9,518 research outputs found
Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses
The Cooperative Patent Classifications (CPC) jointly developed by the
European and US Patent Offices provide a new basis for mapping and portfolio
analysis. This update provides an occasion for rethinking the parameter
choices. The new maps are significantly different from previous ones, although
this may not always be obvious on visual inspection. Since these maps are
statistical constructs based on index terms, their quality--as different from
utility--can only be controlled discursively. We provide nested maps online and
a routine for portfolio overlays and further statistical analysis. We add a new
tool for "difference maps" which is illustrated by comparing the portfolios of
patents granted to Novartis and MSD in 2016.Comment: Scientometrics 112(3) (2017) 1573-1591;
http://link.springer.com/article/10.1007/s11192-017-2449-
Deep Learning for Technical Document Classification
In large technology companies, the requirements for managing and organizing
technical documents created by engineers and managers have increased
dramatically in recent years, which has led to a higher demand for more
scalable, accurate, and automated document classification. Prior studies have
only focused on processing text for classification, whereas technical documents
often contain multimodal information. To leverage multimodal information for
document classification to improve the model performance, this paper presents a
novel multimodal deep learning architecture, TechDoc, which utilizes three
types of information, including natural language texts and descriptive images
within documents and the associations among the documents. The architecture
synthesizes the convolutional neural network, recurrent neural network, and
graph neural network through an integrated training process. We applied the
architecture to a large multimodal technical document database and trained the
model for classifying documents based on the hierarchical International Patent
Classification system. Our results show that TechDoc presents a greater
classification accuracy than the unimodal methods and other state-of-the-art
benchmarks. The trained model can potentially be scaled to millions of
real-world multimodal technical documents, which is useful for data and
knowledge management in large technology companies and organizations.Comment: 16 pages, 8 figures, 9 table
Hybrid image representation methods for automatic image annotation: a survey
In most automatic image annotation systems, images are represented with low level features using either global
methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is
beneficial in annotating images. In this paper, we provide a
survey on automatic image annotation techniques according to
one aspect: feature extraction, and, in order to complement
existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation
Entry and Patenting in the Software Industry
To what extent are firms kept out of a market by patents covering related technologies? Do patents held by potential entrants make it easier to enter markets? We estimate the empirical relationship between market entry and patents for 27 narrowly defined categories of software products during the period 1990-2004. Controlling for demand, market structure, average patent quality, and other factors, we find that a 10% increase in the number of patents relevant to market reduces the rate of entry by 3-8%, and this relationship intensified following expansions in the patentability of software in the mid-1990s. However, potential entrants with patent applications relevant to a market are more likely to enter it. Finally, patents appear to substitute for complementary assets in the entry process, as patents have both greater entry-deterring and entry-promoting effects for firms without prior experience in other markets.
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
Incorporating Prior Knowledge into Task Decomposition for Large-Scale Patent Classification
Abstract. With the adoption of min-max-modular support vector machines (SVMs) to solve large-scale patent classification problems, a novel, simple method for incorporating prior knowledge into task decomposition is proposed and investigated. Two kinds of prior knowledge described in patent texts are considered: time information, and hierarchical structure information. Through experiments using the NTCIR-5 Japanese patent database, patents are found to have time-varying features that considerably affect classification. The experimen-tal results demonstrate that applying min-max modular SVMs with the proposed method gives performance superior to that of conventional SVMs in terms of training time, generalization accuracy, and scalability.
Multipath Routing of Fragmented Data Transfer in a Smart Grid Environment
The purpose of this paper is to do a general survey on the existing
communication modes inside a smart grid, the existing security loopholes and
their countermeasures. Then we suggest a detailed countermeasure, building upon
the Jigsaw based secure data transfer [8] for enhanced security of the data
flow inside the communication system of a smart grid. The paper has been
written without the consideration of any factor of inoperability between the
various security techniques inside a smart gridComment: 5 pages, 2 figure
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