100644 --- a/company_linkage/sql/patent_visualization_data.sql +++ b/company_linkage/sql/patent_visualization_data.sql @@ -11,13 +11,13 @@ WITH FROM staging_ai_companies_visualization.visualization_data_omit_by_rule RIGHT JOIN - staging_ai_companies_visualization.patent_visualization_data_with_by_year + staging_ai_companies_visualization.patent_visualization_data_with_grants_by_year USING (cset_id) WHERE visualization_data_omit_by_rule.cset_id IS NULL) SELECT * FROM - staging_ai_companies_visualization.patent_visualization_data_with_by_year + staging_ai_companies_visualization.patent_visualization_data_with_grants_by_year WHERE CSET_id NOT IN ( SELECT diff --git a/company_linkage/sql/patent_visualization_data_with_grants.sql b/company_linkage/sql/patent_visualization_data_with_grants.sql new file mode 100644 index 00000000..eb1546f7 --- /dev/null +++ b/company_linkage/sql/patent_visualization_data_with_grants.sql @@ -0,0 +1,107 @@ +WITH + aipats AS ( + -- Pulling all the patents from any of our companies + SELECT + * + FROM + staging_ai_companies_visualization.ai_company_patent_grants), + pattable AS ( + -- Getting the count of patents + SELECT + CSET_id, + COUNT(DISTINCT family_id) AS ai_patents_grants, + COUNT(DISTINCT CASE WHEN Physical_Sciences_and_Engineering IS TRUE THEN family_id END) as Physical_Sciences_and_Engineering_pats_grants, + COUNT(DISTINCT CASE WHEN Life_Sciences IS TRUE THEN family_id END) as Life_Sciences_pats_grants, + COUNT(DISTINCT CASE WHEN Security__eg_cybersecurity IS TRUE THEN family_id END) as Security__eg_cybersecurity_pats_grants, + COUNT(DISTINCT CASE WHEN Transportation IS TRUE THEN family_id END) as Transportation_pats_grants, + COUNT(DISTINCT CASE WHEN Industrial_and_Manufacturing IS TRUE THEN family_id END) as Industrial_and_Manufacturing_pats_grants, + COUNT(DISTINCT CASE WHEN Education IS TRUE THEN family_id END) as Education_pats_grants, + COUNT(DISTINCT CASE WHEN Document_Mgt_and_Publishing IS TRUE THEN family_id END) as Document_Mgt_and_Publishing_pats_grants, + COUNT(DISTINCT CASE WHEN Military IS TRUE THEN family_id END) as Military_pats_grants, + COUNT(DISTINCT CASE WHEN Agricultural IS TRUE THEN family_id END) as Agricultural_pats_grants, + COUNT(DISTINCT CASE WHEN Computing_in_Government IS TRUE THEN family_id END) as Computing_in_Government_pats_grants, + COUNT(DISTINCT CASE WHEN Personal_Devices_and_Computing IS TRUE THEN family_id END) as Personal_Devices_and_Computing_pats_grants, + COUNT(DISTINCT CASE WHEN Banking_and_Finance IS TRUE THEN family_id END) as Banking_and_Finance_pats_grants, + COUNT(DISTINCT CASE WHEN Telecommunications IS TRUE THEN family_id END) as Telecommunications_pats_grants, + COUNT(DISTINCT CASE WHEN Networks__eg_social_IOT_etc IS TRUE THEN family_id END) as Networks__eg_social_IOT_etc_pats_grants, + COUNT(DISTINCT CASE WHEN Business IS TRUE THEN family_id END) as Business_pats_grants, + COUNT(DISTINCT CASE WHEN Energy_Management IS TRUE THEN family_id END) as Energy_Management_pats_grants, + COUNT(DISTINCT CASE WHEN Entertainment IS TRUE THEN family_id END) as Entertainment_pats_grants, + COUNT(DISTINCT CASE WHEN Nanotechnology IS TRUE THEN family_id END) as Nanotechnology_pats_grants, + COUNT(DISTINCT CASE WHEN Semiconductors IS TRUE THEN family_id END) as Semiconductors_pats_grants, + COUNT(DISTINCT CASE WHEN Language_Processing IS TRUE THEN family_id END) as Language_Processing_pats_grants, + COUNT(DISTINCT CASE WHEN Speech_Processing IS TRUE THEN family_id END) as Speech_Processing_pats_grants, + COUNT(DISTINCT CASE WHEN Knowledge_Representation IS TRUE THEN family_id END) as Knowledge_Representation_pats_grants, + COUNT(DISTINCT CASE WHEN Planning_and_Scheduling IS TRUE THEN family_id END) as Planning_and_Scheduling_pats_grants, + COUNT(DISTINCT CASE WHEN Control IS TRUE THEN family_id END) as Control_pats_grants, + COUNT(DISTINCT CASE WHEN Distributed_AI IS TRUE THEN family_id END) as Distributed_AI_pats_grants, + COUNT(DISTINCT CASE WHEN Robotics IS TRUE THEN family_id END) as Robotics_pats_grants, + COUNT(DISTINCT CASE WHEN Computer_Vision IS TRUE THEN family_id END) as Computer_Vision_pats_grants, + COUNT(DISTINCT CASE WHEN Analytics_and_Algorithms IS TRUE THEN family_id END) as Analytics_and_Algorithms_pats_grants, + COUNT(DISTINCT CASE WHEN