International Business Machines Corporation

Nec Corporation

Hovey R Strong, Jr

Anca Andreea Chandra

Pawan Raghunath Chowdhary

Jeanette L Blomberg

Eric Kevin Butler

Thomas D Griffin

Divyesh Jadav

Sunhwan Lee

Robert John Moore

Susanne M Glissmann-Hochstein

Shun Jiang

Se Chan Oh

Aly Megahed

Chung-hao Tan

Raphael I Arar

Shubhi Asthana

Sandeep Gopisetty

Neil H A Boyette

Jeffrey Thomas Kreulen

James J Rhodes

Guangjie Ren

Stephen Kane Boyer

Christopher S Campbell

Sechan Oh

Kenneth M Johns

Abhinav Maurya

Suppawong Tuarob

Taiga Nakamura

Tobin Jon Lehman

Samir Tata

Cheryl Ann Kieliszewski

Kevin P Roche

Valeria Becker

Mario H Lichtsinn

Manuel Hernandez

Michael E Rose

Brian D Yost

Systems, methods, and computer program products for providing personalized recommendations of devices for monitoring and/or managing a health condition are disclosed, and generally include receiving first structured information regarding a patient and a first set of one or more patient populations; receiving unstructured information regarding at least the patient and a second set of one or more patient populations; analyzing the unstructured information to derive second structured information; determining one or more health metrics to be monitored for the patient based on analyzing each of the first structured information and the second structured information, using a classification model; and determining an optimum set of devices to be used for monitoring the one or more health metrics. In some embodiments, metrics may be continuously monitored to detect a change exceeding an event trigger threshold, and a new set of recommended devices may be generated.

Personalized device recommendations for proactive health monitoring and management

A computer-implemented method, according to one embodiment, includes: receiving an offer request including one or more desired services, and selecting available offerings, each of which include at least one of the desired services. A determination is made whether available benchmarks exist for each of the at least one desired service included in each of the selected available offerings. For each desired service determined as not having available benchmarks, a draft benchmark is computed for each of a plurality of criteria. A confidence weight is also computed for each of the draft benchmarks. The available benchmarks, the draft benchmarks, and the confidence weights are further used to construct an offer which is submitted in response to the received offer request. Moreover, the draft benchmarks and the corresponding confidence weights are re-computed for each of the respective desired services in response to determining that the submitted offer was not accepted.

Benchmark scalability for services

Computer program products are configured to perform methods for determining likely health conditions based on demographic information and/or determining appropriate wearable technology and services to monitor a patient's health. In one embodiment, a computer program product is configured to perform a method including receiving historical demographic data comprising a plurality of attributes; associating the historical demographic data with labels corresponding to known causes of particular health conditions; building a decision tree model using the historical demographic data and the associated label(s); generating a vector Yusing the model, Yrepresenting probable causes of a plurality of health conditions; and determining likely health conditions for a patient based on comparing the vector Yto a second vector Z, Zrepresenting probable causes of health conditions determined based on a health care record for the patient. Appropriate wearables for tracking the health of the patient may be determined using textual analysis.

Systems and techniques for recommending personalized health care based on demographics

One embodiment provides a method comprising receiving a flight plan request for a drone. The flight plan request comprises a drone identity, departure information, and arrival information. The method further comprises constructing a modified flight plan for the drone based on the flight plan request, wherein the modified flight plan represents an approved, congestion reducing, and executable flight plan for the drone, and the modified flight plan comprises a sequence of four-dimensional (4D) cells representing a planned flight path for the drone. For each 4D cell of the modified flight plan, the method further comprises attempting to place an exclusive lock on behalf of the drone on the 4D cell, and in response to a failure to place the exclusive lock on behalf of the drone on the 4D cell, rerouting the modified flight plan around the 4D cell to a random neighboring 4D cell.

Drone air traffic control and flight plan management

One embodiment provides a method that includes generating, by a processor, a topic graph by converting information to topic nodes and edges in the topic graph. Bridges from a given topic node are provided to a neighbor node based on a weight assignment. The bridges are converted to an electronic form of communication. The processor orders suggested paths based on a determined distance from at least one other topic node to a topic goal node via an earliest topic node on each suggested path. The determined distance is found by a first search of the topic graph.

Topic bridging determination using topical graphs

Embodiments of the present invention provide a method comprising receiving a task set comprising multiple tasks, receiving operational information identifying one or more operating characteristics of multiple drones, and obtaining an initial heuristic ordering of the multiple tasks based on the operational information and the climate information. Each task has a corresponding task location. The method further comprises scheduling the multiple tasks to obtain a final ordering of the multiple tasks. The final ordering represents an order in which the multiple tasks are scheduled, and the final ordering may be different from the initial heuristic ordering.

