# Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation
## 4 Case Study Using AI Patent Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ranking** & \multicolumn{2}{c}{**1990s**} & \multicolumn{2}{c}{**2000s**} & \multicolumn{2}{c}{**2010s**} \\ \cline{2-5}  & **Keyword** & **Width** & **Keyword** & **Width** & **Keyword** & **Width** \\ \hline
1 & object & 0.0907 & object & 0.0536 & object & 0.0699 \\
2 & data & 0.0823 & data & 0.0432 & data & 0.0497 \\
3 & speech & 0.073 & speech & 0.0362 & video & 0.0395 \\
4 & video & 0.0623 & video & 0.0339 & speech & 0.0363 \\
5 & network & 0.048 & image & 0.0233 & image & 0.0328 \\
6 & image & 0.0411 & network & 0.0226 & network & 0.0308 \\
7 & pattern & 0.0396 & pattern & 0.0189 & pattern & 0.0183 \\
8 & spatial & 0.0237 & spatial & 0.0151 & spatial & 0.0176 \\
9 & language & 0.0183 & language & 0.0093 & language & 0.0121 \\
10 & analysis & 0.0152 & feedback & 0.008 & analysis & 0.0097 \\
11 & feedback & 0.0147 & behavior & 0.0079 & feedback & 0.0095 \\
12 & recognition & 0.0125 & analysis & 0.0078 & behavior & 0.0093 \\
13 & behavior & 0.0118 & voice & 0.0067 & recognition & 0.0088 \\
14 & interface & 0.0116 & recognition & 0.0065 & interface & 0.007 \\
15 & sentence & 0.0113 & interface & 0.0063 & corpus & 0.007 \\
16 & vision & 0.0112 & vision & 0.0052 & vision & 0.006 \\
17 & corpus & 0.0098 & sentence & 0.0049 & voice & 0.0057 \\
18 & voice & 0.0094 & corpus & 0.0048 & sentence & 0.0045 \\
19 & representation & 0.0068 & learning & 0.0039 & situation & 0.0045 \\
20 & learning & 0.0062 & representation & 0.0039 & representation & 0.0041 \\
21 & situation & 0.0062 & situation & 0.0038 & conversation & 0.0038 \\
22 & conversation & 0.0057 & ontology & 0.0036 & learning & 0.0038 \\
23 & natural & 0.0051 & conversation & 0.0024 & ontology & 0.0038 \\
24 & figure & 0.0047 & neuro & 0.0024 & sentiment & 0.0038 \\
25 & dialogue & 0.0044 & inference & 0.0019 & inference & 0.0031 \\
26 & inference & 0.0039 & dialogue & 0.0015 & computing & 0.0027 \\
27 & computing & 0.0025 & understanding & 0.0015 & awareness & 0.0023 \\
28 & mind & 0.0025 & natural & 0.0014 & morphological & 0.0016 \\
29 & neuro & 0.0025 & awareness & 0.0013 & natural & 0.0016 \\
30 & ontology & 0.0025 & computing & 0.0011 & dialogue & 0.0014 \\
31 & awareness & 0.0017 & figure & 0.0011 & mind & 0.0014 \\
32 & cognitive & 0.0017 & mind & 0.0011 & figure & 0.0011 \\
33 & collaborative & 0.0017 & morphological & 0.0011 & neuro & 0.0011 \\
34 & morphological & 0.0017 & cognitive & 0.0009 & understanding & 0.0011 \\
35 & sentiment & 0.0017 & collaborative & 0.0008 & cognitive & 0.0008 \\
36 & understanding & 0.0017 & sentiment & 0.0008 & collaborative & 0.0008 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Width ranking of Bayesian prediction intervals: all AI keywords. Figure 2: Technological changes of top 20 AI keywords over time. The interval widths of most AI keywords have decreased, and this result is similar to the result of Figure 2. From the results, we confirmed that the detailed technologies for AI are expanding to various technological keywords without focusing on specific keywords. So, in the future, the AI technology is expected to have a large impact on most other technology fields as well as the society.