Abstract: Provided herein are systems and methods for hyper-personalizing digital guidance for improved user adoption of an underlying computer application. In one exemplary implementation, a method includes identifying an underlying application, gathering usage data at a user level for n days, choosing at least two methods from the group consisting of sequence analysis, top popular analysis, repeat usage analysis, similar users analysis and popular analysis, combining results from the at least two chosen methods, and recommending content to a recommendation user based upon the combined results.

Abstract: Provided herein are systems and methods for personalizing digital guidance for improved user adoption of an underlying computer application. In one exemplary implementation, a method includes identifying an underlying application, identifying different pages of the application, gathering usage data at a user level for n days, creating a user behavior matrix from the gathered data, performing a user similarity calculation for each pair of the users, tabulating a consumption count for each of the users, performing a series of score calculations for a recommendation user, calculating an intermediate score for each piece of content, counting a number of users who clicked on each piece of content, obtaining a final score for each piece of content, deciding on a ranking order of the content based on the final scores, and recommending content to the recommendation user from the ranking step.

Abstract: Provided herein are systems and methods for personalizing digital guidance for improved user adoption of an underlying computer application. In one exemplary implementation, a method includes identifying an underlying application, identifying different pages of the application, gathering usage data at a user level for n days, creating a user behavior matrix from the gathered data, performing a user similarity calculation for each pair of the users, tabulating a consumption count for each of the users, performing a series of score calculations for a recommendation user, calculating an intermediate score for each piece of content, counting a number of users who clicked on each piece of content, obtaining a final score for each piece of content, deciding on a ranking order of the content based on the final scores, and recommending content to the recommendation user from the ranking step.

Abstract: Techniques are described for viewing a plurality of related presentation areas linked to a set of machine-related data. In one example, a machine-related data set associated with a manufacturing process session for manufacturing a particular workpiece is presented, the data set representing a common data set presented in a plurality of presentation areas, each associated with a separate view on the machine-related data set. In one of the presentation areas, a selection of a particular group of data points is identified and that presentation area is updated. Reference values associated with the common data set are identified based on the selected group of data points. For each of the other presentation areas, (1) a particular set of data included in the particular other presentation area corresponding to the identified reference values is identified and (2) the corresponding presentation area is updated based on that identified data set.

Abstract: Techniques for comparing two or more sessions of a manufacturing process are described. In one example, a particular metric associated with execution of a manufacturing process is identified, the particular metric evaluated in a plurality of manufacturing process sessions for at least one manufacturing process. A particular session particular session from the plurality of manufacturing process sessions is selected as a baseline session, wherein at least a portion of the remaining plurality of manufacturing process sessions are to be compared to the baseline session. In a primary portion of a presentation area, a visualization of the values of the identified metric associated with the particular session are presented. In a secondary portion of the presentation area, visualizations of the values of the identified metric associated with at least a portion of the other manufacturing process sessions from the plurality of sessions is presented.

Abstract: Techniques are described for receiving visualizing two-dimensional (2D) metric data in connection with a three-dimensional (3D) visualization data of a manufacturing process. In one example, a 3D visualization of machine-related data from a process session for manufacturing a particular workpiece is presented, the manufacturing process performed by a machine operating in 3D, the machine-related data associated with a path taken by a tool or end-effector associated with the machine and/or the machine itself during the manufacturing process session. At least one 2D data set is provided representing a metric associated with manufacturing process session. A particular set is selected for presentation within the presented 3D visualization of the machine-related data. A connection between values of the 2D metric set and the machine-related data of the 3D visualization is determined and, based on the connection, the selected 2D metric set is incorporated into the 3D visualization of the machine-related data.

Abstract: Techniques are described for receiving visualizing two-dimensional (2D) metric data in connection with a three-dimensional (3D) visualization data of a manufacturing process. In one example, a 3D visualization of machine-related data from a process session for manufacturing a particular workpiece is presented, the manufacturing process performed by a machine operating in 3D, the machine-related data associated with a path taken by a tool or end-effector associated with the machine and/or the machine itself during the manufacturing process session. At least one 2D data set is provided representing a metric associated with manufacturing process session. A particular set is selected for presentation within the presented 3D visualization of the machine-related data. A connection between values of the 2D metric set and the machine-related data of the 3D visualization is determined and, based on the connection, the selected 2D metric set is incorporated into the 3D visualization of the machine-related data.

Abstract: Techniques are described for viewing a plurality of related presentation areas linked to a set of machine-related data. In one example, a machine-related data set associated with a manufacturing process session for manufacturing a particular workpiece is presented, the data set representing a common data set presented in a plurality of presentation areas, each associated with a separate view on the machine-related data set. In one of the presentation areas, a selection of a particular group of data points is identified and that presentation area is updated. Reference values associated with the common data set are identified based on the selected group of data points. For each of the other presentation areas, (1) a particular set of data included in the particular other presentation area corresponding to the identified reference values is identified and (2) the corresponding presentation area is updated based on that identified data set.

Abstract: Techniques for comparing two or more sessions of a manufacturing process are described. In one example, a particular metric associated with execution of a manufacturing process is identified, the particular metric evaluated in a plurality of manufacturing process sessions for at least one manufacturing process. A particular session particular session from the plurality of manufacturing process sessions is selected as a baseline session, wherein at least a portion of the remaining plurality of manufacturing process sessions are to be compared to the baseline session. In a primary portion of a presentation area, a visualization of the values of the identified metric associated with the particular session are presented. In a secondary portion of the presentation area, visualizations of the values of the identified metric associated with at least a portion of the other manufacturing process sessions from the plurality of sessions is presented.

Abstract: A method for generating layered structures is disclosed. The method comprises reacting an azide-functionalized material with an alkyne-functionalized material, one of which can be a nanoparticle or a microparticle and the other can be a substrate or another nanoparticle or microparticle. The method also includes generating layered structures having multiple layers which can be built up in multiple azide-alkyne reaction steps.