Abstract: A computer-implemented method of voice-to-text tagging for transcription of a human voice signal by one of an automatic speech recognition system, or a natural conversation, includes generating a speech-to-text verbal transcript of the verbal vocalizations of an audio signal at a verbal timestamp of the audio signal. A voice-to-text non-verbal transcript of the one or more non-verbal vocalizations of the audio signal is generated at a non-verbal timestamp of the audio signal. The non-verbal transcript and the verbal transcript are combined to generate an enhanced transcript.

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: Techniques for personalizing an AI automated conversational system are provided. In one aspect, a method for personalizing an automated conversational system includes: making predictions of a familiarity of a user with concepts needed to understand a standard output utterance based on the familiarity of an aggregate of users and a background knowledge model of the concepts and related concepts, wherein the standard output utterance assumes that the concepts are known; and giving, by the automated conversational system, an output utterance that is tailored to the user given the predictions. For instance, the automated conversational system can give the standard output utterance to the user when it is predicted that the user is familiar with the concepts, or a nonstandard output utterance when it is it is predicted that the user is unfamiliar with at least one of the concepts.

Abstract: A computer-implemented method, system and computer program product for generating a user interface. A sketch (e.g., wireframe) of a portion of a user interface is received. The sketch is analyzed to predict a set of intended sketches using artificial intelligence based on historical data and/or the user's asset library. A set of intended final sketch renderings of the user interface is then generated and displayed using the set of predicted intended sketches based on historical data or a model trained to extract visual characteristics from existing user interface screens. If the user selects one of the intended final sketch renderings of the user interface as being directed to the intended design of the user interface and indicates that the selected intended final sketch rendering of the user interface corresponds to the final intended design, then code is generated to render the selected final sketch rendering of the user interface.

Abstract: One embodiment provides a method for predicting a next action in a conversation system that includes obtaining, by a processor, information from conversation logs and a conversation design. The processor further creates a dialog graph based on the conversation design. Weights and attributes for edges in the dialog graph are determined based on the information from the conversation logs and adding user input and external context information to an edge attributes set. An unrecognized user input is analyzed and a next action is predicted based on dialog nodes in the dialog graph and historical paths. A guiding conversation response is generated based on the predicted next action.

Abstract: A tool for providing dynamic context-based presentation adjustments across one or more computer devices. The tool receives a presentation from at least one of a plurality of user devices. The tool collects real-time contextual data associated with the presentation from at least one of the plurality of user devices. The tool analyzes the real-time contextual data utilizing a training model. The tool determines one or more adjustment actions based, at least in part, on the analysis of the real-time contextual data. The tool modifies the presentation in real-time using the one or more adjustment actions.

Abstract: In an approach to improve mining and identifying priority relationships and solution packages by identifying client-satisfied solutions packages based on determined relationships, embodiments identify business priorities and software product solutions to recommend to a user. Additionally, embodiments receive electronic representations of the software product solutions utilized by an organization, and generate software product solution relationship maps based on the received electronic representations of software product solutions utilized by the organization. Further, embodiments map received business priorities to the generated software product solution relationship maps based on keyword and key phrase searches or full text similarity using statistical methods and machine learning models, recommend solution packages based on the mapped business priorities to the generated software product solution relationship maps, and output, by a user interface, the solution packages to users.

Abstract: A method, computer system, and a computer program product for contextual transformation is provided. The present invention may include providing a source application for transformation. The present invention may include transforming at least one selection within the source application. The present invention may include communicating the at least one transformed selection to a target application.

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: One embodiment provides a method comprising receiving a first set of information corresponding to a first entity and receiving a second set of information corresponding to a second entity. Each set of information received corresponding to each entity comprises visual information relating to the entity. The method further comprises encoding each set of information, and classifying the first entity and the second entity as either matching entities or non-matching entities based on the encoding.

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 and system of evaluating a user experience (UX) design are provided. A UX design is received. All objects that are identified to be part of a background of the input UI screen are removed to create a filtered input UI screen. The input UI screen is assigned to a cluster. A target UI screen of the input screen is determined and its background removed, to create a filtered target UI cluster. The target UI screen is assigned to a cluster. The filtered input UI screen is used as an input to a deep learning model to predict a target UI cluster. The predicted target UI cluster is compared to the filtered target UI cluster based on the clustering. Upon determining that the filtered target UI cluster is similar to the target UI screen, the UX design is classified as being successful.

Abstract: In one general aspect, a computer-implemented method includes identifying current choices with different verbosity levels for a current turn in a conversation; normalizing multi-dimensional verbosity vectors for each of the current choices to obtain a normalized value for each of the current choices; determining a state definition for the current turn in the conversation, utilizing the normalized values for each of the current choices; providing the state definition for the current turn in the conversation and the normalized values for each of the current choices to a trained reinforcement learning module; receiving, from the trained reinforcement learning module, a score associated with each of the current choices for the current turn in the conversation; and selecting one of the current choices to be entered for the current turn in the conversation, based on the score associated with each of the current choices for the current turn in the conversation.

Abstract: A computer-implemented method of voice-to-text tagging for transcription of a human voice signal by one of an automatic speech recognition system, or a natural conversation, includes generating a speech-to-text verbal transcript of the verbal vocalizations of an audio signal at a verbal timestamp of the audio signal. A voice-to-text non-verbal transcript of the one or more non-verbal vocalizations of the audio signal is generated at a non-verbal timestamp of the audio signal. The non-verbal transcript and the verbal transcript are combined to generate an enhanced transcript.

Abstract: A method and system of automatically identifying topics of a conversation are provided. An electronic data package comprising a sequence of utterances between conversation entities is received by a computing device. Each utterance is classified to a corresponding social action. One or more utterances in the sequence are grouped into a segment based on a deep learning model. A similarity of topics between adjacent segments is determined. Upon determining that the similarity is above a predetermined threshold, the adjacent segments are grouped together. A transcript of the conversation including the grouping of the adjacent segments is stored in a memory.

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 includes identifying a plurality of protected pieces from a conversation. The computer-implemented method further includes generating one or more confidence scores for each protected piece, wherein a confidence score is a degree of associativity between a protected piece and a type of sensitive information. The computer-implemented method further includes determining that the protected piece is associated with the type of sensitive information. The computer-implemented method further includes determining a type of protection action for each protected piece in the plurality of protected pieces. The computer-implemented method further includes performing the type of protection action for each protected piece in the plurality of protected pieces to form a modified conversation that is devoid of the sensitive information. A corresponding computer system and computer program product are also disclosed.

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 creating a profile for a user, the profile including one or more default aspects created automatically by a system and one or more custom aspects created in response to textual input by the user, comparing event data to the profile, and providing a recommendation to the user, based on the comparing.