International Business Machines Corporation

Kyndryl, Inc.

Hemant Kumar Sivaswamy

Shikhar Kwatra

Venkata Vara Prasad Karri

Vijay Ekambaram

Tiberiu (Jim) Suto

Sarbajit Kumar Rakshit

Nadiya Kochura

Zachary A Silverstein

Sonia Lynette Sequeira

Sri Harsha Varada

Sonal Bhatt

Vinod A Valecha

Rohit Jain

Craig M Trim

Gandhi Sivakumar

Raghuveer Prasad Nagar

Namit Kabra

Smitkumar Narotambhai Marvaniya

Sachin K Mahajan

Kiruthikalakshmi Periasamy

Joyce Miryam Habbouche

Afroz Khan I

Vijay Bharti

Jeremiah Joseph

Kamalpreet Sapna

Priyam Aneja

Laxmi V Bhatt

Akanksha Joshi

Meghali V Munoli

Sachin Suhas Patwardhan

Lisa Seacat DeLuca

Jeremy R Fox

Martin G Keen

Aaron K Baughman

Jonathan D Dunne

Robert E Loredo

Seema Nagar

Cesar Augusto Rodriguez Bravo

Charles Evan Beller

Shailendra Moyal

Saraswathi Sailaja Perumalla

Clement Decrop

Girish Dhanakshirur

Indervir Singh Banipal

Girish Padmanabhan

Brent Alan Miller

Hariharan Krishna

Ivan Deleuze

Schuyler Bruce Matthews

Laura Janet Rodriguez

Kamal Kiran Trood Yamala

Dinesh K Bhudavaram

Rachana Vishwanathula

Carlos E Aguilera

Rodrigo Goulart Silva

Aparna Seshadri

Anindita Das

Dinesh Kumar B

Natalie Watkins

Anvita Vyas

Naga Srinivasa Reddy Padala

Vidya Chandrashekar

Norton Samuel Augustus Stanley

Lincoln A Alexander

Ram Kumar Vadlamani

Jaclyn Wakin

Afroz Khan, I

Pritam Chopdar

Pushpender Kumar Garg

Prakalathan Nirmalakumaran

Alberto Pelliccione

Devarajan Raman Karalil

Deepak Rai

Sunil Nambiar

Tanvi Tayal

Kulkarni Nidhi Sanjay

Anshul Shrivastava

Stuti Nair

A method, computer program product, and computer system for network topology optimization. A graph convolutional network (GCN) model is trained using training network topology datasets as input. Each training network topology dataset includes: (i) a specified network topology and an associated optimal network topology and an optimality function value for the optimal network topology, (ii) relative weights of optimality parameters including performance, availability, and scalability, and (iii) an identification of an optimality function of the optimality parameters weighted by the relative weights. The specified network topology includes components, relationships between the components, and metadata pertaining to the components. Each output node in an output layer of the GCN model includes an optimality function value for a different candidate network topology. One of the output nodes identifies an optimum network topology relative to the specified network topology as being the candidate network topology having a highest optimality function value.

Cloud topology optimization using a graph convolutional network model

A method, computer program, and computer system are provided for validating document submissions. A data entry in a data entry field associated with a document associated with a user is identified. A set of validation rules associated with the document is accessed. A determination is made as to whether the identified data entry complies with the accessed set of validation rules. Based on determining that at least a portion of the identified data entry does not comply with a subset of validation rules from among the set of validation rules, feedback is provided to the user corresponding to the portion of the identified data entry that does not comply with the subset of validation rules.

Guiding and validating document submissions

One or more computer processors derive a base corpus by capturing time series haptic data from an artisan performing an artisanal skill comprising a plurality of artisanal actions, wherein each artisanal action in the plurality of artisanal actions is associated with a time interval. The one or more computer processors train a multi-agent reinforcement learning (MARL) model utilizing the derived base corpus, wherein the MARL model outputs a vector representing one or more predicted artisanal actions required to complete the artisanal skill. The one or more computer processors generate a training avatar utilizing a trained generated adversarial network fed with one or more user parameters identified from a user requesting guidance for the artisanal skill and the output vector of the trained MARL model. The one or more computer processors guide the user with the generated training avatar in an execution of the artisanal skill.

Artisan learning system

The illustrative embodiments provide for signature discourse transformation. An embodiment includes detecting a signature discourse of a user by analyzing the user's discourse using corpus linguistics. The embodiment also includes generating computing a collection of words distinct to the user. The embodiment also includes sensing, using tone analysis and a natural language process model, a tone of the collection of words distinct to the user in the signature discourse utterance. The embodiment also includes mapping a cartesian value of the collection of words distinct to the user. The embodiment also includes deriving a signature discourse transformation (SDT) model from the signature discourse utterances, the collection of words, the tone, and the cartesian value. The embodiment also includes transforming, using the signature discourse transformation, an original discourse by the user into an anonymized text.

Signature discourse transformation

According to one embodiment, a method, computer system, and computer program product for transmitting data from a set of edge devices is provided. The embodiment may include identifying one or more Internet-of-Things (IoT) devices within a structure. The embodiment may include combining the identified one or more IoT devices into one or more groups. The embodiment may include identifying a respective primary device for each group of the one or more groups. The embodiment may include deploying a tiny machine learning (ML) model on each identified respective primary device. In response to detection of an event within the structure by a group of the one or more groups, the embodiment may include utilizing the tiny ML model of a primary device of the group to select one or more other groups for activation.

