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 .
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 Judicial Exceptions without significantly more. The claims recite mathematical relationships, mathematical formulas or equations, mathematical calculation and a mental process. This judicial exception is not integrated into a practical application because the recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims only recites generic computer components, which are well-understood, routine, and conventional.
Revised Patent Subject Matter Eligibility Guidance
The USPTO has published revised guidance on the application of § 101. USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“Guidance”). Under the Guidance, the Examiner first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Guidance, Step 2A, prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure (MPEP) § 2106.05(a)-(c), (e)-(h) (9th Ed., Rev. 08.2017, 2018)) (Guidance, Step 2A, prong 2).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do the Examiner then look to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field (see MPEP § 2106.05(d)); or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. (Guidance (Step 2B)).
Evaluate Step 2A Prong One
(a) identify the specific limitation(s) in the claim that recites an abstract idea;
(b) determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
In TABLE 1 below, the Examiner identifies in italics the specific claim limitations that recite an abstract idea.
TABLE 1
Independent Claim 1
Analysis Under Revised Guidance
(a) A method, comprising:
(b) determining query types of L queries to be performed on a target dataset
“determining query types” is an abstract idea, i.e., “a mental process”, which can be performed in the human mind.
(c) determining query sensitivity of each query type of the query types for the target dataset, the query sensitivity of a query type indicating a difference between a first result obtained by performing a query of the query type on the target database and a second result obtained by performing the query of the query type on an adjacent dataset of the target dataset
“determining query sensitivity…” is an abstract idea, i.e., “mental process” or a “mathematical calculation”, mathematical formula”, which can be performed in the human mind or with the aid of pen and paper.
(d) determining, based on query sensitivity corresponding to each query type of the L queries and privacy budget parameter determined for a total set of the L queries, a noise power allocated to each query type of the L queries, respectively.
“determining… a noise power allocated to each query…” is an abstract idea, i.e., “mental process” or a “mathematical calculation”, mathematical formula”, which can be performed in the human mind or with the aid of pen and paper.
(e) obtaining a query result of a target query of a target query type on the target database based on an initial query result of the target query on the target database and a noise power allocated to the target query type.
“obtaining a query result…based on an initial query result… and a noise power…” is an abstract idea, i.e., “mental process” or a “mathematical calculation”, mathematical formula”, which can be performed in the human mind or with the aid of pen and paper.
In view of the above analysis, Claim 1 recites an abstract idea under the Revised Guidance because the limitations (b) – (e) each recite a mental process and/or mathematical relationship, mathematical calculation, mathematical formula. Independent claims 19-20 also recite an abstract idea because it includes similar limitations (b) – (e). Dependent claims 2-18 also recite abstract idea because they include limitations (b) – (e) by virtue of their dependencies to claim 1.
Dependent claims 2-18 further recites additional limitations. However, these limitations are also abstract idea, i.e., “mental process, mathematical concept – mathematical formulas or equations, mathematical calculations” similar to the limitations of claims 1, 19-20 discussed above.
Evaluate Step 2A Prong Two:
Evaluate whether the claim as a whole integrated the recited Judicial exception into a Practical Application of the exception.
Having determined that the claims recite a judicial exception, the analysis under the Guidance turns now to determining whether there are “additional element that integrate the judicial exception into a practical application”. The examiner determines whether the recited judicial exception is integrated into a practical application that exception by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exceptions; and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”.
Independent claim 1 does not recite any additional element that integrate the judicial exception into a practical application. Independent claim 19 recites “a computer system”, “processors”, “storage device”; independent claim 20 recites “a non-transitory storage medium”, “computer executable instructions”, which are simply a generic computer component to store and execute computer instructions, which causes a generic computer system to perform the operations recited in limitations (b)-(c). The “computer system”, “non-transitory medium” “processor” recited in the claims are so generically that is represents no more than mere generic computer component to apply the judicial exception on a computer. The recitation of generic computer and generic computer components does not sufficient to integrate the recited judicial exception into a practical application. Guidance at 52 n.14 (“Performance of a claim limitation using generic computer components does not necessarily preclude the claim limitation from being in the mathematical concepts grouping.”)
