DETAILED ACTION
This action is responsive to the following communications: Original Application filed on June 29, 2022. All references to this application refer to the U.S. Patent Application Publication No. 2024/0005215 A1.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this case. Claims 1, 8, and 15 are the independent claims. Claims 1-20 are rejected.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0017, the fourth sentence begins “AN additive function may be generated based on…” This should recite “An additive function may be generated based on…”
In paragraph 0045, the first sentence recites “In some instances, the aggregator collect’s the gradients…” This should recite “In some instances, the aggregator collects the gradients…”
Appropriate corrections are required.
The use of trademarks has been noted in this application. The term should be accompanied by the generic terminology, if appropriate; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The following trademarks were not properly marked:
BLU-RAY (paragraph 0088)
SMALLTALK (paragraph 0091)
Appropriate corrections are required.
Drawings
The drawings are objected to because of the following informality:
In Fig. 2, box 250, there is a typo on the first word of the second line in the box: “predictiokn set” should be “prediction set”
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the Examiner, the Applicants will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
INFORMATION ON HOW TO EFFECT DRAWING CHANGES
Replacement Drawing Sheets
Drawing changes must be made by presenting replacement sheets which incorporate the desired changes and which comply with 37 CFR 1.84. An explanation of the changes made must be presented either in the drawing amendments section, or remarks, section of the amendment paper. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). A replacement sheet must include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of the amended drawing(s) must not be labeled as “amended.” If the changes to the drawing figure(s) are not accepted by the Examiner, Applicants will be notified of any required corrective action in the next Office action. No further drawing submission will be required, unless Applicants are notified.
Identifying indicia, if provided, should include the title of the invention, inventor’s name, and application number, or docket number (if any) if an application number has not been assigned to the application. If this information is provided, it must be placed on the front of each sheet and within the top margin.
Annotated Drawing Sheets
A marked-up copy of any amended drawing figure, including annotations indicating the changes made, may be submitted or required by the Examiner. The annotated drawing sheet(s) must be clearly labeled as “Annotated Sheet” and must be presented in the amendment or remarks section that explains the change(s) to the drawings.
Timing of Corrections
Applicants are required to submit acceptable corrected drawings within the time period set in the Office action. See 37 CFR 1.85(a). Failure to take corrective action within the set period will result in ABANDONMENT of the application.
If corrected drawings are required in a Notice of Allowability (PTOL-37), the new drawings MUST be filed within the THREE MONTH shortened statutory period set for reply in the “Notice of Allowability.” Extensions of time may NOT be obtained under the provisions of 37 CFR 1.136 for filing the corrected drawings after the mailing of a Notice of Allowability.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 2A, Prong 1.
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1 recites:
A computer-implemented method, comprising:
determining, by a federated learning aggregator, a set of sample ratios for a set of participant systems, each sample ratio associated with a distinct participant system;
generating a set of participant epsilon values for the set of participant systems, each participant epsilon value being associated with a participant system of the set of participant systems;
receiving a set of surrogate data sets for the set of participant systems, each surrogate data set representing a data set of a participant system;
generating, by the federated learning aggregator, a set of local models, each local model generated based on a first global model; and
generating, by the federated learning aggregator, a second global model based on a prediction set generated by the set of participant systems using the set of local models.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human can determine a set of sample ratios, epsilon values for each participant system, receive surrogate data sets, generate a set of local models based on a global model, and generate a second global model based on the local models mentally or with pen and paper.
Step 2A, Prong 1 (Yes).
Step 2A, Prong 2
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The additional elements in this claim are “federated learning aggregator.” This element is recited at a high level of generality and thus is a generic computer component performing computer functions. Thus these are mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
Even when viewed in combination the additional element does not integrate the recited judicial exception into a practical application.
Step 2A, Prong 2 (No).
Step 2B
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, the only additional element is “federated learning aggregator” which at best is mere instructions to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step 2B (No).
Claim 1 is ineligible.
With respect to Claims 8 and 15,
These claims are similar in scope to Claim 1 and are rejected under a similar rationale. The processors, computer-readable storage medium recited in these claims are also generic computing components.
Claims 8 and 15 are ineligible.
Dependent claims:
Claims 2, 7, 9, 14, 16, and 20: These claims only recite further abstract ideas (mental processes) and thus are ineligible. Specifically, they recite additional determination and generation steps for the sample data sets, histogram distributions, and quantile sketches of sample data.
Claims 3-6, 10-13, and 17-19: These claims recite a further abstract idea of transmitting data between the participant systems and the FL aggregator.
With respect to Step 2A, Prong 2 this is mere data gathering recited at a high level of generality and thus is insignificant extra-solution activity. See MPEP 2106.05(g).
With respect to Step 2B, receiving or transmitting data over a network has been found by the courts to be well-understood, routine and conventional activity. See MPEP 2106.05(d), subsection II.
Thus these claims are ineligible.
To expedite a complete examination of the instant application, the claims rejected above under 35 U.S.C. 101, as relating to a judicial exception without significantly more, are further rejected as set forth below in anticipation of amendments to these claims to place them within the four statutory categories of invention.
Examiner’s Note
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.
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. Applicants are 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature Reference entitled “Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning” published by Wang et al., 2022 IEEE 38th International Conference on Data Engineering (ICDE), May 9-12, 2022, pgs. 911-923 (hereinafter Wang), in view of Non-Patent Literature Reference entitled “Fairness and Accuracy in Federated Learning,” by Huang et al., arXiv:2012.10069v1, December 18, 2020 (hereinafter Huang).
