Analysis of NVIDIA's 2024 Patent Filing Trends
파인특허
September 30, 2024
NVIDIA is a global leader in technological innovation, driving advancements in fields such as artificial intelligence (AI), graphics processing units (GPUs), autonomous driving, and data centers. Through its cutting-edge GPU technology, the company plays a pivotal role across industries like gaming, AI, cloud computing, and autonomous vehicles. In this report, we will analyze NVIDIA’s patent filing trends to explore how the company leads in technological innovation. The patent data analysis was conducted using the keyword AP: (NVIDIA) in the Keywert search tool, with duplicate entries removed. This method allowed us to capture the global patent filing trends up to September 30, 2024.
The number of patent filings in the United States (US) stood out the most, especially with a sharp increase to 28 filings in 1999. Most patents were filed in the US, with few filings in other countries. Japan (JP) saw 2 filings in 1997 and 1 in 1999, with no filings in other nations during this period.
The US remained the dominant country for patent filings, with significant annual filings. There were some filings in Japan (JP), but far fewer than in the US. Korea (KR) saw its first filings during this period, with a gradual increase in numbers.
Patent filings in Korea (KR), Europe (EP), and China (CN) became more frequent. Notably, filings in Korea increased sharply, becoming a significant part of NVIDIA’s patent portfolio. While the US still had the most filings, other countries began to hold a more substantial share, indicating a growing global strategy.
Patent filings saw a dramatic rise, likely driven by NVIDIA’s expansion into new technology areas such as AI, autonomous driving, and high-performance computing, leading to a surge in related filings.
The distribution of NVIDIA’s patent filings by country is as follows:
The US holds the largest share of filings, followed by China and international filings.
NVIDIA’s patents reflect its leadership in graphics processing, AI, machine learning, data handling, and user interfaces. The company focuses heavily on GPU performance and advancements in AI and 3D graphics.
These areas underscore NVIDIA’s focus on enhancing AI, graphics, parallel processing, and efficient computing.
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import numpy as np
import nltk
# NLTK 불용어 다운로드
nltk.download('stopwords')
# 특허 문헌에서 자주 사용되는 불용어 세트 정의
patent_stopwords = set([
'method', 'apparatus', 'system', 'device', 'means', 'include', 'comprising', 'claim', 'wherein',
'invention', 'embodiment', 'embodiments', 'described', 'present', 'application', 'may', 'can',
'one', 'said', 'use', 'used', 'step', 'steps', 'provide', 'providing', 'example', 'plurality',
'first', 'second', 'third', 'according', 'thereof', 'therein'
])
# 불용어 세트 (일반 영어 불용어 + 특허 관련 불용어)
stop_words = set(stopwords.words('english')).union(patent_stopwords)
# 텍스트 전처리 함수 정의
tokenizer = RegexpTokenizer(r'\w+')
def preprocess(text):
tokens = tokenizer.tokenize(text.lower()) # 소문자로 변환 후 토큰화
filtered_tokens = [word for word in tokens if word not in stop_words and len(word) > 2] # 불용어 및 짧은 단어 제거
return ' '.join(filtered_tokens)
# 데이터 로드
file_path = 'nvidia_en.xlsx'
data = pd.read_excel(file_path)
# 발명의 명칭과 요약을 결합한 텍스트 칼럼 생성
data['combined_text'] = data['발명의 명칭'].astype(str) + ' ' + data['요약'].astype(str)
# 텍스트 전처리
data['processed_text'] = data['combined_text'].apply(preprocess)
# 벡터화 (CountVectorizer 사용)
vectorizer = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')
text_vectorized = vectorizer.fit_transform(data['processed_text'])
# 다양한 토픽 개수에 대해 LDA 모델을 학습하고 perplexity 값 계산
perplexities = []
topic_range = range(2, 21) # 2~20개의 토픽 시도
for num_topics in topic_range:
lda = LatentDirichletAllocation(n_components=num_topics, random_state=42)
lda.fit(text_vectorized)
perplexities.append(lda.perplexity(text_vectorized)) # perplexity 저장
# 토픽 개수에 따른 perplexity 그래프 그리기
plt.figure(figsize=(10, 5))
plt.plot(topic_range, perplexities, marker='o')
plt.xlabel("Number of Topics")
plt.ylabel("Perplexity")
plt.title("Perplexity by Number of Topics")
plt.show()
# 최적의 토픽 개수 선택
optimal_num_topics = topic_range[perplexities.index(min(perplexities))]
print(f"Optimal number of topics: {optimal_num_topics}")
# 최적의 토픽 수로 LDA 모델 학습
lda_final = LatentDirichletAllocation(n_components=optimal_num_topics, random_state=42)
document_topics = lda_final.fit_transform(text_vectorized)
# 상위 단어 출력 함수 정의
def display_topics(model, feature_names, no_top_words):
for topic_idx, topic in enumerate(model.components_):
print(f"Topic {topic_idx+1}:")
print(" ".join([feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]]))
# 최종 토픽 모델의 상위 10개 단어 출력
no_top_words = 10
tf_feature_names = vectorizer.get_feature_names_out()
display_topics(lda_final, tf_feature_names, no_top_words)
# 각 문서가 가장 많이 할당된 토픽을 계산 (가장 높은 확률의 토픽)
dominant_topic_per_doc = np.argmax(document_topics, axis=1)
# 토픽별 문서 개수 계산
topic_counts = np.bincount(dominant_topic_per_doc, minlength=optimal_num_topics)
print("\nTopic Counts (Number of documents assigned to each topic):")
for i, count in enumerate(topic_counts):
print(f"Topic {i+1}: {count} documents")Using the Latent Dirichlet Allocation (LDA) topic classification method, we identified several key topics across NVIDIA's patent filings. The dominant areas of innovation include.
Other areas include various hardware and software technologies, reflecting NVIDIA’s wide-ranging innovations.
The analysis of NVIDIA's patent filings reveals that the company is continuously pushing the boundaries in cutting-edge technologies like GPU, AI, and parallel processing. NVIDIA plays a crucial role across industries, including AI, autonomous driving, and data centers, as reflected in its patent portfolio. Over the past three years, key areas of focus include AI, image processing, and parallel computing, supporting NVIDIA's strategy to expand its presence in the global market. This technological leadership is expected to keep NVIDIA at the forefront of innovation across multiple industries in the future.
