Abstract: One goal of computer services (e.g., email, web pages, blogs, advertisements, etc.) is to provide a user with Kinds (digital representations of everyday things) that may be relevant and interesting to the user. Users and Kinds may be plotted within a multidimensional matrix as feature vectors based upon their respective characteristics. An unsupervised clustering technique may be executed upon the matrix to create a mathematical cluster of feature vectors having similar characteristics. For example, a clothing cluster may comprise a dress Kind, a shoe Kind, a wool Kind, a watch Kind, etc. because the unsupervised clustering technique may determine these Kinds are plotted within the matrix in such a way that they have similar characteristics relating to clothing. The unsupervised clustering technique may also be utilized in determining which Kinds may be relevant to a user given a particular context with which a user is engaged with a computer resource.

Abstract: An embodiment of the invention is directed toward determining a solution to an objective function that is to be maximized or minimized. An initial feasible basis is determined. An entering variable is determined to enter the initial feasible basis. It is determined if there is an exiting variable. While there is an exiting variable, the exiting variable is removed from the feasible basis, the entering variable is added to the feasible basis, and a new entering variable is determined.

Abstract: A goal of computer development is to understand the user and the data with which the user is engaged. If a better understanding of the user and their data can be accomplished, then additional information and features may be provided based upon the user's intent and interests. Accordingly, as provided herein, Kinds may be created as digital representations of everyday things. Kind classifications may be created to characterize the Kinds. The Kind classifications may be updated based upon user interaction to further characterize Kinds with which the user has interacted. For example, when a user writes an email about using orange peels as an air freshener, an orange Kind classification may be updated to reflect that an orange may be used as an air freshener. Kind feature vectors and user feature vectors may be created to represent the probabilities that the Kind or user relates to particular characteristics.

Abstract: A goal of computer development is to understand the user and the data with which the user is engaged. If a better understanding of the user and their data can be accomplished, then additional information and features may be provided based upon the user's intent and interests. Accordingly, as provided herein, Kinds may be created as digital representations of everyday things. Kind classifications may be created to characterize the Kinds. The Kind classifications may be updated based upon user interaction to further characterize Kinds with which the user has interacted. For example, when a user writes an email about using orange peels as an air freshener, an orange Kind classification may be updated to reflect that an orange may be used as an air freshener. Kind feature vectors and user feature vectors may be created to represent the probabilities that the Kind or user relates to particular characteristics.

Abstract: One goal of computer services (e.g., email, web pages, blogs, advertisements, etc.) is to provide a user with Kinds (digital representations of everyday things) that may be relevant and interesting to the user. Users and Kinds may be plotted within a multidimensional matrix as feature vectors based upon their respective characteristics. An unsupervised clustering technique may be executed upon the matrix to create a mathematical cluster of feature vectors having similar characteristics. For example, a clothing cluster may comprise a dress Kind, a shoe Kind, a wool Kind, a watch Kind, etc. because the unsupervised clustering technique may determine these Kinds are plotted within the matrix in such a way that they have similar characteristics relating to clothing. The unsupervised clustering technique may also be utilized in determining which Kinds may be relevant to a user given a particular context with which a user is engaged with a computer resource.

Abstract: An embodiment of the invention is directed toward determining a solution to an objective function that is to be maximized or minimized. An initial feasible basis is determined. An entering variable is determined to enter the initial feasible basis. It is determined if there is an exiting variable. While there is an exiting variable, the exiting variable is removed from the feasible basis, the entering variable is added to the feasible basis, and a new entering variable is determined.

Abstract: The system and method of the present invention relates to the determining the specific ordering and execution of events from temporal constraints, filtering functions, and execution heuristics. To facilitate specification of event order objects be can associated with events in an object authoring system which provides for interaction, conditional behavior, and fuzzy relationships by dividing all time into past, present, and future. A user or developer can then perform all their work in the editable area marked “now.” Items that may have happened prior to the current work show up in the “past” area and items which might happen in the future show up in the “future” area. A user can then associate and/or dissociate objects associated with events in the editable area, for instance by simply loosely specifying temporal relationships or constraints amongst objects rather than specifying an exact temporal sequence directly.

Abstract: Technologies for transient personal preference customization of environments, devices and systems based on user preference data. Such technologies enable users to maintain personal preference data on mobile or other devices, such as cell phones, personal data assistants (“PDA”), or the like, such that the data can be transferred to devices and systems capable of conforming parameters under their control to the preferences expressed in the data. For example, a frequent traveler may define preferences regarding room temperature, ambient lighting, music style, and the like and maintain these preferences in a mobile device such as a cell phone. Upon arrival at a hotel room, for example, the systems of the hotel may access the preference data and adjust the parameters of the room, and the devices and systems in the room, to conform to the user's preferences while the user is in the room or checked into the hotel.