The GROWTH LAB QUERY (GLquery) is a cloud-based Python natural language processing (NPL) machine learning algorithm that performs queries on large patent text datasets on Google BigQuery's environment.
GLquery is the first of a wider set of tools called High-level Algorithmic Unified Set of Methods to Analyze Nontrivial Nexuses (shortened to H.A.U.S.M.A.N.N.).
IMPORTANT NOTE: This is the public version of GLQuery. The full version of GLQuery (with all features) is available to Growth Lab affiliates upon request.
Make sure the packages below are properly installed:
- HAUSMANN:
pip install -e git+https://github.com/jpalcantara85/HAUSMANN#egg=HAUSMANN- bq_helper:
pip install -e git+https://github.com/SohierDane/BigQuery_Helper#egg=bq_helper- After installing the necessary packages, type:
from HAUSMANN import GLquery, GLquery_examples - Then, type the following (with the necessary modifications) to access Google's environment (.json file):
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/users/[...]/NAME_OF_YOUR_KEY_HERE.json"Read this to learn how to create a key: https://cloud.google.com/iam/docs/creating-managing-service-account-keys#iam-service-account-keys-create-console
- To learn more about GLquery type:
help(GLquery)
GLquery_examples()The public version of GLquery takes the following arguments:
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query (string): choose among one of the options in the next section (see below) (default is "query1")
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country (string) = format "LL" (default is "US")
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start_year (string) = format "YYYY" (default is "1790")
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end_year (string) = format "YYYY" (default is "2019")
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min_count (string) = minimum threshold (default is "100")
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kind_code (string) = format "LN", see kind codes here: https://www.cas.org/support/documentation/references/patkind (default is "B2")
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cpc_code (string) = formant "LNNL", see CPC codes here: https://www.uspto.gov/web/patents/classification/cpc/html/cpc.html (default is "A63F")
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assignee (string) = any format (case sensitive) (default is "President and Fellows of Harvard College")
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keyword (string) = any format (case sensitive) (default is "internet of things")
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budget (number) = any number (default is 1.000 GB) (queries above value 'budget' will not run; queries below 'budget' will use only the minimum amount of memory necessary to run the query, not the full value of 'budget')
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output_to_csv (True or False) = output results as .csv file to your current working directory (default is False)
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plot (True of False) = plot results as .pdf file to your current working directory (default is False) (implemented for queries 1, 2, 3, 10)
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ask_before_running (True or False) = given a query size (in GB), asks user input before running the query (default is False)
As of June/2019, the public version of GLquery can perform the following queries:
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query1: Number of patent applications by country (takes no inputs)
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query2: Number of patents published between years X and Y by country (necessary arguments: start_year and end_year)
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query3: Number of patents published to country Z between years X and Y (necessary arguments: start_year, end_year and country)
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query4: Which patents country Z published between years X and Y? (necessary arguments: start_year, end_year, country, kind_code)
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query5: Most common patenting technology areas by year (takes no inputs)
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query6: Most common patenting technology areas in country Z between years X and Y (necessary arguments: start_year, end_year and country)
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query7: Inventors with over N patents by country (necessary arguments: min_count)
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query8: Patent landscaping of technology T between years X and Y" (necessary arguments: start_year, end_year, min_count and cpc_code) (this query returns patents, firms and countries associated with technology T)
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query9: Which firms is assignee A citing in their patents? (necessary arguments: assignee)
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query10: Number of patent applications with keyword K in country Z (necessary arguments: country and keyword)