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 § 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 2, and 9-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines(“2019 PEG”).
Step 1: Independent claims 1(A method…) and 9(A method…) are directed towards a method. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories s (process, machine, manufacture, or composition of matter).
Claim 1
Step 2A, Prong 1: The claim recites, inter alia:
performing by the computing device a machine learning function utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold
This limitation recites a possible mental process of performing a broad and unspecific machine learning function
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate a judicial exception into a practical application.
Additional element(s):
A method using a computing device to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models
This limitation is recited a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
accessing by a computing device a protected batch of data associated with a machine learning model;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than a judicial exception.
Additional element(s):
A method using a computing device to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models
This limitation is recited a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
accessing by a computing device a protected batch of data associated with a machine learning model;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
Claim 2
Step 2A, Prong 1: The claim recites, inter alia:
wherein the machine learning function is performing […] an inference utilizing at least in-part the simulated batch of data.
This limitation recites a mental process, using evaluation, judgement, and opinion, with aid of pen and paper, to perform a guess using the simulated batch of data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate a judicial exception into a practical application.
Additional element(s):
… by one or more machine learning model…
This limitation is recited a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than a judicial exception.
Additional element(s):
… by one or more machine learning model…
This limitation is recited a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Claim 9
Step 2A, Prong 1: The claim recites, inter alia:
performing by the computing device a machine learning function utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold, the machine learning function performing by one or more machine learning models an inference utilizing at least in part the simulated batch of data.
This limitation recites a mental process, using evaluation, judgement, and opinion, with aid of pen and paper, to perform a guess using the simulated batch of data. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate a judicial exception into a practical application.
Additional element(s):
A method using a computing device to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models
This limitation is recited a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
accessing by a computing device a protected batch of data associated with a machine learning model;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by generic computer equipment. See MPEP 2106.05(g)
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than a judicial exception.
Additional element(s):
A method using a computing device to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models
This limitation is recited a high level of generality and recites use of a generic machine learning model to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic machine learning model in its ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f)
accessing by a computing device a protected batch of data associated with a machine learning model;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
Claim 10
Step 2A, Prong 1: The claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate a judicial exception into a practical application.
Additional element(s):
wherein the one or more comparisons include comparison of a distribution of one or more variables associated with the protected batch of data and a distribution of one or more variables associated with the simulated batch of data.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated performed by generic computer equipment.
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than a judicial exception.
Additional element(s):
wherein the one or more comparisons include comparison of a distribution of one or more variables associated with the protected batch of data and a distribution of one or more variables associated with the simulated batch of data.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
Claim 11
Step 2A, Prong 1: The claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate a judicial exception into a practical application.
Additional element(s):
wherein the one or more comparisons include calculation and comparison of correlation matrices of two or more variables associated with the protected batch of data and the simulated batch of data.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated performed by generic computer equipment.
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than a judicial exception.
Additional element(s):
wherein the one or more comparisons include calculation and comparison of correlation matrices of two or more variables associated with the protected batch of data and the simulated batch of data.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
Claim 12
Step 2A, Prong 1: The claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate a judicial exception into a practical application.
Additional element(s):
wherein the one or more comparisons include generation of a hierarchy cluster to compare all variables in the protected batch of data and the simulated batch of data.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated performed by generic computer equipment.
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than a judicial exception.
Additional element(s):
wherein the one or more comparisons include generation of a hierarchy cluster to compare all variables in the protected batch of data and the simulated batch of data.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
Claim 13
Step 2A, Prong 1: The claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate a judicial exception into a practical application.
Additional element(s):
wherein the one or more comparisons include generation of a relationship correlation between one or more traits displayed by variables included in the protected batch of data and the simulated batch of data.
This limitation represents an insignificant extra-solution activity of selecting a particular type of data to be manipulated performed by generic computer equipment.
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than a judicial exception.
