Adam K. Usadi

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.

Adaptive Parallel Reservoir Simulation

This paper was selected for presentation by an IPTC Programme Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the International Petroleum Technology Conference and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the International Petroleum Technology Conference, its officers, or members. Papers presented at IPTC are subject to publication review by Sponsor Society Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the International Petroleum Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Abstract The availability of multi-core CPUs for personal computers makes desktop parallel computing a reality. Parallel computing for reservoir simulation creates the opportunity for significant run time reduction but also introduces additional technical challenges, particularly in the areas of load balancing and general algorithmic robustness. In this paper we will discuss the development and application of ExxonMobils numerical reservoir simulator, EM powerTM , to parallel desktop systems, focusing on the following areas: • A flexible, hierarchical, object-oriented design that supports fast development, functionality encapsulation, and ease of maintenance • A fully unstructured grid for modeling large and complex reservoirs with fewer gridblocks, yet retaining accurate physics • A highly efficient and robust parallel partition of the fully unstructured grids for modeling large and complex reservoirs • Built-in intelligence for the solver suite and parallel execution to fulfill specific algorithmic needs • Adaptive Implicit Method (AIM) for a high-level of numerical stability with significant run-time speed-up and memory footprint reduction • Dynamic load balancing for seamless integration between shared-memory based parallel computing and adaptive implicit methods We will also describe experiences with real models and briefly discuss mechanisms for addressing problems encountered. We will demonstrate that we can achieve a 3~4 time speedup over serial runs without AIM on real field cases using quad-core machines.

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.