Patent document–keyword count data are typically high-dimensional, sparse, and dominated by zero entries, which makes it difficult to simultaneously reconstruct keyword frequencies and identify meaningful technological structures. This study proposes a machine learning approach to latent structure learning for zero-inflated patent keyword count data. The proposed zero-gated latent factor model (ZG-LFM) combines nonnegative matrix factorization (NMF) with keyword-specific logistic occurrence models. NMF is used to extract interpretable document–factor and factor–keyword representations, while the occurrence gate estimates the probability that each keyword appears in a given patent document. The method was evaluated in an initial domain-specific case study using a document–keyword matrix constructed from 9434 quantum computing patent documents and 175 keywords, of which 87.60% of the entries were zero. Predictive performance was assessed using root mean squared error, mean absolute error, and the area under the receiver operating characteristic curve across different numbers of latent factors. The experimental results showed that NMF provided more accurate keyword count reconstruction, whereas the proposed model consistently achieved better discrimination between zero and nonzero keyword entries. These findings indicate that latent count reconstruction and keyword occurrence modeling provide complementary information for analyzing sparse patent data. The learned latent factors further revealed coherent quantum computing subdomains, including hybrid quantum–classical execution, quantum machine learning, quantum state measurement and error analysis, quantum cryptography, superconducting chips, quantum circuits, optical control, qubit devices, and optimization algorithms. The proposed framework therefore provides interpretable latent technology structures while improving the identification of keyword occurrence patterns in zero-inflated patent data. These findings demonstrate the feasibility of the framework within the analyzed quantum computing corpus; its generalizability across other technological domains remains to be evaluated.

Extended reality (XR), encompassing augmented reality (AR), virtual reality (VR), and mixed reality (MR), is a key enabling technology for virtual worlds, and XR-related patents continue to grow rapidly. However, patent-based XR technology analysis faces a fundamental challenge: document–keyword matrix (DKM) built from patent titles and abstracts are typically high dimensional, sparse, and often exhibit excess zeros, which can distort inference when conventional text mining pipelines are applied without a generative count perspective. In this study, we propose a statistically grounded XR technology analysis framework that combines likelihood-based count modeling with interpretable structure mining to map XR sub-technologies from a patent DKM. Using an XR patent–keyword matrix, we fit Poisson regression (PR), negative binomial regression (NBR), and zero-inflated negative binomial regression (ZINBR) models via maximum likelihood estimation (MLE), controlling for document-length effects. Model selection by Akaike information criterion (AIC) consistently favored NBR for both target keywords, indicating substantial overdispersion in XR patent counts. We interpret exponentiated coefficients as incidence rate ratios (IRRs) and construct a technology relatedness network from significant IRR edges, revealing a dual-axis XR structure: reality is anchored in an AR or VR experience and content axis such as virtual and augment, whereas extend is embedded in a structure and integration axis for example, surface, edge, layer, and connectivity-related terms. To show how the proposed method can be applied to real domains, we searched the XR patent documents, and analyzed them for XR technology analysis.

This study proposes a transformed two-part bootstrap confidence interval (TTB-CI) for zero-inflated count data. The method combines a standard zero-inflated mixture formulation, parametric bootstrap, and monotone transformations to improve inference for practically meaningful estimands, including the marginal mean, zero probability, and positive-part mean. Simulation studies under zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) data-generating processes show that the proposed method maintains nominal or near-nominal coverage while reducing interval width, particularly for the positive-part mean. Compared with conventional Poisson- and negative binomial-based confidence intervals, the proposed TTB-CI provides a more favorable coverage and width tradeoff and yields more informative intervals for positive count inference. These results indicate that the proposed method offers a practical and efficient confidence interval framework for zero-inflated count data.

