RPA DEVELOPMENT ACCELERATION USING DSL VALIDATOR

    公开(公告)号:US20240427563A1

    公开(公告)日:2024-12-26

    申请号:US18341275

    申请日:2023-06-26

    Abstract: 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.

    Intelligent bookmarking with cognitive capability

    公开(公告)号:US10747839B2

    公开(公告)日:2020-08-18

    申请号:US15973044

    申请日:2018-05-07

    Abstract: 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.

    COMPUTER GENERATION OF ILLUSTRATIVE RESOLUTIONS FOR REPORTED OPERATIONAL ISSUES

    公开(公告)号:US20220318125A1

    公开(公告)日:2022-10-06

    申请号:US17301298

    申请日:2021-03-31

    Abstract: 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

    公开(公告)号:US11681603B2

    公开(公告)日:2023-06-20

    申请号:US17301298

    申请日:2021-03-31

    CPC classification number: G06F11/3476 G06F11/3438 G06F11/3612 G06N20/00

    Abstract: 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.

    Protecting a social media post with a hashtag from divergence

    公开(公告)号:US11188613B2

    公开(公告)日:2021-11-30

    申请号:US16701953

    申请日:2019-12-03

    Abstract: 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.

    MITIGATING USER DISSATISFACTION RELATED TO A PRODUCT

    公开(公告)号:US20210241289A1

    公开(公告)日:2021-08-05

    申请号:US16782420

    申请日:2020-02-05

    Abstract: 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.

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