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 .
This action is in response to the application filed on 11/29/2021. Claims 1-14 are pending in the application and have been considered below.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Dependent claims 2, 7 and 11 recite the limitation “the copula function.” There is insufficient antecedent basis for this limitation in the claims.
The claims 3-4, 8-9 and 12-13 are dependent claims of claims 2, 7 and 11 respectively and inherit the deficiency of the claim they depend upon.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
For Step 1, the claim is a method, so it does recite a statutory category of invention.
For Step 2A, Prong 1:
The claim recites the limitation of “applying, to the continuous true data, a probability integral transform and performing an independent uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “applying, to the discrete true data, a distributional transform and performing an independent uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “applying a copula learner to learn a copula from the transformed continuous true data and the transformed discrete true data.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “identifying a first correlated uniform marginal from the learned copula based on the transformed continuous true data.” The identifying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the identifying from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “identifying a second correlated uniform marginal from the learned copula based on the transformed continuous true data.” The identifying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the identifying from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “applying continuous inverse transform sampling on the first correlated uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “applying discrete inverse transform sampling on the second correlated uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components
The claim recites the limitation of “generating the synthetic data using the continuous inverse transform sampling and the discrete inverse transform sampling.” The generating limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the generating from practically being performed in the human mind. This limitation is a mathematical process
For Step 2A, Prong 2, the claim does recite an additional element:
receiving a true dataset on which to model synthetic data, wherein the true
data comprises continuous true data and discrete true data.
The recited additional element of “receiving a true dataset on which to model synthetic data, wherein the true data comprises continuous true data and discrete true data” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Step 2B
Under Subject Matter Eligibility Guidance (SMEG), a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receiving a true dataset on which to model synthetic data, wherein the true data comprises continuous true data and discrete true data” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 2:
Claim 2, which incorporates the rejection of claim 1, recites further limitations such as
“using a normalizing flow to learn the copula function” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible
Regarding Claim 3:
Claim 3, which incorporates the rejection of claim 2, recites an additional element such as
“using an autoregressive density network with the normalizing flow.”
The additional element of “using an autoregressive density network with the normalizing flow” is a generic computer component that amounts to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
The additional element of ““using an autoregressive density network with the normalizing flow” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “using an autoregressive density network with the normalizing flow” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4:
Claim 4, which incorporates the rejection of claim 3, recites an additional element such as
“the autoregressive density network comprises a masked autoregressive network.”
The additional element of “the autoregressive density network comprises a masked autoregressive network.” is a generic computer component that amounts to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
The additional element of ““using an autoregressive density network with the normalizing flow” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “using an autoregressive density network with the normalizing flow” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5:
Claim 5, which incorporates the rejection of claim 1, recites further limitations such as “learning a functional relationship within the continuous true data” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 6:
For Step 1, the claim is a method, so it does recite a statutory category of invention.
For Step 2A, Prong 1:
The claim recites the limitation of “applying a distributional transform and performing an independent uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “applying a copula learner to learn a copula from the transformed discrete true data.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “identifying a correlated uniform marginal from the learned copula.” The identifying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the identifying from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “applying discrete inverse transform sampling on the correlated uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “generating the synthetic data using the he discrete inverse transform sampling.” The generating limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the generating from practically being performed in the human mind. This limitation is a mathematical process.
For Step 2A, Prong 2, the claim does recite an additional element:
receiving a true dataset on which to model synthetic data, wherein the true data comprises discrete true data.
The recited additional element of “receiving a true dataset on which to model synthetic data, wherein the true data comprises discrete true data” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Step 2B
Under Subject Matter Eligibility Guidance (SMEG), a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receiving a true dataset on which to model synthetic data, wherein the true data comprises discrete true data” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 7:
Claim 7, which incorporates the rejection of claim 6, recites further limitations such as
“using a normalizing flow to learn the copula function” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible
Regarding Claim 8:
Claim 3, which incorporates the rejection of claim 2, recites an additional element such as
“using an autoregressive density network with the normalizing flow.”
The additional element of “using an autoregressive density network with the normalizing flow” is a generic computer component that amounts to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
Or
The “using an autoregressive density network with the normalizing flow” step is an intended use and linked to the judicial exception.
The additional element of ““using an autoregressive density network with the normalizing flow” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “using an autoregressive density network with the normalizing flow” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9:
Claim 9, which incorporates the rejection of claim 8, recites an additional element such as “the autoregressive density network comprises a masked autoregressive network.”
The additional element of “the autoregressive density network comprises a masked autoregressive network.” is a generic computer component that amounts to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
The additional element of ““using an autoregressive density network with the normalizing flow” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “using an autoregressive density network with the normalizing flow” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10:
For Step 1, the claim is a method, so it does recite a statutory category of invention.
