Methods, apparatus, systems, and computer-readable media are provided for providing context specific schema files that allow an automated assistant to broker human-to-computer dialogs between a user and an application that is separate from the automated assistant. The context specific schema file can provide the automated assistant with sufficient data to be responsive to user queries without necessarily communicating with a remote device, such as a server. Multiple different context specific schema files can be made available to the automated assistant according to a context in which a user is interacting with the automated assistant. In this way, latency otherwise exhibited by the automated assistant can be mitigated by providing the automated assistant with the information needed to respond to a user without continually retrieving the information over a network.

One embodiment provides a method, comprising: receiving, at a digital assistant of an information handling device, a query from a user; providing, using the digital assistant, a response to the query; determining, during provision of the response, interest of the user in a topic contained within the response; and providing, based on the interest, extended information related to the topic. Other aspects are described and claimed.

Search assistance personalized to a user, may be afforded by combining the application of scoring functions, with the training a Machine Learning (ML) model based upon feedback from that user. A plurality of scoring functions are applied to query results to calculate a plurality of score vectors. The ML model is then applied to generate a ranked list of vectors. Feedback from the user, is incorporated to develop an evolving memory. The feedback may be explicit, or may be implicit—e.g., based upon user selection of particular score vector(s) from the list or based upon user selection of particular query results corresponding to the score vectors. Embodiments may enhance relevance of search assistance by removing past feedback from the evolving memory that is used to retrain the ML model. Embodiments can provide assistance to search text corpuses utilizing scoring functions considering frequency of occurrence of particular words or terms.

A system and method may analyze computer actions on a computer desktop system. Using a data gathering process, a low-level user action information item, describing input by a user (e.g. to the computer desktop system), may be received or gathered. The low-level user action information item may include an input type description and screen window information. Based on a series of low-level user action information items, a process a computer is engaging in with the user may be estimated or determined. The best or most appropriate next low-level user action may be displayed or suggested to the user, e.g. on a computer desktop system to a user.

A computer adjusts a set of presentation materials, comprising. The computer receives, from a source available to the computer, an initial set of presentation materials. The computer, in response to receiving the initial set of presentation materials, determines a reference presentation duration associated with initial set of presentation materials. The computer receives a target presentation duration from a target duration source available to the computer. The computer determines a presentation conversion value representing, at least in part, a ratio of the target presentation duration to the reference presentation duration. The computer applies a Machine Learning (ML) refactoring routine to revise the initial set of presentation materials in accordance, at least partially, with the presentation conversion value, thereby generating a refactored set of presentation materials having a revised conveyance duration substantially the same as the target duration.

A system includes a network interface configured to communicate with a plurality of components across a network. The system also includes a memory system configured to store a plurality of instructions to implement a monitoring system and a processing system configured to communicate with the network interface. The processing device is further configured to execute the instructions to result in accessing one or more monitoring rules that define monitoring criteria associated with the components and monitoring the components to determine whether the monitoring criteria are met. The processing device can also trigger a notification to one or more user systems based on determining that at least one of the monitoring criteria has been met and provide a customization interface to allow user customization of the one or more monitoring rules.

Methods, apparatus, systems, and computer-readable media are provided for providing context specific schema files that allow an automated assistant to broker human-to-computer dialogs between a user and an application that is separate from the automated assistant. The context specific schema file can provide the automated assistant with sufficient data to be responsive to user queries without necessarily communicating with a remote device, such as a server. Multiple different context specific schema files can be made available to the automated assistant according to a context in which a user is interacting with the automated assistant. In this way, latency otherwise exhibited by the automated assistant can be mitigated by providing the automated assistant with the information needed to respond to a user without continually retrieving the information over a network.

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

Aspects of the present disclosure relate to interactive search training. A training canvas comprises results associated with a search query. The training canvas may be used as part of a training session that occurs during normal use of a search platform. When the search platform is first used, the results may be provided based on an existing model. An irrelevant result may be removed from the training canvas, such that a replacement result is added in its place. Additionally, results may be reordered, thereby indicating a ranking with which results should be displayed. Such interactions with the training canvas may be used to generate training data, such that a new model is trained accordingly. Thus, interactions with the training canvas yield high-quality training data that is usable to generate a model having equal or greater performance than a model that was trained using an equivalent amount of implicit training data.

In general, embodiments relates to a method for managing a technical support session, comprising: obtaining technical support question from a technical support person (TSP) that is conducting the technical support session; determining that the technical support question is a duplicate of a prior technical support question; in response to the determination, obtaining a quality score for the technical support question; and displaying the quality score to the TSP in a user interface on a technical support system that the TSP is using the conduct the technical support session.