Measuring_and_Testing IS TRUE THEN family_id END) as Measuring_and_Testing_pats_grants, + COUNT(DISTINCT CASE WHEN Logic_Programming IS TRUE THEN family_id END) as Logic_Programming_pats_grants, + COUNT(DISTINCT CASE WHEN Fuzzy_Logic IS TRUE THEN family_id END) as Fuzzy_Logic_pats_grants, + COUNT(DISTINCT CASE WHEN Probabilistic_Reasoning IS TRUE THEN family_id END) as Probabilistic_Reasoning_pats_grants, + COUNT(DISTINCT CASE WHEN Ontology_Engineering IS TRUE THEN family_id END) as Ontology_Engineering_pats_grants, + COUNT(DISTINCT CASE WHEN Machine_Learning IS TRUE THEN family_id END) as Machine_Learning_pats_grants, + COUNT(DISTINCT CASE WHEN Search_Methods IS TRUE THEN family_id END) as Search_Methods_pats_grants + + FROM aipats + GROUP BY + CSET_id), +grants_all as + -- Pulling CSET_id and name, plus ai_pats +(SELECT + CSET_id, + COALESCE(ai_patents_grants, 0) as ai_patents_grants, + COALESCE(Physical_Sciences_and_Engineering_pats_grants, 0) as Physical_Sciences_and_Engineering_pats_grants, + COALESCE(Life_Sciences_pats_grants, 0) as Life_Sciences_pats_grants, + COALESCE(Security__eg_cybersecurity_pats_grants, 0) as Security__eg_cybersecurity_pats_grants, + COALESCE(Transportation_pats_grants, 0) as Transportation_pats_grants, + COALESCE(Industrial_and_Manufacturing_pats_grants, 0) as Industrial_and_Manufacturing_pats_grants, + COALESCE(Education_pats_grants, 0) as Education_pats_grants, + COALESCE(Document_Mgt_and_Publishing_pats_grants, 0) as Document_Mgt_and_Publishing_pats_grants, + COALESCE(Military_pats_grants, 0) as Military_pats_grants, + COALESCE(Agricultural_pats_grants, 0) as Agricultural_pats_grants, + COALESCE(Computing_in_Government_pats_grants, 0) as Computing_in_Government_pats_grants, + COALESCE(Personal_Devices_and_Computing_pats_grants, 0) as Personal_Devices_and_Computing_pats_grants, + COALESCE(Banking_and_Finance_pats_grants, 0) as Banking_and_Finance_pats_grants, + COALESCE(Telecommunications_pats_grants, 0) as Telecommunications_pats_grants, + COALESCE(Networks__eg_social_IOT_etc_pats_grants, 0) as Networks__eg_social_IOT_etc_pats_grants, + COALESCE(Business_pats_grants, 0) as Business_pats_grants, + COALESCE(Energy_Management_pats_grants, 0) as Energy_Management_pats_grants, + COALESCE(Entertainment_pats_grants, 0) as Entertainment_pats_grants, + COALESCE(Nanotechnology_pats_grants, 0) as Nanotechnology_pats_grants, + COALESCE(Semiconductors_pats_grants, 0) as Semiconductors_pats_grants, + COALESCE(Language_Processing_pats_grants, 0) as Language_Processing_pats_grants, + COALESCE(Speech_Processing_pats_grants, 0) as Speech_Processing_pats_grants, + COALESCE(Knowledge_Representation_pats_grants, 0) as Knowledge_Representation_pats_grants, + COALESCE(Planning_and_Scheduling_pats_grants, 0) as Planning_and_Scheduling_pats_grants, + COALESCE(Control_pats_grants, 0) as Control_pats_grants, + COALESCE(Distributed_AI_pats_grants, 0) as Distributed_AI_pats_grants, + COALESCE(Robotics_pats_grants, 0) as Robotics_pats_grants, + COALESCE(Computer_Vision_pats_grants, 0) as Computer_Vision_pats_grants, + COALESCE(Analytics_and_Algorithms_pats_grants, 0) as Analytics_and_Algorithms_pats_grants, + COALESCE(Measuring_and_Testing_pats_grants, 0) as Measuring_and_Testing_pats_grants, + COALESCE(Logic_Programming_pats_grants, 0) as Logic_Programming_pats_grants, + COALESCE(Fuzzy_Logic_pats_grants, 0) as Fuzzy_Logic_pats_grants, + COALESCE(Probabilistic_Reasoning_pats_grants, 0) as Probabilistic_Reasoning_pats_grants, + COALESCE(Ontology_Engineering_pats_grants, 0) as Ontology_Engineering_pats_grants, + COALESCE(Machine_Learning_pats_grants, 0) as Machine_Learning_pats_grants, + COALESCE(Search_Methods_pats_grants, 0) as Search_Methods_pats_grants, +FROM + high_resolution_entities.aggregated_organizations +LEFT JOIN + pattable +USING + (CSET_id) +) +SELECT + viz.*, + grants_all.