Weichang Li

PatentMiner: topic-driven patent analysis and mining

Patenting is one of the most important ways to protect companys core business concepts and proprietary technologies. Analyzing large volume of patent data can uncover the potential competitive or collaborative relations among companies in certain areas, which can provide valuable information to develop strategies for intellectual property (IP), R&D, and marketing. In this paper, we present a novel topic-driven patent analysis and mining system. Instead of merely searching over patent content, we focus on studying the heterogeneous patent network derived from the patent database, which is represented by several types of objects (companies, inventors, and technical content) jointly evolving over time. We design and implement a general topic-driven framework for analyzing and mining the heterogeneous patent network. Specifically, we propose a dynamic probabilistic model to characterize the topical evolution of these objects within the patent network. Based on this modeling framework, we derive several patent analytics tools that can be directly used for IP and R&D strategy planning, including a heterogeneous network co-ranking method, a topic-level competitor evolution analysis algorithm, and a method to summarize the search results. We evaluate the proposed methods on a real-world patent database. The experimental results show that the proposed techniques clearly outperform the corresponding baseline methods.

Cold Start Approach for Data-Driven Fault Detection

A typical assumption in supervised fault detection is that abundant historical data are available prior to model learning, where all types of faults have already been observed at least once. This assumption is likely to be violated in practical settings as new fault types can emerge over time. In this paper we study this often overlooked cold start learning problem in data-driven fault detection, where in the beginning only normal operation data are available and faulty operation data become available as the faults occur. We explored how to leverage strengths of unsupervised and supervised approaches to build a model capable of detecting faults even if none are still observed, and of improving over time, as new fault types are observed. The proposed framework was evaluated on the benchmark Tennessee Eastman Process data. The proposed fusion model performed better on both unseen and seen faults than the stand-alone unsupervised and supervised models.

Finding nuggets in IP portfolios: core patent mining through textual temporal analysis

Patents are critical for a company to protect its core technologies. Effective patent mining in massive patent databases can provide companies with valuable insights to develop strategies for IP management and marketing. In this paper, we study a novel patent mining problem of automatically discovering core patents (i.e., patents with high novelty and influence in a domain). We address the unique patent vocabulary usage problem, which is not considered in traditional word-based statistical methods, and propose a topic-based temporal mining approach to quantify a patents novelty and influence. Comprehensive experimental results on real-world patent portfolios show the effectiveness of our method.

Generating Breakpoint-based Timeline Overview for News Topic Retrospection

Though news readers can easily access a large number of news articles from the Internet, they can be overwhelmed by the quantity of information available, making it hard to get a concise, global picture of a news topic. In this paper we propose a novel method to address this problem. Given a set of articles for a given news topic, the proposed method models theme variation through time and identifies the breakpoints, which are time points when decisive changes occur. For each breakpoint, a brief summary is automatically constructed based on articles associated with the particular time point. Summaries are then ordered chronologically to form a timeline overview of the news topic. In this fashion, readers can easily track various news topics efficiently. We have conducted experiments on 15 popular topics in 2010. Empirical experiments show the effectiveness of our approach and its advantages over other approaches.

A boosting method for process fault detection with detection delay reduction and label denoising

In this paper we propose a novel fault detection algorithm for process control and maintenance that builds an ensemble of classifiers based on the modified AdaBoost technique. While seeking for the best fault detection accuracy, our algorithm also concentrates on reducing detection delay, which ensures safety and timely equipment service. In addition, the new algorithm can simultaneously detect and remove class-label noise in process data. Training is performed via iteratively optimizing an exponential cost function. The cost function also adaptively changes at each iteration, such that (1) the importance of the fault transition periods is increased to reduce the detection delay and (2) noisy samples are removed from training data. The algorithm was tested on a well known benchmark problem, the Tennessee Eastman Process (TEP), and compared to the baseline AdaBoost ensemble fault detector that does not pay specific attention to minimization of the detection delay and noise removal.

Online subspace and sparse filtering for target tracking in reverberant environment

This paper proposes a class of joint subspace and sparse filtering algorithms with an example application in tracking moving targets in highly reverberant environment. Motivated by recent work in low-rank and sparse matrix decomposition, we have developed filtering algorithms that alternate between tracking the low-rank subspace and estimating the instantaneously sparse components, both of which are recursively updated as new data arrives. The algorithms are particularly suitable for online applications with streaming data or sequential processing of extremely large data sets for which matrix decomposition is computationally infeasible. In contrast to simple signal and noise subspace decomposition in traditional subspace processing, the algorithms we describe here assume a generative model consisting of a low-rank subspace, an additional sparse component and noise. This approach is well suited for tracking a sparse moving target signal in the presence of low-rank reverberations. We demonstrate the target tracking performance via a set of beam space field data.