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公开(公告)号：US20190266291A1
公开(公告)日：2019-08-29
申请号：US15903882
申请日：2018-02-23
摘要： An information request processor analyzes an information request and automatically selects search queries and information sources that are responsive to the information request. Prior reports and portions of browsing history that were generated during the creation of the prior reports are selected based at least on a primary entity included in the information request. The entities extracted from the prior reports using trained Information Extraction (IE) models are mapped to the search terms extracted from the portions of the browsing history in order to identify the successful search queries that provided the information for the prior reports. A report responsive to the information request can be generated either automatically or by receiving user input that validates and rephrases the successful search queries.
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公开(公告)号：US20220335223A1
公开(公告)日：2022-10-20
申请号：US17232991
申请日：2021-04-16
IPC分类号： G06F40/35 , G06N20/00 , G06F40/242 , G06F40/279 , G06F40/40
摘要： The present disclosure relates to automated chatbot generation for different domains from available human-to-human chat logs. The systems and methods may be configured to cluster user utterances as well as agent utterances from the human chat logs. A data miner mines intents and entities from the user utterance clustering and mines actions from agent utterances. The intents, entities and actions mined are used to generate a set of stories or flows which are further used by a machine learning engine to train the chatbot. The stories or flows are also generated automatically by mapping the intents with the actions.
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公开(公告)号：US20210065045A1
公开(公告)日：2021-03-04
申请号：US16555320
申请日：2019-08-29
发明人： Krishna KUMMAMURU , Bibudh LAHIRI , Guruprasad DASAPPA , Arjun ATREYA V , Alexander Frederick John Piers HALL , Sven RUYTINX , Cyrille WITJAS
IPC分类号： G06N20/00 , G06N5/02 , G06F16/93 , G06F16/903 , G06F16/9038
摘要： An Artificial Intelligence (AI)-based innovation data processing system receives at least one query word related to a category. Information material including textual and non-textual data is retrieved from a plurality of data sources using the at least one query word. The information material is tokenized and parsed using a dependency parser for entity recognition, building entity relationships and for generating knowledge graphs. The output of the dependency parser is accessed by a trained classifier for obtaining respective confidence levels for each of the sentences in the textual data. The confidence levels are compared to a predetermined threshold confidence level for determining if the sentences include references to innovations. In addition, trends in the innovations are determined and responses to user queries are generated based on one or more of knowledge graphs and the trends.
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