Abstract: A machine learning model can be run with a first input data to obtain a first output from the machine learning model. A first rule representing an interpretable explanation for the machine learning model predicting the first output can be obtained. A modified rule can be received, which changes the first rule. A transformation function can be generated, which transforms a given input data for inputting to the machine learning model to a new input data based on decision boundaries of the machine learning model and the modified rule. The first rule, the first output, the modified rule, and the transformation function can be stored in a first table.

Abstract: Embodiments of the invention are directed to techniques that include predicting, by a computer system, a number of predicted opportunities and signatures of the predicted opportunities expected in a time window. Based on the signatures of the predicted opportunities, the computer system generates a listing of entities ranked according to signatures of the predicted opportunities. The computer system selects the entities to be assigned to the predicted opportunities based, at least in part, on computing capacity related to sales while accounting for any current opportunities having been assigned to the entities.

Abstract: Aspects of the invention include identifying each solution component of a plurality of solution components described in a text of a solution template of a plurality of solution templates, wherein the solution template includes a first combination of solution components. Identifying each solution component of a plurality of solution component described by an object in the solution template of a plurality of solution templates. Detecting a respective number of instances of each solution component in the solution template and a respective number of instances of each solution component in each other solution template of the plurality of solution templates. Generating analytics for a source company based on the respective number of instances of each solution component in the solution template and the respective number of instances of each solution component in each other solution template of the plurality of solution templates.

Abstract: Technology for computer systems for helping candidates (that is, candidate entities, like people or enterprises) for performing a task to know what portions of the entity's profile could use revision or improvement in order to increase the probability that the candidate entity will, in due course, be selected to perform the task. In some embodiments: (i) the task is the provision of cloud computing services; and (ii) the candidates are various companies in the business of providing cloud computing services.

Abstract: An online machine learning model such as an autonomous agent predicts an action. A processor associated with, or running, the online machine learning model observes an environment for an interval of time for a real reward associated with the action. Responsive to determining that the real reward is not received within the interval of time, the processor determines based on a criterion whether to allocate an immediate reward received within the interval of time to the online machine learning model, where the immediate reward is an approximation of the real reward. Responsive to determining that the immediate reward is to be allocated, the processor allocates the immediate reward to the online machine learning model. The online machine learning model further learns or retrains itself based on the immediate reward.

Abstract: Embodiments for intelligent career planning assessment in a computing environment by a processor. A career planning pathway of a career planning model for a user may be monitored for achieving a career goal. One or more deviations from the career planning pathway may be identified according a user profile, one or more behaviors of the user, one or more environmental factors, or a combination thereof.

Abstract: Embodiments for implementing intelligent opportunity recommendation and management by a processor. A channel selection model mat be applied to a selected opportunity in view of a plurality of opportunity attributes to identify one or more team candidates of an entity or one or more entity partners ranked by alignment with the selected opportunity and determine a recommended opportunity owner from the one or more team candidates of the entity, the one or more entity partners, or a combination thereof.

Abstract: Various embodiments are provided for providing intelligent dialog re-elicitation in a dialog system in a computing environment by a processor. Information, provided during a dialog using the dialog system, may be detected that has been subsequently revised. One or more variables impacted by the revised information provided during the dialog may be dynamically re-elicited.

Abstract: Disclosed are ways to generate and present recommendations which provide a user with the ability to explore the follow-on consequences of accepting the recommendations. In some aspects, a method includes receiving a first user input including a recommendation topic, presenting, via a display, an exploration structure including a node corresponding to the recommendation topic, receiving data corresponding to the node from a knowledge repository, analyzing the received data to determine at least one follow-on recommendation based on the node, and presenting each determined follow-on recommendation in the exploration structure as a child node of the node corresponding to the recommendation topic.

Abstract: Embodiments for intelligent career planning actions in a computing environment by a processor. A career planning model may be created for a user according to a career goal, a user profile, and one or more alternative user profiles and historical data of alternative users having achieved the career goal. A career plan may be generated for the user according to the career planning model.

Abstract: Embodiments for building domain models from dialog interactions by a processor. A domain knowledge may be elicited from one or more dialog interactions with one or more users according to one or more dialog strategies. One or more domain models may be built and/or enhanced according to the domain knowledge.

Abstract: Embodiments for recommending meals by a processor. A collaboration of data capturing a plurality of factors of a group user profile for each user in a group of users may be received for aiding in recommending one or more meals. The one or more meals may be recommended for the group of users according to the group user profile such that the recommending balances a satisfaction level for the one or more meals for the group of users.

Abstract: Various embodiments are provided for understanding user sentiment in a dialog system in a computing environment by a processor. A sentiment of a user may be detected according to a sentiment analysis and user feedback during a dialog with the user. One or more reasons for the sentiment of the user may be identified. Behavior of the dialog system may be adjusted according to the one or more reasons.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems to receive information pertaining to one or more tasks. Embodiments of the present invention can be used to predict a communication time at which a user is available is based, at least in part, on position movements of the user, sentiment of the user, and urgency of a task in the one or more tasks. Embodiments of the present invention can be used to, in response to confirming user availability, select a task from the one or more tasks and initiating a communication event for the task at the predicted communication time.

Abstract: Various embodiments are provided for providing intelligent dialog re-elicitation in a dialog system in a computing environment by a processor. Information, provided during a dialog using the dialog system, may be detected that has been subsequently revised. One or more variables impacted by the revised information provided during the dialog may be dynamically re-elicited.

Abstract: Embodiments for implementing intelligent automation of opportunity transaction workflows by a processor. One or more tasks identified in an existing transaction opportunity workflow suitable for automation may be automated in a current transaction opportunity workflow. The automated tasks may be scheduled and executed in the current transaction opportunity workflow. The automated tasks in the current transaction opportunity workflow may be monitored.

Abstract: Aspects of the present disclosure relate to chat management to address queries. A query can be received. A determination can be made whether the query has already been answered by comparing the query to text within a chat database. In response to determining that the query has not been answered, a set of prospective experts can be identified. Each of the prospective experts of the set of prospective experts can be ranked based on at least one factor. The query can be transmitted to a first ranked expert. An answer to the query can then be received from the first ranked expert.

Abstract: Embodiments for intelligent career planning assessment in a computing environment by a processor. A career planning pathway of a career planning model for a user may be monitored for achieving a career goal. One or more deviations from the career planning pathway may be identified according a user profile, one or more behaviors of the user, one or more environmental factors, or a combination thereof.

Abstract: Various embodiments are provided for applying judgment reasoning knowledge in a dialog system in a computing environment by a processor. A determination is made that a response to a query during a dialog using the dialog system fails to comply with one or more expected response patterns to one of a plurality of query responses. An updated response may be provided to the query using judgment reasoning knowledge for matching the updated response with the one or more expected response patterns.

Abstract: A method, computer system, and a computer program product for sequential pattern mining is provided. The present invention may include receiving a data mining parameter. The present invention may also include computing a plurality of frequent sequences without temporal annotations based on the received parameters. The present invention may then include extracting a subsequence with temporal annotations for each frequent sequence based on the computed plurality of frequent sequences. The present invention may further include clustering a plurality of temporal intervals using a time window based on the extracted subsequence. The present invention may also include producing an output based on the cluster of plurality of temporal intervals.