Creating sets of heterogeneous internet-of-things (IoT) devices from which to upload data as a pre-step to edge computing

Provided is a computer-implemented method, system, and computer program product for dynamically configuring a virtual private network (VPN) protocol of a VPN tunnel. A processor may analyze data activity associated with a first device, where the first device is connected to a second device using a VPN tunnel. The processor may compare the analyzed data activity to a set of policies for determining a VPN protocol to apply to the VPN tunnel. The processor may identify, based on the comparing, a first VPN protocol from a plurality of VPN protocols. The processor may apply the first VPN protocol to the VPN tunnel.

Dynamic virtual private network protocol configuration

A method, computer system, and a computer program product for handheld demonstration is provided. The present invention may include creating an artificial human. The present invention may include determining that assistance of the artificial human is needed by a user. The present invention may include projecting the artificial human onto an augmented reality (AR) or virtual reality (VR) device of the user. The present invention may include monitoring the user's behavior. The present invention may include modifying the projected artificial human based on the user's monitored behavior.

User assistance through demonstration

A computer implemented method processes alerts. A computer system creates a representation of an alert received for processing. The computer system determines a similarity of the alert with previously processed alerts using the representation of the alert and representations of the previously processed alerts. A first evaluator in the computer system evaluates an alert level for the alert based on previously processed similar alerts in response to the similarity being above a similarity threshold for similar alerts. A second evaluator in the computer system evaluates, the alert level for the alert using a machine learning model in response to the similarity not being above the similarity threshold.

Real time behavioral alert processing in computing environments

A computer-implemented method for displaying advertisements. The method includes associating feature metadata with a product video advertisement of a product. The method includes identifying, based on the feature metadata, a feature of the product corresponding with a section of the product video advertisement when a triggering event is detected during playing of the product video advertisement. The method includes displaying a second product video advertisement of a second product that includes the feature of the product.

Real time feature analysis and ingesting correlated advertisements in a video advertisement

A method for dynamically rearranging one or more packages for delivery on a delivery vehicle in response to unforeseen changes in a pre-planned delivery route. The method detects, via one or more sensor-enabled shelves in a delivery vehicle, an address and package details of a respective delivery package. The method further integrates the one or more sensor-enabled shelves with a route map that depicts the pre-planned delivery route. The method further assigns new values to the one or more sensor-enabled shelves, in response to a change in the pre-planned delivery route, indicating a new sequence of delivery for each respective delivery package. The method further notifies a delivery person which delivery package is to be delivered at a current stop by illuminating the one or more sensor-enabled shelves containing the respective delivery package.

Dynamic display of stacked items in a delivery vehicle based on a delivery route change

Embodiments of the present disclosure provide systems and methods for synchronizing an unaligned audio stream and a corresponding unaligned video stream of real time streaming media. A non-limiting disclosed method comprises performing, using a video classifier, video reference point classification of a video stream based on an audio-video dataset; performing, using an audio classifier, audio reference point classification of the audio stream based on the audio-video dataset. The system correlates object related reference points in video segments of the video stream and in audio segments of the audio stream to identify a set of audio-video synchronization candidates. The system compares context of the set of audio-video synchronization candidates to identify an audio-video synchronization candidate to synchronize the audio stream and video stream based on reference point alignment.

Asynchronous content analysis for synchronizing audio and video streams

Systems, methods and/or computer program products for managing the temperature of datacenter during a period of malfunction or inoperability of the cooling system responsible for maintaining temperatures within the datacenter. Temperatures of the datacenter's computing systems are monitored by thermal imaging systems and/or sensors. Computing systems are also monitored for how frequently the systems are accessed during a defined period of time and the number of mission critical deployments by each computing system. Collected parameters, including temperature, frequency of access and number of running mission critical applications are imputed into a scoring algorithm which uses the collected parameters and weightings to generate a ranking of computing systems to shutdown sequentially in response to rising temperatures. As temperatures rise above a target temperature, computing systems with the highest score (and the lowest amount of risk) are shutdown, lowering the temperature of the datacenter until the cooling system becomes operation again.

Datacenter temperature control and management

Data transformation and data quality checking is provided by reading data from a source datastore and storing the data into memory, performing in-memory processing of the data stored in memory, where the data is maintained in-memory for performance of the in-memory processing thereof, and where the in-memory processing includes performing one or more transformations on the data stored in memory, in which the data stored in memory is transformed and stored back into the memory and applying one or more data quality rules to the data stored in-memory, and based on performing the in-memory processing of the data stored and maintained in memory for the in-memory processing, loading to a target datastore at least some of the data processed by the in-memory processing.

Maintaining data in memory for transformation and quality checking when reading from a source and loading to a target

A processor may receive first sensor data associated with detected movement of a user in an environment. The processor may determine, using an AI model, based on the first sensor data, that the movement of the user is associated with a first object. The processor may retrieve sensory data associated with the first object from a repository. In some embodiments, the sensory data may include at least one of auditory, olfactory, and haptic data associated with historical user interactions with the first object. The processor may provide a first sensory signal to the user to indicate a location of the first object relative to a first location of the user.

Navigation assistance device

A method, a computer program product, and a computer system position a plurality of sensors in an area. The method includes receiving data from the sensors positioned at respective first locations where the data is indicative of weather and environmental parameters. The method includes determining weather conditions and an environmental condition based on the weather and environmental parameters. The method includes determining an impact to the environmental condition by the weather conditions. The method includes determining a second location for the sensors based on the impact to the environmental condition. The sensors are configured to generate further environmental parameters with an increased granular level at the second location. The method includes transmitting instructions to a delivery robot to move the sensors to the second location.

Context aware dynamic positioning of sensors

Growing community of inventors

Pune, India

Hemant Kumar Sivaswamy