| |
| from shapely.geometry import Point |
| import pandas as pd |
| from tqdm import tqdm |
| import streamlit as st |
| import numpy as np |
| import json, requests |
| import pandas as pd |
| |
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|
| import matplotlib.pyplot as plt |
| import seaborn as sns |
| from math import radians, cos, sin, asin, sqrt |
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| from sentence_transformers import SentenceTransformer, util |
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| path = "Climate_site/python_scripts/" |
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|
| @st.cache_resource |
| def model_nlp(): |
| model = SentenceTransformer('all-MiniLM-L6-v2') |
| return model |
|
|
| import unicodedata |
|
|
| from metaphone import doublemetaphone |
| from fuzzywuzzy import fuzz |
| from difflib import SequenceMatcher |
| import re |
|
|
| import geopandas as gpd |
| from geopandas import GeoDataFrame |
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|
| @st.cache_data |
| def load_data(): |
| url = path + "institutions.tsv" |
| dic = pd.read_csv(url, delimiter = "\t" , index_col = 0).to_dict('index') |
| return dic |
|
|
| dic_institutions = load_data() |
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| def URL(base_URL , entity_type , filters): |
| url = base_URL + entity_type + filters |
| return url |
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| def get_data(url): |
| url = requests.get(url) |
| text = url.text |
| import json |
| data = json.loads(text) |
| return data |
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| |
| |
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| def norm(vector): |
| return np.sqrt(sum(x * x for x in vector)) |
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| def cosine_similarity2(vec_a, vec_b): |
| norm_a = norm(vec_a) |
| norm_b = norm(vec_b) |
| dot = sum(a * b for a, b in zip(vec_a, vec_b)) |
| return dot / (norm_a * norm_b) |
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| def related_patents(research_words , display): |
| |
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| dic_patents = {} |
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|
| max_count = 0 |
| base_URL_PV = "https://api.patentsview.org/" |
| filter_works = "patents/query?" |
| filter_PV = "q={%22_and%22:[{%22_text_all%22:{%22patent_abstract%22:%22" |
| filter_PV += research_words |
|
|
| filter_PV += "%22}}]}&f=[%22patent_number%22,%22patent_title%22,%22assignee_country%22,%22patent_date%22,%22assignee_organization%22,%22inventor_longitude%22,%22inventor_latitude%22,%22inventor_last_name%22,%22inventor_id%22,%22inventor_first_name%22,%22cpc_subsection_title%22,%22assignee_city%22,%22patent_abstract%22,%22patent_kind%22,%22cpc_group_id%22,%22assignee_organization%22,%22citedby_patent_number%22]" |
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| filter_PV = filter_PV.replace(" " , "%20") |
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| |
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|
| url = URL(base_URL_PV , filter_works, filter_PV) |
| data = get_data(url) |
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|
| if display == True: |
| print( data["total_patent_count"] , elem[-1] ) |
| print(url) |
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| |
| for i in range(data["count"]): |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]] = {} |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["title"] = data["patents"][i]["patent_title"] |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["abstract"] = data["patents"][i]["patent_abstract"] |
|
|
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["assignee"] = str(data["patents"][i]["assignees"][0]["assignee_organization"]) |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["assignee_city"] = str(data["patents"][i]["assignees"][0]["assignee_city"]) |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["assignee_country"] = str(data["patents"][i]["assignees"][0]["assignee_country"]) |
