Abstract: Optimization of smart notifications within an organization can lead to increase productivity and reduction in missed deadlines and human work hours. An approach for to dynamic scheduling of smart notifications may be presented herein. The approach may include obtaining historical decision event data for an organization. Historical decision data may include details regarding the importance of the historical decision event to the organization, individuals within the organization associated with the decision event, and benchmarks of the historical decision event. The approach may include analyzing a future decision event based on a decision event model trained with the historical decision event data. The approach may include identifying one or more priority factors associated with the future decision event data based on the analysis of the decision event priority model. A smart notification may be scheduled for the decision event, based on the identified priority factors.

Abstract: Providing avatar support for trapped seismic event survivors is provided. A survivor is detected in debris of a collapsed structure using a set of sensors. An avatar communicates with the survivor. Fresh air flow drawn from different directions is measured to detect a pathway through the debris to an environment outside the collapsed structure. The pathway is detected through the debris to the environment outside the collapsed structure based on measuring the fresh air flow drawn from the different directions.

Abstract: According to one embodiment, a method, computer system, and computer program product for industrial robot error correction is provided. The present invention may include monitoring a production line for error conditions; responsive to identifying an error condition in an industrial robot comprising the production line, invoking an AI avatar for the industrial robot; gathering, by the AI avatar, data pertaining to the error condition; analyzing, by the AI avatar, the gathered data to produce a plurality of collated data and/or proposed corrections; presenting, by the AI avatar, the collated data and/or the proposed corrections to a subject matter expert; and modifying algorithms of the industrial robot based on the proposed corrections.

Abstract: Embodiments of the present invention provide an approach for enabling medical professionals in determining a genetic predisposition of a medical cause from a family tree analysis. Specifically, the approach involves receiving medical symptoms of a patient and medical narrations about family members, which are organized in a family tree. The medical narrations are collected using questions generated using a supporting symptoms list. The narrations are filtered to provide a summary of relevant information. A linked tree of related words is established based on the summary. A natural language processing engine generates insights based on an analysis of the summary. Likelihood paths of genetic susceptibility to a medical condition through the family are determined by identifying family member symptoms in the linked tree of related words. These likelihood paths are then matched with a known inheritance pattern of a disease to provide an alert to the family members about the disease.

Abstract: In an approach to improve converting conversation to user stories, embodiments capture keywords from a captured discussion, and identify the probability of the keywords being an object attribute or action behavior. Further, responsive to identifying, based on the probability, that the keywords are the object attribute or the action behavior, embodiments determine that the object attribute or the action behavior are not new to a first user story. Additionally, embodiments determine that the attribute or the action behavior are associated with an existing object in a first user story, and update the first user story with the attribute or the action behavior.

Abstract: From transaction data, a graph modeling a set of relationships between transaction elements is constructed, a node of the graph representing a transaction element, an edge of the graph representing a relationship between two transaction elements. Using a part of speech analysis model, a subset of the nodes is tagged with a corresponding first part of speech tag. An alternative part of speech tag is generated for a tagged node. A set of tagged nodes is grouped into a word group including at least one tagged node having an alternative part of speech tag. The word group is validated against a grammar of a natural language. Responsive to the validating determining that the word group conforms to the grammar, a set of additional nodes representing the word group is added to the graph. Using the modified graph, a transaction in the transaction data is validated.

Abstract: Described are techniques for an intelligent code editor that intelligently provides software development suggestions based on the coding techniques of a software developer. The techniques include analyzing source code samples associated with a software developer to determine coding techniques utilized by the software developer. The techniques further include monitoring code editing input received by an intelligent code editor to identify a software design pattern, where the code editing input is associated with the software developer. The techniques further include generating a suggested software design pattern that is based on the software design pattern identified in the code editing input and the coding techniques of the software developer, and displaying the suggested software design pattern in the intelligent code editor to allow the suggested software design pattern to be incorporated into the code editing input using the intelligent code editor.

