International Business Machines Corporation

Jia Liu

Jennifer A Mallette

Steven Ware Jones

Vivek Salve

Shuai Wang

Ruchi Asthana

Jacob Lewis

Srikanth Govindaraj Tamilselvam

James Randal Moulic

Anuradha Bhamidipaty

Daniela Rosu

Mithkal M Smadi

Aliza Rivka Heching

Anshu Narendra Jain

Winnie Wing-Yee Cheng

Hyman David Chantz

Dean Steuer

Zhiyuan Li

A device, system, and a computer-implemented method a for identifying an anomaly in an operation of a device includes comparing, by an electrical power analyzer, a current power draw signature of the device with a known power draw signature of the device. There is a determining as to whether at least one anomaly is present in the current power draw signature. A warning is generated in response to determining the at least one anomaly is present in the current power draw signature.

Electrical power analyzer for large and small scale devices for environmental and ecological optimization

A method, system, and computer program product for routing queries to answer resources based on component parts and intents of a received query is provided. The method receives a query from a user. The query is analyzed to identify a set of entities associated with the query and generate an utterance representing the query. The method generates an intent classification for the utterance and a vector for the query. The vector is generated based on the set of entities, the utterance, and the intent classification. The method determines an answer resource for the query based on the vector and the intent classification of the query. In response to determining the answer resource, the method provides an answer interface based on the query, the vector, and the intent classification. The answer interface dynamically provides a response to the query.

Query routing for bot-based query response

A computer-implemented method, a computer system, and a computer program product for automatically generated conversation output is provided. The present invention may include training an answer-intent classifier to associate an intent expressed in an example question with an example answer that is responsive to the example question. The present invention may further include classifying, using the trained answer-intent classifier, a content transmitted to the trained answer-intent classifier with the intent expressed in the example question in response to determining, using the trained answer-intent classifier, that the content includes relevant information for generating the example answer that is responsive to the example question.

Automatically generated conversation output

When a chatbot application is in a mode of live chat conversation with a user, a trained intent classifier determines an intent that underlies a first live question received by the trained intent classifier from the user. A trained next predictor receives the intent from the intent classifier. The trained next intent predictor generates a set of predicted next intents responsive to receiving the intent. A trained re-ranker selects at least one of the predicted next intents responsive to receiving the set of predicted next intents. A question selection engine sends at least one suggested question to the user responsive to receiving the at least one predicted next intent. As a result of the above, the chatbot application provides to the user at least one suggested next question the user may wish to ask in response to the first question from the user.

Intent-based question suggestion engine to advance a transaction conducted via a chatbot

A method, system and computer program product for improving the understanding of chat transcript data. Chat transcripts are analyzed to classify the utterances into intents and identify products discussed in the chat transcripts. The data of the chat transcripts are divided into categories of utterances associated with products and intents by applying tags to the chat transcripts. The categories of utterances associated with products and intents are then clustered into clusters based on sentence similarity. Once the utterances are grouped, a representative utterance is extracted from a cluster, where the representative utterance is an utterance that has the highest semantic similarity to the utterances in the cluster. In this manner, users will be provided a more accurate guide as to the underlying meaning of the chat transcript data thereby improving the understanding of the chat transcript data more efficiently and accurately than current chat transcript analysis tools.

Analyzing chat transcript data by classifying utterances into products, intents and clusters

An approach is presented for identifying related problem tickets in an information technology (IT) environment. A pattern of actions included in interactions with a computer program is determined to be effective at proactively preventing a problem in the IT environment based on a frequency at which user(s) performed the interactions which perform text and statistical analyses of content of historical problem tickets. A script based on the pattern of actions is generated. A root cause of the problem is determined based on the text and statistical analyses. Responsive to a receipt of a new problem ticket, the script is executed to automatically perform the pattern of actions. The new problem ticket is classified as being in a group of problem tickets which are related to the problem and included in the historical problem tickets. The new problem ticket is determined to specify the problem which has the root cause.

Leveraging user-to-tool interactions to automatically analyze defects in IT services delivery

An approach is presented for identifying related problem tickets in an information technology (IT) environment. User interactions with a computer program are stored. The user interactions include inputs to the computer program to search for problem tickets issued in the IT environment that have the same characteristics. One or more user interaction patterns within the user interactions are recognized. A user interaction pattern of the one or more user interaction patterns is selected based on an evaluation of effectiveness of each of the one or more user interaction patterns. Based on the user interaction pattern, a rule is generated for determining which problem tickets in the IT environment share a common characteristic. The rule is applied to additional problem tickets issued in the IT environment to identify which of the additional problem tickets share the common characteristic.

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Astria, NY, United States of America

Jia Liu