Abstract: A method for generating assembly instructions for a plurality of 3D component models including a first 3D component model and a second 3D component model. The first 3D component model includes a first geometric feature, and the second 3D component model includes a second geometric feature. The method includes determining first assembly instructions for assembling the plurality of 3D component models into a first 3D model assembly, determining a plurality of assembly constraints for assembling the plurality of 3D component models into the first 3D model assembly using the first assembly instructions, modifying the plurality of 3D component models, and generating second assembly instructions for assembling the modified plurality of 3D component models into a second 3D model assembly. The second assembly instructions are different than the first assembly instructions.

Abstract: Systems and methods for passively processing a payment for an activity that leverages location data of a user participating in the activity. User data is received, the user data including a plurality of user location points and a time associated with each of the plurality of user location points. An activity is determined, the activity associated with the user based on the user data, wherein the activity is associated with at least one of transportation and attendance at a venue. An instance of the activity is identified and a payment amount for the user is processed based on the instance of the activity.

Abstract: Systems and computerized methods for determining patterns in user activity such that user contextual information can be provided based on the patterns. Historic data associated with a first user is received and entity information is determined based on the historic data. Current location data associated with the first user is received and prediction information is generated based on a combination of the entity information and the current location data for a current time period. Contextual insight information is determined based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with the current location of the first user.

Abstract: A method for augmenting a three-dimensional model of a component in a computer-aided design (CAD) environment includes indexing a first three-dimensional surface representation of a first component surface of the three-dimensional CAD model as a first function-attributed surface (FAS) element by assigning the first three-dimensional surface representation a unique first FAS ID which associates the first FAS element with the component. The first component surface is one of a plurality of component surfaces of the three-dimensional CAD model of the component. The method further includes storing at least one first scalar attribute for the first FAS element as a first FAS dataset and assigning the unique first FAS ID to the first FAS dataset. The method further includes configuring the three-dimensional CAD model to display a FAS tree that includes a plurality of FAS elements associated with the component.

Abstract: A method for augmenting a three-dimensional model of a component in a computer-aided design (CAD) includes receiving a three-dimensional CAD model of the component. The three-dimensional CAD model includes a plurality of component surfaces. The method further includes generating a first three-dimensional surface representation of a first component surface of the plurality of component surfaces and indexing the first three-dimensional surface representation as a first function-attributed surface (FAS) element by assigning the first three-dimensional surface representation a unique first FAS ID which associates the first FAS element with the component.

Abstract: Systems and computerized methods for determining patterns in user activity such that user contextual information can be provided based on the patterns. Historic data associated with a first user is received and entity information is determined based on the historic data. Current location data associated with the first user is received and prediction information is generated based on a combination of the entity information and the current location data for a current time period. Contextual insight information is determined based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with the current location of the first user.

Abstract: Systems and methods for passively processing a payment for an activity that leverages location data of a user participating in the activity. User data is received, the user data including a plurality of user location points and a time associated with each of the plurality of user location points. An activity is determined, the activity associated with the user based on the user data, wherein the activity is associated with at least one of transportation and attendance at a venue. An instance of the activity is identified and a payment amount for the user is processed based on the instance of the activity.