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公开(公告)号：US20200159836A1
公开(公告)日：2020-05-21
申请号：US16193612
申请日：2018-11-16
发明人： Oznur ALKAN , Adi BOTEA , Elizabeth DALY , Akihiro KISHIMOTO , Radu MARINESCU
摘要： Various embodiments are provided for intelligent resolution of conflicting information in a dialog system in a computing environment by a processor. Conflicting information relating to one or more queries may be detected between one or more users using the dialog system. One or more responses may be provided to resolve the conflicting information according to a knowledge domain.
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公开(公告)号：US20200012954A1
公开(公告)日：2020-01-09
申请号：US16028136
申请日：2018-07-05
发明人： Adi I. BOTEA , Oznur ALKAN , Elizabeth DALY , Matthew DAVIS , Akihiro KISHIMOTO , Vera LIAO , Radu MARINESCU , Biplav SRIVASTAVA , Kartik TALAMADUPULA , Yunfeng ZHANG
摘要： Various embodiments are provided for integrating multiple domain learning and personalization in a dialog system for a user in a computing environment by a processor. One or more problem instances may be defined for multiple domains according to a problem instance template, identified user intent, links to one or more problem solvers associated with the multiple domains, or a combination thereof. A dialog plan may be determined to further define the one or more problem instances in response to user input. A solution may be provided to the user for the one or more problem instances.
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公开(公告)号：US20230259800A1
公开(公告)日：2023-08-17
申请号：US17651213
申请日：2022-02-15
发明人： Oznur ALKAN , Rahul NAIR , Fearghal O'DONNCHA , Ambrish RAWAT
摘要： Embodiments for providing enhanced generative models based assistance for design and creativity in a computing environment by a processor. A partially completed design of an object may be received. A set of recommendations may be generated for completing the partially completed design based on one or more generative models.
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公开(公告)号：US20210110394A1
公开(公告)日：2021-04-15
申请号：US16601445
申请日：2019-10-14
发明人： Bei CHEN , Adi BOTEA , Elizabeth DALY , Oznur ALKAN , Inge VEJSBJERG , Massimiliano MATTETTI
摘要： Embodiments for implementing intelligent automation of opportunity transaction workflows by a processor. One or more tasks identified in an existing transaction opportunity workflow suitable for automation may be automated in a current transaction opportunity workflow. The automated tasks may be scheduled and executed in the current transaction opportunity workflow. The automated tasks in the current transaction opportunity workflow may be monitored.
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公开(公告)号：US20200219495A1
公开(公告)日：2020-07-09
申请号：US16239262
申请日：2019-01-03
发明人： Oznur ALKAN , Adi I. BOTEA , Elizabeth DALY , Matthew DAVIS , Christian MUISE
摘要： Various embodiments are provided for understanding user sentiment in a dialog system in a computing environment by a processor. A sentiment of a user may be detected according to a sentiment analysis and user feedback during a dialog with the user. One or more reasons for the sentiment of the user may be identified. Behavior of the dialog system may be adjusted according to the one or more reasons.
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公开(公告)号：US20230316359A1
公开(公告)日：2023-10-05
申请号：US17657047
申请日：2022-03-29
发明人： Rahul NAIR , Oznur ALKAN , Fearghal O'DONNCHA , Ambrish RAWAT
CPC分类号： G06Q30/0611 , G06Q30/0206 , G06Q30/0619 , G06N20/00
摘要： Intelligent classification for product pedigree identification are presented. A transaction agreement request may be received from a user. A revised transaction agreement request may be generated based on one or more user profiles, a multi-party entity feedback loop, one or more constraints relating to the transaction agreement request, and a transaction agreement fulfillment requirements of the entity.
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公开(公告)号：US20210117506A1
公开(公告)日：2021-04-22
申请号：US16659216
申请日：2019-10-21
发明人： Adi BOTEA , Oznur ALKAN , Elizabeth DALY , Massimiliano MATTETTI , Pablo PEDEMONTE , Abel Nicolas VALENTE , Inge VEJSBJERG
摘要： Various embodiments are provided for providing intelligent dialog re-elicitation in a dialog system in a computing environment by a processor. Information, provided during a dialog using the dialog system, may be detected that has been subsequently revised. One or more variables impacted by the revised information provided during the dialog may be dynamically re-elicited.
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公开(公告)号：US20210065019A1
公开(公告)日：2021-03-04
申请号：US16554233
申请日：2019-08-28
发明人： Oznur ALKAN , Adi BOTEA , Akihiro KISHIMOTO , Radu MARINESCU , Biplav SRIVASTAVA
摘要： Various embodiments are provided for applying judgment reasoning knowledge in a dialog system in a computing environment by a processor. A determination is made that a response to a query during a dialog using the dialog system fails to comply with one or more expected response patterns to one of a plurality of query responses. An updated response may be provided to the query using judgment reasoning knowledge for matching the updated response with the one or more expected response patterns.
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公开(公告)号：US20190073601A1
公开(公告)日：2019-03-07
申请号：US15693765
申请日：2017-09-01
发明人： Oznur ALKAN , Adi I. BOTEA , Akihiro KISHIMOTO , Radu MARINESCU
摘要： Embodiments for recommending meals by a processor. A collaboration of data capturing a plurality of factors of a group user profile for each user in a group of users may be received for aiding in recommending one or more meals. The one or more meals may be recommended for the group of users according to the group user profile such that the recommending balances a satisfaction level for the one or more meals for the group of users.
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公开(公告)号：US20230177388A1
公开(公告)日：2023-06-08
申请号：US17643333
申请日：2021-12-08
发明人： Owen CORNEC , Oznur ALKAN , Rahul NAIR , Elizabeth DALY
摘要： Embodiments are provided for enabling visual editing of machine learning models in a computing environment by a processor. A multidimensional dataset may be received. The multidimensional dataset may be processed. Visualization and exploration of an interactive representation of a plurality of datasets and decision boundaries of one or more machine learning models built upon multidimensional dataset are provided. Behavior of the one or more machine learning models may be edited via the interactive representation using one or more logical rules or moving the decision boundaries of one or more machine learning models.
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