Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/30/2025 has been entered.
Priority
The application priority to CN201910752714.0, filed on 8/15/2019 has been accepted and considered in this office action. The application is a continuation of PCT/CN2019/123937 filed on 12/19/2019.
Status of claims
Claims 1 and 6 are amended. Claim 8 is cancelled. Claims 1-7 and 9-20 are presented for examination.
Response to Arguments
Claim Rejections - 35 USC § 101
In light of amendments and arguments rejection under 101 is withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claim(s) 1 -7 and 9-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Andreas ( US 20190295545) and further in view of Wen (US 11430446 )
Regarding claim 1, Andreas teaches a computer-implemented voice dialogue processing method ,which is applied to a voice customer service server ( Fig 2-3) , wherein the voice customer service server comprises at least an Automatic Speech Recognition (ASR) module, a Natural Language Understanding (NLU) module, natural language generation ( semantic understanding to generate response, Para 0099) a voice response generation ( assistance can recognize speech and generate a response, Para 0018) , and a dialogue management engine, the method comprising: loading a dialogue business customization file by the dialogue management engine, the dialogue business customization file including at least one dialogue flow, and the dialogue flow including a plurality of dialogue nodes in a set order ( loading, into a computer memory, a computer-readable transcript representing an ordered sequence of one or more dialogue events, Para 0037, 0042, Fig 4-5) , the dialogue business customization file being a JSON file (dialog transcript can be in a JSON format, Para 0087 ) ; generating a training sample set for the dialogue management engine based on the dialogue business customization file (generate synthetic data, re-parametrizing the focal sub-command by outputting a plurality of different re-parametrized focal sub-commands wherein, in each re-parametrized focal sub-command, the seed semantic parameter is replaced by one of a plurality of different synthetic semantic parameters Fig 3 -5, Para 0038, 0043 ) , and then training the dialogue management engine using the training sample set ( training a model using the synthetic data, Fig 7-9, Para 0011) performing speech recognition and semantic understanding on a user voice to be processed, by the ASR module and the NLU module, to determine corresponding voice semantics (( responding to the weather query, Para 0018) ; determining a reply sentence for the voice semantics based on the trained dialogue management engine, and generate a customer service voice for replying to the user voice (fig 13, Para 0082- voicing the response , Para 0018)
Although Andreas mentions generating a response based on understanding and speaking the response a it does not explicitly teach a Text To Speech (TTS) module performing natural language generation and speech synthesis on the determined reply sentence by the TTS module, to generate a customer service voice for replying to the user voice
However, Wen teaches Natural Language Generation (NLG) module, a Text To Speech (TTS) module performing natural language generation and speech synthesis on the determined reply sentence by the NLG module and the TTS module, to generate a customer service voice for replying to the user voice (based on natural language understanding generate a text to speech response, Col 7, line 35-50)
It would have been obvious to modify Andreas with the explicitly teaching of Wen to have the tts model so to generate a computer generates response as already suggested in Andreas to improve efficiency of the dialog system (Col 1, line 30-31)
Regarding claim 2, Andreas modified by Laing as above in claim 1, generally teaches domain specific vs general intent ( Para 0042, 0046-0047, Andreas) it does not explicitly teach wherein the dialogue management engine comprises a general dialogue management model and a business dialogue management model, wherein, determining the reply sentence for the voice semantics based on the dialogue management engine includes: determining a user intent indicated by the voice semantics; and using the general dialogue management model to perform a general dialogue operation for the user intent when the user intent belongs to a general intent set, wherein the general dialogue operation includes any one of the following: transferring to manual operation, repeating broadcast operation, exiting dialogue operation, and interjection processing operation
