Abstract: Disclosed in certain embodiments are processes for heavy hydrocarbon removal that implement a regeneration loop that introduces an absorbent into a regeneration gas stream, and systems for implementing the same.

Abstract: Disclosed in certain embodiments are methods of removing water, mercaptans, C5+ or C6+ hydrocarbons, or any combination thereof from a gas feed stream during an adsorption step of an adsorption cycle.

Abstract: Disclosed herein is a system including at least two adsorbent bed containing vessels in adsorption mode, where one has a high pressure PI and one has a low pressure P3, and at least one adsorbent bed containing vessel in regeneration mode. The vessel that is in regeneration mode may have a pressure P2 that is intermediate to the pressures PI and P3 of each of the vessels that are in adsorption mode. The system may be configured to introduce a gas feed stream into the high pressure (P3) vessel to generate a first product stream, followed by passing a slip stream from the first product stream, to act as a regeneration gas, into the vessel that in regeneration mode, followed by passing the regeneration gas into the low pressure (P3) vessel, without passing through a compressor, to generate a second product stream.

Abstract: Disclosed in certain embodiments are processes for heavy hydrocarbon removal that implement a regeneration loop to reduce an amount of liquid hydrocarbons exposed by the separator to the regeneration stream over one or more durations for which an average C5+ hydrocarbon content of the regeneration stream is reduced or minimal.

Abstract: Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users. Machine learning models are trained to control the agent's interactions with the game environment and the user during gameplay. A user may request that a personalized agent join the user's gameplay session. The user device sends a request for the personalized agent to a game platform. The game platform determines whether the user has a license to execute a second instance of the game. When the user has a license to execute a second instance of the game, the second instance of the game may be executed on the user device. Information received from a personalized agent service is used to instantiate a personalized agent in the second instance of the game.

Abstract: Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users. Machine learning models are trained to control the agent's interactions with the game environment and the user during gameplay. As the user continues to play with the agent, the one or more machine learning models develop gameplay styles for the agent that complement the user's preferred playstyle, incorporate the user's preferred strategies, and is generally customized for interaction with the user. The agent personalization data generated during gameplay is stored by the service. An application programming interface is provided by the personalized agent service. Using the API, games can import agent personalization data in order to customize in-game non-player characters (NPCs), thereby customizing the in-game NPCs in accordance with the user's preferences.

Abstract: Disclosed in certain embodiments are adsorbents with improved mechanical properties for capturing heavy hydrocarbons during, for example, via thermal swing adsorption processes.

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

Abstract: Aspects of the present disclosure relate to multidirectional generative editing. In examples, content of a source document is used to produce generated content for a target document. A subpart of the source document may be associated with a subpart of the target document that includes the generated content. As a result of the association, if the subpart of the target document is modified (e.g., to add, remove, or edit natural language content or formatting), the subpart of the target document is used to produce generated content with which to update the source document accordingly. Thus, changes to generated content may be propagated back to a source document from which the generated content was produced.

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: 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: 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: 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 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 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 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.