International Business Machines Corporation

Sujoy Roy

Radha Mohan De

James William Murdock, Iv

Benjamin P Segal

John Martin Prager

James W Murdock, Iv

Mary Swift

Christopher Munjal Nolan

Suman Patra

Somnath Das

Nisarga Markandaiah

Rajib Das

Indrajit Kanjilal

Shivalik Chakravarty

Krishnendu Chowdhury

Jaymin Desai

Priyankar Malakar

A method includes preparing key actions in a process design document (PDD) from historical PDD documents by tokenizing the PDD with part-of-speech (POS) grammar against key verbs and phrases, extracting automation unit pairs from the historical process documents to map the automation unit pairs with the key verbs and phrases, constructing a set of automation tasks against one or more of the key verbs and phrases, coupling different process models with domain verbs for the phrases to mark one or more of the automation unit pairs as actions against one or more of the key verbs, constructing a tree-like mapping where each key action points to different actions in a target specific automation language, and defining a vocabulary interface of a domain specific language (DSL) by using the key actions and phrases.

RPA development acceleration using DSL validator

Techniques are described with respect to a system, method, and computer product for generating relevance alerts. An associated method includes analyzing a multi-party discussion based on a generated profile associated with a user and assigning at least one relevance value associated with the user to the multi-party discussion based on the analysis and an amount of multi-party discussion participation associated with the user. The method further includes generating an alert for the user to participate in the multi-party discussion in response to determining the relevance value exceeding a relevance threshold associated with the multi-party discussion.

Generating alerts on demand for participants in multiparty discussion channels

Provided is a method, computer program product, and system for mitigating user dissatisfaction with a product. A processor may collect a first set of user interaction data and a first set of user sentiment data related to the product. The processor may generate a user profile for the user, including a satisfaction threshold for using the product based in part on the first set of user interaction data and the first set of user sentiment data. The processor may monitor a second set of user interaction data and a second set of user sentiment data. The processor may compare the second set of user interaction data and the second set of user sentiment data to the satisfaction threshold and determine that the user is experiencing dissatisfaction with the product when the satisfaction threshold has been exceeded. In response, the processor may output an action to reduce dissatisfaction of the user.

Mitigating user dissatisfaction related to a product

A method for receiving a historical incident data set with the historical incident data set including a plurality of data records, for each given data record of the plurality of data records, applying a causal analysis algorithm to determine a set of causal factor(s) for the historical instance of an incident corresponding to the given data record to obtain a problems and solutions data set, and automatically, and by machine logic, generating a chatbot based, at least in part, on the problems and solutions data set.

Automatic chatbot generation through causal analysis of historical incidents

An illustrative resolution is generated using a computer for a reported operational issue while using a computer system. A learning model is developed using the computer to facilitate resolving an operational error of a computer system. The learning model can include generating an error report, based on tracking user inputs into a computer system and a plurality of state transitions of the computer system, respectively, when the operational error is not resolved by the user inputs. An error screenshot for each operational step associated with the user inputs is generated. A state transition is cross referenced to a corresponding operational step, and to a best practice in a user's manual, respectively. A deviation of an operational step from a best practice in the user's manual, respectively, is generated. An error screenshot is marked illustratively to show the deviation from the best practice in comparison to the operational step.

Computer generation of illustrative resolutions for reported operational issues

A method for determining an application error from a screenshot includes receiving, by a computing device, a computer application screenshot image indicating a computer error has occurred. The computing device analyzes the computer application screenshot image using a machine learning image analysis to determine one or more error regions in the computer application screenshot image. The computing device further processes the analyzed computer application screenshot to extract text from the one or more error regions in the computer application screenshot image. The computing device determines the application error based upon the extracted text. The computing device further automatically applies an automated error fix based upon the determined application error.

Application error analysis from screenshot

A method modifies a social media post in response to determining that a hashtag for the social media post diverges from how the hashtag is used by other social media posts. A computing device receives a social media post, which contains a hashtag, and analyzes the social media post using natural language processing and a sentiment analysis to determine a sense and sentiment of the social media post. The computing device receives a plurality of reference social media posts, where each of the reference social media posts contains the hashtag, and utilizes the natural language processing and the sentiment analysis to determine a sense and sentiment of the reference social media posts. In response to determining that the sense and sentiment of the social media post do not match the sense and sentiment of the reference social media posts, the computing device causes the social media post to be altered.

Protecting a social media post with a hashtag from divergence

Embodiments may provide a computer implemented method in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to cause the processor to implement a system for bookmarking, the method comprising: receiving, by the processor, at least one search query from a user; returning, by the processor, a set of search results and a search score for each search result to the user; recording, by the processor, behavior of the user with respect to the search results; selecting, by the processor, at least one search result based on recorded behavior and the search score; determining, by the processor, at least one topic for the at least one selected search result; and generating, by the processor, a bookmark for each selected search result, under the at least one topic.

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Kolkata, India

Sujoy Roy