Additional element(s):
wherein the one or more comparisons include generation of a relationship correlation between one or more traits displayed by variables included in the protected batch of data and the simulated batch of data.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional when it is claimed in a merely generic manner (as it is in the present claim).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 9-11, 14-16, 19-21, and 24 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by US 20230074606 A1 by Jesus et al., hereafter ‘606.
Regarding independent claim 1, ‘606 teaches:
A method using a computing device to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models, the method comprising:
accessing by a computing device a protected batch of data associated with a machine learning model; (Paragraph [0044], “Dataset generator 152 is configured to receive an input dataset 140.” The input dataset is a protected batch of data)
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data; (Paragraph [0048], “In an embodiment, (anonymized) biased dataset generator 100 is configured to produce a synthetic dataset with domain constraints 116.” The anonymized biased dataset generator uses the input dataset)
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value; (Paragraph [0058], “…distributions are compared individually through a similarity metric, such as the Jansen-Shannon divergence…”) and
performing by the computing device a machine learning function (Paragraph [0026], “This evaluation is typically performed in two different characteristics of the dataset: a) predictive performance on the generated data, and b) statistical similarity to the seed dataset.” Predictive performance on the generated data requires performing a prediction which is a machine learning function.) utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold. (Paragraph [0059] An objective of the disclosed sampling method is to obtain one or more numerical variables, and underlying distributions, which are capable of producing a linear decision boundary with a given expected functioning point in the ROC space, with user-defined TPR and FPR.” The numerical variables and underlying distributions are similarity values that need to meet or exceed a threshold.)
Regarding claim 2, ‘606 teaches the material disclosed in claim 1, and additionally teaches:
wherein the machine learning function is performing by one or more machine learning model an inference utilizing at least in-part the simulated batch of data. (Paragraph [0026], “This evaluation is typically performed in two different characteristics of the dataset: a) predictive performance on the generated data, and b) statistical similarity to the seed dataset.” Predictive performance on the generated data requires performing a prediction which is an inference. The generated data is the simulated batch of data.)
Regarding claim 3, ‘606 teaches the material disclosed in claim 1, and additionally teaches:
wherein the machine learning function is training a machine learning model with the simulated batch of data. (Paragraph [0042], “In various embodiments, the generated dataset includes training data (used to train a machine learning model)…”)
Regarding claim 4, ‘606 teaches the material disclosed in claim 1, and additionally teaches:
wherein the one or more comparisons include comparison of a distribution of one or more variables associated with the protected batch of data and a distribution of one or more variables associated with the simulated batch of data. (Paragraph [0058], “...distributions are compared individually through a similarity metric...”)
Regarding claim 5, ‘606 teaches the material disclosed in claim 1, and additionally teaches:
wherein the one or more comparisons include calculation and comparison of correlation matrices of two or more variables associated with the protected batch of data and the simulated batch of data. (Paragraph [0058], “...the maximum absolute difference in correlations matrices of the original and generated datasets is calculated…”)
Regarding independent claim 9, ‘606 teaches:
A method using a computing device to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models, the method comprising:
accessing by a computing device a protected batch of data associated with a machine learning model; (Paragraph [0044], “Dataset generator 152 is configured to receive an input dataset 140.” The input dataset is a protected batch of data)
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data; (Paragraph [0048], “In an embodiment, (anonymized) biased dataset generator 100 is configured to produce a synthetic dataset with domain constraints 116.” The anonymized biased dataset generator uses the input dataset)
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value; (Paragraph [0058], “…distributions are compared individually through a similarity metric, such as the Jansen-Shannon divergence…”) and
performing by the computing device a machine learning function (Paragraph [0026], “This evaluation is typically performed in two different characteristics of the dataset: a) predictive performance on the generated data, and b) statistical similarity to the seed dataset.” Predictive performance on the generated data requires performing a prediction which is a machine learning function.) utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold, (Paragraph [0059] An objective of the disclosed sampling method is to obtain one or more numerical variables, and underlying distributions, which are capable of producing a linear decision boundary with a given expected functioning point in the ROC space, with user-defined TPR and FPR.” The numerical variables and underlying distributions are similarity values that need to meet or exceed a threshold.) the machine learning function performing by one or more machine learning models an inference utilizing at least in part the simulated batch of data. (Paragraph [0026], “This evaluation is typically performed in two different characteristics of the dataset: a) predictive performance on the generated data, and b) statistical similarity to the seed dataset.” Predictive performance on the generated data requires performing a prediction which is an inference. The generated data is the simulated batch of data.)