| for j in range(1, len(data["patents"][i]["assignees"])): |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["assignee"] += ", " + str(data["patents"][i]["assignees"][j]["assignee_organization"]) |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["assignee_city"] += ", " + str(data["patents"][i]["assignees"][j]["assignee_city"]) |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["assignee_country"] += ", " + str(data["patents"][i]["assignees"][j]["assignee_country"]) |
|
|
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["list_inventors"] = data["patents"][i]["inventors"] |
|
|
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["inventors"] = str(data["patents"][i]["inventors"][0]["inventor_first_name"]) + " " + str(data["patents"][i]["inventors"][0]["inventor_last_name"]) |
| for j in range(1, len(data["patents"][i]["inventors"])): |
| dic_patents[ "US-" + data["patents"][i]["patent_number"]]["inventors"] += ", " + str(data["patents"][i]["inventors"][j]["inventor_first_name"]) + " " + str(data["patents"][i]["inventors"][j]["inventor_last_name"]) |
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| dic_patents["US-" + data["patents"][i]["patent_number"]]["date"] = data["patents"][i]["patent_date"] |
| dic_patents["US-" + data["patents"][i]["patent_number"]]["number_citations"] = len(data["patents"][i]["citedby_patents"]) |
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|
| if display == True: |
| print(" ") |
| |
| |
| return dic_patents |
| |
| |
| |
| |
| |
| def ranking_patents( research_words, details): |
| |
| model = model_nlp() |
| |
| dic_patents = related_patents( research_words , False) |
| |
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| dic_scores = {} |
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| |
| reference_text = details |
|
|
| encoded_text = model.encode(reference_text, convert_to_tensor=False).tolist() |
| |
| |
| if len(dic_patents ) == 0: |
| return "Select other key words" |
| |
| else: |
| for ids in list(dic_patents.keys()): |
|
|
| dic_scores[ids] = {} |
|
|
| encoded_title = model.encode(dic_patents[ids]["title"], convert_to_tensor=False).tolist() |
| score_title = cosine_similarity2(encoded_title, encoded_text) |
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|
|
| if dic_patents[ids]["abstract"] != None: |
| encoded_abstract = model.encode(dic_patents[ids]["abstract"], convert_to_tensor=False).tolist() |
| score_abstract = cosine_similarity2(encoded_abstract, encoded_text) |
| else: |
| score_abstract = None |
|
|
| dic_scores[ids]["title comparision"] = score_title |
| dic_scores[ids]["abstract comparison"] = score_abstract |
| dic_scores[ids]["title"] = dic_patents[ids]["title"] |
| dic_scores[ids]["citations"] = dic_patents[ids]["number_citations"] |
| dic_scores[ids]["date"] = dic_patents[ids]["date"][:4] |
| dic_scores[ids]["assignee"] = dic_patents[ids]["assignee"] |
| dic_scores[ids]["inventors"] = dic_patents[ids]["inventors"] |
| dic_scores[ids]["number of co-inventors"] = len(dic_patents[ids]["inventors"].split(",")) |
| |
| return dic_patents , dic_scores |
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|
| def get_ranking_own_research(research_key_words, details , size): |
| |
| dic , dic_scores_papers = ranking_patents(research_key_words, details ) |
| |
| if dic_scores_papers == {}: |
| return "No paper found" |
| |
| elif type(dic_scores_papers) == str: |
| return dic_scores_papers |
| |
| else: |
| return pd.DataFrame(dic_scores_papers).T.sort_values(by="abstract comparison" , ascending = False).head(size) |
| |
| |
|
|
| def extract_quantitative_data_patent(patent_id): |
| |
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|
| patent_id = patent_id[3:] |
| |
