International Business Machines Corporation

Lenovo (singapore) Pte. Ltd.

Brian Paul Gaucher

Duixian Liu

Jonathan Lenchner

Valentina Salapura

Ullrich Richard Pfeiffer

Ephraim Bemis Flint

David O S Melville

Thomas M Zwick

Jonathan Hudson Connell, Ii

Robert G Farrell

Jeffrey Owen Kephart

Young Hoon Kwark

Thomas Martin Zwick

Ullrich Richard Rudolf Pfeiffer

Rachel K E Bellamy

Modest M Oprysko

Saila Ponnapalli

Zhi Ning Chen

Janusz Grzyb

Clifford A Pickover

Michael Frank Cina

Thomas Richard Hildner

John Ulrich Knickerbocker

Maharaj Mukherjee

Christopher Vincent Jahnes

John Michael Cotte

Jinjun Xiong

Takeshi Asano

John Harold Magerlein

Chirag Suryakant Patel

Alphonso Philip Lanzetta

Peter S Lee

David Osmond Melville

Cornelia Kang-I Tsang

Gary S Ditlow

Hideyuki Usui

Dario Gil

Alan Michael Webb

William Edward Pence, Iv

Nils Deneke Hoivik

Frank J Canora

David Os Melville

James R Kozloski

Aaron K Baughman

Michael S Gordon

Lawrence Shungwei Mok

Kevin W Brew

Kazuo Fujii

Jayant R Kalagnanam

Mark B Ritter

Troy James Beukema

John Martin Prager

Osamu Yamamoto

Robert Stephen Olyha, Jr

Masaki Oie

Lloyd Alan Treinish

Charles Louis Haymes

Christian Schuster

Shohhei Fujio

Richard Ian Kaufman

Rachel Katherine Emma Bellamy

Paul F Greier

Kazuo Masuda

Thomas Wayne Studwell

Mattias Fitzpatrick

Ranjani Sirdeshmukh

Eprhaim Bemis Flint

David Osmond Scott Melville

Nobutoyo Nakano

Hiroshi Yoshinaga

Hidenao Hamada

Tsutomu Iwasaki

John LeRoy Staples

Changyu Wu

Thomas Hildner

Described are techniques for corroborating anomalous behavior. The techniques include training devices included in an Internet of Things (IoT) mesh network to independently identify occurrences of anomalous behavior in a proximate physical environment. The techniques further include receiving event data from at least a portion of the devices in the IoT mesh network corresponding to a time window, where the event data reports occurrences of at least one type of anomalous behavior. The techniques further include corroborating the at least one type of anomalous behavior to determine that the occurrences of the at least one type of anomalous behavior indicate an anomalous event that meets a reporting threshold for providing notice of the anomalous event. The techniques further include generating a notification regarding the anomalous event.

Corroborating device-detected anomalous behavior

A processor may record a first location at an event with at least one person. The processor may monitor a plurality of actions of that at least one person at the first location. The processor may interpret at least one action of the at least one person that indicates a change of interest to a second location at the event. Based on the at least one action, the processor may determine the second location at the event. The processor may record the second location at the event.

Automatic selection of event video content

Data is obtained specifying a measure of a data set to be analyzed by human experts and allotted time for analysis completion. Based on same, a schedule is created for a primary meeting of human experts, whose aim is to analyze the data. The meeting is evaluated to create rate estimates for hypothetical meeting partitions; and, if the primary meeting is not adhering to schedule and/or can be speeded up, the meeting partitions are simulated until a partitioning scheme is determined that can restore the meeting to schedule and/or speed it up. In another aspect, a model of focus of attention of each member of a group of individuals engaged in an activity requiring cooperation is dynamically updated, and a cooperation index is determined based on the model of focus of attention, an interaction graph, and at least one physiological parameter of at least one of the members.

Predicting outcomes from measures of group cooperation with applications in traffic alert and control

In one embodiment, in accordance with the present invention, a method, computer program product, and system for performing actions based on captured interpersonal interactions during a meeting is provided. One or more computer processors capture the interpersonal interactions between people in a physical space during a period of time, using machine learning algorithms to detect the interpersonal interactions and a state of each person based on vision and audio sensors in the physical space. The one or more computer processors analyze and categorize the interactions and state of each person, and tag representations of each person with the respectively analyzed and categorized interactions and states of the respective person over the period of time. The one or more computer processors then take an action based on the analysis.

