United Services Automobile Association (usaa)

Uipco, LLC

Sean Michael Wayne Craig

Roberto Virgillio Jolliffe

Christopher M Russell

Sean Carl Mitchem

Subhalakshmi Selvam

Angelica Nichole White

Brian Katz

Timothy Blair Chalmers

Robert Lee Black

Timothy Benjamin Czerlinsky

Shayla Leigh Callis

Subhalaskshmi Selvam

The embodiments described herein relate to systems and methods for managing or providing privacy in metaverses and/or to providing protection of a user within metaverses. In certain embodiments, a metaverse user privacy system may be used to prevent or discourage user data or private information from being compromised. Such limitations or prohibitions may be applied or enforced based on various factors, such as privacy or security settings specified by a user. In further aspects, the presently described techniques may facilitate protecting a user from known or suspected 'bad' actors or from entering portions of the metaverse presenting risks to the user. Similarly, in further aspects the presently described techniques may be implemented as configurable parental controls, allowing a parent or guardian to configure or specify the types on interactions and risks a child may be exposed to in metaverses.

Techniques for facilitating privacy and protection in metaverses

Methods and systems described herein are directed to automatedly detecting a vehicle accident occurrence based on a generated accident suspicion. An accident detection system can retrieve a myriad of information as data relating to a driver or a vehicle of the driver. The retrieved information can be assessed to determine assignment of a suspicion of a vehicle accident occurrence. The suspicion assignment can be based on, with respect to historical insurance data, predetermined thresholds and/or machine learning for the retrieved information. The retrieved information can be verified by auxiliary data remote from the driver or the vehicle of the driver. The accident detection system can verify the suspicion by dispatching one or more notifications to the driver, and in the absence of response, determine whether the suspicion is true via the collection of information for a suspected location of the accident or from individuals associated with the driver.

Method and system for automatically detecting a vehicle accident

Methods and systems described herein are directed to automatedly generating and issuing accident data gathering recommendations for a vehicle accident occurrence. In response to a recommendation system detecting an occurrence of a vehicle accident, the system can retrieve initial accident data for the occurrence. Using the initial accident data, the system can generate accident data gathering recommendations to obtain additional accident data via mapping to one or more characteristics for the vehicle accident occurrence. The recommendations, when executed, can yield additional accident data that can be compiled with the initial accident data into a vehicle accident evidence package.

Automatedly generating and issuing accident data gathering recommendations following a vehicle accident

Some implementations of the technology relate to anonymizing personally identifiable information (PII) by creating a quick response (QR) code linked to a website that can verify that driver's license information and insurance information of a driver is available and valid, without showing the PII itself. Some implementations can allow another driver in an automobile accident to scan the QR code, enter her own insurance information, and can push the driver's PII to her insurance company. Some implementations can allow the other driver to download an encrypted file with the PII that can be shared with his insurance company. The QR code can be used in other contexts outside of automobile accidents as well, such as when a driver wishes to test drive a car, rent a car, buy a new car, etc., and needs to provide proof of a valid driver's license and insurance coverage.

Anonymized transfer of personally identifiable information

Methods and systems described herein are directed to automatedly generating entity-specific notifications of a vehicle accident occurrence for entities. In response to a notification system receiving accident data, the system can determine characteristics for the accident occurrence. Using these characteristics, the system can then determine parameters for the notifications defining types of entities which ought to be recipients in accordance with the characteristics. The system can use these parameters to locate specific entities that match the parameters. Having determined the entities, the system can issue notifications to the entities, using templates specific to the entities and with accident characteristics added.

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San Antonio, TX, United States of America

Sean Michael Wayne Craig