International Business Machines Corporation

Slingshot Iot LLC

Kyndryl, Inc.

Doordash, Inc.

Interntaional Business Machines Corporation

Peter Kenneth Malkin

Clifford A Pickover

James R Kozloski

Michael S Gordon

Ashish Kundu

Aaron K Baughman

Christian Eggenberger

Dimitri Kanevsky

John Thomas Richards

Philip Shi-lung Yu

Jacquelyn Annette Martino

Mark N Wegman

Sharon Mary Trewin

Justin David Weisz

Rachel K E Bellamy

Marc Yvon

Peri L Tarr

Tara N Sainath

Evelyn Duesterwald

Thomas David Erickson

Paul Michael Matchen

Brent Tzion Hailpern

Wendy Anne Kellogg

Theresa C Kratschmer

Stanley M Sutton

Calvin Bruce Swart

Tamir Klinger

Murray Robert Cantor

Wilfredo Ferre

Robert J Schloss

Padmanabhan Santhanam

John C Thomas, Jr

Sasha P Caskey

Lin Luo

Wim De Pauw

Jason Benjamin Ellis

Scott Wayne Weller

John Charles Thomas

Bonnie E John

Christian Eggenberger-Wang

Philip Shilung Yu

Malolan Chetlur

James Edward Christensen

Tracee L Wolf

Philip Shi-Lung Yu

Brian M O'Connell

Dong Chen

Mauro Marzorati

Sara H Basson

Robert G Farrell

Diwesh Pandey

Youngja Park

Ching-Yung Lin

Ya Bin Dang

Seetharami R Seelam

Danny Soroker

Vadakkedathu Thomas Rajan

Palani Sakthi

Anni Rosa Coden

Aleksandr Y Aravkin

Brenda Lynn Dietrich

Aliza Rivka Heching

Clay Edwin Williams

Judah M Diament

Shimei Pan

Laurent David Hasson

Marc Hubert Willebeek-LeMair

Younghee Jung

Ping Ping Cheng

William Edward Bennett

Keith C Houck

Craig M Trim

Yu Zhang

Omer Tripp

Vittorio R Castelli

Jonathan Hudson Connell, Ii

Charu Chandra Aggarwal

Nevenko Zunic

Carlos Henrique Cardonha

Samuel Scott Adams

Steve Richard White

Lawrence David Bergman

Yuan Yuan

Mathew S Thoennes

Rahul P Akolkar

Peter Francis Sweeney

Sachiko Yoshihama

Naohiko Uramoto

Lawrence Scott Rich

Edward Charles Snible

Dharmashankar Subramanian

Daniel A Oblinger

Masayoshi Teraguchi

Harold Leon Ossher

Zhong Tian

Alan Michael Webb

Xiao Zhong

Shun Xiang Yang

Guojing Cong

Charles Campbell Palmer

A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Controlling driving modes of self-driving vehicles

A computer-implemented method, system, and/or computer program product controls self-driving vehicles (SDVs). An emergency message is transmitted to a receiver within a self-driving vehicle (SDV). The emergency message describes an emergency state of an emergency vehicle and an identified future route of the emergency vehicle, where the identified future route is a planned route to an emergency destination for the emergency vehicle that includes a first pathway. In response to the SDV receiving the emergency message, the SDV is redirected, via an auto-control hardware system on the SDV, to drive to a second pathway that does not conflict with the identified future route of the emergency vehicle.

Self-driving vehicle's response to a proximate emergency vehicle

Systems and methods for dynamically managing figments are disclosed. A computer-implemented method includes: receiving, by a computing device, a question from a user; answering, by the computing device, the question using a first degree figment; classifying, by the computing device, the question based on topics; forwarding, by the computing device, the question to a set of second degree figments; receiving, by the computing device, answers to the question from the set of second degree figments; ranking, by the computing device, the answers received from the set of second degree figments; and providing, by the computing device, the ranked answers to the user.

Dynamically managing figments in social media

A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including a forecasting device configured to forecast hidden evidence found in relation to a user input in hidden cycle measurements into future forecasted cycle measurements, where the forecasted hidden cycles are transformed into an amplitude versus frequency histogram with each histogram being compared to each other histogram and determined if causation is a candidate, and if causation is a candidate, a probability density function is applied to produce a degree of causation of a causation link for the hidden evidence in relation to the user input.

Modeling a correlation and a causation link of hidden evidence

A method, system, and non-transitory compute readable medium for a fraud detection including a calendar reading device configured to read calendar data of a calendar for a user, a purchase detection device configured to detect an attempted purchase using a credit-card of the user, and a risk detection device configured to detect fraud by calculating a risk based on the calendar data and data related to the attempted purchase.

Bi-directional feed between electronic calendar and credit-card authorization unit

A method, system, and/or computer program product controls a self-driving vehicle (SDV) based on a purchase transaction at a point of sale (POS) device. One or more processors detects a purchase transaction at a device for a product. In response to detecting the purchase transaction at the POS device, the processors(s) transmits instructions to the SDV to transport a passenger associated with the purchase transaction from a location of the POS device to a predetermined location.

Directing movement of a self-driving vehicle based on sales activity

A method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. One or more processors detects that the aerial drone has entered a predetermined airspace, and also determines a physical size of the aerial drone. In response to detecting that the aerial drone has entered the predetermined airspace, one or more processors directs the aerial drone to alter a velocity of the aerial drone based on its physical size.

Operation of an aerial drone inside an exclusion zone

An unmanned aerial vehicle for determining geolocation exclusion zones of animals. The unmanned aerial vehicle includes a processor-based monitoring device to track geolocation information associated with an animal from the unmanned aerial vehicle, an identification device mounted on the unmanned aerial vehicle to identify the animal and to track a position of the animal over time, and a mapping device coupled to the monitoring device to determine locations where the animal has traversed and to identify where an encounter with the animal is reduced.

Unmanned aerial vehicle for generating geolocation exclusion zones

Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Composite propensity profile detector

A method, system, and/or computer program product controls a self-driving vehicle based on a purchase transaction at a point of sale (POS) device. One or more processors detect a purchase transaction at the POS device. In response to detecting the purchase transaction at the POS device, the processor(s) transmit instructions to the SDV to pick up a passenger associated with the purchase transaction at a predetermined location.

A method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. A drone controller device detects a presence of an aerial drone. The drone controller device and the aerial drone negotiate permission to fly within a predetermined airspace under a predefined aerial drone state. In response to successfully negotiating the permission, the drone controller device enables a drone on-board computer to operate the aerial drone within the predetermined airspace in accordance with the predefined aerial drone state.

Growing community of inventors

Ardsley, NY, United States of America

Peter Kenneth Malkin