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 .
Response to Arguments
In response to 35 USC 101 on pages 7-8 of the remarks, filed 6/23/2026, the 35 USC 101 rejection has been withdrawn considering the claim amendment.
In response to 35 USC 112 on pages 8-9 of the remarks, filed 6/23/2026, part of the rejection has been withdrawn. However, as mention in the rejection, the BRI of the claim requires the functional language of a device must somehow “obtaining a size of a hashing domain for one or more hash functions, each configured to process source data to generate an output value; obtaining a probability of including each output value in a corresponding message; obtaining a differential privacy parameter; and generating a variance of the quantity of times the source data was a cause of a message over the one or more hash functions using the size of the hashing domain, the probability, and a value based on the differential privacy parameter”. The recited functions do not flow from the structure recited in the claim. The functional languages are not performed by any element in the claim. For example, what part of the computer are performing these functional languages. Please see MPEP 2173.05(g).
In response to 35 USC 102 and 103 on pages 9-10 of the remarks, filed 6/23/2026, on independent claims 1, 16 and 20 along with their respective dependent claims. Applicant argues that Fang does not generate the variance of using the inputs. Fang evaluates protocol behavior through empirical execution and observation and does not disclose analytically deriving a variance value without performing the protocol.
The examiner does not concede. Fang discloses generating the variance of using the inputs. Fang teaches “generating a variance of the quantity of times the source data was a cause of a message over the one or more hash functions using the size of the hashing domain, the probability, and a value based on the differential privacy parameter”. Fang discloses “selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5][Section 5.3] Please see Algorithms 1 and 2”. The algorithms shows inputs of size of the hashing domain, probability and differential privacy parameter. Fang further teaches experimental results demonstrate that the protocol can achieve a balance between data utility and privacy protection. The prior art further recites “the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Page 5]”.
Fang does not explicitly teach “updating one or more parameters of the LDP protocol based on a determination that generated variance does not meet a threshold variance”.
Applicant’s argument have been considered but are moot, because the newly recited amendment does not rely on the newly recited reference being applied to the prior rejection of record or any teaching or matter specifically challenged in the argument.
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Re. claims 1, 16 and 20; the claims recite “obtaining a size of a hashing domain for one or more hash functions, each configured to process source data to generate an output value; obtaining a probability of including each output value in a corresponding message; obtaining a differential privacy parameter; and generating a variance of the quantity of times the source data was a cause of a message over the one or more hash functions using the size of the hashing domain, the probability, and a value based on the differential privacy parameter”. The BRI of the claim requires the functional language of a device must somehow “obtaining a size of a hashing domain for one or more hash functions, each configured to process source data to generate an output value; obtaining a probability of including each output value in a corresponding message; obtaining a differential privacy parameter; and generating a variance of the quantity of times the source data was a cause of a message over the one or more hash functions using the size of the hashing domain, the probability, and a value based on the differential privacy parameter”. The recited functions do not flow from the structure recited in the claim. The functional language are not performed by any element in the claim.
The boundaries of the functional language is unclear because the claim does not provide a discernable boundary on what performs the function. It is unclear whether the function requires structure or simply a result from the computer. Thus, one of ordinary skill in the art would not be able to draw a clear boundary between what is and is not covered by the claim. Please see MPEP 2173.05(g).
Claims 2-15, 17-19 fall together accordingly as they do not cure the deficiencies of the independent claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fang et al. (“Local differential privacy for human-centered computing”, hereinafter Fang) in view of Cheng (CN 118333139).
Re. claim 1, Fang discloses a computer-implemented method comprising: obtaining a size of a hashing domain for one or more hash functions of a local differential privacy (LDP) protocol, each of the one or more hash functions configured to process source data to generate an output value (Fang discloses size g by using hash function [Section 4.1.2][Section 4.1.5] Please see Algorithms 1 and 2); obtaining a probability of including the output value in a corresponding message (Fang discloses probability p [Section 4.1.1] Please see Algorithms 1 and 2); obtaining a differential privacy parameter of the LDP protocol (Fang discloses the parameter ɛ differential privacy [Section 4.1.1] Please see Algorithms 1 and 2); and generating a variance of the quantity of times the source data was a cause of a message over the one or more hash functions using the size of the hashing domain, the probability, and a value based on the differential privacy parameter (Fang discloses selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5][Section 5.3] Please see Algorithms 1 and 2. The algorithms shows inputs of size of the hashing domain, probability and differential privacy parameter).
