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 .
Information Disclosure Statement
The information disclosure statement filed March 6th, 2024 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the reference Marathe, “Subject Granular Differential Privacy in Federated Learning” is misidentified with a typographical error as “Sbject Granular Differential Privacy in Federated Learning.” It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Each of Claims 3, 10, and 17 fail to further narrow the claim upon which it depends because by the definition of hyperparameter, any clipping threshold used in training a machine learning model is inherently a hyperparameter for the training of the machine learning model – a hyperparameter is merely a parameter or value which effects how the training occurs.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 7, 8-11, and 14 are rejected under 35 U.S.C. 102(a)(1) as anticipated by Marathe et al., “Subject Granular Differential Privacy in Federated Learning.”
Regarding Claim 1, Maranthe teaches a method, comprising: training, using a federation server and a plurality of clients, a machine learning model (Maranthe, pg. 3, 2nd column, 5th paragraph, “LocalSubDP … enforces subject level privacy locally at each user … The federation server samples a random set of users for each training round and sends them a request to perform local training … the server redistributes the updated model and triggers another training round if needed”) on a dataset comprising a plurality of subjects individually comprising one or more data items (Maranthe, Abstract, “data subject level privacy, where a subject is an individual whose private information is embodied by several data items either confined within a single federation user or distributed across multiple federation users”) wherein the training comprises: sampling, at individual ones of the plurality of clients, respective private data sets of the data set to generate respective private mini-batches (Maranthe, pg. 4, Algorithm 2, Step 3, “random sample of B data items from
D
i
” & Abstract, “a subject is an individual whose private information is embodied by several data items either confined within a single federation user or distributed across multiple federation users”); aggregating respective counts of the plurality of subjects in respective private minibatches to generate aggregate counts for the respective subjects (Maranthe, pg. 4, Algorithm 2, “Parameters: … largest group size in a minibatch Z” denotes that each subject’s data in the minibatch has been counted/aggregated – see pg. 2, 2nd column, 2nd paragraph, “a group of data items belonging to the same subject”, and the counts have been aggregated to determine the largest group size Z); computing respective noise values for the respective private mini-batches according to the respective counts and the generated aggregate counts (Maranthe, Algorithm 2, “Parameters: … noise scale
σ
Z
for group size Z” see also pg. 3, 2nd column, last paragraph, “We use Z, and other parameters … to compute the appropriate value of
σ
Z
”); training respective machine learning models by individual ones of the plurality of clients according to the respective private mini-batches to generate respective noisy gradients for individual ones of the plurality of clients, the noisy gradients comprising the respective noise values for the respective private mini-batches (Maranthe, pg. 4, Algorithm 2, Steps 4-11 demonstrates noisy gradients comprising the respective noise values); and accumulating the respective noise gradients to determine respective average gradients providing differential privacy for the respective subjects (Maranthe, pg. 4, Algorithm 2, Steps 11-12).
Regarding Claim 2, Maranthe teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Maranthe further teaches determining respective gradients for individual ones of a plurality of parameters of the respective machine learning models by the individual ones of the plurality of clients (Maranthe, pg. 4, Algorithm 2, Step 6); applying a clipping threshold to the determined respective gradients (Maranthe, pg. 4, Algorithm 2, Step 8); adding the respective noise values for the respective private mini-batches to the determined respective gradients (Maranthe, pg. 4, Algorithm 2, Step 11).
Regarding Claim 3, Maranthe teaches the method of Claim 2 (and thus the rejection of Claim 2 is incorporated). As noted in the 35 U.S.C. 112(d) rejection, a clipping threshold is inherently a hyperparameter for the training of the machine learning model.
Regarding Claim 4, Maranthe teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Maranthe further teaches wherein individual ones of the private data sets of the data set comprise at least a portion of the plurality of subjects individually comprising the one or more data items (Maranthe, pg. 3, 2nd column, last paragraph, “We compute each mini-batch’s group size Z as the largest number of items of any subject appearing in the mini-batch”).
Regarding Claim 7, Maranthe teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Maranthe further teaches wherein the sampling, aggregating, computing, training of the respective machine learning model by a plurality of clients and accumulating are performed for a mini-batch of the plurality of mini-batches of training of the machine learning model (Maranthe, pg. 4, Algorithm 2, lines 3-4 denotes for a mini-batch & lines 5-12 for all of the actions that are performed on the minibatch of line 3).