Zero inflation is pervasive across text mining, event log, and sensor analytics, and it often degrades the predictive performance of analytical models. Classical approaches, most notably the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, address excess zeros but rely on rigid parametric assumptions and fixed model structures, which can limit flexibility in high-dimensional, sparse settings. We propose a Bayesian neural network (BNN) with regularization for sparse zero-inflated data modeling. The method separately parameterizes the zero inflation probability and the count intensity under ZIP/ZINB likelihoods, while employing Bayesian regularization to induce sparsity and control overfitting. Posterior inference is performed using variational inference. We evaluate the approach through controlled simulations with varying zero ratios and a real-world dataset, and we compare it against Poisson generalized linear models, ZIP, and ZINB baselines. The present study focuses on predictive performance measured by mean squared error (MSE). Across all settings, the proposed method achieves consistently lower prediction error and improved uncertainty problems, with ablation studies confirming the contribution of the regularization components. These results demonstrate that a regularized BNN provides a flexible and robust framework for sparse zero-inflated data analysis in information-rich environments.

Zero-inflated count data are characterized by an excessive frequency of zeros that cannot be adequately analyzed by a single distribution, such as Poisson or negative binomial. This problem is pervasive in many practical applications, including document–keyword matrix derived from text corpora, where most keyword frequencies are zero. Conventional statistical approaches, such as the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, explicitly separate a structural zero component from a count component, but they typically assume independent observations and can be unstable when covariates are high-dimensional and sparse. To address these limitations, this paper proposes a graph-based zero-inflated learning framework that combines simple graph convolution (SGC) with zero-inflated count regression heads such as ZIP and ZINB. We first construct an observation graph by connecting similar samples, and then apply SGC to propagate and smooth features over the graph, producing convolutional representations that incorporate neighborhood information while remaining computationally lightweight. The resulting representations are used as covariates in ZIP and ZINB heads, which preserve probabilistic interpretability through maximum likelihood learning. Our experiments on simulated zero-inflated datasets with controlled zero ratios demonstrate that the proposed ZIP+SGC and ZINB+SGC consistently reduce prediction errors compared with their non-graph baselines, as measured by mean absolute error and root mean squared error. Overall, the proposed approach provides an efficient and interpretable way to integrate graph neural computation with zero-inflated modeling for sparse count prediction problems.

Keyword data analysis aims to extract and interpret meaningful relationships from large collections of text documents. A major challenge in this process arises from the extreme sparsity of document–keyword matrices, where the majority of elements are zeros due to zero inflation. To address this issue, this study proposes a probabilistic framework called Bayesian Pattern Mining (BPM), which integrates Bayesian inference into association rule mining (ARM). The proposed method estimates both the expected values and credible intervals of interestingness measures such as confidence and lift, providing a probabilistic evaluation of keyword associations. Experiments conducted on 9436 quantum computing patent documents, from which 175 representative keywords were extracted, demonstrate that BPM yields more stable and interpretable associations than conventional ARM. By incorporating credible intervals, BPM reduces the risk of biased decisions under sparsity and enhances the reliability of keyword-based technology analysis, offering a rigorous approach for knowledge discovery in zero-inflated text data.

Patent keyword analysis involves extracting and examining keywords from patent documents. Since patents contain detailed information about technologies, analyzing them can provide valuable insights for efficient technology management. This paper proposes a novel method for patent keyword analysis that integrates Bayesian factor analysis and social network visualization. Using Bayesian factor analysis, we extract factors representing key technologies within a domain. These factors are used as nodes in a social network analysis to explore their interrelationships. The resulting visualization aids in constructing a technology diagram, enhancing understanding of the technological structure. To evaluate the method, we analyze patents related to digital therapeutic technologies. Experimental results demonstrate the approach’s effectiveness and its applicability to practical technology analysis tasks. Our results indicate that data analysis serves as a core technology in the field of digital therapy, while technologies such as remote patient monitoring, device systems, and signal processing function as supporting technologies for data analysis. The findings contribute to technology management practices, including strategy development, by analyzing target technologies and deriving actionable insights across various domains, including digital therapeutics.

