Trademark Databases Research Articles

Methodology for Developing a Nursing Administration Analysis System

Nursing administration requires a large volume of wide-ranging information, and nurse administrators are limited in their ability to compile and analyze information for nursing administration. The purpose of this study is to create methodology for developing a nursing administration analysis system to aid nurse administrators in performing outcome analysis. In this methodology, information required for nursing administration in the PSYCHOMS&#174 (Psychiatric Outcome Management System, registered trademark) database is analyzed according to the individual needs of nurse administrators. It features a combination of a classification method and an extraction method for obtaining quantitative and qualitative data as information required for nursing administration, and enables nurse administrators to easily obtain analysis results that they directly need. This methodology converts the time required nurse administrators to collect and organize information into time for making considerations in order to devise strategies for improving the quality of nursing care services, and can improve the quality and efficiency of nursing administration. This may lead to an increase of the quality of nursing care services at psychiatric hospitals. This methodology is highly versatile as it can be applied in information management, not only for nursing, but for the entire psychiatric hospital.

FUZZY CLUSTERING OF IMAGE TRADEMARK DATABASE AND PREPROCESSING USING ADAPTIVE FILTER AND KARHUNEN–LOÈVE TRANSFORM

In this paper, an efficient preprocessing module has been described which focuses on building a trademark database that can be used for developing a trademark retrieval system. The preprocessing module focuses on noise removal from the trademark images using an adaptive filtering technique using Wiener filters, followed by Karhunen–Loève transform that makes the trademark search process rotation invariant by rotating the object along positive y direction. Since the registered trademarks are huge in number and will increase invariantly in the future it will be strenuous for the search system to search for similarity in such huge database. Intention was to reduce the search space hence fuzzy clustering has been applied. All these preprocessing steps make a retrieval system more efficient and reduce computation cost.

Tree Representation and Feature Fusion Based Method for Multi-object Binary Image Retrieval

The images with multi-objects are common in multimedia information, such as trademark, symbol and medical image. However state of the art image retrieval systems designed specially for multi-object images are rare. This paper proposes an effective solution for multi-object binary images retrieval by fusing several features. We first employ a Tree Representation Model (TRM) to describe the topology structure of multi-object binary images. Secondly, we propose two new descriptors to describe the density and the spatial location feature of the objects, respectively. In addition, we combine two descriptors and shape feature to distinguish the difference of the image objects. Finally, the similar matching algorithm based on TRM is given and applied to trademark database retrieval. Experiment shows our method is superior to previous methods in search similar binary images with multi-objects.

Software Usability and Legal Informatics

This paper examines software usability from two different perspectives within legal informatics. Firstly, the current state of legal regulation of usability of medical systems is explored briefly. Secondly, different levels of usability in legal information systems are examined through two examples of trademark databases based on a high-usability represented model (Onomatics Quick Search) and the more traditional implementation model (USPTO TESS) with poor usability.

Trademark image retrieval using an integrated shape descriptor

Trademarks are distinctive visual symbols with high reputational value, due to the perception of quality and innovation associated with them. They are important reputational assets used as a marketing tool to convey a certain assurance of quality, innovation, and the standards, which the manufacturer seeks to maintain. This motivates the need for trademark protection by providing a solution to prevent infringement. This problem can be addressed by developing retrieval systems capable of comparing the visual similarity of trademarks. This paper contributes to the research in this field by proposing an innovative trademark retrieval technique with improved retrieval performance due to the integration of global and local descriptors. The global descriptor employed is the Zernike moment’s coefficients. The local descriptor is the edge-gradient co-occurrence matrix, derived from the contour information that is considered very important in human perception of visual similarity. The proposed retrieval technique is tested using the standard MPEG-7 shape database of 1400 images and the MPEG-7 trademark database of 3260 images. The results show 5% precision/recall improvement in the case of the MPEG-7 shape database, as well as 2.35% Bull’s eye score improvement and 19.8% NMRR score improvement for the 10 randomly selected trademarks from the MPEG-7 trademarks database.

Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval

Shape, being an important part of an object, has a special place in the field of shape-based image retrieval (SBIR). To retrieve most appropriate images, various descriptors are applied in SBIR like Zernike moments (ZMs), complex Zernike moments (CZMs) etc. Though ZMs/CZMs are good in SBIR but they are capable of extracting only global details of an image, hence something in addition to this is desirable to improve the performance of SBIR system. This paper presents experimental analysis of pixel-based dense descriptors such as local binary pattern (LBP), local directional pattern (LDP) and their variants. These descriptors are used as local features along with ZMs global features in achieving higher and accurate retrieval rate in SBIR system. We have analyzed these variants of LBP/LDP with various similarity measures on images. In case of ZMs, the magnitude component is used as global features. These methods are tested separately on suitable shape databases. Various databases used in the paper are MPEG-7 CE-2 region-based database, MPEG-7 CE-1 contour-based database and Trademark database. It can be concluded from the experimental analysis that the performance of LDP along with ZMs is better than that of ZMs alone and of ZMs along with other variants of LBP and LDP.

Content based Trademark Retrieval by Integrating Shape with Colour and Texture Information

is one of the most disputed items in Intellectual Property Rights. To grant a trademark, litigation is carried out to investigate if the new trademark applied for matches substantially with an already issued trademark. Considering the fact that the trademark database is very huge and keeps on increasing constantly, the manual search is definitely not a correct solution and an Information and Communication Technology (ICT) based system is required to automate this process. In a content based retrieval system, shape is easily the most important feature for the trademark image. Using shape feature various systems have been developed in the past. Here, a new approach is proposed that integrates shape with color and texture information that improves the results significantly in terms of Precision and Recall. A flexible combination of features is used for improving performance and clustering is applied to the database to enhance retrieval efficiency.

