Abstract: Systems and methods are provided for identifying unsolicited or unwanted electronic communications, such as spam. The disclosed embodiments also encompass systems and methods for selecting content items from a content item database. Consistent with certain embodiments, computer-implemented systems and methods may use a clustering based statistical content matching anti-spam algorithm to identify and filter spam. Such a anti-spam algorithm may be implemented to determine a degree of similarity between an incoming e-mail with a collection of one or more spam e-mails stored in a database. If the degree of similarity exceeds a predetermined threshold, the incoming e-mail may be classified as spam. Further, in accordance with other embodiments, systems and methods may be provided to determine a degree of similarity between a query or search string from a user and content items stored in a database.

Abstract: Systems and methods are provided for identifying unsolicited or unwanted electronic communications, such as spam. The disclosed embodiments also encompass systems and methods for selecting content items from a content item database. Consistent with certain embodiments, computer-implemented systems and methods may use a clustering based statistical content matching anti-spam algorithm to identify and filter spam. Such a anti-spam algorithm may be implemented to determine a degree of similarity between an incoming e-mail with a collection of one or more spam e-mails stored in a database. If the degree of similarity exceeds a predetermined threshold, the incoming e-mail may be classified as spam. Further, in accordance with other embodiments, systems and methods may be provided to determine a degree of similarity between a query or search string from a user and content items stored in a database.

Abstract: Systems and methods are provided for identifying unsolicited or unwanted electronic communications, such as spam. The disclosed embodiments also encompass systems and methods for selecting content items from a content item database. Consistent with certain embodiments, computer-implemented systems and methods may use a clustering based statistical content matching anti-spam algorithm to identify and filter spam. Such a anti-spam algorithm may be implemented to determine a degree of similarity between an incoming e-mail with a collection of one or more spam e-mails stored in a database. If the degree of similarity exceeds a predetermined threshold, the incoming e-mail may be classified as spam. Further, in accordance with other embodiments, systems and methods may be provided to determine a degree of similarity between a query or search string from a user and content items stored in a database.

Abstract: Systems and methods are provided for identifying unsolicited or unwanted electronic communications, such as spam. The disclosed embodiments also encompass systems and methods for selecting content items from a content item database. Consistent with certain embodiments, computer-implemented systems and methods may use a clustering based statistical content matching anti-spam algorithm to identify and filter spam. Such a anti-spam algorithm may be implemented to determine a degree of similarity between an incoming e-mail with a collection of one or more spam e-mails stored in a database. If the degree of similarity exceeds a predetermined threshold, the incoming e-mail may be classified as spam. Further, in accordance with other embodiments, systems and methods may be provided to determine a degree of similarity between a query or search string from a user and content items stored in a database.

Abstract: Systems and methods are provided for identifying unsolicited or unwanted electronic communications, such as spam. The disclosed embodiments also encompass systems and methods for selecting content items from a content item database. Consistent with certain embodiments, computer-implemented systems and methods may use a clustering based statistical content matching anti-spam algorithm to identify and filter spam. Such a anti-spam algorithm may be implemented to determine a degree of similarity between an incoming e-mail with a collection of one or more spam e-mails stored in a database. If the degree of similarity exceeds a predetermined threshold, the incoming e-mail may be classified as spam. Further, in accordance with other embodiments, systems and methods may be provided to determine a degree of similarity between a query or search string from a user and content items stored in a database.

Abstract: A system and method for generating expense information are provided. According to the invention, an employee's bill having itemized charges is received and stored in a data storage system. The itemized charges are processed to determine whether they are business-related, in whole or in part, or not business-related using data sources identifying charges that are known to be at least partially business-related. Charges that cannot be determined as business-related or not business-related are marked as unknown. Employee input is received via a user interface that identifies the unknown charges as business-related or not business-related, and such employee input is stored in the data storage system to assist in identifying charges in the future. Once all charges have been determined to be business-related or not business-related, and have been approved by the employee and, optionally, a supervisor, an expense report may be generated.