Evaluate Step 2B:
Evaluate whether the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well-known. See MPEP 2106.05(g). The claim does not add any specific limitations beyond what is well-understood, routine, and conventional. Here, claims 19, 207 recite “a computer system”, “non-transitory storage medium”, “processors”, which are mere generic computer components that are recited at a high level of generality, and, as disclosed in the specification, is also well-understood, routine, conventional activity when expressed at this high level of generality. Mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the claims do not provide an inventive concept (significantly more than the abstract idea) and is not eligible.
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.
Independent claims 1, 19-20 recite the limitation "the target database" in line 5. There is insufficient antecedent basis for this limitation in the 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)(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.
Claims 1-8, 12-16, 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Damewood et al. (US 2021/0256151 A1), hereinafter “Damewood”.
As per claim 1, Damewood teaches a method comprising:
“determining query types of L queries to be performed on a target dataset” at [0023], [0036];
(Damewood teaches receiving a set of queries 108 submitted by the client 104 to be performed on the database 106, and determining query types of the queries)
“determining query sensitivity of each query type of the query types for the target dataset, the query sensitivity of a query type indicating a difference between a first result obtained by performing a query of the query type on the target database and a second result obtained by performing the query of the query type on an adjacent dataset of the target dataset” at [0024], [0039], [0048]-[0053];
(Damewood teaches determining global and local sensitivity of a query M by performing query M on X and X’, wherein X and X’are two neighboring databases and on of X, X’ has all the same entries as the other, plus one additional entry. Global and local sensitivity indicate a difference between a first result obtain by performing query M on X (i.e., “M(x)”)and a second result obtained by performing query M on X’ (i.e., “M(X)”))
“determining, based on query sensitivity corresponding to each query type of the L queries and a privacy budget parameter determined for a total set of the L queries, a noise power allocated to each query type of the L queries, respectively” at [0026], [0044], [0048]-[0053], [0058]-[0067];
(Damewood teaches perturbing the results released by the database 106 with noises that provides the differential privacy specified by the privacy parameters while enforcing the privacy budget. Damewood teaches iteratively adjusts the noise added to the results to provide differentially private results that satisfy a target accuracy based on the query sensitivity, while minimizing the privacy spend)
“obtaining a query result of a target query of a target query type on the target database based on an initial query result of the target query on the target database and a noise power allocated to the target query type” at [0058]-[0070].
(Damewood teaches obtaining query result and incorporating the noise into the query result to produce differential privacy to result. The DP system then sends the differentially private result to the client in response to the query)
As per claim 2, Damewood teaches the method of claim 1, wherein “the determining the query types of the L queries to be performed on the target dataset includes: receiving L query requests for the target dataset, wherein each query request indicates a query type of the query request” at [0035]-[0039].
As per claim 3, Damewood teaches the method of claim 1, wherein “the determining the query types of the L queries to be performed on the target dataset includes: obtaining a preconfigured number of queries performable on the target dataset and a query type of each of the preconfigured number of queries” at [0035]-[0039].
As per claim 4, Damewood teaches the method of claim 1, wherein “a query type of the query types is one of: a counting query, a maximum value query, a minimum value query, a mean value query, or a variance query” at [0036].
As per claim 5, Damewood teaches the method of claim 1, wherein “the object is one or more of: a user, a commodity, or a business event” at [0019].
As per claim 6, Damewood teaches the method of claim 5, wherein “the business event is one or more of: registration, access, login, or payment” at [0019].
As per claim 7, Damewood teaches the method of claim 1, wherein “the object is a user, and the data of the object is one or more of: age, gender, income, interest and hobbies, physiological indicators, or operation indicators” at [0019].
As per claim 8, Damewood teaches the method of claim 1,wheiren “the determining the query sensitivity of each query type of the query types for the target dataset includes: for each query type of the query types, obtaining the query sensitivity corresponding to the query type based on the greatest absolute difference between a first result and a second result, wherein the first result is a result obtained by performing the type of query on the target dataset, and the second result is a result obtaining by performing the type of query on an adjacent dataset of the target dataset” at [0036]-[0044].
