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公开(公告)号：US11386463B2
公开(公告)日：2022-07-12
申请号：US16866798
申请日：2020-05-05
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Sanjeev Misra , Appavu Siva Prakasam , Ann Eileen Skudlark , Siva Kolachina , Nisha Shahul Hameed , Prashanth Boddhireddy , Lien Tran , Jenq-Chyuan Wang
Abstract: Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.
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公开(公告)号：US20220277351A1
公开(公告)日：2022-09-01
申请号：US17749347
申请日：2022-05-20
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Sanjeev Misra , Appavu Siva Prakasam , Ann Eileen Skudlark , Siva Kolachina , Nisha Shahul Hameed , Prashanth Boddhireddy , Lien Tran , Jenq-Chyuan Wang
Abstract: Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.
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公开(公告)号：US20210182912A1
公开(公告)日：2021-06-17
申请号：US16866798
申请日：2020-05-05
Applicant: AT&T Intellectual Property I, L.P. , Xandr Inc.
Inventor： Sanjeev Misra , Appavu Siva Prakasam , Ann Eileen Skudlark , Siva Kolachina , Nisha Shahul Hameed , Prashanth Boddhireddy , Lien Tran , Jenq-Chyuan Wang
Abstract: Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.
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公开(公告)号：US20220172219A1
公开(公告)日：2022-06-02
申请号：US17106934
申请日：2020-11-30
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Wen-Ling Hsu , Guang-Qin Ma , Guy Jacobson , Jenq-Chyuan Wang , Tan Xu , Kevin McDorman , Brian Economaki , Shuai Zhao
Abstract: A method, computer-readable medium, and apparatus for providing customer care for customers are disclosed. Customer care may be provided for customers by obtaining customer care contact information for a plurality of customers where the customer care contact information includes, for each of a plurality of customers, respective customer care contact data that is based on a sequence of customer care contacts by the customer with one or more customer care agents, determining customer care contact embedding information for the plurality of customers, clustering the customer care contact embedding information for the plurality of customers to form customer care contact clusters, determining customer care contact cluster characterization information for the customer care contact clusters, selecting, from the plurality of customers based on the customer care contact cluster characterization information for the customer care contact clusters, a set of customers, and initiating a customer care action for the set of customers.
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