Fang does not explicitly teach but Cheng teaches updating one or more parameters of the LDP protocol based on a determination that generated variance does not meet a threshold variance (Cheng teaches determined that the initial model parameter w<sub>q</sub> does not exceed the preset space, proceed to step 4 [n0074]. When the initial model parameter w<sub>q</sub> of this iteration does not exceed the preset space, member device A updates the initial model parameter w<sub>q</sub> based on the first gradient g to obtain the updated model parameter w<sub>q+1</sub> of this iteration [n0091]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to modify the method and system disclosed by Fang to include updating one or more parameters of the LDP protocol based on a determination that generated variance does not meet a threshold variance as disclosed by Cheng. One of ordinary skill in the art would have been motivated for the purpose of adapt to the impact of differential privacy pruning operations (Cheng [n0095]).
Re. claim 2, Fang-Cheng teach the method of claim 1, wherein generating the variance of the quantity of times the source data was a cause of a message over the one or more hash functions further comprises using a number of hash functions in the one or more hash functions (Fang discloses smaller hash value domain g by using a hash function [Section 4.1.2]. Selected index from k hash functions [Section 4.1.4] Please see Algorithms 1 and 2).
Re. claim 3, Fang-Cheng teach the method of claim 1, wherein generating the variance of the quantity of times the source data was a cause of a message over the one or more hash functions further comprises using a variance of a total count of the output value over the one or more hash functions (Fang discloses smaller hash value domain g by using a hash function [Section 4.1.2]. Selected index from k hash functions [Section 4.1.4] Please see Algorithms 1 and 2).
Re. claim 4, Fang-Cheng teach the method of claim 3, wherein the variance of the total count of the output value over the one or more hash functions is generated using the size of the hashing domain, the probability, and the value based on the differential privacy parameter (Fang discloses selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5][Section 5.3] Please see Algorithms 1 and 2).
Re. claim 5, Fang-Cheng teach the method of claim 1, further comprising performing an action using the variance of the quantity of times the source data was a cause of a message over the one or more hash functions (Fang discloses selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5][Section 5.3] Please see Algorithms 1 and 2).
Re. claim 6, Fang-Cheng teach the method of claim 5, wherein the action comprises updating one or more parameters based on at least the variance of the quantity of times the source data was a cause of a message over the one or more hash functions (Fang discloses selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5]. The aggregator structures the count sketch matrix and cumulates the number of mapping positions for each attribute value under different hash functions. The server side obtains each data value frequency estimation by matrix count. Estimating the data entry d as an example, the aggregator counts the number of xj’s frequency of the corresponding mapping position under different hash functions and sums up the numbers, as shown in Fig. 2 b. [Section 4.2] Please see Algorithms 1 and 2).
Re. claim 7, Fang-Cheng teach the method of claim 6, wherein updating one or more parameters based on at least the variance of the quantity of times the source data was a cause of a message over the one or more hash functions comprises updating the one or more parameters based on the size of the hashing domain and the variance of the quantity of times the source data was a cause of a message over the one or more hash functions (Fang discloses selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5]. The aggregator structures the count sketch matrix and cumulates the number of mapping positions for each attribute value under different hash functions. The server side obtains each data value frequency estimation by matrix count. Estimating the data entry d as an example, the aggregator counts the number of xj’s frequency of the corresponding mapping position under different hash functions and sums up the numbers, as shown in Fig. 2 b. [Section 4.2]).
Re. claim 8, Fang-Cheng teach the method of claim 6, wherein the one or more parameters comprise any one or more of: the probability, the differential privacy parameter, the size of the hashing domain, or a number of the one or more hash functions (Fang discloses selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5]. The aggregator structures the count sketch matrix and cumulates the number of mapping positions for each attribute value under different hash functions. The server side obtains each data value frequency estimation by matrix count. Estimating the data entry d as an example, the aggregator counts the number of xj’s frequency of the corresponding mapping position under different hash functions and sums up the numbers, as shown in Fig. 2 b. [Section 4.2] Please see Algorithms 1 and 2).
Re. claim 9, Fang-Cheng teach the method of claim 6, further comprising providing data specifying the updated parameters to one or more client devices (Fang discloses the aggregator is designed on the server side to aggregate the reports. When the central server receives all the perturbed reports from the client side, the server will aggregate them through an aggregator [Section 4.2]. Client side sends the report. The choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Page 5]. Analyze the utility of our protocol for different parameters and scenarios [6.4 Experimental metrics] Please see Algorithms 1 and 2).
