Abstract: Methods and systems for cross-platform user profiling based on disparate datasets using machine learning models. Specifically, the methods and systems comprising retrieving a cross-platform profile, wherein the cross-platform profile comprises a profile linked to an account, for a user, that is used across multiple assets. The methods and system may then update a status of the cross-platform profile based on incidents detected using machine learning models. The methods and system may then generate for presentation, in a user interface for the account, the status of cross-platform profile.

Abstract: In some embodiments, native asset data of an asset corresponding to a cross-platform profile may be monitored, where the cross-platform profile includes a profile linked to an account, for a user, that is used across multiple assets. An incident of the user may be detected via a machine learning model based on telemetry data extracted from the native asset data, where the machine learning model is trained to detect known incidents in training data including labeled telemetry data. A status of the cross-platform profile may be updated based on the incident, and the status may be generated for presentation in a user interface for the account. In response to receiving a user request to provide reconstructed asset data based on the incident, the reconstructed asset data may be generated based on the incident for presentation in the user interface for the account.

Abstract: Methods and systems for filtering content in reconstructions of native data in assets. Specifically, the methods and systems extract telemetry data from native game data. The methods and systems then identify Personally Identifiable Information (“PII”) in the telemetry data and determine a masking operation to perform on the PII. The methods and systems then generate for presentation, in a user interface, reconstructed asset data based on the telemetry data, wherein the PII is modified in the reconstructed asset data based on the masking operation.

Abstract: Methods and systems for generating incident recommendations in processed data using machine learning models. Specifically, the methods and system extract telemetry data from native game data. By reducing game data to telemetry data only, the system may reduce the amount of data that must be stored and processed. The system reconstructs game data in order to generate an incident recommendation. This reconstructed data allows for manual user review and/or understanding the context of an incident.

Abstract: Methods and systems for cross-platform user profiling based on disparate datasets using machine learning models. Specifically, the system may monitor native asset data of an asset corresponding to a cross-platform profile, wherein the cross-platform profile comprises a profile linked to an account, for a user, that is used across multiple assets. The system may detect, using a machine learning model, an incident of the user based on telemetry data extracted from the native asset data. The system may update a status of the cross-platform profile based on the incident. The system may generate for presentation, in a user interface for the account, the status of cross-platform profile and reconstructed asset data based on the incident.

Abstract: Methods and systems for generating incident recommendations in processed data using machine learning models. Specifically, the methods and system extract telemetry data from native game data. By reducing game data to telemetry data only, the system may reduce the amount of data that must be stored and processed. The system reconstructs game data in order to generate an incident recommendation. This reconstructed data allows for manual user review and/or understanding the context of an incident.

Abstract: Methods and systems for cross-platform user profiling based on disparate datasets using machine learning models. Specifically, the methods and systems comprising retrieving a cross-platform profile, wherein the cross-platform profile comprises a profile linked to an account, for a user, that is used across multiple assets. The methods and system may then update a status of the cross-platform profile based on incidents detected using machine learning models. The methods and system may then generate for presentation, in a user interface for the account, the status of cross-platform profile.

Abstract: Methods and systems for filtering content in reconstructions of native data in assets. Specifically, the methods and systems extract telemetry data from native game data. The methods and systems then identify Personally Identifiable Information (“PII”) in the telemetry data and determine a masking operation to perform on the PII. The methods and systems then generate for presentation, in a user interface, reconstructed asset data based on the telemetry data, wherein the PII is modified in the reconstructed asset data based on the masking operation.

Abstract: Methods and systems for detecting disparate incidents in processed data using a plurality of machine learning models. For example, the system may receive native asset data. The system may extract telemetry data from the native asset data. The system may input the first feature input into a first machine learning model, wherein the first machine learning model is trained to detect known incidents of a first type in a first set of labeled telemetry data. The system may then detect a first incident based on a first output from the first machine learning model, wherein the first incident is a first event in an asset related to the user's behavior.