Abstract: Architecture that creates a single path for modifying and persisting application's state, via scripts. Accordingly, an application utilizes one or more scripts that include statements that alter the application state. The scripts can be executed at application startup, and thus, the application resumes functioning in the desired state. No additional persistence is required, since the scripts perform this function. If there is an additional need to synchronize (or modify) application state with the server (or another remote party), only a single storage for scripts needs to be synchronized (updated), rather than individual system components.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. According to the proposed method, hardware and/or software parameters that can characterize known behavioral patterns in the computerized system are determined. Known malicious code samples are learned by a machine learning process, such as decision trees, Naïve Bayes, Bayesian Networks, and artificial neural networks, and the results of the machine learning process are analyzed in respect to these behavioral patterns. Then, known and unknown malicious code samples are identified according to the results of the machine learning process.

Abstract: An embodiment of the invention provides a check-in verification system (VCS) that verifies a check-in by a user using a smartphone to check-in at a venue, by verifying that a geolocation provided by the smartphone agrees with a location of the venue and that a password used by the user and the venue are the same.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. Accordingly, hardware and/or software parameters are determined in the computerized system that is can characterize known behavioral patterns thereof. Known malicious code samples are learned by a machine learning process, such as decision trees and artificial neural networks, and the results of the machine learning process are analyzed in respect to the behavioral patterns of the computerized system. Then known and unknown malicious code samples are identified according to the results of the machine learning process.

Abstract: A method of providing impressions of a mobile ad is provided in which a vendor may bid for impressions of the ad responsive to a distribution of a number of potential consumers of a given vendor service and/or product as a function of consumer location and probability of purchasing the given service or product.

Abstract: An embodiment of the invention provides a location based service (LBS) that updates a first version of information provided a mobile terminal responsive to a geo-query relative to a second, later version of information responsive to the geo-query by transmitting portions of the second version to the mobile terminal rather than all of the second version.

Abstract: Location-based notification architecture that provides notification relevance to a user and/or a user goal. The size of the virtual perimeter or boundary is changed dynamically based on changes in relevance to a user and/or user goal, and thus, can be made dependent on various factors. The size of the perimeter can increase or decrease according to user preferences that are learned over time (e.g., preference for a gas station of a specific company). These capabilities improve the relevance of the notification the user receives. The relevance of a notification to the user can be improved by tuning the perimeter size according to known parameters that depend on the point of interest (e.g., business) itself and/or by tuning of the size of virtual perimeter according to parameters associated with user behavior. Other parameters can be considered as well, such as environmental conditions, and traffic conditions, for example.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. According to the proposed method, hardware and/or software parameters that can characterize known behavioral patterns in the computerized system are determined. Known malicious code samples are learned by a machine learning process, such as decision trees, Naïve Bayes, Bayesian Networks, and artificial neural networks, and the results of the machine learning process are analyzed in respect to these behavioral patterns. Then, known and unknown malicious code samples are identified according to the results of the machine learning process.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. Accordingly, hardware and/or software parameters are determined in the computerized system that is can characterize known behavioral patterns thereof. Known malicious code samples are learned by a machine learning process, such as decision trees and artificial neural networks, and the results of the machine learning process are analyzed in respect to the behavioral patterns of the computerized system. Then known and unknown malicious code samples are identified according to the results of the machine learning process.