International Business Machines Corporation

Inge Lise Vejsbjerg

Elizabeth Daly

Oznur Alkan

Massimiliano Mattetti

Adi Ionel Botea

Bei Chen

Thanh Lam Hoang

Gabriele Picco

Natalia Mulligan

Marco Luca Sbodio

Yufang Hou

Vanessa Lopez Garcia

Abel Nicolas Valente

Pablo Pedemonte

Vasileios Vasileiadis

Alice J Chang

Alan Zwiren

Xiaoxi Tian

Denisa Claudia Moga

Benjamin Dubiel

Sanjmeet Abrol

Weiwei Li

The present inventive concept provides for a method for automated decision modelling from text including obtaining a text corpus including a policy. Terms and syntax are identified within the text corpus related to the policy. Sentence similarities and co-references based on the terms and syntax are identified. Discourse and sentence level semantic parsing is performed based on the terms and the sentence similarities and the co-references using machine learning. A decision model template is generated based on the discourse and semantic parsing, and the decision model template is transformed into an automated decision model.

Automated decision modelling from text

Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically updated with one or more changes to a codebase.

Automatic container specification file creation and update for a codebase

Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.

Automatic container specification file generation for a codebase

Embodiments for recommending actions to improve machine learning predictions by a processor. One or more recommended actions may be linked to one or more features that influence a predicted outcome of a prediction model of a machine learning operation. One or more features having one or more negative factors that negatively impact the predicted outcome of the prediction model may be determined and selected. One or more of the linked recommended actions may be applied to one or more of the features to mitigate a negative impact upon the predicted outcome of the prediction model.

Linking actions to machine learning prediction explanations

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.

Intelligent automation of self service product identification and delivery

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.

Chat management to address queries

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.

Intelligent opportunity recommendation

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.

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Kilmainham, Ireland

Inge Lise Vejsbjerg