As per claim 12, Damewood teaches the method according to claim 1, wherein “the query types include a variance query, and the determining the query sensitivity of each query type of the query types for the target dataset includes: determining a greatest value and a smallest value in the target dataset; and determining a product of following factors as the query sensitivity of the variance query: a square of a difference between the greatest value and the smallest value, an amount of data in the target dataset, and a reciprocal of a result obtained after a square operation is performed on the amount of data plus 1” at [0036]-[0044].
As per claim 13, Damewood teaches the method according to claim 1, wherein “the determining, based on the query sensitivity corresponding to each query of the L queries and the privacy budget parameter determined for the total set of the L queries, the noise power allocated to each query of the L queries, respectively, includes: determining a sum of query sensitivity of the L queries based on the query sensitivity of each query; and for a query, determining the noise power allocated to the query based on the query sensitivity of the query, the sum of the query sensitivity, and the privacy budget parameter” at [0026], [0044]-[0055].
As per claim 14, Damewood teaches the method according to claim 13, wherein “the determining the noise power allocated to the query based on the query sensitivity of the query, the sum of the query sensitivity, and the privacy budget parameter includes: obtaining a variable value of a mean variable, wherein the variable value is determined based on a parameter value of the privacy budget parameter and a constraint relationship between the privacy budget parameter and the mean variable in a Gaussian mechanism of differential privacy; and determining a product of following factors as the noise power of the query: the query sensitivity of the query, the sum of the query sensitivity, and a reciprocal of a result obtained after a square operation is performed on the variable value” at [0026], [0044]-[0055].
As per claim 15, Damewood teaches the method according to claim 14, wherein “the privacy budget parameter includes a budget parameter and a relaxation parameter” at [0024], [0034]-[0044].
As per claim 16, Damewood teaches the method according to claim 1, further comprising: “after the determining the noise power allocated to each query allocation, for a target query in the L queries, determining an updated result of the target query by adding to an original query result of the target query a target noise sampled from target noise distribution of differential privacy, wherein the target noise distribution is determined based on a noise power allocated to the target query” at [0058]-[0070].
As per claim 18, Damewood teaches the method according to claim 16, further comprising: “receiving a current query request for the target dataset, wherein the current query request corresponds to a current query type; determining whether a number of processed requests corresponding to the current query type is less than a determined threshold, wherein query requests corresponding to the number of processed requests are directed to the target dataset; and using the current query request as the target query in response to determining that the number of processed requests is less than the predetermined threshold” at [0058]-[0070].
Claims 19-20 recite similar limitations as in claim 1 and are therefore rejected by the same reasons.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Damewood as applied to claims 1-8, 12-16, 18-20 above, and in view of Wu et al. (US 2022/010887 A1), hereinafter “Wu”.
As per claim 9, Damewood teaches the method of claim 1 discussed above. Damewood also teaches “wherein the query types include a counting query” at [0036]. Damewood does not explicitly teach: “the determining the query sensitivity of each query type of the query types for the target dataset includes: determining the query sensitivity of the counting query to be a value of 1” as claimed. However, Wu teaches a similar method for determining query sensitivity of different query types, including “determining the query sensitivity of the counting query to be a value of 1” at [0141]-[0143]. Thus, it would have been obvious to one of ordinary skill in the art to combine Wu with Damewood’s teaching by calculating a proper amount of noise to be injected to the data based on the query sensitivity in order to “simultaneously satisfies that the injected noise does not affect the usability of data” and “reduces the learning cost of different privacy to a large extent, also, reduces the difficulty degree of writing differential privacy programs”, as suggested by Wu at [0035].
As per claim 10, Damewood teaches the method of claim 1 discussed above. Damewood does not explicitly teach “the query types include a maximum value query or a minimum query value query, and the determining the query sensitivity of each query type of the query types for the target dataset includes: determining a greatest value and a smallest value in the target dataset; and determining a result of subtracting the smallest value from the greatest value as the query sensitivity of the maximum value query or the minimum value query” as claimed. However, Wu teaches a similar method for determining query sensitivity of different query types, including “the query types include a maximum value query or a minimum query value query, and the determining the query sensitivity of each query type of the query types for the target dataset includes: determining a greatest value and a smallest value in the target dataset; and determining a result of subtracting the smallest value from the greatest value as the query sensitivity of the maximum value query or the minimum value query” at [0141]-[0169]. Thus, it would have been obvious to one of ordinary skill in the art to combine Wu with Damewood’s teaching by calculating a proper amount of noise to be injected to the data based on the query sensitivity in order to “simultaneously satisfies that the injected noise does not affect the usability of data” and “reduces the learning cost of different privacy to a large extent, also, reduces the difficulty degree of writing differential privacy programs”, as suggested by Wu at [0035].