| url = "https://api.patentsview.org/patents/query?q={%22patent_id%22:%22" + str(patent_id) + "%22}&f=[%22patent_number%22,%22patent_title%22,%22patent_abstract%22,%22patent_date%22,%22inventor_last_name%22,%22inventor_first_name%22,%22assignee_organization%22]" |
| url_google = "https://patents.google.com/patent/US" + str(patent_id) |
| |
| data = get_data(url)["patents"][0] |
| title = data["patent_title"] |
| abstract = data["patent_abstract"] |
| co_inventors = ", ".join([ data["inventors"][i]["inventor_first_name"] + " " + data["inventors"][i]["inventor_last_name"] for i in range(len(data["inventors"])) ]) |
| assignees = ", ".join([ str(data["assignees"][i]["assignee_organization"]) for i in range(len(data["assignees"])) ] ) |
|
|
| return url_google , title , abstract , data["patent_date"] , co_inventors , assignees |
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| |
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|
| ln_suff = ['oster', |
| 'nordre', |
| 'vaster', |
| 'aust', |
| 'vesle', |
| 'da', |
| 'van t', |
| 'af', |
| 'al', |
| 'setya', |
| 'zu', |
| 'la', |
| 'na', |
| 'mic', |
| 'ofver', |
| 'el', |
| 'vetle', |
| 'van het', |
| 'dos', |
| 'ui', |
| 'vest', |
| 'ab', |
| 'vste', |
| 'nord', |
| 'van der', |
| 'bin', |
| 'ibn', |
| 'war', |
| 'fitz', |
| 'alam', |
| 'di', |
| 'erch', |
| 'fetch', |
| 'nga', |
| 'ka', |
| 'soder', |
| 'lille', |
| 'upp', |
| 'ua', |
| 'te', |
| 'ni', |
| 'bint', |
| 'von und zu', |
| 'vast', |
| 'vestre', |
| 'over', |
| 'syd', |
| 'mac', |
| 'nin', |
| 'nic', |
| 'putri', |
| 'bet', |
| 'verch', |
| 'norr', |
| 'bath', |
| 'della', |
| 'van', |
| 'ben', |
| 'du', |
| 'stor', |
| 'das', |
| 'neder', |
| 'abu', |
| 'degli', |
| 'vre', |
| 'ait', |
| 'ny', |
| 'opp', |
| 'pour', |
| 'kil', |
| 'der', |
| 'oz', |
| 'von', |
| 'at', |
| 'nedre', |
| 'van den', |
| 'setia', |
| 'ap', |
| 'gil', |
| 'myljom', |
| 'van de', |
| 'stre', |
| 'dele', |
| 'mck', |
| 'de', |
| 'mellom', |
| 'mhic', |
| 'binti', |
| 'ath', |
| 'binte', |
| 'snder', |
| 'sre', |
| 'ned', |
| 'ter', |
| 'bar', |
| 'le', |
| 'mala', |
| 'ost', |
| 'syndre', |
| 'sr', |
| 'bat', |
| 'sndre', |
| 'austre', |
| 'putra', |
| 'putera', |
| 'av', |
| 'lu', |
| 'vetch', |
| 'ver', |
| 'puteri', |
| 'mc', |
| 'tre', |
| 'st'] |
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| |
| |
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|
| name_del = ['2nd', '3rd', 'Jr', 'Jr.', 'Junior', 'Sr', 'Sr.', 'Senior'] |
|
|
|
|
| def name_delete(string): |
| for elmt in name_del: |
| if f" {elmt}" in string: |
| return string.replace(f" {elmt}","") |
| return string |
|
|
| def ln_suff_merge(string): |
| for suff in ln_suff: |
| if f" {suff} " in string or string.startswith(f"{suff} "): |
| return string.replace(f"{suff} ",suff.replace(" ","")) |
| return string |
|
|
| |
|
|
| def normalize(data): |
| normal = unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore') |
| val = normal.decode("utf-8") |
| |
| val = name_delete(val) |
| |
| val = re.sub(r"[A-Z]{3,}", lambda x: x.group().lower(), val) |
| |
| val = re.sub(r"(\w)([A-Z])", r"\1 \2", val) |
| |
| val = val.lower() |
| |
| val = re.sub('[^A-Za-z0-9 ]+', ' ', val) |
| |
| val = re.sub(' +', ' ', val) |
| |
| val = val.strip() |
| |
| val = ln_suff_merge(val) |
|
|
| return val |
|
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|
|
| def main_inventors( research_key_words , details , size): |
| |
| dic_patents , dic_ranked = ranking_patents(research_key_words , details) |
| dic_patents_co_inventors = {} |
| |
|
|
| for patent in list(dic_ranked.keys())[:size]: |
| for k in range(len(dic_patents[patent]["list_inventors"])): |
| |
| inventor_id = dic_patents[patent]["list_inventors"][k]["inventor_id"] |
| inventor_name = dic_patents[patent]["list_inventors"][k]["inventor_first_name"] + " " + dic_patents[patent]["list_inventors"][k]["inventor_last_name"] |
| inventor_name_norm = normalize(inventor_name).split() |
