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公开(公告)号：US11544762B2
公开(公告)日：2023-01-03
申请号：US16773727
申请日：2020-01-27
Applicant: salesforce.com, inc.
Inventor： Yixin Mao , Sitaram Asur , Na Cheng , Gary Brandeleer , Kavya Murali , Nicholas Beng Tek Geh
Abstract: A system and related processing methodologies for recommending a product based on a work order are described. The system receives an input case description, including a current repair item and a current work type. Historical work orders associating a plurality of products with repair items and work types are searched for a co-occurrence of the repair item matching the current repair item, and the work type matching the current work type. Upon finding a match, the product associated with the match is added to a set of candidate products for the current work order. A similarity measure between the candidate product and current work order description, a current work type category, and popularity of the candidate product is generated and then used in the generation of a probability score for the candidate product and current work order. If the probability score meets a threshold, the candidate product is recommended.
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公开(公告)号：US20210150610A1
公开(公告)日：2021-05-20
申请号：US16773727
申请日：2020-01-27
Applicant: salesforce.com, inc.
Inventor： Yixin Mao , Sitaram Asur , Na Cheng , Gary Brandeleer , Kavya Murali , Nicholas Beng Tek Geh
Abstract: A system and related processing methodologies for recommending a product based on a work order are described. The system receives an input case description, including a current repair item and a current work type. Historical work orders associating a plurality of products with repair items and work types are searched for a co-occurrence of the repair item matching the current repair item, and the work type matching the current work type. Upon finding a match, the product associated with the match is added to a set of candidate products for the current work order. A similarity measure between the candidate product and current work order description, a current work type category, and popularity of the candidate product is generated and then used in the generation of a probability score for the candidate product and current work order. If the probability score meets a threshold, the candidate product is recommended.
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公开(公告)号：US11790894B2
公开(公告)日：2023-10-17
申请号：US17202077
申请日：2021-03-15
Applicant: salesforce.com, inc.
Inventor： Yixin Mao , Zachary Alexander , Victor Winslow Yee , Joseph R. Zeimen , Na Cheng , Chien-Sheng Wu , Wenhao Liu , Caiming Xiong
CPC classification number: G10L15/16 , G10L15/063 , G10L15/08 , G10L15/22 , H04L51/02 , G06F16/3344 , G06F40/56
Abstract: A system uses conversation engines to process natural language requests and conduct automatic conversations with users. The system generates responses to users in an online conversation. The system ranks generated user responses for the online conversation. The system generates a context vector based on a sequence of utterances of the conversation and generates response vectors for generated user responses. The system ranks the user responses based on a comparison of the context vectors and user response vectors. The system uses a machine learning based model that uses a pretrained neural network that supports multiple languages. The system determines a context of an utterance based on utterances in the conversation. The system generates responses and ranks them based on the context. The ranked responses are used to respond to the user.
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公开(公告)号：US20220293094A1
公开(公告)日：2022-09-15
申请号：US17202077
申请日：2021-03-15
Applicant: salesforce.com, inc.
Inventor： Yixin Mao , Zachary Alexander , Victor Winslow Yee , Joseph R. Zeimen , Na Cheng , Chien-Sheng Wu , Wenhao Liu , Caiming Xiong
Abstract: A system uses conversation engines to process natural language requests and conduct automatic conversations with users. The system generates responses to users in an online conversation. The system ranks generated user responses for the online conversation. The system generates a context vector based on a sequence of utterances of the conversation and generates response vectors for generated user responses. The system ranks the user responses based on a comparison of the context vectors and user response vectors. The system uses a machine learning based model that uses a pretrained neural network that supports multiple languages. The system determines a context of an utterance based on utterances in the conversation. The system generates responses and ranks them based on the context. The ranked responses are used to respond to the user.
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