Abstract: In examples, a developer may define a set of computer-controlled agent attributes, which may be processed by a generative multimodal machine learning model in conjunction with background information associated with a virtual environment (e.g., “lore”) and other agent information to generate multimodal model output with which to control the behavior of the computer-controlled agent. Thus, a player may interact with the computer-controlled agent, such that user input from the player is processed using the ML model to generate model output to affect the behavior of the computer-controlled agent, thereby enabling the user and the computer-controlled agent to interact. As compared to manual dialogue authoring, use of agent information to define the behavior of a computer-controlled agent may result in reduced effort on the part of a creator while also offering increased depth and variety for computer-controlled agents of a virtual environment.

Abstract: Aspects of the present disclosure relate to grounded multimodal agent interactions, where a user input is processed using a multimodal machine learning model to generate model output. The model output may then be processed to affect the behavior of an application, for example to enable a user to control the application and/or to facilitate user interactions with a conversational agent, among other examples. In some instances, at least a part of the model output may be executed or parsed, for example to call an application programming interface or function of the application. Thus, use of a multimodal machine learning model according to aspects described herein may enable the use of user-provided natural language input to affect the behavior of an application accordingly.

Abstract: Aspects of the present disclosure relate to grounded multimodal agent interactions, where a user input is processed using a multimodal machine learning model to generate model output. The model output may then be processed to affect the behavior of an application, for example to enable a user to control the application and/or to facilitate user interactions with a conversational agent, among other examples. In some instances, at least a part of the model output may be executed or parsed, for example to call an application programming interface or function of the application. Thus, use of a multimodal machine learning model according to aspects described herein may enable the use of user-provided natural language input to affect the behavior of an application accordingly.

Abstract: Aspects of the present disclosure relate to a machine learning-based dialogue authoring environment. In examples, a developer or creator of a virtual environment may use a generative multimodal machine learning (ML) model to create or otherwise update aspects of a dialogue tree for one or more computer-controlled agents and/or players of the virtual environment. For example, the developer may provide an indication of context associated with the dialogue for use by the ML model, such that the ML model may generate a set of candidate interactions accordingly. The developer may select a subset of the candidate interactions for inclusion in the dialogue tree, which may then be used to generate associated nodes within the tree accordingly. Thus, nodes in the dialogue tree may be iteratively defined based on model output of the ML model, thereby assisting the developer with dialogue authoring for the virtual environment.

Abstract: Aspects of the present disclosure relate to techniques for interactive content generation. In examples, processed content may be produced by a generative model based on a content seed, such as a sentence or paragraph. User input associated with the processed content may be received, for example to revise the processed content or provide additional input with respect to a subpart of the processed content that is associated with a low confidence score. A generative model may produce updated processed content based at least in part on the previously processed content, the user input, and/or, in some examples, additional content, as may be indicated by a user. Thus, a user may iterate on processed content that is produced by such generative models through successive interactions, thereby enabling the user to provide input to the generative model as part of the content generation process.

Abstract: Disclosed herein are processes and systems for removing heavy C5+ hydrocarbon components from a natural gas feed gas by subjecting the natural gas feed gas stream to an adsorber. The adsorber in the processes and systems described herein operating at a pressure that may be associated with improved adsorption capacity and longer breakthrough time.

Abstract: Disclosed in certain embodiments are sorbents for capturing heavy hydrocarbons via thermal swing adsorption processes.

Abstract: Systems and method directed to assistive document generation are described. More specifically, similar documents share large portions of reusable text structures that can be used to generate an initial document thereby saving a user time. To generate the document, an indication to create the document may be received and based on the indication, a plurality of example documents and grounding content may be identified. Example documents may be existing documents that are similar to a target document of the writer. Grounding information may refer to content that is relevant, timely, and accurate when applied to the target document. The plurality of example documents and the grounding content may be received, and a document sketch based on the example documents and the grounding content may be generated and contains a plurality of predicted text sequences based on the example documents and the grounding content.

Abstract: Disclosed in certain embodiments are sorbents for capturing heavy hydrocarbons via thermal swing adsorption processes.

Abstract: Disclosed in certain embodiments are methods of removing water from a gas feed stream comprising hydrocarbons and water during an adsorption step of an adsorption cycle.

Abstract: Disclosed in certain embodiments are sorbents for capturing heavy hydrocarbons via thermal swing adsorption processes.

Abstract: Disclosed in certain embodiments are sorbents for capturing heavy hydrocarbons via thermal swing adsorption processes.

Abstract: Mixed adsorbent/desiccant beds comprising in some embodiments from about 20 vol % (volume percent) to about 90 vol % of one or more adsorbents and from about 10 vol % to about 80 vol % of one or more desiccants, based on the total volume of the adsorbent/desiccant mixture, prevent water reflux during thermal regeneration of adsorption beds in gas processing plants and methods.

Abstract: Disclosed in certain embodiments are sorbents for capturing heavy hydrocarbons via thermal swing adsorption processes.

Abstract: The subject disclosure is directed towards developing a translation model for mapping search query terms to document-related data. By processing user logs comprising search histories into word-aligned query-document pairs, the translation model may be trained using data, such as probabilities, corresponding to the word-aligned query-document pairs. After incorporating the translation model into model data for a search engine, the translation model is used may used as features for producing relevance scores for current search queries and ranking documents/advertisements according to relevance.

Abstract: A digital personal assistant is described that determines a mental or emotional state of a user based on one or more signals and, based on the determined mental or emotional state, provides the user with feedback concerning an item of content generated thereby or an activity to be conducted thereby. An API is described that can be used by diverse applications and/or services to communicate with the digital personal assistant for the purpose of obtaining information about the current mental or emotional state of the user. Content tagging logic is described that identifies one or more items of content generated or interacted with by the user and stores metadata in association with the identified item(s) of content. The metadata includes information indicative of the current mental or emotional state of the user during the time period when the user generated or interacted with the content.

Abstract: A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.

Abstract: Techniques that facilitate the accomplishment of tasks within applications are presented. An intent-based user experience is available through receiving a natural language statement of intent from a user regarding use of an application, such as a productivity application. The graphical user interface for the user can be configured and reconfigured based on the user's intent; thus creating a task-oriented user interface. The user's intent can be determined through classifying and/or mapping the natural language statement of intent to particular tasks, which can then be associated with one or more tools and information that can be used to accomplish the tasks. The one or more tools and information can be surfaced to the user in the graphical user interface.

Abstract: A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.

Abstract: The subject disclosure is directed towards developing a translation model for mapping search query terms to document-related data. By processing user logs comprising search histories into word-aligned query-document pairs, the translation model may be trained using data, such as probabilities, corresponding to the word-aligned query-document pairs. After incorporating the translation model into model data for a search engine, the translation model is used may used as features for producing relevance scores for current search queries and ranking documents/advertisements according to relevance.