As per claim 11, Damewood teaches the method of claim 1 discussed above. Damewood does not explicitly teach: “the query types include a mean value query, and the determining the query sensitivity of each query type of the query types for the target dataset includes: determining a greatest value in the target dataset; and determining a ratio between an absolute value of the greatest value and an amount of data in the target dataset plus 1 as the query sensitivity of the mean value query” as claimed. However, Wu teaches a similar method for determining query sensitivity of different query types, wherein “the query types include a mean value query, and the determining the query sensitivity of each query type of the query types for the target dataset includes: determining a greatest value in the target dataset; and determining a ratio between an absolute value of the greatest value and an amount of data in the target dataset plus 1 as the query sensitivity of the mean value query” at [0141]-[0169]. Thus, it would have been obvious to one of ordinary skill in the art to combine Wu with Damewood’s teaching by calculating a proper amount of noise to be injected to the data based on the query sensitivity in order to “simultaneously satisfies that the injected noise does not affect the usability of data” and “reduces the learning cost of different privacy to a large extent, also, reduces the difficulty degree of writing differential privacy programs”, as suggested by Wu at [0035].
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Damewood as applied to claims 1-8, 12-16, 18-20 above, and in view of Canard et al. (US 2019/0050599 A1), hereinafter “Canard”.
As per claim 17, Damewood teaches the method according to claim 16 discussed above. Damewood also teaches: wherein “the noise distribution uses the noise power of the target query as a variance and uses 0 as a mean” at [0062]-[0067]. Damewood does not teach “the target noise distribution is Gaussian noise distribution” as claimed. However, Canard teaches a similar method for determining the noise level to be added to query data using Gaussian Distribution at [0051], [0150]-[0151]. Thus, it would have been obvious to one of ordinary skill in the art to combine Canard with Damewood’s teaching because “with other distribution, and in particular with a Gaussian distribution, it may be more advantageous to use some other norm for defining the sensitivities involved, e.g., such as an L2 norm”, and “better accuracy can be archived by combining Gaussian noise with sensitivity levels defined using an L2 norm as proposed above”, as suggested by Canard at [0051], [0151].
Response to Arguments
Applicant's arguments filed 3/11/2026 have been fully considered but they are not persuasive. The examiner respectfully traverses Applicant’s arguments.
Regarding the 101 rejection to claims 1-20, Applicant argued that “the claimed “query sensitivity corresponding to each query type of the L queries,” “privacy budget parameter determined for a total set of the L queries” and “noise power allocated to each query type of the L queries” are all technical features necessarily related to the operation of a database and the claims actively recite the implementation of a query operation of a database. As such, the claims are not directed to the alleged abstract idea”. On the contrary, as discussed in the rejection above, each of the claimed limitations recite an abstract idea, i.e., “mental process” or a “mathematical calculation”, mathematical formula”, which can be performed in the human mind or with the aid of pen and paper.
Regarding the 102 rejection to claims 1, 19 and 20, Applicant argued that Damwood does not teach or suggest the query sensitivity recited in claim 1 because the privacy parameter of Damewood do not indicate “a difference between a first result obtained by performing a query of the query type on the target database and a second result obtained by performing the query of the query type on an adjacent data set of the target dataset as recited in claim 1”. On the contrary, Damewood teaches at [0048]-[0053] the privacy parameter includes global and local sensitivity of a query M, wherein the global and local sensitivity is determined by performing query M on X and X’, wherein X and X’ are two neighboring databases and on of X, X’ has all the same entries as the other, plus one additional entry. Global and local sensitivity indicate a difference between a first result (i.e., “M(X) – M(X’)”) obtain by performing query M on X (i.e., “M(x)”) and a second result obtained by performing query M on X’ (i.e., “M(X’)”)
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In light of the foregoing arguments, the 35 U.S.C 102 rejection is hereby sustained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
April 20, 2026
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