International Business Machines Corporation

Salil Ahuja

Scott Harlan Isensee

Rama Kalyani T Akkiraju

William Graham O'Keeffe

Anbang Xu

Cale R Vardy

David Amid

Ateret Anaby-Tavor

Anil Manohar Omanwar

Scott M Lewis

Mitchell Mason

Shirley M Han

Rama K T Akkiraju

Deepak Sekar

Jason M Leonard

Drew Johnson

Rotem Aharonov

Gandhi Sivakumar

Xiaotong Liu

Kiran Challapalli

Debojyoti Mookerjee

An approach is disclosed that retrieves a set of current system data corresponding to a computer system and a set of current outputs from an anomaly detection model that is monitoring the computer system. The current system data and the anomaly detection model outputs are input to a trained anomaly detection supervisor model. The trained anomaly detection supervisor model processes the inputs and provides a set of performance data corresponding to the anomaly detection model. The anomaly detection model is then adjusted when the set of performance data indicates that the anomaly detection model is performing below a threshold.

Performance monitoring in the anomaly detection domain for the IT environment

A system, method, and computer program product are disclosed. The method includes receiving a first text unit, extracting features from the first text unit, receiving a second text unit, extracting features from the second text unit, receiving a portion comprising the first text unit and the second text unit, and aggregating the features extracted from the first text unit and the features extracted from the second text unit. The method also includes generating a set of scores for the first text unit, the second text unit, and the portion, and based on the set of scores, selecting at least one ground truth candidate from the first text unit, the second text unit, and the portion. Additionally, the method includes determining that the at least one ground truth candidate includes at least one confirmed ground truth, and adding the at least one confirmed ground truth to a ground truth repository.

Automatic ground truth selection

A tool for automatically generating incident management process efficiency metrics utilizing real-time communication analysis. The tool retrieves real-time conversation data from one or more communication sources, wherein the real-time conversation data includes one or more messages having data related to an information technology (IT) incident. The tool performs conversation analysis on the one or more messages. The tool determines one or more timestamps of interest for the IT incident from the one or more messages. The tool generates one or more incident management process efficiency metrics for the IT incident utilizing the one or more timestamps of interest. The tool predicts based, at least in part, on historical conversation data, an outcome for the IT incident. The tool sends the one or more incident management process efficiency metrics and the outcome for the IT incident to a user in a notification.

Measuring incident management process efficiency metrics utilizing real-time conversation analysis

A multidimensional system for generating a multimedia search engine is provided. A computer device identifies a plurality of independently separable aspects of a multimedia file. The computing device provides at least one independently separable aspect of the plurality of independently separable aspects as input into an object detection model. The computing device receives, from the object detection model, an identification of at least one object and a corresponding level of confidence that the object is present in the multimedia file. The computing device classifies the object as either confident or not confident, based on whether the level of confidence meets a threshold level of confidence. The computing device generates a multimedia search engine based, at least in part, on the object and the classification.

System for object identification

An intermediary server receives a resource request from a client computer for a requested resource that has a desired resource ability to optimize the client computer. The intermediary server identifies an alternative resource that has the desired resource ability to optimize the client computer, and determines that an ability of the alternative resource to provide the desired resource ability exceeds an ability of the requested resource to provide the desired resource ability. In response to determining that the ability of the alternative resource to provide the desired resource ability exceeds the ability of the requested resource to provide the desired resource ability, the intermediary server instructs a resource server to send the alternative resource instead of the requested resource to the client computer.

Providing an optimal resource to a client computer via interactive dialog

A first user communication request by a first user is detected. The first user communication request is directed to a computer response system. In response to detecting the first user communication request a profile associated with the first user is determined. A first set of computing resources of the computer response system is allocated to a first automated respondent. The allocation is based on the determined profile. A communication between the first user and the first automated responded is assigned based on the determined profile.

Decision based resource allocation in response systems

An intermediary server receives a resource request for a requested resource from a client computer. The intermediary server responds to the resource request by initiating an interactive dialog between the intermediary server and the client computer, where the interactive dialog identifies a desired resource ability of the requested resource. The intermediary server identifies, based on responses to the interactive dialog from the client computer, an alternative resource that best meets the desired resource ability, wherein the requested resource and the alternative resource are a same type of resource, and wherein the interactive dialog uses an interactive exchange that interprets interactive dialog responses from the client computer in order to identify the desired resource ability and the alternative resource that best meets the desired resource ability and then instructs a resource server to send the alternative resource to the client computer.

A method for providing content relevant images for an input question to a deep question answering system is disclosed. The method can include formulating, in response to receiving the input question, an answer to the input question. The method can also include identifying, based on the answer to the input question, a set of candidate images from a corpus of information. The set of candidate images may relate to the answer. The method can also include determining, based on a confidence value of the set of candidate images and a designated display specification, a first subset of the set of candidate images to provide in a display area. The method can also include selecting the first subset to be provided in the display area.

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Austin, TX, United States of America

Salil Ahuja