At&t Intellectual Property I, LP

Warnermedia, LLC

Zhengyi Zhou

Christopher Volinsky

Deirdre Paul

Eric Martin Zavesky

Ann Eileen Skudlark

Appavu Siva Prakasam

Olivia Hong

Sanjeev Misra

Rakhi Seth-Forrest

David Crawford Gibbon

Jean-Francois Paiement

Wen-Ling Hsu

Qiong Wu

Aritra Guha

Louis Alexander

Tan Xu

Mohammed Abdel-Wahab

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.

System for extended reality augmentation of situational navigation

A processing system including at least one processor may obtain description information of a first machine learning model, obtain a set of interpretation criteria for the first machine learning model, and generate, via a second machine learning model, an explanation text providing an interpretation of the first machine learning model in accordance with the set of interpretation criteria and the description information of the first machine learning model.

Automated machine learning model explanation generation

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.

Creating scent models and using scent models in cross-reality environments

In one example, a method performed by a processing system including at least one processor includes establishing a communication group including at least three users of an extended reality environment as members, tracking locations and directional positions of the members of the communication group within the extended reality environment and within physical environments of the members, determining that a second user of the at least three users is an intended recipient of a first utterance made by a first user of the at least three users, and presenting the first utterance to the second user, where a directionality associated with a presentation of the first utterance is based on a location and a directional position of the first user relative to the second user, and presenting a spatial awareness warning to the second user, based on the location and the directional position of the second user.

Managing directed personal immersions in a communication group via awareness warning based on location and directional position

Aspects of the subject disclosure may include, for example, a system or method that determines, based on consumption data and location information, groups of access portals, where the consumption data and the location information correspond to a time period in which the access portals are used to access a service using an account. The groups can be adjusted according to an intended benefit unit for the service and a sharing structure can be determined including benefit unit partitions for the account based on the adjusting. Factors can be determined, based on the benefit unit partitions, that are indicative of account sharing, as well as weights for the factors by applying a learning model to the consumption data. An intervention strategy can be selected from a group of intervention strategies according to the factors and the weights for the factors. Other embodiments are disclosed.

Method and apparatus to profile account credential sharing

Aspects of the subject disclosure may include, for example, a system or method that collects or otherwise accesses information indicating connection patterns such as IP addresses being utilized by communication devices for communication services over a time period, where the communication devices use a same credential of a single account for accessing the communication services. Hubs can be identified according to groups of the communication devices that exhibit a particular sharing pattern such as having used the one or more common IP addresses. A prediction or estimation that the single account is engaging in sharing activity can be made based on an analysis of the hubs, such as based on a number of the hubs. Other embodiments are disclosed.

Method and apparatus to monitor account credential sharing in communication services

A processing system may generate segments of network infrastructure items deployed in a communication network, each segment comprising network infrastructure items grouped in accordance with segment filters and a segment size sparsity threshold, and identify anomalous segments comprising at least a subset of the segments having anomalies of an anomaly type regarding a performance indicator. The processing system may next determine segments from the subset that are defined by sets of segment filter values that are different for less than a threshold number of segment filters, merge the segments from the subset that are different for less than the threshold number to create at least one aggregate segment, and generate a ranking of the subset having the anomalies of the anomaly type, wherein the ranking includes the at least one aggregate segment. The processing system may then perform at least one action in the communication network responsive to the ranking.

Detecting network anomalies for robust segments of network infrastructure items in accordance with segment filters associated via frequent itemset mining

Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

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Chappaqua, NY, United States of America

Zhengyi Zhou