Patent ID: 11900052
Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. A method comprising:
extracting first feature data for a first set of formatted templates associated with a slide-based presentation, wherein the first set of formatted templates are part of a first presentation theme of a plurality of different presentation themes providing a first set of visual style attributes for objects of the first set of formatted templates;
extracting second feature data for a second set of formatted templates associated with a slide-based presentation, wherein the second set of formatted templates are part of a second presentation theme of the plurality of different presentation themes providing a second set of visual style attributes for objects of the second set of formatted templates;
applying trained artificial intelligence (AI) processing, configured for generation of transformations of slide-based templates, to generate a transformation of the first set of formatted templates and a transformation of the second set of formatted templates, wherein the applying of the trained AI processing executes processing operations that comprise:
encoding the first feature data of the first set of formatted templates as a first latent vector providing a distributed representation of the first feature data,
encoding the second feature data of the second set of formatted templates as a second latent vector providing a distributed representation of the second feature data,
propagating the first latent vector to a first decoder network selected from a plurality of decoder networks, wherein each of the plurality of decoder networks is trained based on training data comprising data for slide-based templates having one of the plurality of different presentation themes, wherein the first decoder network is trained based on training data comprising data for slide-based templates having the second presentation theme,
propagating the second latent vector to a second decoder network selected from the plurality of decoder networks, wherein the second decoder network is trained based on training data comprising data for slide-based templates having the first presentation theme,
automatically generating a transformed set of formatted templates for the first set of formatted templates based on analysis of the first latent vector using the first decoder network, wherein the transformed set of formatted templates comprises: one or more transformations of the objects within a slide-based template of the first set of formatted templates and a style transformation modifying one or more visual style attributes of the first set of visual style attributes, and
automatically generating a transformed set of formatted templates for the second set of formatted templates based on analysis of the second latent vector using the second decoder network, wherein the transformed set of formatted templates for the second set of formatted templates comprises:
one or more transformations of the objects within a slide-based template of the first set of formatted templates and a style transformation modifying one or more visual style attributes of the first set of visual style attributes; and

storing the transformed set of formatted templates for the first set of formatted templates and the transformed set of formatted templates for the second set of formatted templates.