# Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation
## 4 Case Study Using AI Patent Data
Morphological, natural, network, neuro, object, ontology, pattern, recognition, representation, sentence, sentiment, situation, spatial, speech, understanding, video, vision, voice. In this paper, we selected AI keywords in sufficient consultation with AI technology experts [24]. Using these keywords, we made the patent-keyword matrix as a structured data for Bayesian data analysis. The rows and columns of this matrix are 13,858 patents and 36 keywords respectively. Each element of this matrix is frequency value of occurred keyword in each patent. First, among the 36 AI technology keywords, we selected 8 keywords representing AI technology highly. Table 3 shows Bayesian intervals with 95% confidence level for the mean frequency of eight representative keywords. In this case, we used five kinds of prior probability distributions, uniform, Jeffrey, exponential, and chi-square suitable for patent frequency data. We found that the differences in the widths of intervals were not large according to the prior probability distributions. From these results, we can see that the confidence interval for the keyword of data is the largest. Next was the speech and video keywords. So, we knew the technology based on data is most important to AI system. For more detailed AI technology forecasting, we estimated Bayesian prediction intervals for all 36 keywords. In addition, we show the intervals separated by time (1990s, 2000s, 2010s). Because technology is developed and evolves over time. Table 4 shows the Bayesian prediction interval with 95% confidence level for the 1990s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Keyword** & **Uniform** & **Jeffrey** & \begin{tabular}{c} **Exponential** \\ **(a = 1)** \\ \end{tabular}  & \begin{tabular}{c} **Gamma** \\ **(a = 3, b = 1)** \\ \end{tabular}  & \ \begin{tabular}{c} **Chi-Square** \\ **(DF = 5)** \\ \end{tabular}  \\ \hline \multirow{2}{*}{
\begin{tabular}{c} analysis \\ awareness \\ behavior \\ cognitive \\ cognitive \\ collaborative \\ computing \\ conversation \\ conversation \\ corpus \\ data \\ dialogue \\ feedback \\ figure \\ figure \\ image \\ inference \\ inference \\ language \\ inference \\ language \\ learning \\ learning \\ learning \\ learning \\ learning \\ \\ morphological \\ natural \\ network  
From the intervals of AI keywords from 1990 to 1999, we can see the characteristics of AI technology during this period. We estimated Bayesian confidence intervals for all AI keywords using five representative prior distributions, uniform, Jeffrey, exponential, gamma, and chi-square, which are used for count data. We can see that the AI keywords with the highest average of their occurred frequencies in the estimated Bayesian intervals are object and data. In other words, we can confirm that the largest weight in the AI object is data. In addition, the keywords of speech, video, network, image, pattern, spatial, language, and analysis are also important in the AI technology field. During this period, AI technology was studied with a focus on data analysis, pattern recognition, speech and image processing, and understanding of natural language. Next, Table 5 illustrates the Bayesian prediction intervals with 95% confidence level for all AI keywords from 2000 to 2009. As shown in the results of Table 4, the AI technology from 2000 to 2009 is affected by technological keywords such as data, object, speech, video, and image. However, this time period shows that the relative weight of feedback and learning keywords is increasing. In other words, we found that active researches related to the learning and behavior technologies for AI are being conducted at this time. Lastly, Table 6 represents the most recent results of Bayesian interval estimation for all AI keywords.