Abstract: A software product for easy-to-use naming, coding, and sharing of locations, including those with no addresses.

Abstract: A system capable of receiving at least one location input, creating a code associated with that input, storing the code, receiving a created code, providing the location, and providing directions to or from the location.

Abstract: A software product for easy-to-use naming, coding, and sharing of locations, including those with no addresses.

Abstract: A method of providing business information data to a user, including allowing the user unlimited access to interactive, customizable modular applications through a single platform. The applications interface with a database to provide real-time business information data to the user, the business information data relating to an entity and being based on a unique corporate identification number associated with the entity.

Abstract: A method of providing business information data to a user, including allowing the user unlimited access to interactive, customizable modular applications through a single platform. The applications interface with a database to provide real-time business information data to the user, the business information data relating to an entity and being based on a unique corporate identification number associated with the entity.

Abstract: Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Abstract: Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Abstract: Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Abstract: Entering and evaluating group(s) of similar dependence hypotheses, such as time dependence hypotheses, and forecasting values of a variable based on the group(s) of similar dependence hypotheses. The group(s) of similar dependence hypotheses may be used to forecast requests for an Internet resource or Internet resources having one or more particular attributes. The entered group(s) of similar dependence hypotheses may be evaluated based on known data, such as past requests for Internet resources or Internet resources of a particular type.

Abstract: Resource usage data is used to infer degrees of influence between users. Once such inferences are made, a directed graph representation of the users and the inferred "influence" between the users can be generated. "Influential rumormongers" can then be determined from the directed graph, for example, by using a greedy graph covering algorithm. In this way, marketing information can be targeted to "influential rumormongers" to optimize its dissemination and impact. If actual (explicit) data regarding the influence between users is known, such data may be used to refine or replace at least some edge values.