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 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Re. claim 1, the claim recite(s) 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, as drafted, is a process that, under its broadest reasonable interpretation, the claim recites a mathematical concept. The claimed invention is generating a variance based on the size of the hashing domain, the probability, and a value based on the differential privacy parameter (e.g., a formula). The mere nominal recitation of a generic computer-implemented. Thus, the claim recites an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. In particular, the claim only recites additional elements such as computer-implemented. The generic computer components (e.g., computer) are recited at a high level of generality (e.g., 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) such that it amounts no more than mere instructions to apply the exception using a generic computer. The obtaining steps is also recited at a high level of generality (i.e., as a general means data gathering), which is a form of insignificant extra-solution activity, and merely automates the obtaining steps. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits practicing the abstract idea. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
As noted previously, the claim as a whole merely describes how to generally apply the concept of 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. Thus, even when viewed as a whole, nothing in the claim adds significantly more (e.g., an inventive concept) to the abstract idea.
Dependent claims 2-15 have also been fully analyzed. Each of these dependent claims are mere recites additional abstract idea or an insignificant, extra-solution activity. Therefore, the dependent claims also fail to integrate the abstract idea into a practical application. Moreover, the claims have also been analyzed regarding whether they recite significantly more than the abstract idea. The dependent claims fail to add significantly more than the abstract idea. Therefore, dependent claims 2-15 are rejected under 35 USC 101.
Re. claim 16, the claim recite(s) 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, as drafted, is a process that, under its broadest reasonable interpretation, the claim recites a mathematical concept. The claimed invention is generating a variance based on the size of the hashing domain, the probability, and a value based on the differential privacy parameter (e.g., a formula). The mere nominal recitation of a generic computer and storage devices. Thus, the claim recites an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. In particular, the claim only recites additional elements such as computer-implemented. The generic computer components (e.g., computer and storage devices) are recited at a high level of generality (e.g., 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) such that it amounts no more than mere instructions to apply the exception using a generic computer. The obtaining steps is also recited at a high level of generality (i.e., as a general means data gathering), which is a form of insignificant extra-solution activity, and merely automates the obtaining steps. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits practicing the abstract idea. Simply implementing the abstract idea on a generic computer and storage device are not a practical application of the abstract idea.
As noted previously, the claim as a whole merely describes how to generally apply the concept of 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. Thus, even when viewed as a whole, nothing in the claim adds significantly more (e.g., an inventive concept) to the abstract idea.
Dependent claims 17-19 have also been fully analyzed. Each of these dependent claims are mere recites additional abstract idea or an insignificant, extra-solution activity. Therefore, the dependent claims also fail to integrate the abstract idea into a practical application. Moreover, the claims have also been analyzed regarding whether they recite significantly more than the abstract idea. The dependent claims fail to add significantly more than the abstract idea. Therefore, dependent claims 17-19 are rejected under 35 USC 101.
Re. claim 20, the claim recite(s) 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, as drafted, is a process that, under its broadest reasonable interpretation, the claim recites a mathematical concept. The claimed invention is generating a variance based on the size of the hashing domain, the probability, and a value based on the differential privacy parameter (e.g., a formula). The mere nominal recitation of a generic computer storage media and computer. Thus, the claim recites an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. In particular, the claim only recites additional elements such as computer-implemented. The generic computer components (e.g., computer storage media and computer) are recited at a high level of generality (e.g., 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) such that it amounts no more than mere instructions to apply the exception using a generic computer. The obtaining steps is also recited at a high level of generality (i.e., as a general means data gathering), which is a form of insignificant extra-solution activity, and merely automates the obtaining steps. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits practicing the abstract idea. Simply implementing the abstract idea on a generic computer and computer storage media are not a practical application of the abstract idea.
As noted previously, the claim as a whole merely describes how to generally apply the concept of 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. Thus, even when viewed as a whole, nothing in the claim adds significantly more (e.g., an inventive concept) to the abstract idea.
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 probability of including each output value in a corresponding message”. The claim indicates each output value in a corresponding message. However, the previous claim limitation indicates a single output value and not a plurality of output value. Furthermore, it is unclear what message the claim is referring to, as there was no message being mention before. For example, which message corresponds to which output value. 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.
Re. claims 1, 16 and 20; the claims recite “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 claim language of “the quantity of times” lacks antecedent basis. One of ordinary skill in the art would not understand to generate a variance of the quantity of times the source data was a cause of a message. Unclear how many times source data was a cause of a message. There is no cause mention before. For the purpose of art, the examiner is interpreting the claim limitation as generating a variance based on the size of the hashing domain, the probability, and a value based on the differential privacy parameter.
Re. claims 1 and 16; 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. 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).
Re. claim 9, the claim recites “providing data specifying the one or more updated parameters to an external system”. The claim is directed to a method claim. The claim never mentions a system, especially an internal system. 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 some other structure (e.g., an internal system). 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. The claims are therefore rendered indefinite.
Claims 2-15, 17-19 fall together accordingly as they do not cure the deficiencies of the independent claims.
Claim Rejections - 35 USC § 102
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fang et al. (“Local differential privacy for human-centered computing”, hereinafter Fang).
Re. claim 1, a computer-implemented method comprising: obtaining a size of a hashing domain for one or more hash functions, each 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 each 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 (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).
Re. claim 2, Fang discloses 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 discloses 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 discloses 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 discloses 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 discloses 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 discloses 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 discloses 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 discloses the method of claim 6, further comprising providing data specifying the one or more updated parameters to an external system (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]).
Re. claim 10, Fang discloses 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 discloses 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 discloses 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 discloses 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 discloses 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 discloses 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN A AYALA whose telephone number is (571)270-3912. The examiner can normally be reached Monday-Thursday 8AM-5PM; Friday: Variable EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jorge Ortiz-Criado can be reached at 571-272-7624. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KEVIN AYALA/Primary Examiner, Art Unit 2496