* EXCEPT (CSET_id) +FROM + staging_ai_companies_visualization.patent_visualization_data_with_by_year AS viz +LEFT JOIN + grants_all +USING + (CSET_id) \ No newline at end of file diff --git a/company_linkage/sql/patent_visualization_data_with_grants_by_year.sql b/company_linkage/sql/patent_visualization_data_with_grants_by_year.sql new file mode 100644 index 00000000..f83ec62a --- /dev/null +++ b/company_linkage/sql/patent_visualization_data_with_grants_by_year.sql @@ -0,0 +1,220 @@ +WITH + aipats AS ( + -- Pulling all the patents from any of our companies + SELECT + * + FROM + staging_ai_companies_visualization.ai_company_patent_grants), + pattable AS ( + -- Getting the count of patents + SELECT + CSET_id, + priority_year, + COUNT(DISTINCT family_id) AS ai_patents, + COUNT(DISTINCT CASE WHEN Physical_Sciences_and_Engineering IS TRUE THEN family_id END) as Physical_Sciences_and_Engineering_pats_grants, + COUNT(DISTINCT CASE WHEN Life_Sciences IS TRUE THEN family_id END) as Life_Sciences_pats_grants, + COUNT(DISTINCT CASE WHEN Security__eg_cybersecurity IS TRUE THEN family_id END) as Security__eg_cybersecurity_pats_grants, + COUNT(DISTINCT CASE WHEN Transportation IS TRUE THEN family_id END) as Transportation_pats_grants, + COUNT(DISTINCT CASE WHEN Industrial_and_Manufacturing IS TRUE THEN family_id END) as Industrial_and_Manufacturing_pats_grants, + COUNT(DISTINCT CASE WHEN Education IS TRUE THEN family_id END) as Education_pats_grants, + COUNT(DISTINCT CASE WHEN Document_Mgt_and_Publishing IS TRUE THEN family_id END) as Document_Mgt_and_Publishing_pats_grants, + COUNT(DISTINCT CASE WHEN Military IS TRUE THEN family_id END) as Military_pats_grants, + COUNT(DISTINCT CASE WHEN Agricultural IS TRUE THEN family_id END) as Agricultural_pats_grants, + COUNT(DISTINCT CASE WHEN Computing_in_Government IS TRUE THEN family_id END) as Computing_in_Government_pats_grants, + COUNT(DISTINCT CASE WHEN Personal_Devices_and_Computing IS TRUE THEN family_id END) as Personal_Devices_and_Computing_pats_grants, + COUNT(DISTINCT CASE WHEN Banking_and_Finance IS TRUE THEN family_id END) as Banking_and_Finance_pats_grants, + COUNT(DISTINCT CASE WHEN Telecommunications IS TRUE THEN family_id END) as Telecommunications_pats_grants, + COUNT(DISTINCT CASE WHEN Networks__eg_social_IOT_etc IS TRUE THEN family_id END) as Networks__eg_social_IOT_etc_pats_grants, + COUNT(DISTINCT CASE WHEN Business IS TRUE THEN family_id END) as Business_pats_grants, + COUNT(DISTINCT CASE WHEN Energy_Management IS TRUE THEN family_id END) as Energy_Management_pats_grants, + COUNT(DISTINCT CASE WHEN Entertainment IS TRUE THEN family_id END) as Entertainment_pats_grants, + COUNT(DISTINCT CASE WHEN Nanotechnology IS TRUE THEN family_id END) as Nanotechnology_pats_grants, + COUNT(DISTINCT CASE WHEN Semiconductors IS TRUE THEN family_id END) as Semiconductors_pats_grants, + COUNT(DISTINCT CASE WHEN Language_Processing IS TRUE THEN family_id END) as Language_Processing_pats_grants, + COUNT(DISTINCT CASE WHEN Speech_Processing IS TRUE THEN family_id END) as Speech_Processing_pats_grants, + COUNT(DISTINCT CASE WHEN Knowledge_Representation IS TRUE THEN family_id END) as Knowledge_Representation_pats_grants, + COUNT(DISTINCT CASE WHEN Planning_and_Scheduling IS TRUE THEN family_id END) as Planning_and_Scheduling_pats_grants, + COUNT(DISTINCT CASE WHEN Control IS TRUE THEN family_id END) as Control_pats_grants, + COUNT(DISTINCT CASE WHEN Distributed_AI IS TRUE THEN family_id END) as Distributed_AI_pats_grants, + COUNT(DISTINCT CASE WHEN Robotics IS TRUE THEN family_id END) as Robotics_pats_grants, + COUNT(DISTINCT CASE WHEN Computer_Vision IS TRUE THEN family_id END) as Computer_Vision_pats_grants, + COUNT(DISTINCT CASE WHEN Analytics_and_Algorithms IS TRUE THEN family_id END) as Analytics_and_Algorithms_pats_grants, + COUNT(DISTINCT CASE WHEN Measuring_and_Testing IS TRUE THEN family_id END) as Measuring_and_Testing_pats_grants, + COUNT(DISTINCT CASE WHEN Logic_Programming IS TRUE THEN family_id END) as Logic_Programming_pats_grants, + COUNT(DISTINCT CASE WHEN Fuzzy_Logic IS TRUE THEN family_id END) as Fuzzy_Logic_pats_grants, + COUNT(DISTINCT CASE WHEN Probabilistic_Reasoning IS TRUE THEN family_id END) as Probabilistic_Reasoning_pats_grants, + COUNT(DISTINCT CASE WHEN Ontology_Engineering IS TRUE THEN family_id END) as Ontology_Engineering_pats_grants, + COUNT(DISTINCT CASE WHEN Machine_Learning IS TRUE THEN family_id END) as Machine_Learning_pats_grants, + COUNT(DISTINCT CASE WHEN Search_Methods IS TRUE THEN family_id END) as Search_Methods_pats_grants + FROM aipats + GROUP BY + CSET_id, + priority_year), + by_year as ( + -- Get the counts by year + SELECT + CSET_id, + ARRAY_AGG(STRUCT(priority_year, + ai_patents) + ORDER BY + priority_year) AS ai_patents_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Physical_Sciences_and_Engineering_pats_grants) + ORDER BY + priority_year) AS Physical_Sciences_and_Engineering_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Life_Sciences_pats_grants) + ORDER BY + priority_year) AS Life_Sciences_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Security__eg_cybersecurity_pats_grants) + ORDER BY + priority_year) AS Security__eg_cybersecurity_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Transportation_pats_grants) + ORDER BY + priority_year) AS Transportation_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Industrial_and_Manufacturing_pats_grants) + ORDER BY + priority_year) AS Industrial_and_Manufacturing_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Education_pats_grants) + ORDER BY + priority_year) AS Education_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Document_Mgt_and_Publishing_pats_grants) + ORDER BY + priority_year) AS Document_Mgt_and_Publishing_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Military_pats_grants) + ORDER BY + priority_year) AS Military_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Agricultural_pats_grants) + ORDER BY + priority_year) AS Agricultural_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Computing_in_Government_pats_grants) + ORDER BY + priority_year) AS Computing_in_Government_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Personal_Devices_and_Computing_pats_grants) + ORDER BY + priority_year) AS Personal_Devices_and_Computing_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Banking_and_Finance_pats_grants) + ORDER BY + priority_year) AS Banking_and_Finance_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Telecommunications_pats_grants) + ORDER BY + priority_year) AS Telecommunications_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Networks__eg_social_IOT_etc_pats_grants) + ORDER BY + priority_year) AS Networks__eg_social_IOT_etc_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Business_pats_grants) + ORDER BY + priority_year) AS Business_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Energy_Management_pats_grants) + ORDER BY + priority_year) AS Energy_Management_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Entertainment_pats_grants) + ORDER BY + priority_year) AS Entertainment_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Nanotechnology_pats_grants) + ORDER BY + priority_year) AS Nanotechnology_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Semiconductors_pats_grants) + ORDER BY + priority_year) AS Semiconductors_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Language_Processing_pats_grants) + ORDER BY + priority_year) AS Language_Processing_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Speech_Processing_pats_grants) + ORDER BY + priority_year) AS Speech_Processing_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Knowledge_Representation_pats_grants) + ORDER BY + priority_year) AS Knowledge_Representation_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Planning_and_Scheduling_pats_grants) + ORDER BY + priority_year) AS Planning_and_Scheduling_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Control_pats_grants) + ORDER BY + priority_year) AS Control_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Distributed_AI_pats_grants) + ORDER BY + priority_year) AS Distributed_AI_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Robotics_pats_grants) + ORDER BY + priority_year) AS Robotics_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Computer_Vision_pats_grants) + ORDER BY + priority_year) AS Computer_Vision_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Analytics_and_Algorithms_pats_grants) + ORDER BY + priority_year) AS Analytics_and_Algorithms_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Measuring_and_Testing_pats_grants) + ORDER BY + priority_year) AS Measuring_and_Testing_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Logic_Programming_pats_grants) + ORDER BY + priority_year) AS Logic_Programming_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Fuzzy_Logic_pats_grants) + ORDER BY + priority_year) AS Fuzzy_Logic_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Probabilistic_Reasoning_pats_grants) + ORDER BY + priority_year) AS Probabilistic_Reasoning_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Ontology_Engineering_pats_grants) + ORDER BY + priority_year) AS Ontology_Engineering_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Machine_Learning_pats_grants) + ORDER BY + priority_year) AS Machine_Learning_pats_grants_by_year, + ARRAY_AGG(STRUCT(priority_year, + Search_Methods_pats_grants) + ORDER BY + priority_year) AS Search_Methods_pats_grants_by_year, + + FROM + high_resolution_entities.aggregated_organizations + LEFT JOIN + pattable + USING + (CSET_id) + GROUP BY + CSET_id +) + -- Pulling CSET_id and name, plus ai_pats_grants +SELECT + viz.*, + by_year.* EXCEPT (CSET_id) +FROM + staging_ai_companies_visualization.patent_visualization_data_with_grants AS viz +LEFT JOIN + by_year +USING + (CSET_id) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 7551a29c..4473fe92 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,7 +10,7 @@ google-auth google-cloud-bigquery-storage google-cloud-bigquery google-cloud-core -google-cloud-translate +google-cloud-translate==1.6.0 google-crc32c google-resumable-media googleapis-common-protos diff --git a/web/gui-v2/src/components/DetailViewPatents.jsx b/web/gui-v2/src/components/DetailViewPatents.jsx index d571818d..5ec53561 100644 --- a/web/gui-v2/src/components/DetailViewPatents.jsx +++ b/web/gui-v2/src/components/DetailViewPatents.jsx @@ -72,9 +72,9 @@ const DetailViewPatents = ({ text: <>growth in {data.name}'s AI patenting ({yearSpanNdash}), }, { - key: "ai-patent-applications", - stat: <>{commas(data.patents.ai_patent_applications.total)}, - text:
AI patent applications were filed by {data.name} ({yearSpanNdash})
, + key: "ai-patent-grants", + stat: <>{commas(data.patents.ai_patents_grants.total)}, + text:
AI patents were granted to {data.name} ({yearSpanNdash})
, }, { key: "ai-focused-percent", diff --git a/web/gui-v2/src/components/ListView.test.js b/web/gui-v2/src/components/ListView.test.js index 30fc4f5e..5a027a79 100644 --- a/web/gui-v2/src/components/ListView.test.js +++ b/web/gui-v2/src/components/ListView.test.js @@ -25,16 +25,16 @@ describe("ListView", () => { ); // Filter by Europe and verify that the count updates - expect(screen.getByText('Viewing 1760 companies')).toBeVisible(); + expect(screen.getByText('Viewing 1779 companies')).toBeVisible(); const regionHeader = screen.getByRole('columnheader', { name: /country/i }); await user.click(getByRole(regionHeader, 'button')); const menu = screen.getByRole('listbox'); await user.click(getByText(menu, 'China')); - expect(screen.getByText('Viewing 267 of 1760 companies')).toBeVisible(); + expect(screen.getByText('Viewing 269 of 1779 companies')).toBeVisible(); // Reset the filters and verify that the count updates await user.click(screen.getByRole('button', { name: /reset filters/i })); - expect(screen.getByText('Viewing 1760 companies')).toBeVisible(); + expect(screen.getByText('Viewing 1779 companies')).toBeVisible(); }, 20000); @@ -49,7 +49,7 @@ describe("ListView", () => { await user.click(getByRole(companyHeader, 'combobox')); const menu = screen.getByRole('listbox'); await user.click(getByText(menu, 'S&P 500')); - expect(screen.getByText('Viewing 499 of 1760 companies')).toBeVisible(); + expect(screen.getByText('Viewing 503 of 1779 companies')).toBeVisible(); }, 20000); diff --git a/web/gui-v2/src/static_data/data.js b/web/gui-v2/src/static_data/data.js index a2e7122b..0d540c82 100644 --- a/web/gui-v2/src/static_data/data.js +++ b/web/gui-v2/src/static_data/data.js @@ -1,3 +1,3 @@ -const company_data = [{"cset_id": 803, "country": "Ireland", "website": "https://www.accenture.com/us-en", "crunchbase": {"text": "0d5171b3-68b3-37c3-cb50-8cd8ccb8930b", "url": "https://www.crunchbase.com/organization/accenture"}, "child_crunchbase": [], "linkedin": ["https://www.linkedin.com/company/accenture"], "stage": "Mature", "name": "Accenture PLC", "patent_name": "accenture plc", "continent": "Europe", "local_logo": "accenture_plc.png", "aliases": "Accenture; Andersen", "permid_links": [{"text": 