Regarding claim 10, ‘606 teaches the material disclosed in claim 9, and additionally teaches:
wherein the one or more comparisons include comparison of a distribution of one or more variables associated with the protected batch of data and a distribution of one or more variables associated with the simulated batch of data. (Paragraph [0058], “...distributions are compared individually through a similarity metric...”)
Regarding claim 11, ‘606 teaches the material disclosed in claim 9, and additionally teaches:
wherein the one or more comparisons include calculation and comparison of correlation matrices of two or more variables associated with the protected batch of data and the simulated batch of data. (Paragraph [0058], “...the maximum absolute difference in correlations matrices of the original and generated datasets is calculated…”)
Regarding independent claim 14, ‘606 teaches:
A method using a computing device to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models, the method comprising:
accessing by a computing device a protected batch of data associated with a machine learning model; (Paragraph [0044], “Dataset generator 152 is configured to receive an input dataset 140.” The input dataset is a protected batch of data)
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data; (Paragraph [0048], “In an embodiment, (anonymized) biased dataset generator 100 is configured to produce a synthetic dataset with domain constraints 116.” The anonymized biased dataset generator uses the input dataset)
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value; (Paragraph [0058], “…distributions are compared individually through a similarity metric, such as the Jansen-Shannon divergence…”) and
performing by the computing device a machine learning function (Paragraph [0026], “This evaluation is typically performed in two different characteristics of the dataset: a) predictive performance on the generated data, and b) statistical similarity to the seed dataset.” Predictive performance on the generated data requires performing a prediction which is a machine learning function.) utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold, (Paragraph [0059] An objective of the disclosed sampling method is to obtain one or more numerical variables, and underlying distributions, which are capable of producing a linear decision boundary with a given expected functioning point in the ROC space, with user-defined TPR and FPR.” The numerical variables and underlying distributions are similarity values that need to meet or exceed a threshold.) the machine learning function training a machine learning model with the simulated batch of data. (Paragraph [0042], “In various embodiments, the generated dataset includes training data (used to train a machine learning model)…”)
Regarding claim 15, ‘606 teaches the material disclosed in claim 14, and additionally teaches:
wherein the one or more comparisons include comparison of a distribution of one or more variables associated with the protected batch of data and a distribution of one or more variables associated with the simulated batch of data. (Paragraph [0058], “...distributions are compared individually through a similarity metric...”)
Regarding claim 16, ‘606 teaches the material disclosed in claim 14, and additionally teaches:
wherein the one or more comparisons include calculation and comparison of correlation matrices of two or more variables associated with the protected batch of data and the simulated batch of data. (Paragraph [0058], “...the maximum absolute difference in correlations matrices of the original and generated datasets is calculated…”)
Regarding independent claim 19, ‘606 teaches:
A computer system to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models, the computer system comprising:
one or more computer processors; (Paragraph [0105], “The process may be performed by a system or processor”)
one or more computer-readable storage media; (Paragraph [0116], “It is to be appreciated that certain embodiments of the disclosure as described herein may be incorporated as code (e.g., a software algorithm or program)residing in firmware and/or on computer useable medium...”)