Re. claim 10, Fang-Cheng teach the method of claim 1, further comprising: determining a data batch comprising a plurality of messages, each generated by processing a corresponding source data of a plurality of source data using one of the one or more hash functions to generate the output value; and predicting a quantity of times the source data was a cause of a message over the one or more hash functions by using a total count of the output value, the size of the hashing domain, the probability, a number of messages in the plurality of messages, and the value based on the differential privacy parameter (Fang discloses it takes each report w(i) and transforms it to x(i). Then, the server constructs the sketch matrix MH and add x(i) to row j(i), column l(i) of MH. Next, it uses the transpose Hadamard matrix to transform the rows of sketch back. At last, the server estimates the count of entry d∈|D| by debiasing the count and averaging over the corresponding hash entries in MH. [Section 4.1.4] Please see Algorithms 1 and 2).
Re. claim 11, Fang-Cheng teach the method of claim 10, wherein generating the variance of the quantity of times the source data was a cause of a message over the one or more hash functions further comprises using the quantity of times the source data was a cause of a message over the one or more hash functions (Fang discloses selecting parameters and concerning the size of the data. For example, the choice of parameters k and m in the HCMS algorithm greatly influences data variance and utility, and different tasks need to identify different suitable parameters [Section 4.1.5]. The aggregator structures the count sketch matrix and cumulates the number of mapping positions for each attribute value under different hash functions. The server side obtains each data value frequency estimation by matrix count. Estimating the data entry d as an example, the aggregator counts the number of xj’s frequency of the corresponding mapping position under different hash functions and sums up the numbers, as shown in Fig. 2 b. [Section 4.2] Please see Algorithms 1 and 2).
Re. claim 12, Fang-Cheng teach the method of claim 10, wherein each of the plurality of messages is further generated by: determining whether to include the output value in the message according to the probability; and in response to determining to include the output value in the message, generating the message that includes a quantity of noise values and the output value (Fang discloses when a user generates data, the local perturbator selects a random hash function to encode the data as a one-hot encoding and adds the Laplace noises in the mapping location. Then, the local perturbator sends the report containing the selected hash function index and the noised mapping location to the central server. Since the client-side algorithm satisfies the LDP definition, even if the adversary has the relevant background knowledge and acquires another user’s data, the adversary cannot infer which data are the user’s data [Section 4.2][Section 7.3]).
Re. claim 13, Fang-Cheng teach the method of claim 10, wherein each of the plurality of messages is further generated by: determining whether to include the output value in the message according to the probability; and in response to determining not to include the output value in the message, generating the message that includes a quantity of noise values (Fang discloses when a user generates data, the local perturbator selects a random hash function to encode the data as a one-hot encoding and adds the Laplace noises in the mapping location. Then, the local perturbator sends the report containing the selected hash function index and the noised mapping location to the central server. Since the client-side algorithm satisfies the LDP definition, even if the adversary has the relevant background knowledge and acquires another user’s data, the adversary cannot infer which data are the user’s data [Section 4.2][Section 7.3]).
Re. claim 14, Fang-Cheng teach the method of claim 10, further comprising obtaining the total count of the output value using a matrix that maintains anonymized data for messages (Fang discloses Then, the server constructs the sketch matrix MH and add x(i) to row j(i), column l(i) of MH. Next, it uses the transpose Hadamard matrix to transform the rows of sketch back [Section 4.1.4][Section 4.2]).
Re. claim 15, Fang-Cheng teach the method of claim 10, further comprising performing an action using the predicted quantity of times the source data was the cause of a message over the one or more hash functions (Fang discloses The aggregator is designed on the server side to aggregate the reports. When the central server receives all the perturbed reports from the client side, the server will aggregate them through an aggregator. The aggregator structures the count sketch matrix and cumulates the number of mapping positions for each attribute value under different hash functions [Section 4.2] Please see Algorithms 1 and 2).
Re. claims 16 and 20, claims 16 and 20 are rejected with the same rationale as applied in claim 1. Fang further teaches a system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations (Fang discloses client and server [Section3.4]. CPU and RAM [Section 6.3])
Re. claim 17, rejection of claim 16 is included and claim 17 is rejected with the same rationale as applied in claim 2 above.
Re. claim 18, rejection of claim 16 is included and claim 18 is rejected with the same rationale as applied in claim 3 above.
Re. claim 19, rejection of claim 16 is included and claim 19 is rejected with the same rationale as applied in claim 5 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (CN 115664676) teaches a differential privacy mechanism that can be used to privatize user data collected for crowdsourcing.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KEVIN AYALA/Primary Examiner, Art Unit 2496
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