4295903017, "url": "https://permid.org/1-4295903017"}], "parent_info": null, "agg_child_info": null, "unagg_child_info": null, "market_filt": [{"text": "NYSE:ACN", "url": "https://www.google.com/finance/quote/acn:nyse"}], "market_full": [{"text": "FRA:CSA", "url": "https://www.google.com/finance/quote/csa:fra"}, {"text": "FWB:CSA", "url": "https://www.google.com/finance/quote/csa:fwb"}, {"text": "MEX:ACNN", "url": "https://www.google.com/finance/quote/acnn:mex"}, {"text": "LSE:0Y0Y", "url": "https://www.google.com/finance/quote/0y0y:lse"}, {"text": "NYSE:ACN", "url": "https://www.google.com/finance/quote/acn:nyse"}], "crunchbase_description": "Accenture is a professional services company, providing services and solutions in strategy, consulting, digital, technology and operations.", "groups": {"sp500": true, "global500": true}, "fields": [{"field_name": "Deep learning", "field_count": 9}, {"field_name": "Convolutional neural network", "field_count": 5}, {"field_name": "Artificial neural network", "field_count": 5}, {"field_name": "Sentence", "field_count": 4}, {"field_name": "Topic model", "field_count": 4}, {"field_name": "Cluster analysis", "field_count": 4}, {"field_name": "Semantic similarity", "field_count": 3}, {"field_name": "Sentiment analysis", "field_count": 3}, {"field_name": "Biometrics", "field_count": 3}, {"field_name": "Robot", "field_count": 3}], "clusters": [{"cluster_id": 12638, "cluster_count": 9}, {"cluster_id": 7496, "cluster_count": 5}, {"cluster_id": 5109, "cluster_count": 5}, {"cluster_id": 19500, "cluster_count": 3}, {"cluster_id": 1149, "cluster_count": 3}, {"cluster_id": 20825, "cluster_count": 3}, {"cluster_id": 75688, "cluster_count": 2}, {"cluster_id": 12712, "cluster_count": 2}, {"cluster_id": 2621, "cluster_count": 2}, {"cluster_id": 53938, "cluster_count": 2}], "company_references": [{"ref_CSET_id": 101, "referenced_count": 194}, {"ref_CSET_id": 163, "referenced_count": 119}, {"ref_CSET_id": 115, "referenced_count": 73}, {"ref_CSET_id": 87, "referenced_count": 59}, {"ref_CSET_id": 803, "referenced_count": 31}, {"ref_CSET_id": 6, "referenced_count": 14}, {"ref_CSET_id": 127, "referenced_count": 14}, {"ref_CSET_id": 1126, "referenced_count": 13}, {"ref_CSET_id": 792, "referenced_count": 13}, {"ref_CSET_id": 21, "referenced_count": 12}], "tasks": [{"referent": "classification", "task_count": 29}, {"referent": "artificial_intelligence_and_machine_learning", "task_count": 14}, {"referent": "decision_making", "task_count": 11}, {"referent": "information_extraction", "task_count": 7}, {"referent": "system_identification", "task_count": 6}, {"referent": "natural_language_processing", "task_count": 6}, {"referent": "software_defect_prediction", "task_count": 6}, {"referent": "knowledge_base", "task_count": 6}, {"referent": "multi_task_learning", "task_count": 6}, {"referent": "image_processing", "task_count": 5}], "methods": [{"referent": "mad_learning", "method_count": 27}, {"referent": "vqa_models", "method_count": 17}, {"referent": "q_learning", "method_count": 16}, {"referent": "recurrent_neural_networks", "method_count": 14}, {"referent": "convolutional_neural_networks", "method_count": 12}, {"referent": "auto_classifier", "method_count": 12}, {"referent": "double_q_learning", "method_count": 10}, {"referent": "symbolic_deep_learning", "method_count": 10}, {"referent": "griffin_lim_algorithm", "method_count": 9}, {"referent": "natural_language_processing", "method_count": 9}], "articles": {"highly_cited": {"counts": [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], "total": 22, "isTopResearch": false, "rank": 1, "sp500_rank": 1, "fortune500_rank": 1}, "all_publications": {"counts": [114, 112, 107, 129, 137, 167, 173, 192, 161, 67, 4], "total": 1363, "isTopResearch": false, "rank": 156, "sp500_rank": 120, "fortune500_rank": 57}, "ai_publications": {"counts": [7, 10, 6, 20, 14, 32, 27, 44, 46, 7, 1], "total": 214, "isTopResearch": false, "rank": 77, "sp500_rank": 60, "fortune500_rank": 23}, "ai_publications_growth": {"counts": [], "total": -5.758063729078221, "isTopResearch": false, "rank": 1238, "sp500_rank": 299, "fortune500_rank": 356}, "ai_pubs_top_conf": {"counts": [0, 1, 1, 0, 1, 4, 2, 3, 6, 1, 0], "total": 19, "isTopResearch": false, "rank": 82, "sp500_rank": 44, "fortune500_rank": 27}, "citation_counts": {"counts": [29, 49, 78, 71, 99, 162, 254, 400, 562, 361, 20], "total": 2085, "isTopResearch": false, "rank": 130, "sp500_rank": 81, "fortune500_rank": 39}, "cv_pubs": {"counts": [0, 0, 0, 1, 1, 2, 4, 3, 6, 2, 0], "total": 