DETAILED ACTION
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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mazza et al. (US 2019/0182382 A1), hereinafter “Mazza”, and in view of Magliozzi et al. (US 2024/0414109 A1), hereinafter “Magliozzi”, and further in view of Potti et al. (US 2017/0255542 A1), hereinafter “Potti”.
As per claim 1, Mazza teaches a method of auto-provisioning artificial intelligence-based dialog services for a plurality of target applications, the method comprising:
“providing automated provisioning for deployment of artificial intelligence-based dialogs without manual intervention” at [0078];
(Mazza teaches automatically generating chatbots from sample dialogue data without input from a system administrator)
“extracting metrics, by an automated subroutine, from a plurality of interactions with human beings” at [0069]-[0072];
(Mazza teaches storing customer data and interaction data, e.g., details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.)
“aggregating and normalizing historical dialog into an artificial intelligence-based corpus” at [0077]-[0079];
(Mazza teaches the sample dialogue data may include transcripts of chat conversation between customer and human agents of the contact center. The sample dialogue data are cleaned and normalized by the data extraction module, the interactions are clustered by topic using their normalized transcript, a dialogue graph is generated for each topic)
“delivering specific recommendations based on pre-formatted templates, extracted analytics and associated metrics” at [0074];
(Mazza teaches data extraction module configured to extract information from transcripts of prior chat interactions, displaying suggestion extracted by the data extraction module, allowing the human designer to approve, reject or edit the suggestion)
“automatically correlating language n-grams with sentence-level n-grams to assemble statement sets, wherein the statement sets are further assembled into machine-to-human dialogs” at [0080]-[0099] and Figs. 4A-C;
(Mazza teaches corelating key terms or phrase n-grams with sentence-level n-gram to assemble a dialogue graph represent different types of conversion paths. The chatbot generation module generates a chatbot based on the extracted dialogue graph)
Mazza does not teach “periodically scheduling updates to an artificial intelligence-based corpus using periodic text, application and page crawling subroutines”. However, Magliozzi teaches a process for operating and training a text-based chatbot, including the step of “periodically scheduling updates to an artificial intelligence-based corpus using periodic text, application and page crawling subroutines” at [0042]-[0043], [0118]-[0119]. Thus, it would have been obvious to one of ordinary skill in the art to combine Magliozzi with Mazza’s system so that “the neural network can generalize categories from the categories it was trained on so the category encodings improve continuously as knowledge is added. And the entire encoding process in the neural network can also be improved with periodic retraining”, as suggested by Magliozzi at [0118].
Mazza and Magliozzi, as combined, do not teach “interfacing with a plurality of AI-based subsystems to automatically retrieve and act on credentialed deployment codes or tokens by an access control tokenizer that retrieves deployment codes from an AI-based decisioning subsystem and retrieves authorization token or keys from the customer’s chosen platform, wherein the credentialed deployment codes or tokens are further deployed automatically on a customer’s chosen platform without manual intervention” as claimed. However, Potti teaches a system for automated code validation and deployment including the steps of “interfacing with a plurality of AI-based subsystems to automatically retrieve and act on credentialed deployment codes or tokens by an access control tokenizer that retrieves deployment codes from an AI-based decisioning subsystem and retrieves authorization token or keys from the customer’s chosen platform, wherein the credentialed deployment codes or tokens are further deployed automatically on a customer’s chosen platform without manual intervention” at [0034]-[0038] and Fig. 1. . Particularly, Potti teaches the code validation and deployment module 300 (mapped to the claimed “access control tokenizer”) that retrieves deployment codes from code database 400, which stores the codes that are to be deployed on a customer’s chosen platform. The code validation and deployment module 300 is configured to identify the task identifier (ID) (mapped to the claimed “authorization token or key”,) platforms and applicable software. Thus, it would have been obvious to one of ordinary skill in the art to combine Potti with Mazza’s teaching in order to provide a desired system for “management and automation of all phases of code deployment, including, but not limited to, code validation, validation approval, code deployment and data reporting/auditing. As such, the desired system should be able to automatically validate code regardless of which standards applied to the entities, platforms and or applications associated with a given deployment. In addition, the desired systems should provide the user the flexibility to implement whichever code deployment tool and/or reporting/auditing tool that is applicable to a given deployment/migration. Moreover, the desired systems and the like should be capable of being integrated with any new platform, new software applications/packages and/or updates/revisions to applications/software packages without having to modify or reconfigure the existing flexible and customizable system. Additionally, the desired system should be capable of tracking/logging each action/event that occurs throughout the process, so that resulting data provides requisite analysis and audit trials”, as suggested by Potti at [0005].
As per claim 2, Mazza-Magliozzi and Potti teach the method of claim 1 discussed above. Magliozzi also teaches: wherein “the periodically scheduled updates are used by one or more automatic update algorithm to automatically train and retrain AI subroutines” at [0118]-[0119]
As per claim 3, Mazza-Magliozzi and Potti teach the method of claim 1 discussed above. Mazza also teaches: wherein “the artificial intelligence-based dialogs facilitate dialog between one or more users and one or more social site timelines or web sites” at [0044]-[0045], [0066]-[0067].
As per claim 4, Mazza-Magliozzi and Potti teach the method of claim 1 discussed above. Mazza also teaches: wherein “the metrics comprises one or more of: customer tone, personality, relevance, response time and response length” at [0069]-[0072].
As per claim 5, Mazza-Magliozzi and Potti teach the method of claim 1 discussed above. Mazza also teaches: “assembling and uploading pre-formatted template-based data to derive actionable insights” at [0074].
As per claim 6, Mazza-Magliozzi and Potti teach the method of claim 1 discussed above. Mazza also teaches: “automatically parsing text and extracting relevant data, based on use; and filtering based upon customer preferences, specific customer use cases, and relevant question and answer behavior” at [0074], [0083]-[0097].
As per claim 7, Mazza-Magliozzi and Potti teach the method of claim 1 discussed above. Mazza also teaches: “assembling and re-assembling dialog and answers accounting for tone, personality and length of answer for a particular target audience” at [0121]-[0123], [0137]-[0148].
As per claim 8, Mazza-Magliozzi and Potti teach the method of claim 7 discussed above. Mazza also teaches: “ranking and weighting the answers and presenting the answers in priority order” at [0092]-[0097].
As per claim 9, Mazza-Magliozzi and Potti teach the method of claim 1 discussed above. Magliozzi also teaches: “performing notification and escalation to a user based on an automatic upload and distribution of data from an automated recommendations subroutine” at [0102]-[0105].
As per claim 10, Mazza-Magliozzi and Potti teach the method of claim 9 discussed above. Magliozzi a also teaches: “the notification and the escalation is based on real-time sentiment analysis” at [0102]-[0105].
Claims 11-20 recite similar limitations as in claims 1-10 and are therefore rejected by the same reasons.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
August 27, 2026
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