International Business Machines Corporation

Kyndryl, Inc.

Pritpal Arora

Gopal Sarma Pingali

Laxmikantha Sai Nanduru

Bijo Sebastian Kappen

Klaus Koenig

Sarbajit Kumar Rakshit

Maja Vukovic

Muhammed Fatih Bulut

Naga A Ayachitula

Dennis Anthony Perpetua, Jr

Hongtan Sun

Jigar Navanitrai Kapasi

Jonathan Richard Young

Adinarayana Sakala

Sathis Kumar Duraisamy

Clea Anne Zolotow

Tedrick Neal Northway

James E Olson

Saravanan Devendran

Steven Joseph Kurtz

Anil Kumar Narigapalli

Omar Odibat

Aditya M Burli

Bhupesh Gupta

Mouleswara Reddy Chintakunta

Ronald George Gottschalk

Ramachandra S Byahatti

Nicola Jane Ankcorn McKenna

Mark Vincent Chitti

A method of mitigating risks in a service level agreement (SLA), including: identifying relationships between an infrastructure and an application associated with the SLA; identifying changes to a technological environment of the SLA based on collected data associated with the SLA and the identified relationships, the collected data including the infrastructure and the application; establishing a baseline for a service level objective (SLO) of the SLA by analyzing the collected data of the infrastructure and the application; determining risk impact to the SLA based on an assessment of requirements to the SLO and the changes to the technological environment of the SLA; generating a solution option for the SLA by applying an explainable artificial intelligence (XAI) model based on processing the risk impact with the baseline in the XAI model; and updating the SLA based on the solution option.

Risk mitigation in service level agreements

Computer systems that use artificial intelligence to predict strategies, scenarios and or game plans for sports games that are already in progress. In some embodiments a feedback loop is used so that the artificial intelligence takes into account events occurring in and/or around the game as they occur to provide updated predictions.

Artificial intelligence assisted sports strategy predictor

A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.

Automatic generation of user experience mockups using adaptive gaze tracking

An approach for providing solutions to ad in the process recovery in manufacturing systems to resume manufacturing activity is disclosed. The outcome of the approach can include the following advantages, minimizing waste, reducing cost of recovery, and increasing efficiency in a manufacturing process. The approach includes collecting initial factory data; identifying and categorizing one or more shop floor activities; determining initial recovery process; creating a digital twin copy of the factory; performing one or more initial simulation scenarios; generating factory improvement plan; and executing factory improvement plan.

AI-enabled process recovery in manufacturing systems using digital twin simulation

A tool for providing contextual policy-based computer vision control. The tool identifies one or more camera devices in an environment. The tool determines a plurality of vision areas in a field of view within the environment. The tool determines one or more contextual policies for the plurality of vision areas in the field of view. The tool determines one or more vision augmentations for the plurality of vision areas based, at least in part, on an aggregate computer vision capability of the one or more camera devices in the environment and the one or more contextual policies for the plurality of vision areas in the field of view. The tool applies the one or more vision augmentations to at least one of the one or more camera devices in the environment.

Contextual policy-based computer vision control

A computer-implemented method, according to one embodiment, includes logically partitioning a storage system into a plurality of compartment constructs, and mapping hosts in communication with the storage system to the compartment constructs, thereby enabling interoperability among the hosts and the compartment constructs. The interoperability of the hosts and the compartment constructs is analyzed, and the interoperability is based on storage software and/or firmware versions being run by the hosts. The method further includes defining, based on the analysis, risk profiles for applications run on the hosts, and determining, based on the risk profiles, recommendations for assignment and mapping of the hosts with the compartment constructs. Ownership of storage objects is assigned to the compartment constructs based on the recommendations. Each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

Dynamically assigning storage objects to compartment constructs of a storage system to reduce application risk

Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Root cause identification and analysis

A computer implemented method is provided that includes using historic migration data to label key performance indicators (KPIs) in a migration model including a scale that indicates a level of successful migration to a remote provider. Employing the migration model to predict successful migration of a local application having one or more of said one or more of local key performance indicators for the local application. Migrating the local application to a remote provider when the model to predict successful migration indicates a greater than threshold value for successful migration.

Machine learning for determining suitability of application migration from local to remote providers

Techniques regarding providing artificial intelligence problem descriptions are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include, at least: a query component that generates key performance indicators from a query, determines a subset of key performance indicators that individually have a performance below a threshold, and maps the subset of key performance indicators to operational metrics; a learning component that generates, using artificial intelligence, problem descriptions from one or more of the subset of key performance indicators or the operational metrics and transmits the problem descriptions to a database.

Artificial intelligence based problem descriptions

A method for optimizing device-to-device communication protocol selection in an edge computing environment is provided. The method includes: receiving a request for a service from a user device, wherein the computing system is one of plural edge computing devices in an edge computing environment; determining computational tasks performed in providing the service; selecting, using a machine learning model, a set of the edge computing devices to perform the computational tasks and communication protocols for the set of the edge computing devices to use while performing the computational tasks, wherein the machine learning model is configured to select the set of the edge computing devices and the communication protocols based on minimizing a time to perform the computational tasks; and sending instructions to perform the computational tasks, thereby causing the set of the edge computing devices to perform the service in response to the request from the user device.

Optimizing device-to-device communication protocol selection in an edge computing environment

An approach for securely accessing self-sovereign data via a bot-chain ledger may be provided. A bot may request access to a piece distributed data at a bot-chain client. A bot registry service may validate the requesting bot is registered with the bot-ledgering client. The bot-ledgering client may generate a token for the requesting bot and provide the identity of a data bot with permission to access the piece of distributed data. A data bot may request to read the piece of distributed data at the bot-ledgering client. The bot-ledgering client may verify the data bot is registered with the bot-chain. The bot-ledgering client may generate an access token and send it to the data bot.

Self-sovereign data access via bot-chain

Providing enterprise recommendations includes determining a change of a maturity score of an enterprise architecture assessment domain model associated with an enterprise architecture. The change of the maturity score is indicative of a change of a maturity state of the enterprise architecture assessment domain model. One or more maturity model types that exceed a predetermined degree of change are determined, and at least one assessment capability group for each maturity model type is determined. One or more assessment capabilities are selected for each assessment capability group based on one or more selection criteria. One or more recommendation templates are selected for each selected assessment capability based upon one or more template selection rules. A first set of recommendations are compiled based on the selected recommendation templates, and the first set of recommendations are output.

Operations augmented enterprise collaborative recommender engine

An approach is provided in which an information handling system loads a set of event data corresponding to an information technology (IT) ecosystem into a blockchain framework. The blockchain framework, in turn, generates a set of anomaly data based on the set of event data. The information handling system identifies a set of parameter values to adjust corresponding to the IT ecosystem based on the set of anomaly data, and the information handling system then optimizes the IT ecosystem by adjusting the identified set of parameter values in the IT ecosystem.

Cognitive balancing IT ecosystems

In an approach for dynamically determining a suitable SME for a particular critical situation, a processor receives an initial problem statement. A processor constructs a collaboration channel associated with the initial problem statement. A processor identifies an SME based on a rating that associates the SME with the initial problem statement. A processor sends an invitation to the SME to join the collaboration channel.

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Bengaluru, India

Pritpal Arora