Abstract: A computer-implemented method according to one embodiment includes determining a starting folder within a file system, computing, for each child folder of the starting folder, a similarity metric indicating a level of similarity to a file, selecting two child folders of the starting folder having greatest similarity metrics, comparing a difference between the greatest similarity metrics of the two child folders to a predetermined threshold, and conditionally selecting the starting folder as a recommended folder to which the file is saved, based on the comparing.

Abstract: According to one embodiment, a computer-implemented method for dynamically modifying placeholder text in a conversational interface includes: processing a conversation log reflecting a conversation between a human user and an automated agent; determining, based at least in part on the processing: one or more capabilities of the automated agent; and/or a trajectory of the conversation; and dynamically modifying placeholder text in the conversational interface based at least in part on: the one or more capabilities of the automated agent; the trajectory of the conversation; or both the one or more capabilities of the automated agent and the trajectory of the conversation. Other embodiments in the form of systems and computer program products are also disclosed.

Abstract: A method, computer system, and a computer program product for matching one or more users is provided. The present invention may include collecting a user's information. The present invention may include determining that there is an overlap between the user's information and information contained within a knowledge base. The present invention may include ranking one or more matches of the user. The present invention may include displaying the ranked one or more matches to the user. The present invention may include collecting feedback from the user.

Abstract: One embodiment of the present invention provides a method comprising running a monitoring agent on a connected electronic device, and determining, via the monitoring agent, a level of hearing impairment of an individual user associated with the connected electronic device. The method further comprises, in response to determining the individual user has some level of hearing impairment, selecting a notification mechanism suitable for notifying the individual user based on the level of hearing impairment, and invoking the monitoring agent to notify the individual user of an event in accordance with the notification mechanism selected.

Abstract: Embodiments relate to an intelligent computer platform to utilize machine learning techniques to minimize compliance risk. Features are extracted from one or more data sources across two or more domains and activity data is identified. Features are identified within the activity data which are used to create one or more models. Domain activity impact is measured and a risk probability is calculated. A policy is generated based on the calculated risk probability and one or more compliance activities are orchestrated. One or more encoded actions are executed in compliance with the orchestration.

Abstract: A method, computer system, and a computer program product for feedback analysis is provided. The present invention may include, in response to identifying, using a natural language processing (NLP) component, a verb-related tag in a feedback data associated with a current version of a product or service, classifying the feedback data into a temporal class based on the identified verb-related tag. The present invention may also include, analyzing, using the NLP component, the classified feedback data within the temporal class. The present invention may further include, determining, based on the analyzed feedback data, a user perception associated with the current version of the product or service relative to the temporal class of the analyzed feedback data.

Abstract: A computer-implemented method, according to one embodiment, is used to creating customized templates and template recommendations. The computer-implemented method includes: extracting text content from user information which corresponds to a user, and using the text content extracted from the user information to generate feature vectors. The feature vectors are further used to form one or more clusters of existing user editions. For each of the one or more formed clusters, a tree structure is generated for each user edition in the cluster. Moreover, the tree structures in the cluster are combined into a merged tree structure. A customized template is generated for each of the one or more formed clusters using the respective merged tree structure, and at least one of the customized templates is recommended to the user based on the user information.

Abstract: A computer-implemented method, according to one embodiment, includes: setting a target power demand corresponding to a consumer, and performing a process. The process includes: determining an actual power demand presented to a utility by the consumer, and determining a current error. The current error is the difference between the actual power demand and the target power demand. A determination is also made as to whether the actual power demand is adjustable in a direction that reduces the current error. In response to determining that the actual power demand is adjustable in the direction that reduces the current error, the current error is reduced by adjusting the actual power demand. Moreover, in response to determining that the actual power demand is not adjustable in the direction that reduces the current error, the target power demand is modified.

Abstract: A computer-implemented method according to one embodiment includes determining a starting folder within a file system, computing, for each child folder of the starting folder, a similarity metric indicating a level of similarity to a file, selecting two child folders of the starting folder having greatest similarity metrics, comparing a difference between the greatest similarity metrics of the two child folders to a predetermined threshold, and conditionally selecting the starting folder as a recommended folder to which the file is saved, based on the comparing.

Abstract: A computer-implemented method according to one embodiment includes determining a starting folder within a file system, computing, for each child folder of the starting folder, a similarity metric indicating a level of similarity to a file, selecting two child folders of the starting folder having greatest similarity metrics, comparing a difference between the greatest similarity metrics of the two child folders to a predetermined threshold, and conditionally selecting the starting folder as a recommended folder to which the file is saved, based on the comparing.

Abstract: A method, computer system, and a computer program product for matching one or more users is provided. The present invention may include collecting a user's information. The present invention may include determining that there is an overlap between the user's information and information contained within a knowledge base. The present invention may include ranking one or more matches of the user. The present invention may include displaying the ranked one or more matches to the user. The present invention may include collecting feedback from the user.

