International Business Machines Corporation

Utopus Insights, Inc.

Massachusetts Institute of Technology

Bei Chen

Adi Ionel Botea

Akihiro Kishimoto

Beat Buesser

Mathieu Sinn

Elizabeth Daly

Thanh Lam Hoang

Rahul Nair

Oznur Alkan

Massimiliano Mattetti

Dakuo Wang

Joern Ploennigs

Anika Schumann

Tiep Mai

Johann-Michael Thiebaut

Chuang Gan

Eric P Bouillet

Si Er Han

Ji Hui Yang

Pascal Pompey

Randall Lee Cogill

Marco Laumanns

Vincent Petrus Anthonius Lonij

John Sheehan

Jean-Baptiste Remi Fiot

Jing James Xu

Yufang Hou

Dhavalkumar C Patel

Syed Yousaf Shah

Inge Lise Vejsbjerg

Radu Marinescu

Jun Song Wang

Peter Daniel Kirchner

Horst Cornelius Samulowitz

Long H Vu

Gregory Bramble

Saket Sathe

Xuan-Hong Dang

Abel Nicolas Valente

William Karol Lynch

Djallel Bouneffouf

Alan Zwiren

Anup Kalia

Olivier Verscheure

Lingfei Wu

Jin Xiao

Petros Zerfos

Yassine Lassoued

Sijia Liu

Jun Wang

Fearghal O'Donncha

Martin Mevissen

Wesley M Gifford

Jakub Marecek

Sean A McKenna

Hiroshi Kajino

Martin Wistuba

Claudio Gambella

Arunima Chaudhary

Parikshit Ram

Daniel Karl I Weidele

Carlos A Alzate Perez

Anton Dekusar

Tim Nonner

Jacint Szabo

Yi Wang

John Dillon Eversman

Voranouth Supadulya

Ulrike Fischer

Takahiko Koyama

Xiaoxi Tian

Long Vu

Karol W Lynch

Xuye Liu

John Savage

Alice J Chang

Vincent Pa Lonij

Sanjmeet Abrol

Weiwei Li

Kelsey Dipietro

Ambrish Rwat

Dongyu Liu

Yi Sun

Benjamin Dubiel

A system receives messaging, video and/or audio input streams including dialogue spoken by users at a group meeting. From these inputs, the system obtains single or multiple interaction records including natural language text memorializing content spoken by each speaker at a meeting, analyzes the content, and identifies single or multiple action item tasks in the interaction records. The system then generates summaries indicating the action item tasks for the users. From the dialogue content, the system further detects whether each action item is addressed, and whether the action item for a user has a solution, or not. The system further detects whether one action item is a precondition for resolving another action item by the user or in conjunction with another user. Using a pre-configured template, the system generates action item summaries, any associated solution, and any relationship or precondition between action items and presents the summary to a user.

Generating summary and next actions in real-time for multiple users from interaction records in natural language

Embodiments of the invention are directed to a computer-implemented method. A non-limiting example of the computer-implemented method includes accessing, using an explanation generator module of a processor system, information of a recommendation associated with an application, information of the application, and information of a user of the application. The explanation generator module of the processor system is used to determine an explanation format of an explanation of the recommendation based at least in part on the information of the recommendation associated with the application, the information of the application, and the information of the user of the application.

Automatically generating personalized and context-aware explanation formats for a recommendation

Use a computerized trained graph neural network model to classify an input instance with a predicted label. With a computerized graph neural network interpretation module, compute a gradient-based saliency matrix based on the input instance and the predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of the model. With a computerized user interface, obtain user input responsive to the gradient-based saliency matrix. Optionally, modify the trained graph neural network model based on the user input; and re-classify the input instance with a new predicted label based on the modified trained graph neural network model.

Interpretable visualization system for graph neural network

Machine learning model change management in an online Software as a Medical Device ('SaMD') is provided. One or more machine learning models implemented in a medical domain may be monitored where the one or more machine learning models are continuously updated. One or more changes to the one or more machine learning models. The one or more machine learning models, having the one or more changes, are certified as being in compliance with performance characteristics and compliance criteria.

