International Business Machines Corporation

Other

Eric Young Liu

Guangjie Ren

Lei Huang

Shun Jiang

Robert John Moore

Aly Megahed

Raphael I Arar

Peifeng Yin

Chung-hao Tan

Saurabh Mishra

Divyesh Jadav

Pawan Raghunath Chowdhary

Sunhwan Lee

Margaret Helen Szymanski

Sungeun An

Mei-Kao Liu

Jueguang Wang

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.

Personalizing automated conversational system based on predicted level of knowledge

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.

System for voice-to-text tagging for rich transcription of human speech

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.

Dynamic modification of placeholder text in conversational interfaces

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.

Using artificial intelligence to iteratively design a user interface through progressive feedback

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.

Customization and recommendation of tree-structured templates

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.

AI-assisted UX design evaluation

One embodiment provides a method including receiving authoring conversational training data. A machine learning based conversational agent is trained with the conversational training data. The training includes: creating and storing example transcripts of user utterances, creating and storing example transcripts of agent utterances, sequencing utterance transcripts using the example transcripts of user utterances and the example transcripts of agent utterances, forming a corpus from the sequenced utterance transcripts, marking speech patterns that represent social actions from tagging the sequenced utterance transcripts, and forming a patterned corpus from the marked speech patterns.

Program dialog by example

A thermal scavenging system to remove remnant lubricants from interior of bright annealing steel tubes is provided. The system is retrofitted to bright annealing furnace with a conveyer belt and hydrogen gas source and comprises hydrogen-blowing rack, flexible rubber hoses and a lighter. The tubes are placed on the conveyer belt. The hydrogen-blowing rack comprises a hydrogen gas manifold and outlet nozzles connected to tailing ends of the tubes by flexible rubber hoses. At the leading ends of the tubes, a lighter ignites the hydrogen gas to insure all tubes are filled with hydrogen gas, instead of atmospheric air. Then rubber hoses are unplugged from the leading ends and the hydrogen-filled tubes are fed into the furnace for heat treatment. At high annealing temperature, lubricant remnants are burned off the tube's interior surfaces. A negative difference in atmospheric pressure, combustion products of hydrocarbons are scavenged out from the tailing ends.

Thermal scavenging system to remove residue from interior surface of seamless tube in a bright annealing furnace

An optoelectric module includes a housing having a front face with an opening designed to receive an optical transceiver nestingly engaged therein. The optoelectric module includes a discontinuity in an outer surface positioned to cooperate with a spring latch formed in an inner surface of the cage to latch the module in a fully nested orientation within the cage. An actuator is mounted on an outer surface of the housing and movable from an orientation in which the housing is latched in the cage to an orientation in which the actuator engages the spring latch and disengages it from the discontinuity. A bail latch is pivotally mounted for movement between a stored position and a module removal position and is engaged with the actuator to move from the stored position to the module removal position with movement of the actuator from the latched orientation to the delatched orientation.

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San Jose, CA, United States of America

Eric Young Liu