Detecting interaction during meetings

A meeting management method, system, and non-transitory computer readable medium, include a target determination circuit configured to determine a target of an agenda for a meeting, the target of the agenda including which users correspond to a topic, a behavior capturing circuit configured to capture behavior of the users during the meeting, a tracking and identifying circuit configured to track the behavior of the users during the meeting and identify a user of the users corresponding to the behavior, and a difference determination circuit configured to determine if a difference between the behavior of the users and the target of the agenda is greater than a threshold value.

Meeting management system

In an approach to determining an introduction of a user in a meeting, one or more computer processors retrieve a document containing information associated with a user, wherein the document includes one or more fragments describing the user. The one or more computer processors determine an audience of a meeting associated with the user. The one or more computer processors determine a description of the meeting. The one or more computer processors prioritize one or more of the one or more fragments, based, at least in part, on the audience of the meeting and on the description of the meeting. The one or more computer processors select one or more of the one or more fragments for the introduction of the user in the meeting, based, at least in part, on the prioritization of the one or more fragments.

Greeting agent for meeting introductions

A computer-implemented method manages remote electronic drop-ins on local conversations. A local audio sensor transmits a captured conversation from a local cluster of persons to a remote communication device where members of the local cluster of persons are within a predefined distance of one another, and where the remote communication device is at a location that is beyond a human hearing range from the local audio sensor. One or more processors determine that the captured conversation is about a particular topic. A request from a remote user is received from the remote communication device to electronically drop in on a particular remote cluster of persons who are having a conversation about the particular topic. In response to receiving the request from the remote user, one or more processors selectively connect a local communication device proximate to the cluster of persons to the remote communication device.

Managing drop-ins on focal points of activities

A system and method are provided for shared machine learning. The method includes providing a model to a plurality of agents included in a machine learning system. The model specifies attributes and attribute value data types for an event in which the agents act. The method further includes receiving agent-provided inputs during an instance of the event. The agent-provided inputs include estimated attribute values that are consistent with the attribute value data types. The method also includes determining expertise weights for at least some agents in response to at least one ground-truth which is learned from the estimated attribute values. The method additionally includes determining an estimate value for one or more of the attributes using respective adaptive mixtures of the estimated attribute values.

Interactive learning

A method for managing and rendering one or more information nodes relative to a current focus. The method receives one or more principal topics from an analysis of the information nodes. The method determines a topic vector from a similarity of a first information node to each of the principal topics and a map from the topic vector to a storage location of the first information node. The method determines one or more current topics from a second information node that has been recently accessed by a user and a current focus vector from a similarity of the one or more current topics to the one or more principal topics. The method renders the current focus vector and the first information node according to a rendering algorithm that is applied to the current focus vector and the first information node.

Access and presentation of files based on semantic proximity to current interests

A computer-implemented method manages drop-ins on conversations near a focal point of proximal activity in a gathering place. One or more processors receive a first set of sensor data from one or more sensors in a gathering place, and then identify a focal point of proximal activity based on the first set of received sensor data received from the one or more sensors. One or more processors characterize a conversation near the focal point based on a second set of received sensor data from the one or more sensors, and then present a characterization of the conversation to an electronic device. One or more processors enable the electronic device to allow a user to drop-in on the conversation.

A computer program product for managing and rendering one or more information nodes relative to a current focus. The computer program product receives one or more principal topics from an analysis of the information nodes. The computer program product determines a topic vector from a similarity of a first information node to each of the principal topics and a map from the topic vector to a storage location of the first information node. The computer program product determines one or more current topics from a second information node that has been recently accessed by a user and a current focus vector from a similarity of the one or more current topics to the one or more principal topics. The computer program product renders the current focus vector and the first information node according to a rendering algorithm that is applied to the current focus vector and the first information node.

A method, system and apparatus for operating an energy-using device are disclosed. Current data related to operation of the energy-using device is received at a remote device. An operating specification for the energy-using device is received at the remote device from a database. A recommended setting of the energy-using device is determined from the current data and the operating specification. The remote device communicates the recommended setting to the energy-using device. A control unit at the energy-using device receives the recommended setting and implements the recommended setting at the energy-using device.

Interactive energy device for environmental stewardship

Growing community of inventors

Brookfield, CT, United States of America

Brian Paul Gaucher