19, "isTopResearch": true, "rank": 143, "sp500_rank": 89, "fortune500_rank": 40}, "nlp_pubs": {"counts": [4, 5, 2, 7, 4, 14, 5, 10, 16, 1, 1], "total": 69, "isTopResearch": true, "rank": 36, "sp500_rank": 26, "fortune500_rank": 14}, "robotics_pubs": {"counts": [0, 0, 0, 1, 1, 3, 0, 3, 3, 0, 0], "total": 11, "isTopResearch": true, "rank": 106, "sp500_rank": 77, "fortune500_rank": 29}, "citations_per_article": {"counts": [4.142857142857143, 4.9, 13.0, 3.55, 7.071428571428571, 5.0625, 9.407407407407407, 9.090909090909092, 12.217391304347826, 51.57142857142857, 20.0], "total": 9.742990654205608, "isTopResearch": false, "rank": 550, "sp500_rank": 205, "fortune500_rank": 159}}, "patents": {"ai_patents": {"counts": [7, 11, 25, 60, 116, 161, 143, 167, 48, 0, 0], "total": 738, "table": null, "rank": 37, "sp500_rank": 30, "fortune500_rank": 9}, "ai_patents_growth": {"counts": [], "total": 14.798732002630056, "table": null, "rank": 314, "sp500_rank": 147, "fortune500_rank": 100}, "ai_patent_applications": {"total": 42, "table": null, "rank": 1, "sp500_rank": 1, "fortune500_rank": 1}, "all_patents": {"counts": [70, 110, 250, 600, 1160, 1610, 1430, 1670, 480, 0, 0], "total": 7380, "table": null, "rank": 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"wikipedia_description": "SZ DJI Technology Co., Ltd. or Shenzhen DJI Sciences and Technologies Ltd. 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{"economic": "Technology", "business": "Technology Equipment"}, "671": {"economic": "Technology", "business": "Technology Equipment"}, "812": {"economic": "Technology", "business": "Software & IT Services"}, "23": {"economic": "Consumer Cyclicals", "business": "Retailers"}, "1850": {"economic": "Consumer Non-Cyclicals", "business": "Consumer Goods Conglomerates"}, "2551": {"economic": "Technology", "business": "Technology Equipment"}, "796": {"economic": "Technology", "business": "Software & IT Services"}, "2050": {"economic": "Technology", "business": "Technology Equipment"}, "2291": {"economic": "Industrials", "business": "Industrial Goods"}, "2479": {"economic": "Industrials", "business": "Industrial Goods"}} \ No newline at end of file diff --git a/web/scripts/retrieve_data.py b/web/scripts/retrieve_data.py index 52f60c7b..5fbfe15e 100644 --- a/web/scripts/retrieve_data.py +++ b/web/scripts/retrieve_data.py @@ -90,7 +90,7 @@ def get_exchange_link(market_key: str) -> dict: :param market_key: exchange:ticker :return: A dict mapping market_key to the input market_key and link to the link, if successfully found, else None """ - time.sleep(5) + time.sleep(1) # for some mysterious reason, the ticker/market ordering is alphabetical in google finance first, last = sorted(market_key.strip(":").split(":")) gf_link = f"https://www.google.com/finance/quote/{first}:{last}" @@ -100,7 +100,7 @@ def get_exchange_link(market_key: str) -> dict: r = requests.get(gf_link, headers=headers) if "No results found" in r.text: gf_link = f"https://www.google.com/finance/quote/{last}:{first}" - time.sleep(5) + time.sleep(1) r = requests.get(gf_link, headers=headers) return {"market_key": market_key, "link": None if "No results found" in r.text else gf_link} else: @@ -338,12 +338,12 @@ def get_translation(desc: str, client, parent) -> str: # Check if desc appears to be in English, and if not, translate it if details[0][1].lower() != "en": print("Translating "+desc) - response = client.translate_text( - parent=parent, - contents=[desc], - mime_type="text/plain", - target_language_code="en" - ) + response = client.translate_text(request = { + "parent": parent, + "contents": [desc], + "mime_type": "text/plain", + "target_language_code": "en" + }) translation = response.translations[0].translated_text.strip() return translation return None @@ -366,7 +366,7 @@ def add_supplemental_descriptions(rows: list) -> None: } with open(SUPPLEMENTAL_DESCRIPTIONS) as f: client = translate.TranslationServiceClient() - parent = client.location_path("gcp-cset-projects", "global") + parent = "projects/gcp-cset-projects/locations/global" for row in csv.DictReader(f): company_name = row["company_name"] name_to_desc_info[company_name] = {desc_info[k]: row[k].strip() for k in desc_info} @@ -600,7 +600,6 @@ def clean_misc_fields(js: dict, refresh_images: bool, lowercase_to_orig_cname: d "global500": "in_fortune_global_500" } js["groups"] = {k: js.pop(v, False) for k, v in group_keys_to_names.items()} - js.pop("grid") def get_top_10_lists(js: dict) -> None: @@ -698,9 +697,9 @@ def get_category_counts(js: dict) -> None: "counts": [], "total": get_growth(counts, is_patents=True) }, - # spoof ai patent applications https://github.com/georgetown-cset/parat/issues/146 - "ai_patent_applications": { - "total": 42, + "ai_patents_grants": { + "counts": [], + "total": get_yearly_counts(js.pop("ai_patents_grants_by_year", {}), "ai_patents")[1], }, # spoof all patents https://github.com/georgetown-cset/parat/issues/125 "all_patents": { diff --git a/web/tests/test_data/alphabet_input.json b/web/tests/test_data/alphabet_input.json index 5664f21a..188ea45f 100644 --- a/web/tests/test_data/alphabet_input.json +++ b/web/tests/test_data/alphabet_input.json @@ -132,9 +132,6 @@ "crunchbase_url": "https://www.crunchbase.com/organization/verily-2" } ], - "grid": [ - "grid.497059.6" - ], "linkedin": [ "https://www.linkedin.com/company/intrinsic", "https://www.linkedin.com/company/alphabet-inc", @@ -2429,6 +2426,132 @@ "ai_patents": 64 } ], + "ai_patents_grants_by_year": [ + { + "priority_year": 1988, + "ai_patents": 1 + }, + { + "priority_year": 1991, + "ai_patents": 1 + }, + { + "priority_year": 1994, + "ai_patents": 6 + }, + { + "priority_year": 1995, + "ai_patents": 2 + }, + { + "priority_year": 1996, + "ai_patents": 7 + }, + { + "priority_year": 1997, + "ai_patents": 5 + }, + { + "priority_year": 1998, + "ai_patents": 5 + }, + { + "priority_year": 1999, + "ai_patents": 6 + }, + { + "priority_year": 2000, + "ai_patents": 5 + }, + { + "priority_year": 2001, + "ai_patents": 5 + }, + { + "priority_year": 2002, + "ai_patents": 5 + }, + { + "priority_year": 2003, + "ai_patents": 4 + }, + { + "priority_year": 2004, + "ai_patents": 9 + }, + { + "priority_year": 2005, + "ai_patents": 14 + }, + { + "priority_year": 2006, + "ai_patents": 12 + }, + { + "priority_year": 2007, + "ai_patents": 18 + }, + { + "priority_year": 2008, + "ai_patents": 15 + }, + { + "priority_year": 2009, + "ai_patents": 25 + }, + { + "priority_year": 2010, + "ai_patents": 56 + }, + { + "priority_year": 2011, + "ai_patents": 74 + }, + { + "priority_year": 2012, + "ai_patents": 89 + }, + { + "priority_year": 2013, + "ai_patents": 119 + }, + { + "priority_year": 2014, + "ai_patents": 91 + }, + { + "priority_year": 2015, + "ai_patents": 150 + }, + { + "priority_year": 2016, + "ai_patents": 251 + }, + { + "priority_year": 2017, + "ai_patents": 332 + }, + { + "priority_year": 2018, + "ai_patents": 264 + }, + { + "priority_year": 2019, + "ai_patents": 267 + }, + { + "priority_year": 2020, + "ai_patents": 133 + }, + { + "priority_year": 2021, + "ai_patents": 25 + }, + { + "priority_year": 2022, + "ai_patents": 2 + } + ], "Physical_Sciences_and_Engineering_pats_by_year": [ { "priority_year": 1995, diff --git a/web/tests/test_data/alphabet_output.json b/web/tests/test_data/alphabet_output.json index 17ba5d31..f2904926 100644 --- a/web/tests/test_data/alphabet_output.json +++ b/web/tests/test_data/alphabet_output.json @@ -521,9 +521,10 @@ "table": null, "total": 12.192163477671421 }, - "ai_patent_applications": { + "ai_patents_grants": { + "counts": [], "table": null, - "total": 42 + "total": 1634 }, "all_patents": { "counts": [ diff --git a/web/tests/test_data/hugging_face_input.json b/web/tests/test_data/hugging_face_input.json index 65b60eb6..63b8a13b 100644 --- a/web/tests/test_data/hugging_face_input.json +++ b/web/tests/test_data/hugging_face_input.json @@ -24,7 +24,6 @@ "crunchbase_url": "https://www.crunchbase.com/organization/hugging-face" }, "child_crunchbase": [], - "grid": [], "linkedin": [ "https://www.linkedin.com/company/huggingface" ], diff --git a/web/tests/test_data/hugging_face_output.json b/web/tests/test_data/hugging_face_output.json index 225af4ad..6505305f 100644 --- a/web/tests/test_data/hugging_face_output.json +++ b/web/tests/test_data/hugging_face_output.json @@ -402,17 +402,18 @@ 0, 0 ], - "total": 0.0, + "total": 0, "table": null }, "ai_patents_growth": { "counts": [], "table": null, - "total": 0.0 + "total": 0 }, - "ai_patent_applications": { + "ai_patents_grants": { + "counts": [], "table": null, - "total": 42 + "total": 0 }, "all_patents": { "counts": [