Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Abstract: Implementations relate to identifying actions performed by a user while the user is interacting with multiple applications and providing a routine suggestion to the user based on the identified actions. While a user is interacting with one or more applications, the user actions are determined. The user actions are compared to one or more template routines and a template routine is selected that matches the actions of the user and provided to the user as a suggested routine. The suggested routine can be implemented by an automated assistant to perform the actions of the template by providing a corresponding command.

Abstract: Implementations relate to determining a rendering type for an application that is executing automatically. Based on user interactions with an application that is associated with specified input from the user while the user is interacting with the application, a confidence metric is generated for each specified input and a rendering type is determined based on the confidence metrics. Subsequently, when the user requests that a sequence of actions be performed, the application will be displayed according to the rendering type.

Abstract: Implementations relate to identifying actions performed by a user while the user is interacting with multiple applications and providing a routine suggestion to the user based on the identified actions. While a user is interacting with one or more applications, the user actions are determined. The user actions are compared to one or more template routines and a template routine is selected that matches the actions of the user and provided to the user as a suggested routine. The suggested routine can be implemented by an automated assistant to perform the actions of the template by providing a corresponding command.

Abstract: Implementations relate to determining a rendering type for an application that is executing automatically. Based on user interactions with an application that is associated with specified input from the user while the user is interacting with the application, a confidence metric is generated for each specified input and a rendering type is determined based on the confidence metrics. Subsequently, when the user requests that a sequence of actions be performed, the application will be displayed according to the rendering type.

Abstract: Implementations relate to determining a rendering type for an application that is executing automatically. Based on user interactions with an application that is associated with specified input from the user while the user is interacting with the application, a confidence metric is generated for each specified input and a rendering type is determined based on the confidence metrics. Subsequently, when the user requests that a sequence of actions be performed, the application will be displayed according to the rendering type.

Abstract: Implementations relate to identifying actions performed by a user while the user is interacting with multiple applications and providing a routine suggestion to the user based on the identified actions. While a user is interacting with one or more applications, the user actions are determined. The user actions are compared to one or more template routines and a template routine is selected that matches the actions of the user and provided to the user as a suggested routine. The suggested routine can be implemented by an automated assistant to perform the actions of the template by providing a corresponding command.

Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Abstract: Implementations set forth herein relate to an automated assistant that operates according to a variety of different location-based biasing modes for rendering responsive content for a user and/or proactively suggesting content for the user. The user can provide condensed spoken utterances to the automated assistant, when the automated assistant is operating according to one or more location-based biasing modes, but nonetheless receive accurate responsive outputs from the automated assistant. A responsive output generated by biasing toward a subset of location characteristic data that has been prioritized over other subsets of location characteristic data. The biasing allows the automated assistant to compensate for any details that may be missing from a spoken utterance, but allows the user to provide shorter spoken utterances, thereby reducing an amount of language processing when processing inputs from the user.

Abstract: Implementations set forth herein relate to an automated assistant that operates according to a variety of different location-based biasing modes for rendering responsive content for a user and/or proactively suggesting content for the user. The user can provide condensed spoken utterances to the automated assistant, when the automated assistant is operating according to one or more location-based biasing modes, but nonetheless receive accurate responsive outputs from the automated assistant. A responsive output generated by biasing toward a subset of location characteristic data that has been prioritized over other subsets of location characteristic data. The biasing allows the automated assistant to compensate for any details that may be missing from a spoken utterance, but allows the user to provide shorter spoken utterances, thereby reducing an amount of language processing when processing inputs from the user.

Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Abstract: In various embodiments, the invention relates to poly(propylene fumarate) (PPF)-based star-shaped copolymers synthesized using a core-first approach that uses a multi-functional alcohols as an initiator, and Mg(BHT)2(THF)2 as catalyst for controlled ring opening copolymerization (ROCOP) of maleic anhydride (MAn) with propylene oxide (PO). In some embodiments, these star-PPF copolymers have lower viscosities than their linear analogs, allowing a decrease in DEF fraction in resin formulation, as well as the use of higher molecular weights. These star-shape PPF can be used to prepare PPF:DEF resins containing as much as 70% by weight of the multi-arm PPF star copolymers, and have a low complex viscosity of high Mn star PPF resin that affords rapid printing with a Mn nearly eight times larger than the largest linear PPF oligomer printed previously.

Abstract: Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.