Abstract: In an approach to improve converting conversation to user stories, embodiments capture keywords from a captured discussion, and identify the probability of the keywords being an object attribute or action behavior. Further, responsive to identifying, based on the probability, that the keywords are the object attribute or the action behavior, embodiments determine that the object attribute or the action behavior are not new to a first user story. Additionally, embodiments determine that the attribute or the action behavior are associated with an existing object in a first user story, and update the first user story with the attribute or the action behavior.

Abstract: A method, computer program product, and computer system for controlling access to presentation content to present to multiple participants in a virtual meeting. The presentation content is received and includes restricted information. First participants who will be provided access to the presentation content during the virtual meeting, and second participants will be denied access to respective portions of the presentation content containing the restricted information during the virtual meeting, are identified. Entry by the first participants to a safe zone within the virtual meeting is provided. The presentation content is presented in the safe zone to the first participants during the virtual meeting. The restricted information is denied to the second participants during the virtual meeting. The first participants in the safe zone have full access to all content presented in the virtual meeting, outside the safe zone and not presented in any other safe zone within the virtual meeting.

Abstract: In an approach for detecting when a resource gap has opened in a project and deciding how to best utilize the project's resources to fill the resource gap, a processor monitors a degree of progress of one or more projects on a project dashboard. Responsive to detecting a project with a resource gap, a processor obtains a set of data regarding the project. A processor determines a productivity trajectory. Responsive to determining a team member will not meet the productivity trajectory, a processor determines one or more alternative combinations of team members who can meet the productivity trajectory. A processor measures a degree of amplification required to achieve the productivity trajectory by the one or more alternative combinations of team members. A processor identifies one or more resources available to complete a backlog of one or more tasks of the first project. A processor determines an optimum variation.

Abstract: Optimization of smart notifications within an organization can lead to increase productivity and reduction in missed deadlines and human work hours. An approach for to dynamic scheduling of smart notifications may be presented herein. The approach may include obtaining historical decision event data for an organization. Historical decision data may include details regarding the importance of the historical decision event to the organization, individuals within the organization associated with the decision event, and benchmarks of the historical decision event. The approach may include analyzing a future decision event based on a decision event model trained with the historical decision event data. The approach may include identifying one or more priority factors associated with the future decision event data based on the analysis of the decision event priority model. A smart notification may be scheduled for the decision event, based on the identified priority factors.

Abstract: From transaction data, a graph modeling a set of relationships between transaction elements is constructed, a node of the graph representing a transaction element, an edge of the graph representing a relationship between two transaction elements. Using a part of speech analysis model, a subset of the nodes is tagged with a corresponding first part of speech tag. An alternative part of speech tag is generated for a tagged node. A set of tagged nodes is grouped into a word group including at least one tagged node having an alternative part of speech tag. The word group is validated against a grammar of a natural language. Responsive to the validating determining that the word group conforms to the grammar, a set of additional nodes representing the word group is added to the graph. Using the modified graph, a transaction in the transaction data is validated.

Abstract: An embodiment for creating a virtual ambient workstation is provided. The embodiment may include receiving a request to reserve an ambient space for a determined time period. The embodiment may also include identifying one or more workstations that are occupied by one or more other individuals. The embodiment may further include identifying combined vocal-fold sound patterns provided to a cognitive system based on the identified one or more workstations occupied by the one or more other individuals. The embodiment may also include evaluating data obtained from the one or more workstations and the combined vocal-fold sound patterns. The embodiment may further include generating an ambient noise remediation sound using an acoustic beam system. The embodiment may also include training the cognitive system. The embodiment may further include determining whether a level of ambient sound is sufficient and providing feedback to the cognitive system through machine learning.

Abstract: An embodiment for creating a virtual ambient workstation is provided. The embodiment may include receiving a request to reserve an ambient space for a determined time period. The embodiment may also include identifying one or more workstations that are occupied by one or more other individuals. The embodiment may further include identifying combined vocal-fold sound patterns provided to a cognitive system based on the identified one or more workstations occupied by the one or more other individuals. The embodiment may also include evaluating data obtained from the one or more workstations and the combined vocal-fold sound patterns. The embodiment may further include generating an ambient noise remediation sound using an acoustic beam system. The embodiment may also include training the cognitive system. The embodiment may further include determining whether a level of ambient sound is sufficient and providing feedback to the cognitive system through machine learning.