Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating personal responses through retrieval-augmented generation. In particular, the disclosed systems can generate a query embedding from a query generated by an entity and determine data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity. The disclosed systems can provide the data context to a large language model and generate a personalized response informed by the data context. Subsequently, the disclosed systems can provide the personalized response for display on a client device associated with the entity.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating personal responses through retrieval-augmented generation. In particular, the disclosed systems can generate a query embedding from a query generated by an entity and determine data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity. The disclosed systems can provide the data context to a large language model and generate a personalized response informed by the data context. Subsequently, the disclosed systems can provide the personalized response for display on a client device associated with the entity.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating composite actions for a user account. In particular, in one or more embodiments, the disclosed systems determine a set of tasks performable by the user account using software tools on a client device. In some embodiments, the disclosed systems generate a task initialization prompt to provide to a large language model. Additionally, in some implementations, the disclosed systems generate a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks. Moreover, in some embodiments, the disclosed systems provide access to the composite action and the set of content items via a user interface of the client device. Furthermore, in some implementations, the disclosed systems generate and insert predicted content into a content item without user input.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating composite actions for a user account. In particular, in one or more embodiments, the disclosed systems determine a set of tasks performable by the user account using software tools on a client device. In some embodiments, the disclosed systems generate a task initialization prompt to provide to a large language model. Additionally, in some implementations, the disclosed systems generate a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks. Moreover, in some embodiments, the disclosed systems provide access to the composite action and the set of content items via a user interface of the client device. Furthermore, in some implementations, the disclosed systems generate and insert predicted content into a content item without user input.

Abstract: A method of safe file transmission and reputation lookup is provided. As a part of the safe file transmission and reputation lookup methodology, a data file that is to be made available to a data file receiver is accessed and it is determined whether the data file needs to be provided a protective file. The data file is wrapped in a protective file to create a non-executing package file. Access is provided to the non-executing package file where the associated data file is prevented from being executed until data file reputation information is received.

Abstract: Technologies for an application reputation service to assist users with minimizing their computerized machines' exposure to and infection from malware, including an application reputation service that contains the reputations for elements (e.g., applications) that are known to be non-malicious as well as those known to be malicious. In one example, when a user attempts to install or execute a new application, the service is queried by the user's machine with a set of identities for the element. The service determines the reputation of the application by referencing a knowledge base of known reputations and returns an indication (e.g., an overall rating, or a flag) of how safe that application would be to install and run on the user's computer.

Abstract: A method of safe file transmission and reputation lookup is provided. As a part of the safe file transmission and reputation lookup methodology, a data file that is to be made available to a data file receiver is accessed and it is determined whether the data file needs to be provided a protective file. The data file is wrapped in a protective file to create a non-executing package file. Access is provided to the non-executing package file where the associated data file is prevented from being executed until data file reputation information is received.

Abstract: Technologies described herein relate to estimating engagement of a person with respect to content being presented to the person. A sensor outputs a stream of data relating to the person as the person is consuming the content. At least one feature is extracted from the stream of data, and a level of engagement of the person is estimated based at least in part upon the at least one feature. A computing function is performed based upon the estimated level of engagement of the person.

Abstract: A method of safe file transmission and reputation lookup is provided. As a part of the safe file transmission and reputation lookup methodology, a data file that is to be made available to a data file receiver is accessed and it is determined whether the data file needs to be provided a protective file. The data file is wrapped in a protective file to create a non-executing package file. Access is provided to the non-executing package file where the associated data file is prevented from being executed until data file reputation information is received.

Abstract: The claimed subject matter is directed to the use of an application reputation service to assist users with minimizing their computerized machines' exposure to and infection from malware. Specifically, the claimed subject matter provides a method and system of an application reputation service that contains the reputations for elements that are known to be non-malicious as well as those known to be malicious. One embodiment of the claimed subject matter is implemented as a method to determine the reputation of an element (e.g., an application). When a user attempts to install or execute a new application, the Application Reputation Service is queried by the user's machine with a set of identities for the element. The Application Reputation Service determines the reputation of the application by referencing a knowledge base of known reputations and returns an indication (e.g., an overall rating, or a flag) of how safe that application would be to install and run on the user's computer.

Abstract: The claimed subject matter is directed to the use of an application reputation service to assist users with minimizing their computerized machines' exposure to infection from malware. The claimed subject matter provides an application reputation service that contains the reputations for elements that are known to be non-malicious as well as those known to be malicious. One embodiment is implemented as a method to determine the reputation of an element (e.g., an application). When a user attempts to install or execute a new application, the Application Reputation Service is queried by the user's machine with a set of identities for the element. The reputation of the application is determined by referencing a knowledge base of known reputations and returns an indication (e.g., an overall rating, or a flag) of how safe that application would be to install and run on the user's computer.

Abstract: Technologies described herein relate to estimating engagement of a person with respect to content being presented to the person. A sensor outputs a stream of data relating to the person as the person is consuming the content. At least one feature is extracted from the stream of data, and a level of engagement of the person is estimated based at least in part upon the at least one feature. A computing function is performed based upon the estimated level of engagement of the person.