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This study aimed to investigate the relationship between AI patents owned by companies, company earnings, industry type, and company size. The research question guiding the study was: How do AI patents owned by companies affect their earnings, and do these effects differ across industries and company sizes?

Three hypotheses were developed to explore this question:

1. AI patents owned by companies have a positive effect on company earnings.

2. Patenting contributes more to a company’s earnings in high-tech industries than in low- tech industries.

3. The effect of owned patents on earnings is less pronounced the larger a company is.

Using a quantitative approach, the researchers employed multiple linear regression analysis on a sample of companies across various industries and sizes. Data was collected from public databases, including patent records and financial statements.

The analysis led to the following conclusions regarding the three initial hypotheses:

Hypothesis 1: The findings indicate a positive but statistically insignificant relationship between AI patents and company earnings, suggesting that there may be a positive effect, but the analysis could not establish this relationship with statistical certainty.

Hypothesis 2: The study did not find enough evidence to support or reject the hypothesis that patenting contributes more to earnings in high-tech industries than in low-tech industries. This may be due to limitations in the dataset or the analysis approach employed.

Hypothesis 3: The influence of company size on the relationship between patents and earnings remains inconclusive. Although the results showed a positive relationship between the number of employees and earnings, the analysis did not provide sufficient evidence to determine the interaction effect between company size and patent ownership.

These inconclusive findings suggest that further research is necessary to better understand the relationship between AI patents, company earnings, industry type, and company size. Future studies could address the limitations of this study by incorporating more granular data on different industries, conducting industry-specific analyses, and employing alternative statistical methods or longitudinal data. This would help to enhance our understanding of the complex relationships between these factors and provide more actionable insights for businesses, investors, academics, and policymakers.