Drone carrier

Embodiments of the present invention provide a method for detecting a temporal change of name associated with performance data. The method comprises receiving at least one candidate name replacement pair comprising a pair of names. The method further comprises, in a training stage, for each known name replacement pair included in the performance data, determining a window of time covering a most recent appearance of a first name of the known name replacement pair. The window of time is determined based on quantitative features of a time series model comprising performance data for the first name and a second name of the known name replacement pair. The method further comprises, in the training stage, training a machine learning classifier based on quantitative features computed using a portion of the performance data for the first name and the second name, where the portion is within the window of time determined.

Quantitative discovery of name changes

A computer-implemented method, according to one embodiment, includes: setting a target power demand corresponding to a consumer, and performing a process. The process includes: determining an actual power demand presented to a utility by the consumer, and determining a current error. The current error is the difference between the actual power demand and the target power demand. A determination is also made as to whether the actual power demand is adjustable in a direction that reduces the current error. In response to determining that the actual power demand is adjustable in the direction that reduces the current error, the current error is reduced by adjusting the actual power demand. Moreover, in response to determining that the actual power demand is not adjustable in the direction that reduces the current error, the target power demand is modified.

Computer-implemented method, system, and computer program product for setting a target power demand corresponding to a consumer

Embodiments of the present invention provide a system, method and computer program product for forecasting a time series based on actuals and a plan. A method comprises generating a first and a second sequence of segments that rise and fall alternately based on a first and a second sequence of data values over time, respectively. The first and second sequence of data values represent actual data for an ongoing project and estimated data for a planned project, respectively. The method further comprises aligning at least one segment of the second sequence of segments with at least one segment of the first sequence of segments, and adjusting the estimated data by replacing at least one segment of the second sequence of segments with a scaled version of at least one corresponding aligned segment of the first sequence of segments. The scaled version maintains an estimated total for the planned project.

Forecasting a time series based on actuals and a plan

One embodiment provides a method comprising maintaining a multi-dimensional data structure partitioned into cells utilizing a tree data structure ('tree') comprising intervals for each dimension of a multi-dimensional space. To partition an interval for a node of the tree into multiple subintervals, multiple leaf nodes ('leaves') are generated, each leaf descending from the node. To merge multiple intervals for multiple nodes of the tree, a parent node (&#x201c;parent&#x201d;) and multiple leaves descending from the parent are generated, the parent and the leaves are time constrained, and the leaves are scheduled for a merger. When transient data in cells included in a list that corresponds to a leaf scheduled for merger expires, each cell in the list is converted into a cell for inclusion in a different list corresponding to a parent of the leaf, each leaf of the parent removed, and the parent turned into a leaf.

Drone management data structure

A computer-implemented method according to one embodiment includes creating a profile for a user, the profile including one or more default aspects created automatically by a system and one or more custom aspects created in response to textual input by the user, comparing event data to the profile, and providing a recommendation to the user, based on the comparing.

Providing recommendations utilizing a user profile

One embodiment provides a method for predicting revenue change in a ledger including receiving, by a processor device, revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A statistical classification model is trained to predict the revenue change. A set of recent histories is converted into a quantitative health value.

Predicting ledger revenue change behavior of clients receiving services

One embodiment provides optimizing potential revenue savings when predicting client revenue change including receiving revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A weight is assigned to each data point proportional to value of revenue. A model is trained to optimize a weighted linear combination of losses over each data point. A set of recent histories is converted into a quantitative health value.

Optimizing revenue savings for actionable predictions of revenue change

One embodiment provides a computer program product for cognitive visual and ontological mapping of tabular data. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to receive a data query and ontology information relating to the data query, receive context information relating to a display device, determine a measure of interest for the data query and one or more constraints for the measure of interest, determine saliency features based on the context information, and generate a user interface comprising query results corresponding to the data query arranged in a dynamic tabular format for display on the display device. The dynamic tabular format is based on the ontology information, the measure of interest, the one or more constraints for the measure of interest, and the saliency features.

Cognitive visual and ontological mapping of tabular data based on a distribution of a measure of interest

One embodiment provides a method comprising maintaining a weather model based on predicted weather conditions for an air traffic control zone. A hash table comprising multiple hash entries is maintained. Each hash entry comprises a timestamped predicted weather condition for a cell in the zone. A flight plan request for a drone is received. The request comprises a planned flight path for the drone. For at least one cell on the planned flight path, same latitude or same longitude cells, whichever is most closely orthogonal to a direction of the planned flight path, are heuristically probed. Weather conditions for the at least one cell are estimated based on predicted weather conditions for the same latitude or same longitude cells. An executable flight plan is generated if the planned flight path is feasible based on the estimated weather conditions; otherwise, a report including an explanation of infeasibility is generated instead.

Growing community of inventors

San Jose, CA, United States of America

Hovey R Strong, Jr