program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising (Paragraph [0116], “Such a computer system typically includes memory storage configured to provide output from execution of the code which configures a processor in accordance with the execution.” Code is program instructions.):
program instructions to access a protected batch of data associated with a machine learning model; (Paragraph [0044], “Dataset generator 152 is configured to receive an input dataset 140.” The input dataset is a protected batch of data)
program instructions to access a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data; (Paragraph [0048], “In an embodiment, (anonymized) biased dataset generator 100 is configured to produce a synthetic dataset with domain constraints 116.” The anonymized biased dataset generator uses the input dataset)
program instructions to access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value; and (Paragraph [0058], “…distributions are compared individually through a similarity metric, such as the Jansen-Shannon divergence…”) and
program instructions to perform a machine learning function (Paragraph [0026], “This evaluation is typically performed in two different characteristics of the dataset: a) predictive performance on the generated data, and b) statistical similarity to the seed dataset.” Predictive performance on the generated data requires performing a prediction which is a machine learning function.) utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold. (Paragraph [0059] An objective of the disclosed sampling method is to obtain one or more numerical variables, and underlying distributions, which are capable of producing a linear decision boundary with a given expected functioning point in the ROC space, with user-defined TPR and FPR.” The numerical variables and underlying distributions are similarity values that need to meet or exceed a threshold.)
Regarding claim 20, ‘606 teaches the material disclosed in claim 19, and additionally teaches:
wherein the one or more comparisons include comparison of a distribution of one or more variables associated with the protected batch of data and a distribution of one or more variables associated with the simulated batch of data. (Paragraph [0058], “...distributions are compared individually through a similarity metric...”)
Regarding claim 21, ‘606 teaches the material disclosed in claim 19, and additionally teaches:
wherein the one or more comparisons include calculation and comparison of correlation matrices of two or more variables associated with the protected batch of data and the simulated batch of data. (Paragraph [0058], “...the maximum absolute difference in correlations matrices of the original and generated datasets is calculated…”)
Regarding independent claim 24, ‘606 teaches:
A computer program product to determine whether synthetic data is sufficient for utilization in connection with one or more machine learning models, the computer program product comprising:
one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method (Paragraph [0116], “It is to be appreciated that certain embodiments of the disclosure as described herein may be incorporated as code (e.g., a software algorithm or program)residing in firmware and/or on computer useable medium...” Code is program instructions.), the method comprising:
accessing by a computing device a protected batch of data associated with a machine learning model; (Paragraph [0044], “Dataset generator 152 is configured to receive an input dataset 140.” The input dataset is a protected batch of data)
accessing by the computing device a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data; (Paragraph [0048], “In an embodiment, (anonymized) biased dataset generator 100 is configured to produce a synthetic dataset with domain constraints 116.” The anonymized biased dataset generator uses the input dataset)
access results of one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value; (Paragraph [0058], “…distributions are compared individually through a similarity metric, such as the Jansen-Shannon divergence…”) and
performing by the computing device a machine learning function (Paragraph [0026], “This evaluation is typically performed in two different characteristics of the dataset: a) predictive performance on the generated data, and b) statistical similarity to the seed dataset.” Predictive performance on the generated data requires performing a prediction which is a machine learning function.) utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold. (Paragraph [0059] An objective of the disclosed sampling method is to obtain one or more numerical variables, and underlying distributions, which are capable of producing a linear decision boundary with a given expected functioning point in the ROC space, with user-defined TPR and FPR.” The numerical variables and underlying distributions are similarity values that need to meet or exceed a threshold.)
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 6, 12, 17, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over '606, in view of US 20230377421 A1 by Russ et al., hereafter '421.
Regarding claim 6, ‘606 teaches the material disclosed in claim 1:
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a hierarchy cluster to compare all variables in the protected batch of data and the simulated batch of data.
‘421 teaches:
Comparisons of data including generation of hierarchical clusters. ((‘421) Paragraph [0116], “In some aspects, the neural network may support various clustering algorithms, such as partitive (or centroidal) or hierarchical clustering, both of which separate data into groups whose members share maximum similarity as defined (typically) by a distance metric.”)
‘421 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of hierarchical clusters, as taught by ‘421, into the other comparisons taught by ‘606. The motivation for this would be to include a hierarchical method of data comparison as real-world data often contains hierarchical structures. This combination of a specific type of comparison taught by ‘421 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to the claim 6 of the instant application.