Claims 8-11 and 14 recite one or more non-transitory, computer-readable storage media, storing instructions that when executed on or across a plurality of computing devices, cause the plurality of computing devices to perform precisely the method of Claims 1-4 and 7, respectively. As Maranthe teaches a federation server and users with computer devices to perform their method (Maranthe, pg. 4, 1st column, last paragraph, “federation server”& pg. 1, 2nd column, 2nd paragraph, “cross-device federated learning”) in which such a storage medium is inherent, Claims 8-11 and 14 are rejected for reasons set forth in the rejections of Claims 1-4 and 7, respectively.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5, 6, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Maranthe in view of Akdeniz, US PG Pub 2023/0068386.
Regarding Claim 5, Maranthe teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Maranthe does not teach, but Akdeniz, in the analogous art of federated learning, teaches, wherein the noise values are computed at a noise shuffler different from the federation server and the plurality of clients (Akdeniz, Fig. 23, displays a second server specifically to generate noise). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a trusted server, such as that of Akdeniz, to generate the noise. The motivation to do so is to ensure privacy of the generated noise.
Regarding Claim 6, the Maranthe/Akdeniz combination of Claim 5 teaches the method of Claim 1 (by inheritance, and thus the rejection of Claim 5 is incorporated). The combination, as described in the rejection of Claim 5, further teaches wherein one of the plurality of clients is an aggregating user (take the combination of Claim 5, and relabel “Central Server” as an aggregated user and is one of the plurality of clients of the “Trusted Server”/relabeled as federation server) wherein the accumulating is performed by the aggregating user and wherein the method further comprises: applying the respective average gradients to a machine learning mode of the aggregating user to generate an updated machine learning model (Maranthe, pg. 4, Algorithm 2, line 22); and distributed the updated machine learning model by the aggregating user to individual ones of the plurality of clients other than the aggregating user (Maranthe, pg. 3, 2nd column, 5th paragraph, “the server redistributes the updated model and triggers another training round if needed”).
Claims 12 and 14 recite one or more non-transitory, computer-readable storage media, storing instructions that when executed on or across a plurality of computing devices, cause the plurality of computing devices to perform precisely the method of Claims 5 and 6, respectively. As Maranthe teaches a federation server and users with computer devices to perform their method (Maranthe, pg. 4, 1st column, last paragraph, “federation server”& pg. 1, 2nd column, 2nd paragraph, “cross-device federated learning”) in which such a storage medium is inherent, Claims 12 and 13 are rejected for reasons set forth in the rejections of Claims 5 and 6, respectively.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 8 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 7 and 14, respectively, of copending Application No. 17/663,009 in view of Shi et al., “Make Landscape Flatter in Differentially Private Federated Learning.” The reference claims teach all of the limitations of the corresponding instant claims except for a) the reference application recites generic parameter updates instead of the instant claims’ gradients and b) the reference application recites aggregating respective counts of the plurality of subjects in respective private mini-batches to generate aggregate counts for the respective subjects while the reference application recites determining a count of the largest number of items in the portion of the dataset associated with any single group of the plurality of groups of the portion of the dataset. Regarding a) Shi teaches adding noise and accumulating and sending to the server gradients rather than generic parameter updates. It would have been obvious to one of ordinary skill in the art before the effective filing date to compute gradients as the respective parameter updates, because that is the standard method of learning, e.g. backpropagation. Regarding b) it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a count of the largest number of items associated with any single group in a set by determining counts of items associated with each of the groups in the set, and then identifying the largest count (i.e. the method recited in the claims), because to determine the largest of a set of counts, we would want to determine each element of the set of counts.
This is a provisional nonstatutory double patenting rejection.
Conclusion
Claims 15, 16, and 18-20 are allowed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Maranthe et al., “Subject Level Differential Privacy with Hierarchical Gradient Averaging” was published within the one-year grace period of 35 U.S.C. 102(b)(1), however names an additional author, and as such by MPEP 2153.01(a) should be treated by the examiner as valid prior art. This prior art also appears to anticipate independent Claims 1 and 8.
Shi et al., “Make Landscape Flatter in Differentially Private Federated Learning” teaches federated learning with a plurality of clients and aggregating counts of items in mini-batches, but does not teach noise values as functions of those counts.
Fu et al., “Adapt DP-FL: Differentially Private Federated Learning with Adaptive Noise” teaches the noise values for federated learning as functions of “aggregation weights” which may be related to different counts of items at the different clients.
Wang et al., “Protect Privacy from Gradient Leaking Attack in Federated Learning” explicitly links aggregation weights to counts of items at the different clients.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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