International Business Machines Corporation

Kyndryl, Inc.

Shikhar Kwatra

Sarbajit Kumar Rakshit

Jeremy R Fox

Aaron K Baughman

Craig M Trim

Zachary A Silverstein

Adam Lee Griffin

Indervir Singh Banipal

Paul N Krystek

Sushain Pandit

Komminist S Weldemariam

John David Wilson

Joseph N Kozhaya

Raghuveer Prasad Nagar

Nadiya Kochura

Mauro Marzorati

Corville O Allen

Vijay Ekambaram

Andrew Ronald Freed

Hemant Kumar Sivaswamy

Tiberiu (Jim) Suto

Gray Franklin Cannon

Seng Chai Gan

Christopher J Hardee

Scott Edward Schneider

Jennifer L Szkatulski

Sourav Mazumder

Liam S Harpur

Peeyush Jaiswal

Priyansh Jaiswal

Robert Huntington Grant

Stephen Carl Hammer

Caleb Miles

Martin G Keen

Venkata Vara Prasad Karri

Cesar Augusto Rodriguez Bravo

Moitreyee Mukherjee-Roy

James R Kozloski

Maharaj Mukherjee

Abdigani Mohamed Diriye

Pritesh Patel

Michael S Gordon

Vittorio R Castelli

Florian Pinel

Vinod A Valecha

Jeronimo Irazabal

Clement Decrop

Smitkumar Narotambhai Marvaniya

Victor Povar

Frederik Frank Flöther

Abhishek Malvankar

Michael Treadway

Rodrigo Goulart Silva

Michael Bender

Diwesh Pandey

Padmanabha Venkatagiri Seshadri

Spencer Thomas Reynolds

Reji Jose

Saurabh Yadav

Melissa Restrepo Conde

Gary William Reiss

Eric Jeffery

Michael Nicholas Virnoche

Iranna Dharmaraya Ankad

Steven Lee Fisher-Stawinski

Robert E Loredo

Jun Zhu

Shailendra Moyal

Edgar Adolfo Zamora Duran

Hyman David Chantz

Roxana Monge Nunez

Mary E Rudden

Stefan Ravizza

Micah Forster

Amitava Kundu

Rosanna Mannan

Kelly Camus

Emily A Ray

Laura Grace Ellis

Bryan Baysinger

Clifford A Pickover

Gregory J Boss

Paul R Bastide

Gandhi Sivakumar

Maja Vukovic

Qiqing Christine Ouyang

Petr Novotny

Vijay Kumar Ananthapur Bache

Saravanan Sadacharam

Gary Francis Diamanti

Natalie Brooks Powell

Steven Joroff

Michael Man Behrendt

Srinivas Babu Tummalapenta

Ryan R Anderson

Richard Daniel Gunjal

HuyAnh Dinh Ngo

Komminist Weldemariam

Kavitha Hassan Yogaraj

Sujatha B Perepa

Aspects of the present disclosure relate generally to reminder systems and, more particularly, to a contextual item discovery and pattern inculcated reminder mechanism and methods of use. A computer-implemented method includes: determining, by a computing device, an event in which a user will participate; associating, by the computing device, one or more items with the event; determining a location of the one or more items associated with the event including that the user does not possess any combination of the one or more items; and providing an alert to the user that the any of the one or more items associated with the event is not in the possession of the user.

Contextual item discovery and pattern inculcated reminder mechanism

Energy conversion and distribution can include converting sound energy to electrical energy using one or more energy converters positioned within a predetermined area. Multiple devices can be classified to indicate an energy requirement of each device, and the electrical energy converted from sound energy can be allocated to one or more of the devices based on the classifying. Delivery of the electrical energy allocated to each of the one or more devices can be controlled using a switching mechanism to create a transmission channel with respect to each of the one or more devices.

Directed energy conversion and distribution

Using a probability distribution and a search result history, a probability of selecting a specified number of relevant samples and a specified number of irrelevant samples from a set of contents is computed, each content in the set of contents comprising a content copied to a first system clipboard of a first device. Using the probability distribution, each content in the set of contents is weighted. Using the weighted set of contents, a set of predicted searches is generated. The set of predicted searches is presented for selection responsive to an indication of intent to perform a search. A selected search within the set of predicted searches is caused to be performed.

Clipboard based search term prediction

From a first model parameter, an autoencoder network is generated. A reconstruction error for the autoencoder network is measured, the reconstruction error comprising a difference between an input to the autoencoder network and a corresponding output from the autoencoder network, the input to the autoencoder network comprising a portion of an initial set of data. The reconstruction error and a confidence score corresponding to a complexity level of the autoencoder network are aggregated into a level of difficulty score of the autoencoder network. From the level of difficulty score and an initial data access policy level corresponding to the initial set of data, a derived data access policy level corresponding to the initial data access policy level is generated, the derived data access policy level enforcing access to a transformed set of data generated by applying a transformation to the initial set of data.