Abstract: A method, a computer program product, and a computer system counterbalance a developed bias of user reviews. The method includes determining a developed bias of an existing plurality of first user reviews for a first item. The method includes determining a tendency value of a designated user indicative of a tendency of a user sentiment exhibited in user reviews for respective second items provided by the designated user deviating from an average sentiment of the respective second items. The method includes determining an influential prompt in which the designated user provides an input for the first item, the influential prompt being offset by an offset value based on the developed bias and the tendency value. The method includes prompting the designated user with the influential prompt and receiving the input from the designated user. The method includes updating the tendency value based on the input.

Abstract: A computer-implemented method according to one embodiment includes identifying one or more historical instances of device usage; determining historical contextual data for each of the one or more historical instances of the device usage; creating an event in association with the one or more historical instances of the device usage and the historical contextual data; training a classifier to identify the event by inputting the historical contextual data and an associated event identifier into a machine learning algorithm; identifying, using the trained classifier, a real-time occurrence of the event, utilizing real-time contextual data and the historical contextual data, where the trained classifier analyzes the real-time contextual data to identify the real-time occurrence of the event; and in response to identifying the real-time occurrence of the event, adjusting one or more aspects of a user interface of the device based on the one or more historical instances of the device usage.

Abstract: One embodiment of the present invention provides a method comprising running a monitoring agent on a connected electronic device, and determining, via the monitoring agent, a level of hearing impairment of an individual user associated with the connected electronic device. The method further comprises, in response to determining the individual user has some level of hearing impairment, selecting a notification mechanism suitable for notifying the individual user based on the level of hearing impairment, and invoking the monitoring agent to notify the individual user of an event in accordance with the notification mechanism selected.

Abstract: A computer-implemented method, according to one embodiment, is used to creating customized templates and template recommendations. The computer-implemented method includes: extracting text content from user information which corresponds to a user, and using the text content extracted from the user information to generate feature vectors. The feature vectors are further used to form one or more clusters of existing user editions. For each of the one or more formed clusters, a tree structure is generated for each user edition in the cluster. Moreover, the tree structures in the cluster are combined into a merged tree structure. A customized template is generated for each of the one or more formed clusters using the respective merged tree structure, and at least one of the customized templates is recommended to the user based on the user information.

Abstract: A method, computer system, and a computer program product for feedback analysis is provided. The present invention may include, in response to identifying, using a natural language processing (NLP) component, a verb-related tag in a feedback data associated with a current version of a product or service, classifying the feedback data into a temporal class based on the identified verb-related tag. The present invention may also include, analyzing, using the NLP component, the classified feedback data within the temporal class. The present invention may further include, determining, based on the analyzed feedback data, a user perception associated with the current version of the product or service relative to the temporal class of the analyzed feedback data.

Abstract: Embodiments relate to an intelligent computer platform to utilize machine learning techniques to minimize compliance risk. Features are extracted from one or more data sources across two or more domains and activity data is identified. Features are identified within the activity data which are used to create one or more models. Domain activity impact is measured and a risk probability is calculated. A policy is generated based on the calculated risk probability and one or more compliance activities are orchestrated. One or more encoded actions are executed in compliance with the orchestration.

Abstract: Facilitation of optimized transportation selection relative to user travel objectives and user context is provided. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an objective component that determines travel objectives of a user; a context component that determines context of the user; an analyzer component that analyzes transportation related data relevant to the travel objectives of the user, wherein the analyzer component performs a utility-based analysis that weighs costs versus benefits associated with respective transportation options relative to the travel objectives of the user; and an optimization component that outputs a travel itinerary to the user that includes an optimized transportation selection relative to the user travel objectives and user context.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an energy consumption profile which spans multiple intervals in a period of time, and predicting a net energy demand of a consumer system over the period of time. Moreover, a first multiple is determined which, when applied to the received energy consumption profile, produces an updated energy consumption profile which corresponds to an amount of energy that is capable of satisfying the predicted net energy demand of the consumer system. A greatest amount of underprediction is estimated. A greatest amount of overprediction is also estimated. Furthermore, an initial state of an energy storage device electrically coupled to the consumer system is computed according to the updated energy consumption profile. The initial state of the energy storage device is also based on a second multiple applied to each of the greatest amount of underprediction, and the greatest amount of overprediction.

Abstract: A computer-implemented method, according to one embodiment, includes: setting a target power demand corresponding to a consumer, and performing a process. The process includes: determining an actual power demand presented to the utility by the consumer based on a reward table, determining a current error, determining whether the actual power demand is adjustable in a direction that reduces the current error, reducing the current error by adjusting the actual power demand in response to determining that the actual power demand is adjustable in the direction that reduces the current error, and modifying the target power demand in response to determining that the actual power demand is not adjustable in the direction that reduces the current error. Determining the actual power demand includes measuring the power demand over a period of time in accordance with a process for measuring stability of power demand received from the utility.