For Step 2A, Prong 1:
The claim recites the limitation of “applying a distributional transform and performing an independent uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “applying a copula learner to learn a copula from the transformed continuous true data.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “identifying a correlated uniform marginal from the learned copula.” The identifying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim precludes the identifying from practically being performed in the human mind. This limitation is a mental process.
The claim recites the limitation of “applying continuous inverse transform sampling on the correlated uniform marginal.” The applying limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
The claim recites the limitation of “generating the synthetic data using the continuous inverse transform sampling.” The generating limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This limitation is a mathematical concept.
For Step 2A, Prong 2, the claim does recite an additional element:
receiving a true dataset on which to model synthetic data, wherein the true data comprises continuous true data.
The recited additional element of “receiving a true dataset on which to model synthetic data, wherein the true data comprises continuous true data” step is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Step 2B
Under Subject Matter Eligibility Guidance (SMEG), a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B.
Here the “receiving a true dataset on which to model synthetic data, wherein the true data comprises continuous t true data” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i).
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Regarding Claim 11:
Claim 11, which incorporates the rejection of claim 10, recites further limitations such as
“using a normalizing flow to learn the copula function” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible
Regarding Claim 12:
Claim 12, which incorporates the rejection of claim 11, recites an additional element such as
“using an autoregressive density network with the normalizing flow.”
The additional element of “using an autoregressive density network with the normalizing flow” is a generic computer component that amounts to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
The additional element of ““using an autoregressive density network with the normalizing flow” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “using an autoregressive density network with the normalizing flow” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13:
Claim 13, which incorporates the rejection of claim 12, recites an additional element such as
“the autoregressive density network comprises a masked autoregressive network.”
The additional element of “the autoregressive density network comprises a masked autoregressive network.” is a generic computer component that amounts to mere instructions to apply the abstract idea. See MPEP 2106.05(f).
The additional element of ““using an autoregressive density network with the normalizing flow” does not amount to significantly more for the reasons set forth in step 2A above.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “using an autoregressive density network with the normalizing flow” to perform the claim steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14:
Claim 14, which incorporates the rejection of claim 13, recites further limitations such as “learning a functional relationship within the continuous true data” that are part of the abstract idea.
There are no additional elements recited in this claim that amount to an integration of the judicial exception into a practical application or significantly more than the judicial exception. Therefore, the claim is not eligible.
Examiner’s comments
For claims 1-14, no art rejection is made for these claims, they are only rejected under 35 USC 101 and 112 as explained above in this office action.
Generative models, such as Generative Adversarial Networks (GANs) are state-of-the-art for synthetic data due to their ability to capture complex data distributions and produce high-fidelity, privacy-preserving, and realistic, yet, often uninterpretable, data.
Applicant’s prior art by Kamthe et al. (“Copula Flows for Synthetic Data Generation”) teaches an interpretable probabilistic synthetic data generator based on the copula theory to model complex data distributions to create high-fidelity data.
Applicant’s prior art cannot be used because the disclosure is less than a year.
The prior art by Li et al. (“SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources”) teaches a unique synthetic data generation task called downscaling and a multi-stage framework called SYNC (Synthetic Data Generation via Gaussian Copula). However, the prior does teach the limitations as recited in the claim.
The prior art by Jeong et al. (“Copula-Based Approach to Synthetic Population Generation”) teaches the building of construction of a joint distribution from a given reference joint distribution and target margins that are discrete distributions and the procedure to be extended to continuous variables. However, the prior does teach the limitations as recited in the claim.
The prior art by Li et al. (“Differentially Private Synthesization of Multi-Dimensional Data using Copula Functions”) teaches DPCopula, a differentially private data synthesization technique using Copula functions for multi-dimensional data to produce highly accurate synthetic multi-dimensional data with significantly better utility than state-of-the-art techniques. However, the prior does teach the limitations as recited in the claim.
There is/are no other prior art(s) to cover the following claim limitations:
Claim 1
“identifying a first correlated uniform marginal from the learned copula based
on the transformed continuous true data;
identifying a second correlated uniform marginal from the learned copula
based on the transformed discrete true data;
applying continuous inverse transform sampling on the first correlated
uniform marginal;
applying discrete inverse transform sampling on the second correlated
uniform marginal; and
generating the synthetic data using the continuous inverse transform
sampling and the discrete inverse transform sampling.”
Claim 10
“applying a copula learner to learn a copula from the transformed continuous
true data;
identifying a correlated uniform marginal from the learned copula;
applying continuous inverse transform sampling on the correlated uniform
marginal; and
generating the synthetic data using the continuous inverse transform sampling.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABABACAR SECK whose telephone number is (571)270-7146. The examiner can normally be reached Monday-Friday 8:00 A.M.-6:00 P.M..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lamardo Viker can be reached on 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ABABACAR SECK/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147