Abstract: Disclosed in some examples are methods, systems, and machine readable mediums which provide summaries of topics determined within a corpus of documents. These summaries may be used by customer service associates, analysts, or other users to quickly determine both topics discussed and contexts of those topics over a large corpus of text. For example, a corpus of documents may be related to customer complaints and the topics may be summarized to produce summaries such as “credit report update due to stolen identity.” These summarizations may be used to efficiently spot trends and issues.

Abstract: A mileage prediction and optimization system (FIG. 1) is disclosed for optimizing the mileage of a vehicle. The mileage prediction and optimization system comprises a prediction and optimization module (300) adapted to determine variation in mileage of the vehicle at least based on the current fuel volume, current speed, current fuel density, the current tire air pressure, the current tire air temperature, current fetched values from an ECU module (700) and the pre-defined mileage data of the vehicle. Further, the prediction and optimization module (300) is adapted to optimize the mileage of the vehicle by reducing variation of fuel density, reducing variation of tire air pressure and informing optimal gear-speed combinations to a user regardless of whether the vehicle is stationary or in motion.

Abstract: An adhesive tape is disclosed that can simplify the process of pasting a torn, cut off, additional piece of material into its position. The adhesive tape comprising a strip that comprises of flaps made up of flexible material extending radially from a common axis along the length of the strip. The surfaces of the flap are either both adhesive surfaces or one adhesive surface and another is non-adhesive surface. The invention provides a removable layer which protects an adhesive quality of the adhesive surface on which corresponding removable layer is pasted. The invention also provides a method of easily tracing the removable layer which is pasted onto the tape which relates to the method to know the approximate/exact amount of tape left in a roll.

Abstract: A mileage prediction and optimization system (FIG. 1) is disclosed for optimizing the mileage of a vehicle. The mileage prediction and optimization system comprises a prediction and optimization module (300) adapted to determine variation in mileage of the vehicle at least based on the current fuel volume, current speed, current fuel density, the current tire air pressure, the current tire air temperature, current fetched values from an ECU module (700) and the pre-defined mileage data of the vehicle. Further, the prediction and optimization module (300) is adapted to optimize the mileage of the vehicle by reducing variation of fuel density, reducing variation of tire air pressure and informing optimal gear-speed combinations to a user regardless of whether the vehicle is stationary or in motion.

Abstract: The present subject matter relates to a vehicle mileage optimization system (300) for optimizing mileage of a vehicle by informing user about correct gear and speed combination. The vehicle mileage optimization system (300) has a speed and fuel measuring module (305) coupled with a processor (301) to collect speed and fuel data of the vehicle. Further, the vehicle mileage optimization system has a mileage and distance estimation module (306) coupled with the processor (301) to estimate fuel efficiency and correct gear and speed combination in the vehicle based on the collected speed and fuel data.

Abstract: The present subject matter relates to a vehicle mileage optimization system (300) for optimizing mileage of a vehicle by informing user about correct gear and speed combination. The vehicle mileage optimization system (300) has a speed and fuel measuring module (305) coupled with a processor (301) to collect speed and fuel data of the vehicle. Further, the vehicle mileage optimization system has a mileage and distance estimation module (306) coupled with the processor (301) to estimate fuel efficiency and correct gear and speed combination in the vehicle based on the collected speed and fuel data.

Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival.

Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival.

Abstract: An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient/donor; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of disease is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival.

Abstract: A technique for valuation of intangible assets is provided. The technique includes determining a present value of all future earnings from an intangible asset based on a current earning and one or more financial ratios. The technique further includes determining a value of the intangible asset based on the present value of all future earnings.

Abstract: A method, system, and apparatus for classifying intangible assets are provided. The method includes determining an objective of classification. The method further includes constructing, via a processor, a Discriminant Analysis (DA) model using one or more test sets of intangible assets. The DA model includes one or more discriminant functions operable to classify the one or more test set of intangible assets into two or more groups based on a set of attributes associated with one or more intangible assets of the test set of intangible assets to meet the objective of classification. Thereafter, the method includes classifying a target set of intangible assets via the DA model.

Abstract: Software intended to operate in a clustered environment can be tested for appropriate failover behavior through the use of an automated tool which allows failover to be simulated without requiring that the application be deployed in a cluster environment and observing the effects of actual failover. Such an automated tool can measure the characteristics of one or more session objects created by the application and provide appropriate messages for a developer when those characteristics indicate improper coding for failover.

Abstract: Software intended to operate in a clustered environment can be tested for appropriate failover behavior through the use of an automated tool which allows failover to be simulated without requiring that the application be deployed in a cluster environment and observing the effects of actual failover. Such an automated tool can measure the characteristics of one or more session objects created by the application and provide appropriate messages for a developer when those characteristics indicate improper coding for failover.

Abstract: The present invention is directed toward a framework for consistent and minimized development of Web-based applications. A preferred Web-based application of the present invention is built on a web server framework utilizing industry standard technologies relating to Web development and deployment. These technologies fall into three main areas: browser technology, Web server technology, and application server technology. A preferred Web architecture of the present invention is preferably based on HTTP, utilizing a browser, Middleware, and a Web Server, the Web Server comprising Java servlets, Java Server Pages, Java Beans, and a Web Server Framework. This architecture follows a standard Model, View, Controller pattern. Preferably, the initial servlet is the Controller, the Java Beans are the Model, and the JSP is the View. Preferred frameworks include an Error Framework, a Logging and Tracing Framework, a Connection Framework, a Reference Data Framework, a Security Framework, and an International Framework.