Regarding claim 12, ‘606 teaches the material disclosed in claim 9:
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a hierarchy cluster to compare all variables in the protected batch of data and the simulated batch of data.
‘421 teaches:
Comparisons of data including generation of hierarchical clusters. ((‘421) Paragraph [0116], “In some aspects, the neural network may support various clustering algorithms, such as partitive (or centroidal) or hierarchical clustering, both of which separate data into groups whose members share maximum similarity as defined (typically) by a distance metric.”)
‘421 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of hierarchical clusters, as taught by ‘421, into the other comparisons taught by ‘606. The motivation for this would be to include a hierarchical method of data comparison as real-world data often contains hierarchical structures. This combination of a specific type of comparison taught by ‘421 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to claim 12 of the instant application.
Regarding claim 17, ‘606 teaches the material disclosed in claim 14:
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a hierarchy cluster to compare all variables in the protected batch of data and the simulated batch of data.
‘421 teaches:
Comparisons of data including generation of hierarchical clusters. ((‘421) Paragraph [0116], “In some aspects, the neural network may support various clustering algorithms, such as partitive (or centroidal) or hierarchical clustering, both of which separate data into groups whose members share maximum similarity as defined (typically) by a distance metric.”)
‘421 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of hierarchical clusters, as taught by ‘421, into the other comparisons taught by ‘606. The motivation for this would be to include a hierarchical method of data comparison as real-world data often contains hierarchical structures. This combination of a specific type of comparison taught by ‘421 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to claim 17 of the instant application.
Regarding claim 22, ‘606 teaches the material disclosed in claim 19:
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a hierarchy cluster to compare all variables in the protected batch of data and the simulated batch of data.
‘421 teaches:
Comparisons of data including generation of hierarchical clusters. ((‘421) Paragraph [0116], “In some aspects, the neural network may support various clustering algorithms, such as partitive (or centroidal) or hierarchical clustering, both of which separate data into groups whose members share maximum similarity as defined (typically) by a distance metric.”)
‘421 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of hierarchical clusters, as taught by ‘421, into the other comparisons taught by ‘606. The motivation for this would be to include a hierarchical method of data comparison as real-world data often contains hierarchical structures. This combination of a specific type of comparison taught by ‘421 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to claim 22 of the instant application.
Claim(s) 7, 8, 13, 18, 23 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over '606, in view of provisional application 63/460642 with publication US 20240354647 A1 by Rashidi et al., hereafter '642.
Regarding claim 7, ‘606 teaches the material disclosed in claim 1.
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a relationship correlation between one or more traits displayed by variables included in the protected batch of data and the simulated batch of data.
‘642 does teach:
Generation of relationship correlations between traits displayed by variables included in the real protected batch of data and the synthetic simulated batch of data. ((‘642) Paragraph [0035], “Regarding bivariate relationships, the pairwise correlations were shown for the same set of variables in the real and synthetic datasets”)
‘642 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of relationship correlations, as taught by ‘642, into the other comparisons taught by ‘606. The motivation for this would be further evaluations using commonly used statistics that many would be able to understand. This combination of a specific type of comparison taught by ‘642 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to claim 7 of the instant application.
Regarding claim 8, ‘606 teaches the material disclosed in claim 1.
‘606 does not explicitly teach:
wherein the computing device displays an output of the one or more comparisons, the output displaying a difference in the protected batch of data and the simulated batch of data.
‘642 does teach:
Displaying an output of comparisons ((‘642) Table 4, Table 4 is a display of the output of comparisons. ) and the outputs displaying a difference in the protected real batch of data and the synthetic simulated batch of data. ((‘642) Table 2; Paragraph [0027d], “Cross-classification metric measures the similarity of the prediction performances from the synthetic and real datasets.” Cross-classification metrics are part of the outputs. Measuring similarities also measures differences. Pairwise correlation difference is a difference.)
