scholarly journals Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

2020 ◽  
Vol 10 (2) ◽  
pp. 570 ◽  
Author(s):  
Daiho Uhm ◽  
Jea-Bok Ryu ◽  
Sunghae Jun
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Bayesian Inference ◽  
Prior Knowledge ◽  
Interval Estimation ◽  
Patent Data ◽  
Domain Experts ◽  
Technology Analysis ◽  
Patent Documents

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

scholarly journals Technology analysis of artificial intelligence using Bayesian inference for neural networks

2018 ◽  
Vol 7 (2.3) ◽  
pp. 43
Author(s):  
Sunghae Jun
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Industrial Revolution ◽  
Statistical Modelling ◽  
Fourth Industrial Revolution ◽  
Research Institutes ◽  
Patent Documents ◽  
The Internet Of Things

At present, artificial intelligence (AI) technology is receiving much attention and applied in each field of society. AI is one of the key technologies to lead the fourth industrial revolution along with the internet of things and big data. Therefore, many companies and research institutes are trying to systematically analyze AI technology in order to understand the AI itself correctly. In this paper, we also study on a method to analyze AI technology based on quantitative approach. We correct the patent documents related to AI technology, and analyze them using statistical modelling. We use Bayesian inference for neural networks to build our proposed method. To verify the validity of our research, we carry out a case study using the AI patent documents.

scholarly journals Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis

2020 ◽  
Vol 12 (2) ◽  
pp. 505
Author(s):  
Sangsung Park ◽  
Sunghae Jun
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Bayesian Network ◽  
Network Modeling ◽  
Real Problem ◽  
Patent Data ◽  
Disaster Area ◽  
Proposed Model ◽  
Bayesian Network Modeling

At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends on multivariate data analysis. In order to show how the proposed model can be applied to a real problem, we carried out a case study to collect and analyze the patent data related to disaster AI.

scholarly journals Patent Keyword Analysis using Time Series and Copula Models

2019 ◽  
Vol 9 (19) ◽  
pp. 4071 ◽  
Author(s):  
Kim ◽  
Yoon ◽  
Hwang ◽  
Jun
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Patent Data ◽  
Analysis Model ◽  
Copula Models ◽  
Keyword Analysis ◽  
Logit Function ◽  
Patent Documents ◽  
Major Factors ◽  
Over Time

The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time.

scholarly journals Technology Integration and Analysis Using Boosting and Ensemble

2021 ◽  
Vol 7 (1) ◽  
pp. 27
Author(s):  
Sunghae Jun
Keyword(s):  
Autonomous Driving ◽  
Gradient Boosting ◽  
Patent Data ◽  
Integrated Technology ◽  
Technology Analysis ◽  
Technological Field ◽  
Extreme Gradient Boosting ◽  
Real Domain ◽  
Technological Integration

Most of the studies related to technology analysis have focused on one specific technological field such as autonomous driving or blockchain. Most technologies have large and small relationships with each other. Therefore, it is necessary not only to perform technology analysis focusing on one target technology, but also to analyze several integrated technologies at the same time. In this paper, we propose a methodology for integrating technologies and analyzing the integrated technologies. We integrate patent big data for technological integration and use text mining, boosting, and ensemble for integrated technology analysis. To evaluate the performance of proposed method, we search the patent documents related to disaster artificial intelligence (AI) and extended reality (XR). In our case study, we integrate the patent data from disaster AI and XR technologies and analyze the integrated patent data using regression trees, random forest, extreme gradient boosting, and ensemble models. Therefore, we illustrate how our proposed method can be applied to the real domain.

Teaching Computer Based Historical Data Analysis

1994 ◽  
Vol 6 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Charles Anderson ◽  
Robert J. Morris
Keyword(s):  
Data Analysis ◽  
Social History ◽  
Historical Data ◽  
Good Practice ◽  
Computer Assisted ◽  
Historical Data Analysis ◽  
University Of Edinburgh ◽  
Computer Based ◽  
The University

A case study ofa third year course in the Department of Economic and Social History in the University of Edinburgh isusedto considerandhighlightaspects of good practice in the teaching of computer-assisted historical data analysis.

KAJIAN PENYELENGGARAAN PAUD DI KOTA BANJARMASIN DITINJAU DARI ASPEK PERENCANAAN

2018 ◽  
Vol 2 (2) ◽  
pp. 159
Author(s):  
Lisna Sulinar Sari
Keyword(s):  
Early Childhood ◽  
Data Analysis ◽  
Early Childhood Education ◽  
Educational Needs ◽  
Needs Analysis ◽  
Case Study Approach ◽  
Childhood Education ◽  
Study Approach ◽  
Early Childhood Institutions

Abstrak: Permasalahan dalam penelitian ini yaitu dari jumlah lembaga PAUD yang ada diKota Banjarmasin belum semuanya memiliki perencanaan khususnya pada analisispeningkatan legalitas kelembagaan PAUD dan analisis kebutuhan pendidikan untuk anak usiadini (AUD). Penelitian ini menggunakan pendekatan studi kasus dengan analisis data deskrtifkuantitatif dan kualitataif. Hasil studi menunjukkan bahwa: i) Disdik Kota Banjarmasin danLembaga PAUD sampel tidak melakukan perencanaan yang baik untuk pendataan analisiskebutuhan pendidikan AUD; ii) Belum semua lembaga PAUD sampel memiliki izinoperasional dikarenakan adanya persyaratan yang belum dapat dipenuhi karena memerlukanbiaya yang cukup besar seperti, pembuatan akta notaris; iii) Belum semua lembaga PAUDmemiliki sarpras sesuai dengan pedoman sarana dan prasarana dari pusat; iv) untuk membantuketersediaan sarpras, Disdik Kota Banjarmasin sudah mengalokasikan dana APBD II berupabantuan RKB, rehab kelas rusak ringan dan berat, serta bantuan APE Dalam dan Luar berupabarang. Abstract: The problem in this study is from the number of early childhood institutions in thecity of Banjarmasin not all have plans in particular to the analysis of institutional legalityincrease early childhood education and educational needs analysis for early childhood (AUD).This study uses a case study approach to data analysis of quantitative and qualitative deskrtif.The study shows that: i) Disdik Banjarmasin and Institutions ECD sample is not doing betterplanning for data analysis AUD educational needs; ii) Not all the samples of early childhoodinstitutions have an operating permit because of the requirements can not be met because itrequires significant costs such as notary deed; iii) Not all early childhood institutions haveinfrastructure accordance with the guidelines of the central infrastructure; iv) to assist theavailability infrastructure, Disdik Banjarmasin already allocated budget II in the form ofclassroom assistance, rehabilitation of damaged light and heavy classes, as well as the In andOut APE assistance in the form of goods.

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