However Wen in the same field of endeavor teaches wherein the dialogue management engine comprises a general dialogue management model and a business dialogue management model, wherein, determining the reply sentence for the voice semantics based on the dialogue management engine includes: determining a user intent indicated by the voice semantics ( detect intent, Col 7, line 35-50) ; and using the general dialogue management model to perform a general dialogue operation for the user intent when the user intent belongs to a general intent set ( global vs general rules, Col 12, line 5-25) , wherein the general dialogue operation includes any one of the following: transferring to manual operation, repeating broadcast operation, exiting dialogue operation, and interjection processing operation ( ( general and specific routine, ( policy engine and local state machines) , policy engine performs the fig 5 and fig 6 policy engine 44 access the global rules which includes start stop or transition operation, Col 19, line 50-67, Col 20, line 1-10; Col 30, line 45-50)
It would have been obvious having the teachings of Mazza to further include the concept of Wen before
effective filing date to improve the efficiency of dialog system (Col 1, line 30-31)
Regarding claim 3, Wen as above in claim 2, teaches using the business dialogue management model to perform a business operation including the following to determine the reply sentence, when the user intent does not belong to the general intent set: determining a target dialogue flow corresponding to the user intent; determining the reply sentence according to dialogue nodes in the determined target dialogue flow ( Fig 5-6, Wen)
Regarding claim 4, Andreas as above in clam 1, teaches the interaction process wherein the dialogue business customization file is obtained from a dialogue flow design server, and the dialogue flow design server is configured to interact with the dialogue flow design client to construct the dialogue business customization file ( fig 11)
Regarding claim 5, Wen as above in claim 1, teaches wherein the dialogue nodes include a dialogue start node ( start node, Fig 5) , a user communication node ( communication node, Col 25, line 5-10) , a user information identification node ( phone/date time nodes, Col 25, line 20-30) , and a slot filling node ( col 26, line 20-25; col 28, line 12-20)
Regarding claim 9, Andreas teaches An electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform steps of the method claim 1 (Fig 2-3)
Regarding claim 10, Andreas as above in claim 1, teaches A non-transitory computer-readable storage medium storing a computer program, wherein the computer program implements steps of the method of claim 1 when executed by a processor ( fig 1-3)
Regarding claim 11, Wen as above in claim 2, teaches wherein the dialogue nodes include a dialogue start node ( start node, Fig 5) , a user communication node ( communication node, Col 25, line 5-10) , a user information identification node ( phone/date time nodes, Col 25, line 20-30) , and a slot filling node ( col 26, line 20-25; col 28, line 12-20)
Regarding claim 12, Wen as above in claim 3, teaches wherein the dialogue nodes include a dialogue start node ( start node, Fig 50 , a user communication node ( communication node, Col 25, line 5-10) , a user information identification node ( phone/date time nodes, Col 25, line 20-30) , and a slot filling node ( col 26, line 20-25; col 28, line 12-20)
Regarding claim 13, Wen as above in claim 4, teaches wherein the dialogue nodes include a dialogue start node ( start node, Fig 5) , a user communication node ( communication node, Col 25, line 5-10) , a user information identification node ( phone/date time nodes, Col 25, line 20-30) , and a slot filling node ( col 26, line 20-25; col 28, line 12-20)
Regarding claim 14, arguments analogous to claim 2, are applicable.
Regarding claim 15, arguments analogous to claim 3, are applicable.
Regarding claim 16, arguments analogous to claim 4, are applicable.
Regarding claim 17, arguments analogous to claim 5, are applicable.
Regarding claim 18, arguments analogous to claim 11, are applicable.
Regarding claim 19, arguments analogous to claim 12, are applicable.
Regarding claim 20, arguments analogous to claim 13, are applicable.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Andreas ( US 20190295545) and further in view of Smullen ( US 20170048170) and further in view of Wen (US 11430446 )
Regarding claim 6, Andres teaches a computer-implemented voice dialogue processing method involving at least a voice customer service server and a dialogue flow design server (embodied within a system as in Fig 3, Para 0026-0029) , wherein the voice customer service server ( dialog system, Para 0018, 0099-0101) comprises at least an Automatic Speech Recognition (ASR) module (recognize speech, Para 0099-0105) , a Natural Language Understanding (NLU) module ( intent recognition, Para 0096) , a Natural Language Generation (NLG) module ( generate response, Para 0018) , a Text To Speech (TTS) module ( speaks the response back, Para 0018) , and a dialogue management engine, the method comprising: obtaining, by the dialogue flow design server, a dialogue flow design request from a dialogue flow design client (seed dialogue acquisition acquires data; wherein the seed data is a customization file as it pertains to a particular domain for e.g. whether, response message etc., para 0031) , and determining at least one dialogue flow corresponding to the dialogue flow design request, wherein the dialogue flow comprises a plurality of dialogue nodes having a set order (seed dialogue is an ordered sequence, Para 0031) ; generating, by the dialogue flow design server, a dialogue business customization file by determining the at least one dialogue flow of each ordered node in the at least one dialogue flow ( generate a dialogue file using the seed semantic parameter 0032-0034, 0042, Fig 5; Each category-specific subdomain may in turn be associated with one or more utterance annotations (e.g., comprising “leaves” of the hierarchical tree structure), and/or additional hierarchically-nested category-specific subdomains, as illustrated by the numerous branches of hierarchical menu 1112 shown in FIG. 11B, Para 0064- node type can be category/sub category etc. ) , the dialogue business customization file being a JSON file ( dialog transcript being a JSON file and annotated file can be a JSON file, Para 0076, 0087, 0089); loading the dialogue business customization file by the dialogue management engine of the voice customer service server ( annotated dialog goes to the synthetic data generation, Fig 3, Para 0034) generating a training sample set for the dialogue management engine based on the dialogue business customization file ( generate synthetic data, re-parametrizing the focal sub-command by outputting a plurality of different re-parametrized focal sub-commands wherein, in each re-parametrized focal sub-command, the seed semantic parameter is replaced by one of a plurality of different synthetic semantic parameters Fig 3 -5, Para 0043) and then training the dialogue management engine using the training sample set, by the voice customer service server ( training a model using the synthetic data, Fig 7-9, Para 0011; wherein the voice service server is embodied within a dialogue flow design server ) ; performing speech recognition and semantic understanding on a user voice to be processed, by the ASR module and the NLU module, to determine corresponding voice semantics ( responding to the weather query, Para 0018); determining a reply sentence for the voice semantics, by the voice customer service server, based on the trained dialogue management engine ( response is generated, Para 0018, Fig 14) ; and generate a customer service voice for replying to the user voice ( voicing the response, Para 0018-0020, 010-0102)
Andreas does not explicitly teach generating, by the dialogue flow design server, a dialogue business customization file by parsing the at least one dialogue flow to automatically identify a node content and node type of each ordered node in the at least one dialogue flow
However, Smullen teaches generating, by the dialogue flow design server, a dialogue business customization file by parsing the at least one dialogue flow to automatically identify a node content and node type of each ordered node in the at least one dialogue flow ( Then the user clicks on a button (e.g. “Create”) to initiate the process that converts the VXML into an automated human interface module 2202. In so doing the VXML file is parsed, and VXML data is validated to ensure proper formatting, Para 0170; each contain a node graph that provides a content type and node types, Para 0291, 174)
It would have been obvious having the teachings of Andreas to further include the concept of Smullen before effective filing date so that bot/business/domain specific file can parsed and used for a specific purposes (Para 0170, Smullen)
Andres does not explicitly teach TTS module and performing natural language generation and speech synthesis on the determined reply sentence, by the NLG module and the TTS module, to generate a customer service voice for replying to the user voice
Wen teaches teaches TTS module and performing natural language generation and speech synthesis on the determined reply sentence, by the NLG module and the TTS module, to generate a customer service voice for replying to the user voice( text to speech to reply to the user, Para 0018, 0059, 0195)
It would have been obvious to modify Andreas with the explicitly teaching of Jiang to have the tts model so to generate a computer generates response as already suggested in Andreas.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Andreas ( US 20190295545) and further in view of Smullen ( US 20170048170) and further in view of Wen (US 11430446 ) and further in view of Liang ( US 20190066660)
Regarding claim 7, Andreas as above in claim 6, does not explicitly teach wherein the dialogue flow design client is configured to have a graphical interface for a user to drag and drop a dialogue node box, wherein the dialogue flow design client is used to receive a corresponding dialogue flow design request generated with respect to a user operation of the graphical interface
However Jiang teaches wherein the dialogue flow design client is configured to have a graphical interface for a user to drag and drop a dialogue node box, wherein the dialogue flow design client is used to receive a corresponding dialogue flow design request generated with respect to a user operation of the graphical interface ( drag and drop, Fig 2e, Col 7, line 30-42)
It would have been obvious having the teachings of Andreas and Liang to further include the concept of Jiang before effective filing date to make it easier for user to edit and customize ( Col 7, line 30-42, Jiang)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vilbert US-20160042735
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/Richa Sonifrank/Primary Examiner, Art Unit 2654