Automatically adjusting data access policies in data analytics

By analyzing a natural language content concurrently with a presentation of the natural language content, a first language of the natural language content is detected. By analyzing the natural language content concurrently with the presentation of the natural language content, it is detected that a subset of the natural language content is expressed in a second language, wherein the second language is different from the first language, wherein a set of known languages comprises the first language, wherein the set of known languages excludes the second language. From the subset of the natural language content, a modified subset expressed in the first language is generated. The modified subset is inserted into the natural language content.

Contextual and personalized real time content modification for language assistance

A method, computer system, and a computer program product for dynamic speech modulation is provided. The present invention may include transmitting a first response to a received command. The present invention may include determining the first response is not understood by a user. The present invention may include transmitting a second response to the received command.

Dynamic virtual assistant speech modulation

A method, a structure, and a computer system for playback optimization. The exemplary embodiments may include transcribing an audio stream at normal and at high playback speed, as well as determining whether portions of the audio stream are unintelligible at the high playback speed. The exemplary embodiments may further include reducing a speed of the high playback speed for portions determined unintelligible, and transcribing the audio stream with the unintelligible portions played at the reduced high playback speed. The exemplary embodiments may further include determining whether the unintelligible portions are still unintelligible at the reduced high playback speed and, if not, identifying the reduced high playback speed as an optimal playback speed of the unintelligible portions. The exemplary embodiments may additionally include, in response to receiving a request to play the audio stream at the high playback speed, playing the unintelligible portions at the optimal playback speed.

Audio playback and captioning

Embodiments are directed to techniques for secure network connectivity. The techniques including a system having a credential server storing a Personal-Public (PP) Service Set. Identifier (SSID) profile configured according to registration information provided from a personal computing device. The system further including a Wireless Access Point (WAP) communicatively coupled to the credential server and configured to implement a PP SSID connection using the PP SSID profile to create a single-device, single-use, password-protected, unadvertised, and encrypted networking channel between the personal computing device and the Internet.

Establishing a personal-public service set identifier connection between a personal device and the internet

A method, system, and computer program product for occupant-based elevator actions are provided. The method identifies a set of subjects approaching a set of elevators to generate a set of identities for the set of subjects. A set of initial priority statuses are generated for the set of subjects. Based on the set of identities and the set of initial priority statuses, an elevator dispatch solution is determined. The method updates at least a portion of the initial priority statuses to generate a set of subsequent priority statuses in response to a subset of subjects entering at least one elevator. The method determines an isolation response for a first subject of the subset of subjects and modifies the elevator dispatch solution based on the isolation response to generate an isolation dispatch solution. The at least one elevator executes the isolation dispatch solution.

Occupant-based intelligent elevator actions

Embodiments receive a data metrics input which corresponds with a microservice issue, determine a final outcome by multiplying each metric of the data metrics input by a weight, determine a link output based on the final outcome, determine a cost function based on the link output, evaluate that a cost output of the cost function has a minimized error, output a data metrics output based on the cost output of the cost function having the minimized error, train at least one machine learning (ML) model using the data metrics output, receive a new data metrics input which corresponds with another microservice issue, and provide a recommendation corresponding to the new data metrics input using the trained at least one ML model.

Alert management

A system includes: a platform; a payload extending from a platform; an adjustable tether connecting the payload to the platform; and a sail extending from the payload. The system further includes a computer program that is executable to relocate the platform to the area of interest based on a threshold based decision which may include providing a tether and sail configuration.

Relocation based coverage area enhancement system

A computer-implemented method, a computer program product for predictive device maintenance, and system for a plurality of Internet of Things (IoT) devices. One embodiment may comprise generating a risk score as a weighted measure of input data about an Internet of Things (IoT) device and a frequency of failure of the IoT device, determining an optimal time to trigger a predictive notification pertaining to the IoT device using the risk score, and at the determined optimal time, issuing the predictive notification. The risk score in some embodiments may indicate an estimate of loss of function of the IoT device.

Predictive device maintenance

The exemplary embodiments disclose a method, a computer program product, and a computer system for determining the context of media. The exemplary embodiments may include a user consuming media, collecting data of the media, extracting one or more features from the collected data, and determining a media context based on the extracted one or more features and one or more models.

Displaying contextual information of media

A computer-implemented method includes: presenting, by a computing device, a virtual event user interface to an attendee of a virtual event; obtaining, by the computing device, data about actions performed by the attendee within the virtual event; creating, by the computing device, a tensile sphere kernel that is specific to the attendee using the obtained data; creating, by the computing device, a mixed tensile sphere kernel using the tensile sphere kernel that is specific to the attendee and another tensile sphere kernel of another attendee; determining, by the computing device, an event recommendation of the virtual event using the mixed tensile sphere kernel; and presenting, by the computing device, the event recommendation to the attendee by changing the virtual event user interface to show the event recommendation.

Event experience representation using tensile sphere mixing and merging

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

Growing community of inventors

San Jose, CA, United States of America

Shikhar Kwatra