Text data often exhibits high sparsity and zero inflation, where a substantial proportion of entries in the document-keyword matrix are zeros. This characteristic presents challenges to traditional count-based models, which may suffer from reduced predictive accuracy and interpretability in the presence of excessive zeros and overdispersion. To overcome this issue, we propose an effective analytical framework that integrates imbalanced data handling by undersampling with classical probabilistic count models. Specifically, we apply Poisson's generalized linear models, zero-inflated Poisson, and zero-inflated negative binomial models to analyze zero-inflated text data while preserving the statistical interpretability of term-level counts. The framework is evaluated using both real-world patent documents and simulated datasets. Empirical results demonstrate that our undersampling-based approach improves the model fit without modifying the downstream models. This study contributes a practical preprocessing strategy for enhancing zero-inflated text analysis and offers insights into model selection and data balancing techniques for sparse count data.

Technology keyword analysis (TKA) requires a different approach compared to general keyword analysis. While general keyword analysis identifies relationships between keywords, technology keyword analysis must find cause–effect relationships between technology keywords. Because the development of new technologies depends on previously researched and developed technologies, we need to build a causal inference model, in which the previously developed technology is the cause and the newly developed technology is the effect. In this paper, we propose a technology keyword analysis method using casual inference modeling. To understand the causal relationships between technology keywords, we constructed a graphical causal model combining a graph structure with causal inference. To show how the proposed model can be applied to the practical domains, we collected the patent documents related to the digital therapeutics technology from the world patent databases and analyzed them by the graphical causal model. We expect that our research contributes to various aspects of technology management, such as research and development planning.

Patent keyword analysis is used to analyze the technology keywords extracted from collected patent documents for specific technological fields. Thus, various methods related to this type of analysis have been researched in the industrial engineering fields, such as technology management and new product development. To analyze the patent document data, we have to search for patents related to the target technology and preprocess them to construct the patent–keyword matrix for statistical and machine learning algorithms. In general, a patent–keyword matrix has an extreme zero-inflated problem. This is because each keyword occupies one column even if it is included in only one document among all patent documents. General zero-inflated models have a limit at which the performance of the model deteriorates when the proportion of zeros becomes extremely large. To solve this problem, we applied a Bayesian inference to a general zero-inflated model. In this paper, we propose a patent keyword analysis using a Bayesian zero-inflated model to overcome the extreme zero-inflated problem in the patent–keyword matrix. In our experiments, we collected practical patents related to digital therapeutics technology and used the patent–keyword matrix preprocessed from them. We compared the performance of our proposed method with other comparative methods. Finally, we showed the validity and improved performance of our patent keyword analysis. We expect that our research can contribute to solving the extreme zero-inflated problem that occurs not only in patent keyword analysis, but also in various text big data analyses.

For text big data analysis, we preprocessed text data and constructed a document–keyword matrix. The elements of this matrix represent the frequencies of keywords occurring in a document. The matrix has a zero-inflation problem because many elements are zero values. Also, in the process of preprocessing, the data size of the document–keyword matrix is reduced. However, various machine learning algorithms require a large amount of data, so to solve the problems of data shortage and zero inflation, we propose the use of generative models based on statistics and machine learning. In our experimental tests, we compared the performance of the models using simulation and practical data sets. Thus, we verified the validity and contribution of our research for keyword data analysis.

Patents contain detailed information of researched and developed technologies. We analyzed patent documents to understand the technology in a given domain. For the patent data analysis, we extracted the keywords from the patent documents using text mining techniques. Next, we built a patent document–keyword matrix using the patent keywords and analyzed the matrix data using statistical methods. Each element of the matrix represents the frequency of a keyword that occurs in a patent document. In general, most of the elements were zero because the keyword becomes a column of the matrix even if it occurs in only one document. Due to this zero-inflated problem, we experienced difficulty in analyzing patent keywords using existing statistical methods such as linear regression analysis. The purpose of this paper is to build a statistical model to solve the zero-inflated problem. In this paper, we propose a regression model based on quantile cumulative distribution function to solve this problem that occurs in patent keyword analysis. We perform experiments to show the performance of our proposed method using patent documents related to blockchain technology. We compare regression modeling based on a quantile cumulative distribution function with convenient models such as linear regression modeling. We expect that this paper will contribute to overcoming the zero-inflated problem in patent keyword analysis performed in various technology fields.

