International Business Machines Corporation

Aly Megahed

Samir Tata

Mohamed Mohamed

Taiga Nakamura

Kugamoorthy Gajananan

Peifeng Yin

Shubhi Asthana

Shun Jiang

Mark A Smith

Guangjie Ren

Hovey R Strong, Jr

Mari Abe Fukuda

Hamid Reza Motahari Nezhad

Valeria Becker

Jeanette L Blomberg

Lei Huang

Eric Young Liu

Abhinav Maurya

Eric Kevin Butler

Robert Engel

Yuya Jeremy Ong

Bing Zhang

Nitin Ramchandani

Alaa Elwany

Michael K Firth

Ramani Ranjan Routray

Pawan Raghunath Chowdhary

Mark Andrew Smith

Raphael I Arar

Hovey Ramond Strong, Jr

Ahmed El Harouni

Ilyas Mohamed Iyoob

German H Flores

Krishna Teja Rekapalli

Hogun Park

Michael E Rose

Mohammed Saeed Abuelmakarm Shafae

Brian D Yost

Rohit Avinash Mujumdar

Ali Balma

Nejib Ben Hadj-Alouane

Hana Teyeb

A method of using a computing device to compare performance of multiple algorithms. The method includes receiving, by a computing device, multiple algorithms to assess. The computing device further receives a total amount of resources to allocate to the multiple algorithms. The computing device additionally assigns a fair share of the total amount of resources to each of the multiple algorithms. The computing device still further executes each of the multiple algorithms using the assigned fair share of the total amount of resources. The computing device additionally compares the performance of each of the multiple based on at least one of multiple hardware relative utility metrics describing a hardware relative utility of any given resource allocation for each of the multiple algorithms.

Fair simultaneous comparison of parallel machine learning models

Embodiments are provided for ticket-agent matching and agent skillset development. In some embodiments, a system includes a processor that executes computer-executable components stored in memory. The computer-executable components can include a matching component that determines, using a ticket profile and a space of agent profiles, a ticket-agent pair including a ticket identifier of a service request and an agent identifier of a particular agent within a pool of agents. The computer-executable components also can include a rematching component that assigns a second agent identifier to the service request to develop a skillset of a second particular agent within the pool of agents, the second agent identifier being associated with an unsatisfactory skill score for a defined skill to resolve the service request.

Ticket-agent matching and agent skillset development

A computer-implemented method, system and computer program product for generating a user interface. A sketch (e.g., wireframe) of a portion of a user interface is received. The sketch is analyzed to predict a set of intended sketches using artificial intelligence based on historical data and/or the user's asset library. A set of intended final sketch renderings of the user interface is then generated and displayed using the set of predicted intended sketches based on historical data or a model trained to extract visual characteristics from existing user interface screens. If the user selects one of the intended final sketch renderings of the user interface as being directed to the intended design of the user interface and indicates that the selected intended final sketch rendering of the user interface corresponds to the final intended design, then code is generated to render the selected final sketch rendering of the user interface.

Using artificial intelligence to iteratively design a user interface through progressive feedback

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

Computer systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set. PII replacement is mapped consistently across entire data packages and the crowdsourced pattern library is updated with generated regular expressions.

Cognitive data pseudonymization

One embodiment provides a method comprising receiving training data and experts' intuition, training a machine learning model based on the training data, predicting a class label for a new data input based on the machine learning model, estimating a degree of similarity of a target attribute of the new data input relative to the training data, and selectively applying a correction to the class label for the new data input based on the degree of similarity prior to providing the class label as an output. The target attribute is an attribute related to the experts' intuition.

Resolving conflicts between experts' intuition and data-driven artificial intelligence models

A method, a computer program product, and a system for predicting low-frequency sensor signal predictions using a hierarchical prediction model. The method includes receiving a historical dataset of high-frequency sensor signal data and low-frequency sensor signal data. The method also includes generating a Gaussian process regression model using the historical dataset and sensor parameters and outputting high-frequency sensor signal predictions. The method also includes generating a hierarchical Gaussian process model using the historical dataset and the high-frequency sensor signal predictions and predicting low-frequency sensor signal predictions.

Sensor signal prediction at unreported time periods

One embodiment provides a method comprising receiving data relating to a tenant utilizing a cloud computing environment, and determining one or more classifications for a variation in current workload resource consumption of the tenant based on the data. The current workload resource consumption is indicative of current usage of one or more computing resources of the cloud computing environment.

Cognitive classification of workload behaviors in multi-tenant cloud computing environments

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

A system for cognitive prioritization for report generation may include a processor and a memory cooperating therewith. The processor may be configured to accept a request for a new report from a user, the request having a user profile importance associated therewith and generate a predicted completion time for the new report based upon a historical completion time prediction model based upon historical data for prior reports. The processor may be configured to generate a predicted importance of the new report based upon a historical importance prediction model based upon the historical data for prior reports and determine a combined predicted importance based upon the user profile importance and the predicted importance. The processor may also be configured to generate a prioritization of the new report among other reports based upon the predicted completion time and the combined predicted importance and generate the new report based upon the prioritization.

Cognitive prioritization for report generation

Techniques for augmenting data visualizations based on user interactions to enhance user experience are provided. In one aspect, a method for providing real-time recommendations to a user includes: capturing user interactions with a data visualization, wherein the user interactions include images captured as the user interacts with the data visualization; building stacks of the user interactions, wherein the stacks of the user interactions are built from sequences of the user interactions captured over time; generating embeddings for the stacks of the user interactions; finding clusters of embeddings having similar properties; and making the real-time recommendations to the user based on the clusters of embeddings having the similar properties.

Interaction-based visualization to augment user experience

A computer-implemented method according to one embodiment includes identifying a plurality of policies to be implemented within a system, aggregating the plurality of policies to create an aggregated policy, disseminating the aggregated policy within the system, receiving data collected according to the aggregated policy, and disaggregating the data.

Systemic adaptive data management in an internet of things environment

One embodiment provides a method for augmenting missing values in historical or market data for deals. The method comprises receiving information relating to a set of deals. For any service included in one or more deals of the set of deals but not included in one or more other deals of the set of deals, the method further comprises augmenting, for any or all of the one or more other deals that does not include the service, one or more missing values for the service with one or more recommended values based on a recommendation algorithm. The service may be at any service level of a hierarchy of services.

Augmenting missing values in historical or market data for deals

A method and system of evaluating a user experience (UX) design are provided. A UX design is received. All objects that are identified to be part of a background of the input UI screen are removed to create a filtered input UI screen. The input UI screen is assigned to a cluster. A target UI screen of the input screen is determined and its background removed, to create a filtered target UI cluster. The target UI screen is assigned to a cluster. The filtered input UI screen is used as an input to a deep learning model to predict a target UI cluster. The predicted target UI cluster is compared to the filtered target UI cluster based on the clustering. Upon determining that the filtered target UI cluster is similar to the target UI screen, the UX design is classified as being successful.

AI-assisted UX design evaluation

One embodiment provides a method for estimating unit price reduction of services in a new in-flight deal using data of historical deals and market reference deals cost structures. The method includes receiving a detailed cost structure for historical information, market deals information, services quantity information and deals metadata for a first year. For each service: peer deals to the in-flight deal are selected based on the detailed cost structure; missing cost data values in the peer deals are augmented; unit cost reduction values for the peer deals estimated; the unit cost reduction for the in-flight deal from each year in total contract years to a next year without a last contract year are estimated; and a total cost for the in-flight deal for all years in the total contract years beyond the first year are estimated.

Growing community of inventors

San Jose, CA, United States of America

Aly Megahed