Abstract: A computer-implemented method for saving, renaming, or moving a file includes receiving a request to save, rename or move a file, determining real-time context data and meta-data for the file in response to receiving the request to save, rename or move the file, generating a suggested pathname using the real-time context data and presenting the suggested pathname to a user. The suggested pathname may include a folder or directory name and a filename. The method may also include enabling the user to edit and approve the suggested pathname. Examples of context data include a password hint for the file, storage attributes for the file, collaboration data for the file, calendar data for the user, a file naming policy for an organization, real-time IoT data, and a topic determined from content within the file. A corresponding system and computer program product for executing the above method are also disclosed herein.

Abstract: According to one embodiment, a method, computer system, and computer program product for utilizing a pricing engine is provided. The present invention may include analyzing pricing factors of one or more products in an online listing; recommending a best price proposal to list the one or more products based on the analyzed pricing factors; analyzing buyer factors of a potential buyer; and recalculating the best price proposal based on the analyzed buyer factors.

Abstract: An approach for optimizing storage on a local storage device. The approach identifies a stored or being stored on a user's local storage device. The approach extracts metadata from the media file. The approach searches the user-associated storage locations for a matching media file based on the metadata. If the approach locates the matching media file, then the approach, using artificial intelligence (AI), predicts if the local device storage should be optimized for the media file, then the approach, using AI, stores a link to the matching media file on the local device storage and removes the media file from the local device storage.

Abstract: A head mounted display device includes a frame with multiple tubes inside the frame. The tubes contain a number of speakers and valves. The valves can be actuated on demand, changing the air column length relative to the speakers. Based on an input sound, speakers can be selected and valves can be actuated to improve sound quality.

Abstract: A processor may receive user data associated with a voice conversation having one or more participating users. The processor may analyze the user data for voice content associated with the voice conversation and the one or more participating users. The processor may determine a security policy associated with the voice conversation. The processor may identify one or more permission rules from the security policy associated with the voice conversation. The processor may dynamically execute the one or more permission rules to a portion of the voice content associated with at least one participating user. Dynamically executing the one or more permission rules may include modifying the portion of the voice content.

Abstract: In an approach to providing contextual justification for a virtual assistant response, one or more computer processors receive a first voice command from a user. One or more computer processors determine one or more boundary conditions associated with the first voice command. Based on the one or more boundary conditions, one or more computer processors determine a first response to the first voice command and a contextual justification of the first response. One or more computer processors respond to the user with the response to the first voice command and the contextual justification of the response.

Abstract: A method includes: predicting a jeopardy associated with an edge server included in the server cluster that communicates with a central server; responsive to predicting the jeopardy, detaching the edge server from the server cluster; determining a patch based on the jeopardy; pushing the patch to the edge server; validating the patch by performing regression testing; and responsive to validating the patch, inducting the edge server into the server cluster.

Abstract: A processor may analyze a target area. The processor may identify, from the analyzing, one or more objects in the target area. The processor may evaluate each of the one or more objects in the target area. Evaluating each of the one or more objects may include measuring each of the one or more objects and determining a purpose for each of the one or more objects. The processor may generate, based on the evaluating, a placement for each of the one or more objects.

Abstract: Automated delivery coordination and meeting scheduling for an order including a plurality of recipients includes identifying, by a computer, an online business transaction performed by a user. The online business transaction including the order being placed by the user for the plurality of recipients. The computer determines whether the order is associated with an event that includes the plurality of recipients and, in response to the order being associated with the event, collects information corresponding to the event. The computer uses the collected information to generate a first recommendation during checkout including a synchronized delivery option for the plurality of recipients. Further, using the collected information, the computer generates a second recommendation during the checkout including a synchronized meeting scheduling option for the plurality of recipients.

Abstract: A method for receiving an app-use contextual data set, applies artificial intelligence style machine logic to the app-use contextual data in order to generate a recommendation that the app should be subject to a recommended revision, in response to the generation of the recommended revision, makes the recommended revision in a dynamic manner, stores the app-use contextual data set in the form of a plurality of blockchain data structures, and operates around contextual reset/roll-back/restore scenarios in a hybrid cloud environment.

Abstract: An embodiment of the present invention monitors a process across a plurality of systems associated with a supply chain. A context is determined for an order, where at least one of the features of the context includes information from a networked sensing device. One or more first machine learning models identify a plurality of orders of an order history having features corresponding to the order. A second machine learning model is trained with the identified plurality of orders to predict the monitoring interval for the order. The second machine learning model predicts the monitoring interval for the order based on the context of the order. The monitoring interval for the order is dynamically adjusted based on the predicted monitoring interval, and the order is monitored through an order fulfillment process across the plurality of systems associated with the supply chain in accordance with the adjusted monitoring interval.

