Abstract: In one embodiment, a method includes receiving a multimodal input from a first client system associated with a first user via an assistant xbot, wherein the multimodal input comprises first images captured by cameras of the first client system and voice inputs by the first user, wherein the voice inputs comprise personalized labels corresponding to the first images, storing the first images and the personalized labels as a first digital memory of the first user, receiving a user request by the first user referencing one or more of the personalized labels from the first client system via the assistant xbot, generating a response for the first user based on the first digital memory and the referenced personalized labels, and sending instructions for presenting the response to the first user to the first client system via the assistant xbot.

Abstract: The disclosed computer-implemented method may include (i) detecting a collection of media files captured by a wearable media device, (ii) determining a selection of the media files representing a common set of user experiences accumulated over a continuous period, (iii) grouping the selection of the media files into a customizable container, and (iv) sharing the customizable container with one or more target recipients for viewing within a secure application portal. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one embodiment, a method includes accessing visual signals comprising images portraying textual content in a real-world environment associated with a first user from a client system associated with the first user, recognizing the textual content based on machine-learning models and the visual signals, determining a context associated with the first user with respect to the real-world environment based on the visual signals, executing tasks determined based on the textual content and the determined context for the first user, and sending instructions for presenting execution results of the tasks to the first user to the client system.

Abstract: In one embodiment, a method includes accessing from a client system associated with a first user sensor signals captured by sensors of the client system, wherein the client system comprises a plurality of sensors, and wherein the sensors signals are accessed from the sensors based on cascading model policies, wherein each cascading model policy utilizes one or more of a respective cost or relevance associated with each sensor, detecting a change in a context of the first user associated with an activity of the first user based on machine-learning models and the sensor signals, wherein the change in the context of the first user satisfies a trigger condition associated with the activity, and responsive to the detected change in the context of the first user automatically capturing visual data by cameras of the client system.

Abstract: Performing Splunk code deployment in existing environments has been a challenge for support teams due to the large infrastructure footprint and the number of moving parts. An embodiment of the present invention is directed to an Orchestration Engine to automatically execute the Splunk Deployment releases with reduced downtime and enhanced logging and traceability. This automation will not only help eliminate inefficient and resource-intensive manual processes involved in promoting changes to production, but also carry out validations and reduce human errors thereby providing a more stable and reliable platform for end users.

Abstract: Performing Splunk code deployment in existing environments has been a challenge for support teams due to the large infrastructure footprint and the number of moving parts. An embodiment of the present invention is directed to an Orchestration Engine to automatically execute the Splunk Deployment releases with reduced downtime and enhanced logging and traceability. This automation will not only help eliminate inefficient and resource-intensive manual processes involved in promoting changes to production, but also carry out validations and reduce human errors thereby providing a more stable and reliable platform for end users.

Abstract: The present disclosure generally relates to an automated method and system for generating a three-dimensional (3D) representation of a skin structure of a subject. The method comprises: acquiring a plurality of two-dimensional (2D) cross-sectional images of the skin structure, specifically, using optical coherence tomography (OCT) technique; computing a cost for each 2D cross-sectional image based on a cost function, the cost function comprising an edge-based parameter and a non-edge-based parameter; constructing a 3D graph from the 2D cross-sectional images; and determining a minimum-cost closed set from the 3D graph based on the computed costs for the 2D cross-sectional images, wherein the 3D representation of the skin structure is generated from the minimum-cost closed set.

Abstract: The present disclosure generally relates to an automated method and system for generating a three-dimensional (3D) representation of a skin structure of a subject. The method comprises: acquiring a plurality of two-dimensional (2D) cross-sectional images of the skin structure, specifically, using optical coherence tomography (OCT) technique; computing a cost for each 2D cross-sectional image based on a cost function, the cost function comprising an edge-based parameter and a non-edge-based parameter; constructing a 3D graph from the 2D cross-sectional images; and determining a minimum-cost closed set from the 3D graph based on the computed costs for the 2D cross-sectional images, wherein the 3D representation of the skin structure is generated from the minimum-cost closed set.