Abstract: A processing system including at least one processor may obtain at least a first objective associated with a demeanor of at least a first participant for a conversation and may activate at least one machine learning model associated with the at least the first objective. The processing system may then apply a conversation content of the at least the first participant as at least a first input to the at least one machine learning model and perform at least one action in accordance with an output of the at least one machine learning model.

Abstract: Aspects of the subject disclosure may include, for example, capturing a first depiction of an exhibit environment, wherein the first depiction of the exhibit environment comprises an image of at least one artifact and an image of at least one interface element, and wherein the at least one interface element is located in the exhibit environment at a first location relative to the at least one artifact; obtaining a first user profile of a first user, wherein the first user profile comprises at least one first preference of the first user, and wherein the first user is located in a first remote environment; and facilitating a presenting of a first modified version of the first depiction of the exhibit environment in the first remote environment, wherein the first modified version moves the image of the at least one interface element to a second location relative to the at least one artifact, wherein the second location is different than the first location, and wherein the second location is selected based upon

Abstract: An architecture related to the creation of, and attribution to, experiences based on location, popularity, captured media, and/or objects. A method can comprise receiving social media data associated with a user associated with a user identity, generating an attributable identity token based on the social media data and a learning model, wherein the attributable identity token is associated with the user identity and is representative of location data and experience data, and wherein the learning model comprises a collection of rules, and disseminating, by the device, the attributable identity token to a group of user identities associated with a social media operator entity.

Abstract: Creating scent models and using scent models in cross-reality environments can include capturing experience data identifying a scent detected and a context in which the scent was detected. The experience data can be provided to a scent modeling service to generate a scent model that can represent perceived scents and perceived scent intensities for a user. The scent model can be used to generate cross-reality session data to be used in a cross-reality session presented by a cross-reality device. The cross-reality device can include a scent generator and can generate the cross-reality session using data obtained from the user device. The cross-reality device can generate a further scent during the cross-reality session based on the scent model.

Abstract: Tasks associated with users can be managed for efficient workflow management. A task management component (TMC) can analyze, including performing artificial intelligence-based analysis on, task-related information relating to associated with a user(s), assessment information relating to assessing performance or expertise associated with a task, biometric information relating to health, diet, and activity associated with the user(s), and/or user(s) feedback information. Based on the analysis, TMC can adaptively adjust respective attributes associated with respective tasks, resulting in respective adjusted attributes associated with the respective tasks. Based on the respective adjusted attributes, TMC can determine task information and can present the task information to a device(s) associated with the user(s) to facilitate performance of the tasks.

Abstract: Transfer knowledge from auxiliary data for more inclusive machine learning models is provided. A method can include generating a common feature space comprising first data features, wherein the first data features are present in training data used to train a first machine learning model, and wherein the first data features are present in auxiliary data that are independent of the training data; generating a combined learned feature representation, the combined learned feature representation being representative of the first data features of the common feature space and second data features that are unique to the training data; and training a second machine learning model based on the combined learned feature representation.

Abstract: Aspects of the subject disclosure may include, for example, obtaining contextual information associated with a user, wherein the user is engaged in an immersive environment using a target user device, and wherein the contextual information comprises user profile data, data regarding a location of the user, data regarding one or more inputs provided by the user, or a combination thereof, receiving data regarding a metaverse object in the immersive environment, determining a relevance of the metaverse object to the user based on the contextual information and the data regarding the metaverse object, responsive to the determining the relevance of the metaverse object to the user, generating a personalized recommendation or review of the metaverse object for the user, and causing the personalized recommendation or review to be provided to the user in the immersive environment for user consumption. Other embodiments are disclosed.

Abstract: Extended reality augmentation of situational navigation (e.g., using a computerized tool) is enabled. For example, a system can comprise: a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on a route of a vehicle, determining a navigational instruction to be displayed via an augmented reality interface of the vehicle, and displaying the navigational instruction via the augmented reality interface, wherein displaying the navigational instruction comprises displaying a virtual leading vehicle to be followed by the vehicle.

Abstract: A method performed by a processing system including at least one processor includes identifying an insufficiency in a representation of a subpopulation in training data for a machine learning model, generating simulated data to mitigate the insufficiency in the representation, and training the machine learning model using an enhanced training data set that includes the training data and the simulated data to produce a trained machine learning model. In some examples, the generating and the training may be repeated in response to determining that an output of the trained machine learning model still reflects the insufficiency in the representation of the subpopulation or reflects an insufficiency in a representation of another subpopulation. In other examples, the simulated data may be stored for future reuse.