Abstract: A computer simulation object such as a chair is described by voice or photo input to render a 2D image. Machine learning may be used to convert voice input to the 2D image. The 2D image is converted to a 3D asset and the 3D asset or portions thereof are used in the computer simulation, such as a computer game, as the object such as a chair.

Abstract: Systems and methods for generating images are described. A method includes receiving a textual description describing a first image. The method further includes executing an artificial intelligence (AI) model to compute a weight for each word of the textual description and to extract contextual biasing words based on the computed weights. The method further includes generating a suggestion including a proposed modification to the first image based on the contextual biasing words and receiving input data responsive to the suggestion. The method further includes generating a second image based on the suggestion and the input data and providing the second image to a client device for display.

Abstract: A method including accessing an avatar including a plurality of physiological characteristics, wherein the avatar includes a plurality of motion profiles. The method including receiving editing of a motion profile of the avatar via a user interface. The method including modifying each of the plurality of motion profiles to be consistent with the motion profile that edited. The method including automatically generating a prompt to apply the motion profile that is edited to the avatar, wherein the prompt is provided as input for an image generation artificial intelligence system configured to implement latent diffusion. The method including generating a sequence of video frames using the image generation artificial intelligence system showing the avatar in motion based on the motion profile that is edited. The method including presenting the sequence of video frames in the user interface.

Abstract: A method for building an artificial intelligence (AI) model. The method includes accessing data related to monitored behavior of a user. The data is classified, wherein the classes include an objective data class identifying data relevant to a group of users including the user, and a subjective data class identifying data that is specific to the user. Objective data is accessed and relates to monitored behavior of a plurality of users including the user. The method includes providing as a first set of inputs into a deep learning engine performing AI the objective data and the subjective data of the user, and a plurality of objective data of the plurality of users. The method includes determining a plurality of learned patterns predicting user behavior when responding to the first set of inputs. The method includes building a local AI model of the user including the plurality of learned patterns.

Abstract: Embodiments for port reconfiguration for passive intermodulation interference mitigation are presented herein. A base station device comprises a signal processing component comprising a passive intermodulation interference component and an antenna configuration component. The passive intermodulation interference component determines passive intermodulation interference corresponding to uplink signals that have been received, via a configurable cellular antenna array of the base station device, from respective wireless devices of a group of wireless devices that have been communicatively coupled to the base station device. The antenna configuration component selects a defined configuration of a group of cellular antenna ports of the configurable cellular antenna array to facilitate a reduction of the passive intermodulation interference corresponding to the uplink signals.

Abstract: Systems and methods help the user revise their goals including prompt-based goal revisions and AI-based prediction that based on financial behaviors of the user that shown the system can recommend revised goals. In some instances, a decision tree feedback loop is utilized to automatically modify the goals.

Abstract: Embodiments are directed towards a lift system for extracting a fluid from a well. The system may include a production tube and at least one bladder located inside a pump barrel which is connected to the production tube. The bladder may include a top connector configured to receive a concentric tubing. The lift system may further include a compression system connected to the concentric tubing. The compression system may be configured to provide a compressed gas through the concentric tubing to the at least one bladder.

Abstract: Techniques and devices are described for building 3D drawings within a 3D scene sourced from a game engine (or other video source) without the benefit of 3D data and other metadata from the game engine itself. Accordingly, in one aspect an apparatus may include at least one processor assembly that is configured to access a parameterization of a virtual 3D space and to receive user input directed to an object represented in the virtual 3D space. The at least pone processor assembly may also be configured to, based on the user input, change the parameterization of the virtual 3D space to represent the user input within the virtual 3D space from different viewing angles.

Abstract: A 3D neural radiance field (NeRF) is used to generate an impulse response (IR) characterization, which can then be input to a convolutional reverberation engine to create an audio experience that reflects the in-game world on a 2.0 stereo speaker system. The NeRF recreates a background geometry and the impulse response of a virtual 3D space generated using NeRF is input to the convolutional reverberation engine to transform game sounds/music to appear as though they are occurring inside the 3D space of the game. The same may be done for the player's real-world space in which the virtual IR and real IR are blended together in real-time, and the real-life player tracked as he moves around the room to create audio as it would sound were the player moving through the virtual space while adjusting for the acoustics of the real space.