‘642 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined displaying an output showing a difference between real and synthetic data , as taught by ‘642, into the calculations of the differences and comparisons taught by ‘606. The motivation for this would be so that viewers would be able to understand the output information. This combination of a display of comparison taught by ‘642 with calculation of the comparison taught by ‘606 would not cause a change in the function of either the display or calculation of comparison metrics and would yield a predictable result that is equivalent to claim 8 of the instant application.
Regarding claim 13, ‘606 teaches the material disclosed in claim 9.
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a relationship correlation between one or more traits displayed by variables included in the protected batch of data and the simulated batch of data.
‘642 does teach:
Generation of relationship correlations between traits displayed by variables included in the real protected batch of data and the synthetic simulated batch of data. ((‘642) Paragraph [0035], “Regarding bivariate relationships, the pairwise correlations were shown for the same set of variables in the real and synthetic datasets”)
‘642 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of relationship correlations, as taught by ‘642, into the other comparisons taught by ‘606. The motivation for this would be further evaluations using commonly used statistics that many would be able to understand. This combination of a specific type of comparison taught by ‘642 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to claim 13 of the instant application.
Regarding claim 18, ‘606 teaches the material disclosed in claim 14.
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a relationship correlation between one or more traits displayed by variables included in the protected batch of data and the simulated batch of data.
‘642 does teach:
Generation of relationship correlations between traits displayed by variables included in the real protected batch of data and the synthetic simulated batch of data. ((‘642) Paragraph [0035], “Regarding bivariate relationships, the pairwise correlations were shown for the same set of variables in the real and synthetic datasets”)
‘642 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of relationship correlations, as taught by ‘642, into the other comparisons taught by ‘606. The motivation for this would be further evaluations using commonly used statistics that many would be able to understand. This combination of a specific type of comparison taught by ‘642 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to claim 18 of the instant application.
Regarding claim 23, ‘606 teaches the material disclosed in claim 19.
‘606 does not explicitly teach:
wherein the one or more comparisons include generation of a relationship correlation between one or more traits displayed by variables included in the protected batch of data and the simulated batch of data.
‘642 does teach:
Generation of relationship correlations between traits displayed by variables included in the real protected batch of data and the synthetic simulated batch of data. ((‘642) Paragraph [0035], “Regarding bivariate relationships, the pairwise correlations were shown for the same set of variables in the real and synthetic datasets”)
‘642 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined including generation of relationship correlations, as taught by ‘642, into the other comparisons taught by ‘606. The motivation for this would be further evaluations using commonly used statistics that many would be able to understand. This combination of a specific type of comparison taught by ‘642 with other metrics for comparison taught by ‘606 would not cause a change in the function of any of the comparison metrics and would yield a predictable result that is equivalent to claim 23 of the instant application.
Regarding claim 25, ‘606 teaches the material disclosed in claim 24.
‘606 does not explicitly teach:
wherein the computing device displays an output of the one or more comparisons, the output displaying a difference in the protected batch of data and the simulated batch of data.
‘642 does teach:
Displaying an output of comparisons ((‘642) Table 4, Table 4 is a display of the output of comparisons. ) and the outputs displaying a difference in the protected real batch of data and the synthetic simulated batch of data. ((‘642) Table 2; Paragraph [0027d], “Cross-classification metric measures the similarity of the prediction performances from the synthetic and real datasets.” Cross-classification metrics are part of the outputs. Measuring similarities also measures differences. Pairwise correlation difference is a difference.)
‘642 and ‘606 are analogous art because they contain the same area of invention, that being use of synthetic data generation for analysis.
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to have combined displaying an output showing a difference between real and synthetic data , as taught by ‘642, into the calculations of the differences and comparisons taught by ‘606. The motivation for this would be so that viewers would be able to understand the output information. This combination of a display of comparison taught by ‘642 with calculation of the comparison taught by ‘606 would not cause a change in the function of either the display or calculation of comparison metrics and would yield a predictable result that is equivalent to claim 25 of the instant application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to synthetic data generation.
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D.H.L.
Examiner
Art Unit 2144
/TAMARA T KYLE/ Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights