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 1-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-8, 9-13, 14-20 are directed to a method, medium and apparatus of noise addition for differential privacy with preservation of data utility. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 9 and 14 these claims recite
obtaining a first embedding generated for first data, wherein the first embedding comprises a plurality of values for a corresponding plurality of dimensions; scaling each of the plurality of values of the first embedding to generate a scaled embedding, wherein scaling each of the plurality of values comprises scaling the value relative to a minimum value and a maximum value, wherein the minimum and maximum values for each dimension were previously selected from a plurality of embeddings generated from experimental data; and adding noise to the scaled embedding.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0015-0022], Fig. 1. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 9, 14 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 9 and 14: The claims do not include additional elements, alone or in combination, 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 elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). Further the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “obtainin…”, “scaling”… “adding…”, etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 10, 15
Claim 2, 10, 15 merely recite other additional elements that define adding noise to the scaled embedding which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 12, 16
Claim 3, 12, 16 merely recite other additional elements that check if the scaled embedding with added noise has sensitive data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 13, 17
Claim 4, 13, 17 merely recite other additional elements that check inputting the scaled embedding with added noise to a classifier which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 18
Claim 5, 18 merely recite other additional elements that adding the scaled embedding with added noise with a label into a training dataset which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 11, 19
Claim 6, 11, 19 merely recite other additional elements that generating the embeddings for the data and selecting the range values for each dimension which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7-8
Claim 7-8 merely recite other additional elements that generating the data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 20
Claim 20 merely recite other additional elements that scaling embedding the values corresponding to min./max. range identified for each dimension which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dang et al. (Dang) US 20240403708 in view of Beigi et al. (Beigi) US 11763093
In regard to claim 1, Dang disclose A method comprising:
obtaining a first embedding generated for first data, wherein the first embedding comprises a plurality of values for a corresponding plurality of dimensions; ([0034]-[0041] [0073]-[0078] extract feature embeddings generated from data samples, the embeddings are vector values corresponding to the dimensions)
scaling each of the plurality of values of the first embedding to generate a scaled embedding, wherein scaling each of the plurality of values comprises scaling the value relative to a minimum value and a maximum value, ([0034]-[0045] [0073]-[0078][0098]-[0101] scaling the vector values of the embeddings to generate scaled embeddings, and the vector values with range from min. to max. for each of the dimensions of the feature vectors) wherein the minimum and maximum values for each dimension were previously selected from a plurality of embeddings generated from experimental data; ([0034]-[0045] [0073]-[0078][0098]-[0101] vector values with range from min. to max. for each of the dimensions of the feature vectors are selected from the embeddings generated from data samples)
But Dang fail to explicitly disclose “and adding noise to the scaled embedding.”
Beigi disclose and adding noise to the scaled embedding. (col. 3, line 4-55, col. 7, line 28-48, add the noise to the data representation)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Beigi’s privacy preserving text representation learning into Dang’s invention as they are related to the same field endeavor of method of machine learning. The motivation to combine these arts, as proposed above, at least because Beigi’s machine learning with privacy preservation by adding noise would help to provide more machine learning method into Dang’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made providing more ML methods would help to improve prediction accuracy of the ML.
In regard to claim 2, Dang and Beigi disclose The method of claim 1,
But Dang fail to explicitly disclose “wherein adding noise to the scaled embedding comprises adding noise to the scaled embedding with the Laplace mechanism.”
Beigi disclose wherein adding noise to the scaled embedding comprises adding noise to the scaled embedding with the Laplace mechanism. (col. 3, line 4-55, col. 7, line 28-48, add the noise to the data representation with Laplace mechanism)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Beigi’s privacy preserving text representation learning into Dang’s invention as they are related to the same field endeavor of method of machine learning. The motivation to combine these arts, as proposed above, at least because Beigi’s machine learning with privacy preservation by adding noise would help to provide more machine learning method into Dang’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made providing more ML methods would help to improve prediction accuracy of the ML.
In regard to claim 3, Dang and Beigi disclose The method of claim 1,
But Dang fail to explicitly disclose “further comprising determining if the scaled embedding with added noise comprises sensitive data.”
Beigi disclose further comprising determining if the scaled embedding with added noise comprises sensitive data. (col. 9, line 5-col. 10, line 3, determine if the scaled data representations with added noise has private information)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Beigi’s privacy preserving text representation learning into Dang’s invention as they are related to the same field endeavor of method of machine learning. The motivation to combine these arts, as proposed above, at least because Beigi’s machine learning with privacy preservation by adding noise would help to provide more machine learning method into Dang’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made providing more ML methods would help to improve prediction accuracy of the ML.
In regard to claim 4, Dang and Beigi disclose The method of claim 3,
But Dang fail to explicitly disclose “wherein determining if the scaled embedding with added noise comprises sensitive data comprises inputting the scaled embedding with added noise into a trained classifier, wherein the trained classifier was trained to predict whether inputs comprise sensitive data.”
