DETAILED ACTION
In the response filed September 16, 2025, the Applicant amended claims 1, 5, and 6. Claims 1-20 are pending in the current application.
Notice of 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 .
Response to Arguments
The drawings were objected to for informalities. Examiner thanks the Applicant for revising and amending the disclosure and hereby withdraws the objections from the previous Office action.
Claim 1 was objected to for informalities. Examiner thanks the Applicant for revising and amending the claim language and hereby withdraws the objections from the previous Office action.
Claims 5 and 6 were rejected under 35 U.S.C. 112(b) as being indefinite. Examiner thanks the Applicant for revising and amending the claim language and hereby withdraws the rejection from the previous Office action.
Applicant’s arguments for claims 1-20 with respect to the 35 U.S.C. 101 rejection have been considered but are unpersuasive. Applicant argues that the claims are not directed to a judicial exception. Examiner respectfully disagrees. Here, under broadest reasonable interpretation, the steps as amended describe or set-forth training a machine learning system to receive data and output audible content with title and value information, which commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Applicant argues that the claims are not directed to a judicial exception as they provide a technological solution to a technical problem – in particular, permitting interoperability among disparate systems. Examiner respectfully disagrees. The claims do not describe any solutions to disparate systems and do not address any issues regarding incompatibility among such disparate systems. Here, the requirement to execute the claimed steps/functions using “a system including at least an application plane layer, a control plane layer including a cognitive computing unit;” “a training input to the system including an input for receiving content for training during the machine learning, and a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “interfacing the control plane layer with the application plane layer via the application plane layer interface,” “the control plane further including a (1) a difference engine coupled to at least a first data set of stored data and a second data set of stored data, the first and second data sets including at least audio data and (2) a transformation engine,” “the data plane layer being coupled to the control plane layer,” and “transferring the data input content information to the cognitive computing unit,” (claim 1), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s arguments remain unpersuasive. The 35 U.S.C. 101 rejection is hereby maintained.
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1, line 22, “(2) a transformation engine,” should read “(2) the transformation engine,” as it is a typographical error the transformation engine as found in line 9. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the phrase “the control plane further including a (1) a difference engine coupled to at least a first data set of stored data and a second data set of stored data, the first and second data sets including at least audio data and (2) a transformation engine,” in lines 18-22. It is unclear as to what the difference engine is coupled to and as to whether the at least a first set of stored data and a second set of stored data is stored on the difference engine. As such, the claim is indefinite for failing to particularly point out and distinctly claim the invention. For purposes of examination, the difference engine is being interpreted as merely analyzing the at least a first set of stored data and a second set of stored data as this appears to be Applicant’s intent (See current disclosure, Par. [0089]).
Dependent claims 2-20 are rejected by virtue of their dependence on independent claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-20 are drawn to a process, which is within the four statutory categories (e.g., a process, a machine). (Step 1: YES).
Step 2A – Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1 recites/describes the following steps:
“…using at least machine learning for training of the cognitive computing unit,”
“…receive and store data input content from one or more data sources other than the control plane layer, …the data input content being subject to transformation into audible content… for output from the system, and a data output for the transformed audible content,”
“receiving …instructions regarding operation of the system,”
“… receive information related to the instructions regarding operation of the system,”
“training the cognitive computing unit at least in part by utilizing the content for training input during the machine learning, and translating … the instructions …, and receiving …data input content information,”
“synthesizing audible output content at least in part by transforming the data input content into the audible output content,”
“providing the audible output content to the data output,”
“receiving … information relating to title and value transfer element,”
“processing the input and outputting title and value information.”
These steps, under broadest reasonable interpretation, describe or set-forth training a machine learning system to receive data and output audible content with title and value information, which commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Each of the depending claims 2-20 likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Any elements recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim.
Step 2A – Prong Two: The claims recite the additional elements/limitations of: “a system including at least an application plane layer, a control plane layer including a cognitive computing unit;” “a training input to the system including an input for receiving content for training during the machine learning, and a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “interfacing the control plane layer with the application plane layer via the application plane layer interface,” “the control plane further including a (1) a difference engine coupled to at least a first data set of stored data and a second data set of stored data, the first and second data sets including at least audio data and (2) a transformation engine,” “the data plane layer being coupled to the control plane layer,” and “transferring the data input content information to the cognitive computing unit,” (claim 1).
The requirement to execute the claimed steps/functions using “a system including at least an application plane layer, a control plane layer including a cognitive computing unit;” “a training input to the system including an input for receiving content for training during the machine learning, and a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “interfacing the control plane layer with the application plane layer via the application plane layer interface,” “the control plane further including a (1) a difference engine coupled to at least a first data set of stored data and a second data set of stored data, the first and second data sets including at least audio data and (2) a transformation engine,” “the data plane layer being coupled to the control plane layer,” and “transferring the data input content information to the cognitive computing unit,” (claim 1), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Remaining dependent claims 2-20 either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: As discussed above in “Step 2A – Prong 2,” the requirement to execute the claimed steps/functions using “a system including at least an application plane layer, a control plane layer including a cognitive computing unit;” “a training input to the system including an input for receiving content for training during the machine learning, and a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “interfacing the control plane layer with the application plane layer via the application plane layer interface,” “the control plane further including a (1) a difference engine coupled to at least a first data set of stored data and a second data set of stored data, the first and second data sets including at least audio data and (2) a transformation engine,” “the data plane layer being coupled to the control plane layer,” and “transferring the data input content information to the cognitive computing unit,” (claim 1), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Remaining dependent claims 2-20 either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: NO).