Abstract: Machine learning is used to transform screen renders from the viewpoint of the player's character to be from the viewpoint of other non-player characters (NPC) in the room. One or more neural networks are trained using game images captured during a human play session, and then subsequently the neural networks are used to create realistic video from the NPC viewpoints. To avoid rendering multiple viewpoints simultaneously, a single viewpoint is rendered, and neural networks are used to transform it for the viewpoints of other NPCs in the area. A group of NPCs may be treated as a batch and a single viewpoint transformed to multiple viewpoints in a single inference pass. For cloud gaming the game video can be rendered/sent immediately for the player but the actions of the NPCs are delayed for a frame while the neural network generates the behavior.

Abstract: An unsupervised multi-hop search across a corpus of documents in a database or other data resource permits the identification of relationships between two entities mentioned in the corpus in cases where the two entities are not co-mentioned within any documents in the corpus (or not mentioned within document(s) with sufficient frequency or proximity to infer the relationship). The search can employ a beam search algorithm anchored by word embeddings and an A* graph traversal to calculate semantic distance between the entities as different paths through the corpus for different entity co-mentions are evaluated.

Abstract: An entity-based platform stores a database of entity mentions and related, entity-based information obtained by processing text in a corpus of documents such as content from a public network or content from a document management system. The platform may analyze changes to a previously-processed document in order to align mentions in the modified document to mentions in a prior document and/or to identify new mentions not present in the previously-processed document. As the underlying documents in the corpus change over time, this advantageously permits sustained use of knowledge graphs and other entity-based representations or analysis linked to specific entity mentions.

Abstract: Systems and methods help the user revise their goals including prompt-based goal revisions and AI-based prediction that based on financial behaviors of the user that shown the system can recommend revised goals. In some instances, a decision tree feedback loop is utilized to automatically modify the goals.

Abstract: A method generating an image according to a style is described. The method includes receiving a textual description describing a first image. The method further includes applying an artificial intelligence (AI) model to determine the style of a second image based on the textual description, the first image, a plurality of descriptions, and a plurality of images to generate a suggestion. The style provides a context to the second image. The style is lacking in the first image. The method includes generating the second image with the suggestion according to the style and providing the second image to a client device for display.

Abstract: Systems and methods help the user revise their goals including prompt-based goal revisions and AI-based prediction that based on financial behaviors of the user that shown the system can recommend revised goals. In some instances, a decision tree feedback loop is utilized to automatically modify the goals.

Abstract: Systems and methods help the user revise their goals including prompt-based goal revisions and AI-based prediction that based on financial behaviors of the user that shown the system can recommend revised goals. In some instances, a decision tree feedback loop is utilized to automatically modify the goals.

Abstract: A method, system and computer program product for training a control input system involve taking an integral of an output value from a Motion Decision Neural Network for one or more movable joints to generate an integrated output value and comparing the integrated output value to a backlash threshold. A subsequent output value is generated using a machine learning algorithm that includes a sensor value and a previous joint position if the integrated output value does not at least meet the threshold. A position of the one or more movable joints is simulated based on an integral of the subsequent output value; and the Motion Decision Neural Network is trained with the machine learning algorithm based upon at least a result of the simulation of the position of the one or more movable joints.

Abstract: A media system employs techniques to create a framework for evaluating gameplay content in a vector-space. These techniques include monitoring frames for content streams that correspond to gameplay in a game environment, determining feature-values for features associated with the frames, mapping the content streams to position vectors in the vector-space based on the feature-values, and assigning a set of position vectors in the vector-space to an area in the game environment based on a relative proximity of the set of position vectors in the vector-space.

Abstract: Embodiments are directed towards a lift system for extracting a fluid from a well. The system may include a production tube and at least one bladder located inside a pump barrel which is connected to the production tube. The bladder may include a top connector configured to receive a concentric tubing. The lift system may further include a compression system connected to the concentric tubing. The compression system may be configured to provide a compressed gas through the concentric tubing to the at least one bladder.