Morphological Pattern Based Approach for Trademark Retrieval

In the current scenario of the economic developments in the world market and growth in the international trade practices, Trademark registration is becoming a critical issue. The number of company and brand is increasing day by day and they all demand a trademark registration for same trade both in the home country as well as in foreign markets. In this paper we propose an invariant approach to break the character and shape of the trademark (logo or design) to capture the shape and related attributes therein to build an efficient trademark retrieval tool. We create the input image in a negative image format for ignoring the background and objects reverse intensities values. The paper illustrates the morphological opening approach for removing the very small objects which can be acting as a noise. The paper also proposes how the extracted images from the trademark database will be matched further for similar input trademarks on the basis of trademark retrieval process from the database.

Combining block and corner features for content-based trademark retrieval

In order to retrieve a similarly look trademark from a large trademark database, an automatic content based trademark retrieval method using block hit statistic and corner Delaunay Triangulation features was proposed. The block features are derived from the hit statistic on a series of concentric ellipse. The corners are detected based on an enhanced SUSAN (Smallest Univalue Segment Assimilating Nucleus) algorithm and the Delaunay Triangulation of corner points are used as the corner features. Experiments have been conducted on the MPEG-7 Core Experiment CE-Shape-1 database of 1 400 images and a trademark database of 2 000 images. The retrieval results are very encouraging.

Gestalt-based feature similarity measure in trademark database

Motivated by the studies in Gestalt principle, this paper describes a novel approach on the adaptive selection of visual features for trademark retrieval. We consider five kinds of visual saliencies: symmetry, continuity, proximity, parallelism and closure property. The first saliency is based on Zernike moments, while the others are modeled by geometric elements extracted illusively as a whole from a trademark. Given a query trademark, we adaptively determine the features appropriate for retrieval by investigating its visual saliencies. We show that in most cases, either geometric or symmetric features can give us good enough accuracy. To measure the similarity of geometric elements, we propose a maximum weighted bipartite graph (WBG) matching algorithm under transformation sets which is found to be both effective and efficient for retrieval.

Trademark image retrieval by distance–angle pair‐wise histogram

AbstractBecause of the continuously increasing number of registered trademarks, it is more and more difficult to design and register a new trademark without conflicting with the registered trademarks or logos. Traditionally, trademark indexing by text description is applied for trademark image databases. However, as more and more trademarks have been designed with complex content, describing a trademark only with text description is not an efficient approach any more. Therefore, trademark indexing by its content is required for comparing, retrieving and classifying similar trademarks. In this article, a content‐based trademark image retrieval system is proposed. The newly designed trademarks can be avoided from being similar with the registered ones with such a system. This scheme makes use of the distance–angle pair‐wise histogram as a feature vector of trademark shape for retrieving similar registered trademarks. Several moment‐based trademark retrieval schemes have also been implemented in computer simulations. Experiments have been conducted on registered trademark databases. Impressive results are shown to demonstrate the robustness of the proposed scheme. Moreover, it is quite simple to construct the distance–angle pair‐wise histogram for a trademark object. Therefore, the retrieval speed is acceptable and the system can be accessed through internet on a web browser. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 103–113, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20043

News on patent trade mark and design databases on the Internet

News on patent trade mark and design databases on the Internet

News on patent, trade mark and design databases on the Internet

News on patent, trade mark and design databases on the Internet

News on patent, trade mark and design databases on the Internet

News on patent, trade mark and design databases on the Internet

News on patent, trade mark and design databases on the internet

News on patent, trade mark and design databases on the internet

News on patent, trade mark and design databases on the Internet

News on patent, trade mark and design databases on the Internet

State Trademark and Company Name Web Sites

SUMMARYThis paper briefly discusses the importance of searching state trademarks and reviews the library and legal literature on issues involved in such a search. It summarizes the development of search services, from Dialog's release of Trademarkscan(r) Statein January 1987 to the present proliferation of individual states' free company name and trademark databases on the Internet. It describes commonly available search features and unusual special features, and compares their availability in different states' web sites. A spreadsheet gives details and web addresses for the fifty states, Puerto Rico, and the District of Columbia.

Finding Your Way Through the Trademark Information Maze

SUMMARYThis article gives guidance in searching trademarks, owner names, goods and services, and international classes using sample queries in the trademark databases of the United States Patent and Trademark Office (USPTO). Web and DVD databases are described. Both systems have advantages and disadvantages. The author presents an introduction to trademark terminology and the basic application process. Five queries are completed showing interesting uses of trademarks as well as the strengths and weaknesses of the databases.

Content-based retrieval from trademark databases

Since the number of registered trademarks is increasing rapidly, the job of identifying infringement of similar trademarks by human inspection becomes laborious and time-consuming. To deal with the problem, we propose an automatic content-based trademark retrieval method. The proposed method automatically selects appropriate features based on feature selection principles to discriminate trademarks. The database trademarks are softly clustered into classes using a fuzzy approach to increase the retrieval speed. The user can submit a query through trademark examples to get a list of database trademarks ordered by similarity ranks. The query results can be iteratively refined by the feedback presented by the user until the trademarks of interest are retrieved. Experiments are conducted on a trademark database containing 1000 images and the retrieval results are very encouraging.

Content-based similarity retrieval of trademarks using relevance feedback

This paper addresses the problem of how to efficiently and effectively retrieve images similar to a query from a trademark database purely on the basis of low-level feature analysis. It investigates the hypothesis that the low-level image features used to index the trademark images can be correlated with image contents by applying a relevance feedback mechanism that evaluates the feature distributions of the images the user has judged relevant, or not relevant and dynamically updates both the similarity measure and query in order to better represent the user's particular information needs. Experimental results on a database of 1100 trademarks are reported and commented.