Beigi disclose wherein determining if the scaled embedding with added noise comprises sensitive data comprises inputting the scaled embedding with added noise into a trained classifier, wherein the trained classifier was trained to predict whether inputs comprise sensitive data. (col. 9, line 5-col. 10, line 3, input the scaled data representations with added noise into a classifier and the classifier is trained to predict if the input has private information)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Beigi’s privacy preserving text representation learning into Dang’s invention as they are related to the same field endeavor of method of machine learning. The motivation to combine these arts, as proposed above, at least because Beigi’s machine learning with privacy preservation by adding noise would help to provide more machine learning method into Dang’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made providing more ML methods would help to improve prediction accuracy of the ML.
In regard to claim 5, Dang and Beigi disclose The method of claim 4,
But Dang fail to explicitly disclose “further comprising adding the scaled embedding with added noise and a label indicating whether the scaled embedding with added noise comprises sensitive data into a training dataset for ongoing learning of the trained classifier.”
Beigi disclose further comprising adding the scaled embedding with added noise and a label indicating whether the scaled embedding with added noise comprises sensitive data into a training dataset for ongoing learning of the trained classifier. (col. 9, line 5-col. 10, line 3, col. 11, line 10- 39, col. 15, line 13-25, adding the scaled data representations with added noise and with a label to indicate the data has sensitive data into a training dataset)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Beigi’s privacy preserving text representation learning into Dang’s invention as they are related to the same field endeavor of method of machine learning. The motivation to combine these arts, as proposed above, at least because Beigi’s machine learning with privacy preservation by adding noise would help to provide more machine learning method into Dang’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made providing more ML methods would help to improve prediction accuracy of the ML.
In regard to claim 6, Dang and Beigi disclose The method of claim 1,
Dang disclose further comprising: generating the plurality of embeddings from the experimental data; and selecting, for each dimension of the plurality of dimensions, the minimum and maximum values identified at the dimension. ([0034]-[0045] [0073]-[0078][0098]-[0101] vector values with range from min. to max. for each of the dimensions of the feature vectors are selected from the embeddings generated from data samples identified at the dimension)
In regard to claim 7, Dang and Beigi disclose The method of claim 6,
Dang disclose further comprising generating the experimental data. ([0106] [0126]-[0131] generate the input data)
In regard to claims 9-10, 12-13, claims 9-10, 12-13 are medium claims corresponding to the method claims 1-4 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-4
In regard to claim 11, claim 11 is a medium claim corresponding to the method claim 6 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 6, Dang further disclose wherein the instructions to scale the value relative to the minimum value and the maximum value comprise instructions to scale the value relative to corresponding ones of the minimum and maximum values. ([0034]-[0045] [0073]-[0078][0098]-[0101][0110] vector values are scaled with range from min. to max. for each of the dimensions of the feature vectors corresponding to min. and max. values)
In regard to claims 14-19, claims 14-19 are apparatus claims corresponding to the method claims 1-6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-6.
In regard to claim 20, The apparatus of claim 19, Dang further disclose wherein the instructions executable by the processor to cause the apparatus to scale each of the plurality of values relative to the corresponding minimum and maximum values comprise instructions executable by the processor to cause the apparatus to scale, for each dimension of the plurality of dimensions, the corresponding one of the plurality of values relative to the minimum value and a maximum value identified at that dimension. ([0034]-[0045] [0073]-[0078][0098]-[0101][0110] vector values are scaled with range from min. to max. for each of the dimensions of the feature vectors corresponding to min. and max. values identified for the dimension)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Dang et al. (Dang) US 20240403708 and Beigi et al. (Beigi) US 11763093 as applied to claim 7, further in view of Dai et al. (Dai) US 20260064743
In regard to claim 8, Dang and Beigi disclose The method of claim 7,
But Dang and Beigi fail to explicitly “disclose wherein generating the experimental data comprises generating the experimental data based on prompting a language model.”
Dai disclose disclose wherein generating the experimental data comprises generating the experimental data based on prompting a language model. ([0006]-[0012][0056]-[0059][0105]-[0106] generating synthetic training dataset base don prompting a LLM)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Dai’s prompt based data generation into Beigi and Dang’s invention as they are related to the same field endeavor of method of generating training data for machine learning. The motivation to combine these arts, as proposed above, at least because Dai’s prompt based data generation using LLM would help to provide more training data generation method into Beigi and Dang’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made providing more training data generation method would help to improve prediction accuracy of the ML.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20230032705 A1 2023-02-02 Navalpakkam et al.
Differentially Private Heatmaps
Navalpakkam et al. disclose Improved methods are provided for generating heatmaps or other summary map data from multiple users' data (e.g., probability distributions) in a manner that preserves the privacy of the users' data while also generating heatmaps that are visually similar to the ‘true’ heatmap. These methods include decomposing the average of the users' data (the ‘true’ heatmap) into multiple different spatial scales, injecting random noise into the data at the multiple different spatial scales, and then reconstructing the privacy-preserving heatmap based on the noisy multi-scale representations. The magnitude of the noise injected at each spatial scale is selected to ensure preservation of privacy while also resulting in heatmaps that are visually similar to the ‘true’ heatmap… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights