Abstract: A system for predicting vegetation encroachment into a utility asset that includes a remote sensing controller that stores a set of 3D data characterizing a utility asset and vegetation in a digital twin database. The digital twin database stores 3D data characterizing the utility asset and vegetation over time. The system further includes a biomass engine that classifies instances of vegetation characterized by the set 3D data stored in the digital twin database as a particular type of vegetation. The biomass engine further generates a growth curve for each instance of vegetation based on the set of 3D data stored in the digital twin database and the particular type of vegetation. Additionally, the biomass engine predicts future growth for each instance of vegetation based on the growth curve and determines if and when each instance of vegetation will exceed an encroachment threshold associated with the utility asset.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: A Personal TV System receives a plurality of video segments constituting a TV program and information describing each segment, and controls the display of the segments to a viewer in accordance with preferences of the viewer and with the description of each segment. Segments may be omitted or replaced with substitute segments. The viewer may review his preferences and edit or replace them. In a multi-viewer household, the system may prompt a viewer to confirm her preferences. A TV program may contain interactive segments, and when the viewer chooses to interact with a particular segment, the system automatically stores all subsequently received segments for later viewing while the viewer interacts with the selected segment. The system may present TV program choices to the viewer selected and arranged in accordance with her preferences.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: The method and system for TV user profile data prediction and modeling allows accurate and narrowly focused behavioral clustering. A client-side system classifies television consumers into representative user profiles. The profiles target individual user advertising and program preference category groups. A contextual behavioral profiling system determines the user's monitor behavior and content preferences, and the system may be continually updated with user information. A behavioral model database is queried by various system modules. The programming, including targeted advertising for television and interactive television is based on the profile data prediction, modeling and preference determination. The system is enabled to present a complete program sequence to the viewer based on the preference determination and stored programming.

Abstract: A Personal TV System receives a plurality of video segments constituting a TV program and information describing each segment, and controls the display of the segments to a viewer in accordance with preferences of the viewer and with the description of each segment. Segments may be omitted or replaced with substitute segments. The viewer may review his preferences and edit or replace them. In a multi-viewer household, the system may prompt a viewer to confirm her preferences. A TV program may contain interactive segments, and when the viewer chooses to interact with a particular segment, the system automatically stores all subsequently received segments for later viewing while the viewer interacts with the selected segment. The system may present TV program choices to the viewer selected and arranged in accordance with her preferences.