A large part of big data consists of text documents such as papers, patents or articles. To analyze text data, we have to preprocess the text documents and build a structured data based on a document-word matrix using various text mining techniques. This is because statistics and machine learning algorithms used in text analysis require structured train data. The row and column of the matrix are document and word, respectively. The element of the matrix represents the frequency value of the word occurring in each document. In general, because the number of words is much larger than the number of documents, most elements have zero values. Due to the sparsity problem caused by inflated zeros, the performance of the predictive model has decreased. In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. We perform compound Poisson linear modeling to make the proposed method. To show the performance of our proposed method, we collect and analyze the patent documents from patent databases. In our experimental results, we compared the value of the Akaike information criterion (AIC) of the proposed model with traditional models, such as linear model, generalized linear model and zero-inflated Poisson model. Additionally, we illustrated that the AIC value of our proposed model is smaller than others. Therefore, we verify the validity of this paper.

In big data analysis, various zero-inflated problems are occurring. In particular, the problem of inflated zeros has a great influence on text big data analysis. In general, the preprocessed data from text documents are a matrix consisting of the documents and terms for row and column, respectively. Each element of this matrix is an occurred frequency of term in a document. Most elements of the matrix are zeros, because the number of columns is much larger than the rows. This problem is a cause of decreasing model performance in text data analysis. To overcome this problem, we propose a method of zero-inflated text data analysis using generative adversarial networks (GAN) and statistical modeling. In this paper, we solve the zero-inflated problem using synthetic data generated from the original data with zero inflation. The main finding of our study is how to change zero values to the very small numeric values with random noise through the GAN. The generator and discriminator of the GAN learned the zero-inflated text data together and built a model that generates synthetic data that can replace the zero-inflated data. We conducted experiments and showed the results, using real and simulation data sets to verify the improved performance of our proposed method. In our experiments, we used five quantitative measures, prediction sum of squares, R-squared, log-likelihood, Akaike information criterion and Bayesian information criterion to evaluate the model’s performance between original and synthetic data sets. We found that all performances of our proposed method are better than the traditional methods.

통계학과 머신러닝에서 제공하는 다양한 분석기법에 의한 텍스트 데이터 분석을 위해서는 먼저 텍스트 마이닝을 이용한 텍스트 문서의 전처리가 이루어져야 한다. 일반적으로 전처리된 텍스트 데이터는 문자와 키워드로 이루어진 행렬 구조를 갖는다. 이 행렬의 원소는 문서에 포함된 키워드의 빈도수를 나타내는데 영의 값이 지나치게 많이 포함되어 영과잉 문제가 발생한다. 따라서 텍스트 데이터의 분석 결과에 대한 성능을 높이기 위해서는 영과잉 문제를 해결해야 한다. 본 논문에서는 행렬 인수분해와 베이지안 모델링을 이용하여 전처리된 텍스트 데이터의 영과잉 문제를 해결하려고 하였다. 제안 방법의 성능평가와 실제 사용에 대한 활용을 보이기 위하여 실제 특허문서를 수집하고 분석하였다.

특허문서는 새롭게 개발된 기술에 대한 상세하고 표준화된 정보를 포함하고 있기 때문에 특허데이터의 분석을 통하여 다양한 기술분석이 수행된다. 특히, 통계학과 머신러닝이 제공하는 여러 가지 분석기법을 이용한 정량적 특허데이터분석이 활발히 이루어지고 있다. 정량적 특허분석을 위하여 특허문서는 정형화된 데이터 형태로 전처리되어야 하는데 이 과정에서 0의 값이 지나치게 많이 포함되는 영과잉 문제가 발생한다. 영과잉 포아송 회귀모형 등 영과잉 문제를 해결하기 위한 통계적 분석기법들이 제안되었지만 0의 값이 지나치게 많게 되면 이에 비례하여 모형의 설명력이 떨어진다. 이와 같은 문제점을 해결하기 위하여 본 연구에서는 딥러닝 모형인 변이형 오토인코더를 이용하여 정형화된 데이터로부터 재현데이터를 생성하는 방법을 제안한다. 정형화된 데이터를 그대로 사용하는 것보다 제안방법에 의한 재현 데이터를 사용할 때 모형의 설명력이 높아지는 것을 확인하기 위하여 실제 특허 데이터를 이용한다.

Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big data analysis using statistics and machine learning algorithms. However, as the use of big data increases, problems also occur. One of them is a zero-inflated problem in structured data preprocessed from big data. Most count values are zeros because a specific word is found in only some documents. In particular, since most of the patent data are in the form of a text document, they are more affected by the zero-inflated problem. To solve this problem, we propose a generation of synthetic samples using statistical inference and tree structure. Using patent document and simulation data, we verify the performance and validity of our proposed method. In this paper, we focus on patent keyword analysis as text big data analysis, and we encounter the zero-inflated problem just like other text data.

빅데이터는 다양한 산업군의 혁신을 이끌며, 많은 융복합 기술의 발전을 유도했다. 기업과 연구기관은 이러한 기술 시장에서 생존하기 위해 기술을 활용한 연구 개발 전략을 활용하고 있다. 특허는 기술 시장 분석을 위해 활용되는 빅데이터 중 하나이다. 그러나 특허의 수가 증가함에 따라 효율적인 분석이 어려워지고 있다. 본 연구의 목적은 가치가 높은 특허를 선별하고, 이를 설명할 수 있는 방법을 제안하는 것이다. 이를 통해, 특허 빅데이터 중 가치가 높은 특허를 통해 선행기술 조사나 공백기술 예측 등의 심층 분석이 가능하다. 따라서 우리는 특허의 가치를 예측할 수 있는 모델을 학습한 뒤, 이를 앙상블하여 예측 성능을 향상시키고자 한다. 그리고 특허의 가치 평가에 영향을 주는 변수를 추출하고, 시각화하고자 한다. 실험은 제안된 방법의 실제 적용 가능성을 검토하기 위해 에너지 자원 관련 미국 특허 11,423건을 수집하여 진행했다. 실험 결과, 신재생 에너지와 리튬 이온 배터리 전극 표면 개선을 위한 기술의 가치가 높을 것으로 추론하는 것이 가능했다.

제4차 산업혁명은 많은 산업 분야에서의 기술적 융합을 선도했다. 이로 인해, 다양한 산업과 기술이 탄생하고 사장되면서 자율주행이나 블록체인과 같은 미래 지향적 기술이 발전했다. 그러나 기업 간의 개발 경쟁이 치열해짐에 따라 기술의 권리화에 대한 중요성이 증가하고 있다. 이에 많은 기업들이 특허를 활용한 연구 개발을 시도하고 있다. 특허분석은 기술 기장에서의 자사 경쟁력뿐만 아니라 경쟁사의 기술 발전 동향까지 파악할 수 있다. 특히, 기술분류는 성공적인 특허분석을 위한 필수 요소 중 하나이다. 선행연구에서는 사전에 정의된 분류 체계를 활용하거나 특허의 인용정보와 텍스트로 기술을 분류했다. 본 연구의 목적은 계층적 군집분석을 활용하여 기술의 계층적 관계를 도출하고, 세부기술의 토픽을 추출하는 것이다. 실험은 제안된 방법의 실제 적용 가능성 검토를 위해 신재생 에너지 특허 28,298건으로 진행됐다. 실험 결과, 신재생 에너지 기술로부터 8개의 세부기술을 도출하는 것과 세부기술 간의 계층적 관계를 시각화하는 것이 가능했다.

Cognitive artificial intelligence (CAI) is an intelligent machine that thinks and behaves similar to humans. CAI also has an ability to mimic human emotions. With the development of AI in various fields, the interest and demand for CAI are continuously increasing. Most of the current AI research focuses on the realization of intelligence that can make optimal decisions. Existing AI studies have not conducted in-depth research on human emotions and cognitive perspectives. However, in the future, the demand for the use of AI that can imitate human emotions in various fields, such as healthcare and education, will continue. Therefore, we propose a method to build CAI in this paper. We also use Bayesian inference and computing based on the hybrid Monte Carlo algorithm for CAI development. To show how the proposed method for CAI can be applied to practical problems, we create an experiment using simulation data.