# Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation
## 4 Case Study Using AI Patent Data  
Using the patent documents related to AI technology, we carried out a case study to show the validity of our research. This illustrates how our study could be applied to practical problems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Prior Distribution, P(\(\lambda\))** & **Posterior Distribution, P(\(\lambda|x\))** \\ \hline Uniform & \(1\) & \(Gamma\!\left(\alpha=\sum\limits_{i=1}^{n}x_{i}+1,\ \beta=n\right)\) \\ Jeffrey’s & \(\lambda^{-1/2}\) & \(Gamma\!\left(\alpha=\sum\limits_{i=1}^{n}x_{i}+\frac{1}{2},\ \beta=n\right)\) \\ Exponential (\(a\)) & \(\text{ae}^{-a\lambda}\) & \(Gamma\!\left(\alpha=\sum\limits_{i=1}^{n}x_{i}+1,\ \beta=n+a\right)\) \\ Gamma (\(a\), \(b\)) & \(\frac{\lambda^{a-1}\text{ge}^{-a\lambda}}{\Gamma(a)\times b^{-a}}\) & \(Gamma\!\left(\alpha=\sum\limits_{i=1}^{n}x_{i}+a,\ \beta=n+b\right)\) \\ Chi-square (\(df\)) & \(\frac{\lambda^{df/2-1}\text{ge}^{-a\lambda/2}}{\Gamma(df/2)\times 2^{df/2}}\) & \(Gamma\!\left(\alpha=\sum\limits_{i=1}^{n}x_{i}+\frac{df}{2},\ \beta=n+\frac{1}{2}\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prior and posterior distributions for Bayesian interval estimation. We searched the AI patent documents from the patent databases [22; 23]. The searching keywords equation is defined as follow;  
**Searching keywords equation** = (big OR data) OR (neural OR network) OR ((Linear OR Logistic)AND Regression) OR (Support AND Vector) OR (Naive AND Bayesian) OR (Hidden AND Markov) OR (Conditional AND Random*) OR (Decision AND Tree) OR Cluster* OR ((Dimension OR feature) AND Reduction) OR (nearest AND neighbor) or (((((big OR data) OR mining) OR ((DB OR big) AND data)) OR knowledge OR (find OR detect OR discovery))))) or ((((ontology OR OWL OR (DAML AND OIL) OR SWRL OR (Semantic AND Web AND Rule AND Language) OR SEMANTIC) OR (represent* OR expres* OR design OR induct* OR deduct* OR reason* OR inference*))) or ((((Boltzma* A/1 Machin*) OR ((deep* OR Convolution* OR Recurrence* OR unsupervised* OR supervised* OR Reinforcement*) OR learning)) AND (KNN* OR k-nearest* OR (data OR mining) OR (big AND data*) OR neural OR network OR ((Linear OR Logistic) AND Regression) OR (Support AND Vector*) OR (Naive AND Bayesian*) OR (Hidden AND Markov*) OR (Conditional AND Random*) OR (Decision AND Tree) OR Cluster* OR ((Dimension OR feature) AND Reduction) OR (nearest AND neighbor*)))) or pattern* OR (aware* OR realiz* OR cognit* OR recogn* OR percept* OR understand* OR comprehensive* OR estimat* OR assumpt* OR presump* OR anal*) OR (cognit* OR percept*) AND (computing OR process* OR application OR program) OR (humanlife OR \"human life\" OR living* OR livelihood OR lifelong OR \"life log\") OR (aware* OR realiz* OR cognit* OR recogn* OR percept* OR understand* OR comprehens* OR intercept* OR semit*) or (emotion* OR sentiment* OR feel*) OR (aware* OR realiz* OR cognit* OR recogn* OR percept* OR understand* OR comprehens* or estimat* or assumpt* or presump* or anal*) OR space* OR (aware* OR realiz* OR cognit* OR recogn* or percept* or understand* or comprehens* or estimat* or assumpt* or presump* or anal*) or (collabor* or collect*) OR intel* OR (((image* OR video* OR movie OR picture) OR (object* OR target* OR non-rigid* OR nonrigid*) OR (extract* OR awareness* OR realizat* OR cognit*OR capture*))))  
From the collected patent documents, we removed the patent documents not related to AI or duplicated, and selected 13,858 valid patents from 1995 to 2016. In this case study, we use the R computing language and its text mining package to preprocess and analyze the collected patent data [19; 20]. We also extracted 36 keywords representing AI technology from the valid patent documents by text mining technique. The keywords are as follow; analysis, awareness, behavior, cognitive, collaborative, computing, conversation, corpus, data, dialogue, feedback, figure, image, inference, interface, language, learning, mind.