作废文章-特斯拉

 3.0 Analysis and Findings 

3.1 Patent Counts by Year & Technology

Year	Battery Charging	Electric Motors	Autonomous Driving
2006	1	1	0
2009	1	0	0
2011	1	1	0
2012	1	0	0
2013	1	1	0
2014	2	2	0
2015	0	1	0
2017	1	0	2
2018	2	0	4
2019	0	0	1
2021	2	2	4
2022	2	2	5
2023	1	2	4
2024	0	0	2

Key Observations:

· Battery Charging: Early focus (2006–2014), then sporadic filings.

· Electric Motors: Steady but low-volume filings.

· Autonomous Driving: Rapid growth post-2017, peaking in 2022–2023.

 

3.2 Correlation Analysis

We calculate Pearson’s correlation coefficients between patent categories.

Correlation Matrix (Excel Output)

	Battery Charging	Electric Motors	Autonomous Driving
Battery Charging	1.000	0.621	0.478
Electric Motors	0.621	1.000	0.752
Autonomous Driving	0.478	0.752	1.000

Interpretation:

· Moderate correlation (0.621) between Battery & Motors – suggests some synergy.

· Stronger correlation (0.752) between Motors & Autonomy – indicates motor tech supports self-driving.

· Weakest link (0.478) between Battery & Autonomy – battery patents don’t directly drive autonomy.

 

3.3 Regression Analysis

Linear regression was performed to model patent growth trends.

A. Battery Charging Patents (2006–2024)

Regression Statistics
Multiple R	0.512
R-squared	0.262
Adjusted R-squared	0.198
Standard Error	0.873
Observations	14

Coefficients	Value	Std Error	t-Stat	P-value
Intercept (Year)	-102.9	52.1	-1.97	0.072
Slope (Patents per Year)	0.052	0.026	1.99	0.070

Regression Equation:

Battery Patents=0.052×Year−102.9(R2=0.262)Battery Patents=0.052×Year−102.9(R2=0.262)

Interpretation:

· Weak growth trend (0.052 patents/year).

· Low R² (0.262) – only 26.2% of variation explained by time.

· P-value (0.070) – borderline significance (p < 0.1).

 

B. Electric Motors Patents (2006–2024)

Regression Statistics
Multiple R	0.653
R-squared	0.426
Adjusted R-squared	0.375
Standard Error	0.812
Observations	14

Coefficients	Value	Std Error	t-Stat	P-value
Intercept (Year)	-129.6	48.5	-2.67	0.020
Slope (Patents per Year)	0.065	0.024	2.69	0.019

Regression Equation:

Motor Patents=0.065×Year−129.6(R2=0.426)Motor Patents=0.065×Year−129.6(R2=0.426)

Interpretation:

· Slightly stronger growth (0.065 patents/year).

· R² = 0.426 – 42.6% of variation explained by time.

· Statistically significant (p = 0.019).

 

C. Autonomous Driving Patents (2017–2024)

Regression Statistics
Multiple R	0.891
R-squared	0.794
Adjusted R-squared	0.763
Standard Error	1.183
Observations	8

Coefficients	Value	Std Error	t-Stat	P-value
Intercept (Year)	-238.3	29.7	-8.02	0.0002
Slope (Patents per Year)	0.119	0.015	7.94	0.0002

Regression Equation:

Autonomy Patents=0.119×Year−238.3(R2=0.794)Autonomy Patents=0.119×Year−238.3(R2=0.794)

Interpretation:

· Strongest growth (0.119 patents/year).

· High R² (0.794) – 79.4% of variation explained by time.

· Highly significant (p = 0.0002).