Allowable Subject Matter
Claims 1-20 would be allowable subject matter if revised and amended to overcome the rejections under 35 U.S.C. 101 as set forth in this Office action.
The closest prior art of record is:
Wu (U.S. Pub. No. 2018/0174020 A1, June 21, 2018), hereinafter Wu;
Krasser et al. (U.S. Pub. No. 2018/0198800 A1, July 12, 2018), hereinafter Krasser;
Agiomyrgiannakis et al. (U.S. Pub. No. 2016/0140951 A1, May 19, 2016), hereinafter Agiomyrgiannakis;
Sun et al., “Voice conversion using deep bidirectional long short-term memory based recurrent neural networks.” 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4869-4873 (Year: 2015), hereinafter Sun.
Wu discloses a system for emotionally intelligent automated chatting are provided. The systems and method provide emotionally intelligent automated (or artificial intelligence) chatting by determining a context and an emotion of a conversation with a user. Based on these determinations, the systems and methods may select one or more responses from a database of responses to a reply to a user query. Further, the systems and methods are able update or train based on user feedback and/or world feedback.
Krasser discloses a system to determine a validation dataset, determine a computational model using the validation dataset, or determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processing unit can determine signatures of individual training data streams. The processing unit can determine, based at least in part on the signatures and a predetermined difference criterion, a training set and a validation set of the training data streams. The processing unit can determine a computational model based at least in part on the training set. The processing unit can then operate the computational model based at least in part on a trial data stream to provide a trial model output. Some examples include determining the validation set based at least in part on the training set and the predetermined criterion for difference between data streams.
Agiomyrgiannakis discloses a system for building a speech database for a text-to-speech (TTS) synthesis system from multiple speakers recorded under diverse conditions. For a plurality of utterances of a reference speaker, a set of reference-speaker vectors may be extracted, and for each of a plurality of utterances of a colloquial speaker, a respective set of colloquial-speaker vectors may be extracted. A matching procedure, carried out under a transform that compensates for speaker differences, may be used to match each colloquial-speaker vector to a reference-speaker vector. The colloquial-speaker vector may be replaced with the matched reference-speaker vector. The matching-and-replacing can be carried out separately for each set of colloquial-speaker vectors. A conditioned set of speaker vectors can then be constructed by aggregating all the replaced speaker vectors. The condition set of speaker vectors can be used to train the TTS system.
Sun investigates the use of Deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks (DBLSTMRNNs) for voice conversion. Temporal correlations across speech frames are not directly modeled in frame-based methods using conventional Deep Neural Networks (DNNs), which results in a limited quality of the converted speech. To improve the naturalness and continuity of the speech output in voice conversion, Sun et al. proposes a sequence-based conversion method using DBLSTM-RNNs to model not only the frame-wised relationship between the source and the target voice, but also the long-range context-dependencies in the acoustic trajectory. Experiments show that DBLSTM-RNNs outperform DNNs where Mean Opinion Scores are 3.2 and 2.3 respectively. Also, DBLSTM-RNNs without dynamic features have better performance than DNNs with dynamic features.
As per claim 1, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest “a data plane layer, the data plane layer including an input interface to receive and store data input content from one or more data sources other than the control plane layer, the data plane layer further including a title and value transfer element including a processor, the data input content being subject to transformation into audible content by a transformation engine for output from the system, and a data output for the transformed audible content, the method comprising the steps of: receiving at the application plane layer instructions regarding operation of the system, the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface, interfacing the control plane layer with the application plane layer via the application plane layer interface to receive information related to the instructions regarding operation of the system, the control plane further including a (1) a difference engine coupled to at least a first data set of stored data and a second data set of stored data, the first and second data sets including at least audio data and (2) a transformation engine, training the cognitive computing unit at least in part by utilizing the content for training input during the machine learning, translating within the control plane layer the instructions of the application plane layer to the data plane layer, receiving, at the data plane layer, data input content information, the data plane layer being coupled to the control plane layer, transferring the data input content information to the cognitive computing unit, synthesizing audible output content at least in part by transforming the data input content into the audible output content, providing the audible output content to the data output, and receiving at the input interface of the data plane layer information relating to title and value transfer element, processing the input and outputting title and value information.” This combination of functions/features would not have been obvious to a PHOSITA in view of the prior art.
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 Patrick Kim whose telephone number is (571)272-8619. The examiner can normally be reached Monday - Friday, 9AM - 5PM EST.
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/P.K./Examiner, Art Unit 3628
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3628
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