Abstract: An embodiment for resource management is provided. The embodiment may include receiving created text of an assigned activity to a proposed assignee. The embodiment may also include identifying information about the assigned activity. The embodiment may further include predicting resources and capabilities required to complete the assigned activity. The embodiment may also include identifying the proposed assignee. The embodiment may further include analyzing the resources and capabilities available on one or more devices of the proposed assignee. The embodiment may also include in response to determining the proposed assignee is able to complete the assigned activity, displaying to an assignor a predicted start time and time of completion of the assigned activity and in response to determining the proposed assignee is unable to complete the assigned activity, recommending to the assignor another assignee that is able to complete the assigned activity.

Abstract: Dynamic logging includes generating parsed event data by at least one natural language processor responsive to event data of a log for transactions of a target application. In response to the parsed event data, a first classifier classifies context states of the respective transactions of the target application. In response, a second classifier classifies trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels. When a logic module determines, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction, the logic module sends an increased log detail selection to the target application, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

Abstract: An approach for optimizing storage on a local storage device. The approach identifies a stored or being stored on a user’s local storage device. The approach extracts metadata from the media file. The approach searches the user-associated storage locations for a matching media file based on the metadata. If the approach locates the matching media file, then the approach, using artificial intelligence (AI), predicts if the local device storage should be optimized for the media file, then the approach, using AI, stores a link to the matching media file on the local device storage and removes the media file from the local device storage.

Abstract: A computer system implemented method for a user includes gathering, by a computer system, records of sources that each have no website and that the computer system, therefore, deems to be nondigital sources. The gathering includes the computer system receiving identifications of the nondigital sources based on transactions of other users with the respective sources. The computer system selects candidate sources from among the nondigital sources based on a geolocation of the first user and geolocations of the sources. The computer system selects a final source from among the candidate sources, wherein the computer system selecting the final source includes an artificial intelligence model fitting attributes of the candidate sources to attributes valued by the first user. The computer system presents the final source to the first user as a recommendation by the computer system.

Abstract: In an approach for a shopper-based commerce driven presentation of required-but-missing product related information, a processor detects a user viewing a physical product via a mixed reality device. A processor identifies a preferred environment for the physical product from a personal shopper profile of the user. A processor generates via the mixed reality device a depiction of the preferred environment. A processor determines whether the physical product can be overlaid in the preferred environment. A processor enables the user to purchase a product based on the determination.

Abstract: In an approach to predicting and managing a collaboration delay, one or more computer processors detect a start of a first collaborative meeting. One or more computer processors identify one or more attendees of the first collaborative meeting. One or more computer processors determine a status of one or more missing participants. Based on the status, one or more computer processors predict a delay duration and a reason for a delay of at least one of the one or more missing participants. Based on the prediction, one or more computer processors determine a first reschedule recommendation for the first collaborative meeting. One or more computer processors present the first reschedule recommendation to the one or more attendees. One or more computer processors reschedule the first collaborative meeting. One or more computer processors notify the one or more attendees of the first collaborative meeting of the reschedule.

Abstract: Dynamic logging includes generating parsed event data by at least one natural language processor responsive to event data of a log for transactions of a target application. In response to the parsed event data, a first classifier classifies context states of the respective transactions of the target application. In response, a second classifier classifies trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels. When a logic module determines, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction, the logic module sends an increased log detail selection to the target application, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

Abstract: A computer-implemented process includes the following operations. Interface data for a first computer application having a first interface configured to exchange data with a second computer application is identified. The interface data is aggregated using a machine learning engine, and the machine learning engine performs contextual analysis on the aggregated interface data to identify a context. A fix pack for the first computer application is generated using the context from the contextual analysis. The fix pack is caused to be applied to the first computer application. The fix pack includes an installable for the first application to transform notations used by the second computer application when communication with the first application.

Abstract: In an approach to providing contextual justification for a virtual assistant response, one or more computer processors receive a first voice command from a user. One or more computer processors determine one or more boundary conditions associated with the first voice command. Based on the one or more boundary conditions, one or more computer processors determine a first response to the first voice command and a contextual justification of the first response. One or more computer processors respond to the user with the response to the first voice command and the contextual justification of the response.