| inventor_name_norm = inventor_name_norm[0] + " " + inventor_name_norm[-1] |
|
|
| if inventor_name_norm not in dic_patents_co_inventors: |
| dic_patents_co_inventors[inventor_name_norm] = {} |
| dic_patents_co_inventors[inventor_name_norm]["Inventor's name"] = inventor_name |
| dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"] = inventor_id |
| dic_patents_co_inventors[inventor_name_norm]["Number of occurence"] = 1 |
| dic_patents_co_inventors[inventor_name_norm]["Number of related citations"] = dic_patents[patent]["number_citations"] |
|
|
| else: |
| if inventor_id not in dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"] : |
| dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"] += ", " + inventor_id |
| if inventor_name not in dic_patents_co_inventors[inventor_name_norm]["Inventor's name"] : |
| dic_patents_co_inventors[inventor_name_norm]["Inventor's name"] += ", " + inventor_name |
| dic_patents_co_inventors[inventor_name_norm]["Number of occurence"] += 1 |
| dic_patents_co_inventors[inventor_name_norm]["Number of related citations"] += dic_patents[patent]["number_citations"] |
|
|
|
|
| dic_patents_co_inventors = {k: v for k, v in sorted(dic_patents_co_inventors.items(), key=lambda item: item[1]["Number of occurence"] , reverse = True)} |
| |
| if dic_patents_co_inventors == {}: |
| |
| |
| return "No patent, select other key words" |
| else: |
| |
| for inventor_name_norm in list(dic_patents_co_inventors.keys()): |
| list_inventors = dic_patents_co_inventors[inventor_name_norm]["PatentsView inventor's id"].split(", ") |
| work_count = 0 |
| cited_by_count = 0 |
| |
| for elem in list_inventors: |
| url = "https://api.patentsview.org/inventors/query?q={%22inventor_id%22:[%22" + elem + "%22]}&f=[%22inventor_total_num_patents%22,%22patent_num_cited_by_us_patents%22]" |
| data = get_data(url)["inventors"][0] |
| work_count += int(data["inventor_total_num_patents"]) |
| for k in range(len(data["patents"])): |
| cited_by_count += int(data["patents"][k]["patent_num_cited_by_us_patents"]) |
| |
| dic_patents_co_inventors[inventor_name_norm]["Number of patents"] = work_count |
| dic_patents_co_inventors[inventor_name_norm]["Number of US patents citations"] = cited_by_count |
| |
| |
| |
| |
| return pd.DataFrame(dic_patents_co_inventors , index = ["Inventor's name", "PatentsView inventor's id", "Number of occurence" , "Number of patents" ,"Number of US patents citations" , "Number of related citations"]).T.style.hide(axis="index") |
|
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|
| |
| def map_inventors(research_key_words , details , size): |
| display = False |
| dic_patents , dic_ranked = ranking_patents(research_key_words , details) |
| dic_patents_co_inventors = {} |
| count = 0 |
| |
| for patent in list(dic_ranked.keys())[:size]: |
| for k in range(len(dic_patents[patent]["list_inventors"])): |
| |
| dic_patents_co_inventors[count] = {} |
| |
| dic_patents_co_inventors[count]["latitude"] = dic_patents[patent]["list_inventors"][k]["inventor_latitude"] |
| dic_patents_co_inventors[count]["longitude"] = dic_patents[patent]["list_inventors"][k]["inventor_longitude"] |
| dic_patents_co_inventors[count]["longitude"] = dic_patents[patent]["list_inventors"][k]["inventor_longitude"] |
| dic_patents_co_inventors[count]["inventor_name"] = str(dic_patents[patent]["list_inventors"][k]["inventor_first_name"]) + " " + str(dic_patents[patent]["list_inventors"][k]["inventor_last_name"]) |
| dic_patents_co_inventors[count]["patent_date"] = dic_patents[patent]["date"] |
| count += 1 |
| |
| if dic_patents_co_inventors == {}: |
| return "No patent, select other key words" |
| |
| |
| map_df = pd.DataFrame(dic_patents_co_inventors).T |
| map_df["longitude"]=map_df['longitude'].astype(float) |
| map_df['latitude']=map_df['latitude'].astype(float) |
| map_df = map_df[map_df["latitude"].notnull()] |
|
|
| return map_df |
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