Abstract: Disclosed are methods, computer program products, and systems for maximizing renewals of purchase orders. One embodiment of the method may comprise utilizing a classifier machine learning model to identify metrics that are most relevant to whether customers will renew purchase orders, predicting respective risks of non-renewal for the purchase orders using the identified metrics, applying a tone analyzer natural language processing (NLP) model to determine current sentiments for respective customers, and recommending which of the respective customers to pursue with additional resources based the respectively determined sentiments and risks of non-renewal.

Abstract: Text is received from a user describing item(s) for migration to a computing environment with cloud feature(s), resulting in item description(s), the text including unstructured text that are processed separately. Text mining is performed on the unstructured text to extract item feature(s). For each listing a portion of the unstructured text is extracted, resulting in an extracted text portion for each listing from which an entity is identified. Each entity or item feature is mapped to cloud feature(s) available from solution(s) with cloud feature(s). Based on the cloud feature(s), recommendation(s) are made to the user regarding cloud feature(s) of the solution(s) for optional consideration by the user. Explanation(s) for the recommended cloud feature(s) from explainability model(s) may be provided to the user.

Abstract: A computer-implemented method in accordance with one embodiment includes, in response to a submission of an input dataset to an artificially intelligent application, receiving an explanation from each module of the application. The modules are configured within the application in a serial sequence in which each module, upon receiving the input dataset and any input generated by an immediately preceding module of the serial sequence, generates output that is forwarded as input to a next module, if any, in the sequence. A determination is made that at least two of the received explanations are semantically inconsistent.

Abstract: A computer implemented method includes receiving a dataset for use with respect to a current machine learning model, wherein the dataset comprises one or more features, analyzing one or more external datasets to identify a set of similar features, appending the similar features to the received dataset to generate an updated dataset, applying the updated dataset to the current machine learning model to generate an updated machine learning model, and assessing performance of the updated machine learning model. The method may further include categorizing the features of the dataset into categorical text features and unstructured text features. The method may additionally include recommending one or more actions based on the performance assessment of the updated machine learning model. The method may further include converting the one or more features into numerical feature vectors and identifying a vectoral distance between the one or more features and the set of similar features.

Abstract: 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.

Abstract: Systems, methods, and computer programming products for versioning machine learning models. Changes between new and existing datasets are detected, quantified and compared using statistical and semantic feature comparisons. Recommendations for versioning existing models are in response to detecting changes between the feature importance of datasets used in the application of the machine learning model and new datasets that introduce new features or features that evolve over time in such a manner that feature importance has shifted away from one or more features of the first dataset to the new dataset. Based on the changes in feature importance, statistical changes and semantic feature comparisons, the recommendations provided describe whether models should be updated with a re-trained model, or that the existing features of the model do not indicate a need for re-training.

Abstract: A computer implemented method for selecting service providers includes receiving a set of client requirements and analyzing available service providers based on the received set of client requirements. The method additionally includes scoring the available service providers based on the analysis. The method further includes identifying one or more unstructured external data sources corresponding to the available service providers and analyzing the reliability of the one or more unstructured external data sources with respect to the available service providers. The method further includes adjusting the scoring of the service providers based, at least in part, on the data source reliability, and subsequently providing an optimal selection of service providers based on the adjusted scoring. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: 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.

Abstract: A computer-implemented method in accordance with one embodiment includes, in response to a submission of an input dataset to an artificially intelligent application, receiving an explanation from each module of the application. The modules are configured within the application in a serial sequence in which each module, upon receiving the input dataset and any input generated by an immediately preceding module of the serial sequence, generates output that is forwarded as input to a next module, if any, in the sequence. A determination is made that at least two of the received explanations are semantically inconsistent.

Abstract: Systems and methods for generating user-centric and event-sensitive text summaries are described. For example, summaries may be generated based on user selected reading parameters and user workflow. According to some embodiments, a reinforcement learning module is used to modify a change summarization network based on user feedback. For example, a text summary may change in real-time based on changes to the reader or event context. In some cases, user actions and feedback (e.g., a number of edits to a text summary or the editing time taken by a user) are used to improve prediction of future summaries.

Abstract: 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.

Abstract: An explainable artificially intelligent (XAI) application contains an ordered sequence of artificially intelligent software modules. When an input dataset is submitted to the application, each module generates an output dataset and an explanation that represents, as a set of Boolean expressions, reasoning by which each output element was chosen. If any pair of explanations are determined to be semantically inconsistent, and if this determination is confirmed by further determining that an apparent inconsistency was not a correct response to an unexpected characteristic of the input dataset, nonzero inconsistency scores are assigned to inconsistent elements of the pair of explanations.

Abstract: 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.

Abstract: A method for predicting service requests volume includes generating a machine learning model predicting a number of service requests in time series data, based upon a plurality of actually received service requests in the time series data. The method recommends service request features for use in predicting the service requests volume. The method receives a determination from an human-in-the-loop indicating whether the generated machine learning model correctly predicts the number of service requests in time series data, based on the plurality of actually received service requests in the time series data and the recommended service request features. The method selectively updates the machine learning model predicting the number of service requests in times series data, based upon the determination from the human-in-the-loop. The method predicts, using the updated machine learning model, a number of service requests in time series data incoming during a future time period.

Abstract: Embodiments relate to systematic explanation of neural model behavior and effective deduction of its vulnerabilities. Input data is received for the neural model and applied to the model to generate output data. Accuracy of the output data is evaluated with respect to the neural model, and one or more neural model vulnerabilities are identified that correspond to the output data accuracy. An explanation of the output data and the identified one or more vulnerabilities is generated, wherein the explanation serves as an indicator of alignment of the input data with the output data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 Yk using the model, Yk representing probable causes of a plurality of health conditions; and determining likely health conditions for a patient based on comparing the vector Yk to a second vector Zk, Zk representing probable causes of health conditions determined based on a health care record for the patient.

Abstract: 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.