Model change management of online software as a medical device

Learning and correcting errors in text input fields to an artificial intelligence (AI) system includes receiving, by the AI system from a target system, an input text. The AI system executes a text processing operation on the input text by applying at least one transformer from a library of transformers to the input text to generate transformed text. At least one proposed correction to the input text is output by the AI system based on an analysis of the transformed text. Feedback data, associated with the at least one proposed correction, is then received from a user of the target system to iteratively learn, by the AI system, which of the transformers need be applied on future input text to accurately generate future proposed corrections on the future input text.

Syntactic and semantic autocorrect learning

Various embodiments manage energy generation in a power generation and distribution system. In one embodiment, a set of residual load data is obtained for a given period of time measured at one or more nodes within a power generation and distribution system. The set of residual load data encodes a set of power flow signals. The set of residual load data is analyzed. An amount of power contributed to the set of residual load data by at least one energy generator class is determined based on the analysis of the set of residual load data.

Real-time estimation of contributions from classes of energy generators in residual load signals

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.

Online machine learning with immediate rewards when real rewards are delayed

To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

Automated time series forecasting pipeline generation

In an approach for discourse-level text optimization, a processor receives an initial text in a first language. A processor applies one or more operators to modify the initial text. A processor evaluates the modified text using a scoring function. A processor determines whether a score generated from the scoring function on the modified text is above a predefined threshold. In response to determining the score is above the predefined threshold, a processor outputs the modified text.

Discourse-level text optimization based on artificial intelligence planning

Embodiments for intelligent dynamic parking for autonomous vehicles by a processor. A dynamic parking plan may be determined for an autonomous vehicle to travel from a selected location and returning to the selected location within a selected time window according to a transportation network and one or more a variable parking functions in lieu of waiting at the selected location.

Intelligent dynamic parking for autonomous vehicles

An embodiment for dynamically assigning vehicle parking is provided. The embodiment may include receiving one or more preferences regarding parking. The embodiment may also include in response to determining a detector vehicle detects a vacant parking spot, creating a network of vehicles within a pre-defined threshold of the vacant parking spot. The embodiment may further include notifying each vehicle in the network about the vacant parking spot and receiving one or more requests for the vacant parking spot from one or more requestor vehicles. The embodiment may also include identifying real-time information associated with roads within the pre-defined threshold of the vacant parking spot. The embodiment may further include assigning the vacant parking spot to a particular requestor vehicle in the network of vehicles. The embodiment may also include displaying an indicator placed adjacent to the particular requestor vehicle that is assigned the vacant parking spot.

Dynamic vehicle parking assignment with user feedback

Embodiments for managing a wave energy converter (WEC) device by one or more processors are described. At least one environmental characteristic associated with a WEC device in a body of water is received. A prediction of wave conditions on the body of water is calculated based on the at least one environmental characteristic. A signal representative of the prediction of wave conditions is generated.

Methods and systems for wave energy generation prediction and optimization

Embodiments for intelligent discovery of preferred chemical reaction pathways to a synthesized target compound by a processor. Target compound data of a target compound a user intends to synthesize may be received. A preference score for one or more chemical reaction pathways to the target compound may be calculated based on one or more chemical reactions. One or more preferred chemical reaction pathways to the targeted compound may be determined using a modified retrosynthetic analysis according to the preference score for one or more chemical reactions.

Intelligent personalized chemical synthesis planning

Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

Visualization of a model selection process in an automated model selection system

Embodiments for detection of energy consumption anomalies in one or more energy consumption systems in a cloud computing environment by a processor. Energy consumption anomalies associated with a selected facility, a selected object, or a combination thereof may be detected and diagnosed according to feedback and simulation data generated from a scenario-based simulation operation.

Operational energy consumption anomalies in intelligent energy consumption systems

Growing community of inventors

Dublin, Ireland

Bei Chen