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公开(公告)号：US20220038577A1
公开(公告)日：2022-02-03
申请号：US17213335
申请日：2021-03-26
申请人： Five9, Inc.
发明人： Jonathan Rosenberg
摘要： A method for creating a textual summary of a call includes transcribing speech to text in real time using a speech-to-text generating unit configured for execution upon one or more data processors, automatically matching, in real-time, text to predetermined intents and extracted entities using an intent recognizing unit for execution upon the one or more data processors, automatically mapping the predetermined intents and extracted entities into a call summary using one or more mapping functions, and displaying the call summary using an agent user interface for execution upon the one or more data processors. A contact center call summarization system may include a contact center communication device, a speech-to-text generating unit, an intent recognizing unit, and an agent user interface.
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2.发明申请公开(公告)号：US20210006662A1
公开(公告)日：2021-01-07
申请号：US16917968
申请日：2020-07-01
申请人： Five9, Inc.
发明人： Jonathan Rosenberg
摘要： Systems and methods are described herein for providing a Voice over Internet Protocol (VoW) call. In an embodiment, a load balancing processor receives a re-initiated HTTP request from a client processor upon detection that an initial call server is no longer active, and sends the re-initiated HTTP request to a second call server. The second server generates updated call resource information that identifies the second server as the new server resource for the call, and sends the updated call resource information over the IP network to the client processor. Subsequent HTTP requests from the client processor for sending and receiving signaling and media data for the call are received at the second server using the updated call resource information.
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公开(公告)号：US11228681B1
公开(公告)日：2022-01-18
申请号：US17213335
申请日：2021-03-26
申请人： Five9, Inc.
发明人： Jonathan Rosenberg
摘要： A method for creating a textual summary of a call includes transcribing speech to text in real time using a speech-to-text generating unit configured for execution upon one or more data processors, automatically matching, in real-time, text to predetermined intents and extracted entities using an intent recognizing unit for execution upon the one or more data processors, automatically mapping the predetermined intents and extracted entities into a call summary using one or more mapping functions, and displaying the call summary using an agent user interface for execution upon the one or more data processors. A contact center call summarization system may include a contact center communication device, a speech-to-text generating unit, an intent recognizing unit, and an agent user interface.
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4.发明授权公开(公告)号：US11172069B2
公开(公告)日：2021-11-09
申请号：US16917968
申请日：2020-07-01
申请人： Five9, Inc.
发明人： Jonathan Rosenberg
摘要： Systems and methods are described herein for providing a Voice over Internet Protocol (VoIP) call. In an embodiment, a load balancing processor receives a re-initiated HTTP request from a client processor upon detection that an initial call server is no longer active, and sends the re-initiated HTTP request to a second call server. The second server generates updated call resource information that identifies the second server as the new server resource for the call, and sends the updated call resource information over the IP network to the client processor. Subsequent HTTP requests from the client processor for sending and receiving signaling and media data for the call are received at the second server using the updated call resource information.
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公开(公告)号：US20220319496A1
公开(公告)日：2022-10-06
申请号：US17705484
申请日：2022-03-28
申请人： Five9, Inc.
摘要： Systems and methods are provided for training natural language processing (NLP) models in a contact center. The systems and methods provided may render the process of training an NLP model faster, easier to perform, and more accessible to non-experts. In embodiments of the present disclosure, a method for training an NLP model includes a first step of initializing an NLP model with an intent and one or more seed phrases. The next step may be to receive a customer interaction. Next, a matched utterance can be generated based on the customer interaction and the NLP model. Then, a suggested training phrase may be generated based on the matched utterance. The suggested training phrase may be confirmed. Thereafter, the NLP model can be updated with the confirmed training phrase.
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