With respect to independent claim 1, Wang discloses a computer-implemented method, comprising:
Determining, by a federated learning aggregator, a set of sample ratios for a set of participant systems, each sample ratio associated with a distinct participant system; Wang discloses determining, by a federated learning (FL) aggregator a set of sample ratios (e.g., proportional participation) for each of the participant systems (see Wang, Sec. I, page 911. [horizontal FL systems applies to scenarios where participants share the same feature space but have different samples where the global model is obtained by aggregating the local participant parameters], Sec. I, page 912 [how to accurately measure contributions of each participant system, by defining the utility function as the change in loss on the validation dataset then analyze the impact of each participant system on the global gradients and calculate the Shapley value; based on the per-epoch contribution, design a participant reweight mechanism to improve model training], Sec. II A, pgs. 912-913 [each participant updates a local model with local training data, sends the local model to the server for aggregation to train a global model]).
Generating a set of participant epsilon values for the set of participant systems, each participant epsilon value being associated with a participant system of the set of participant systems; Wang discloses generating a set of participant epsilon values associated with the participant system (see Wang, Sec. II.B., pg. 913 [describing the calculation of the Shapely value to measure how each participant system contributes to the global model; when each participant system joins the system, the marginal contribution is calculated and transmitted to the server for aggregation]).
Receiving a set of surrogate data sets for the set of participant systems, each surrogate data set representing a data set of a participant system; Wang discloses receiving a set of surrogate data for each participant system (see Wang, Sec. II.B., pg. 914 [participant systems update local models using local training data and send local gradients to the server for aggregation, the server evaluates the contribution of each participant in each epoch and determines impact and contribution to the global model]).
Generating, by the federated learning aggregator, a set of local models, each local model generated based on a first global model; Wang discloses generating a set of local models based on a first global model (see Wang, Sec. II.B, described supra).
Generating, by the federated learning aggregator, a second global model…; Wang discloses generating a second global model (see Wang, Sec. II.B, described supra).
Wang fails to expressly disclose generating the second global model based on a prediction set generated by the set of participant systems using the set of local models.
However, Huang teaches generating the second global model in an FL aggregator based on a prediction set generated by the participant systems using the local models (see Huang, Sec. 3, pgs. 4-6 [describing how the accuracy of the global model is determined based on the local models from the participant systems using appropriate weighting strategies for each local model; including Algorithms 1 and 2]
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Wang and Huang before him before the effective filing date of the claimed invention, to modify the method of Wang to incorporate generating the second global model based on the prediction sets of the local models as taught by Huang. One would have been motivated to make such a combination because this ensures greater accuracy in the global model, as taught by Huang (see Huang, abstract [“This paper proposes an algorithm to achieve more fairness and accuracy in federated learning (FedFa). It introduces an optimization scheme that employs a double momentum gradient, thereby accelerating the convergence rate of the model. An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed. This procedure assists in addressing the issue of unfairness in federated learning due to preferences for certain clients. Our results show that the proposed FedFa algorithm outperforms the baseline algorithm in terms of accuracy and fairness.”]).
With respect to dependent claim 2, Wang, as modified by Huang, teaches the method of claim 1, as described above.
Wang further teaches the method wherein determining the set of sample ratios further comprises:
Determining a number of samples within a data set of each participant system; Wang further teaches determining the number of samples within a data set for each participant system (see Wang, Sec. I, page 912, described supra, claim 1).
Determining a total sample size for the set of participant systems; Wang further teaches determining the total sample size for the participant systems (see Wang, Sec. I, page 912 and Sec. II.A, page 913, described supra, claim 1).
Generating a sample ratio for each participant system based on the total sample size and the number of samples within a data set of each participant system; Wang further teaches determining the proportional participant participation amounts for the participant systems (see Wang, Sec. I, page 912, Sec. II.A, page 913, and Sec. II.B, page 914, described supra, claim 1).
With respect to dependent claim 3, Wang, as modified by Huang, teaches the method of claim 2, as described above.
Wang further teaches the method wherein generating the set of participant epsilon values further comprises:
Generating a global epsilon parameter; Wang further teaches generating a global epsilon value (see Wang, Sec. II.B., pg. 913, described supra, claim 1).
For each participant system, generating a participant epsilon value based on the global epsilon parameter and the sample ratio associated with that participant system; Wang further teaches generating local epsilon values for each participant system based on the global epsilon value (see Wang, Sec. II.B., pg. 913, described supra, claim 1).
Transmitting the set of participant epsilon values to the set of participant systems, each participant system receiving the participant epsilon value generated based on the sample ratio of that participant system; Wang further teaches transmitting the local epsilon values to each participant system (see Wang, Sec. II.B., pg. 913, described supra, claim 1).
With respect to dependent claim 4, Wang, as modified by Huang, teaches the method of claim 2, as described above.
Wang further teaches the method wherein the receiving the set of surrogate data sets further comprises:
Generating, by each participant system, a surrogate histogram distribution based on the participant epsilon value and the data set of the participant system; Wang further teaches generating a surrogate histogram distribution (e.g., training date bin sizes) based on the participant epsilon values (see Wang, Sec. II.B, described supra, claim 1).
Transmitting, by each participant system, the surrogate histogram distribution to the federated learning aggregator; Wang further teaches transmitting the surrogate histogram data to the server for incorporation into the global model (see Wang, Sec. II.B, described supra, claim 1).
With respect to dependent claim 5, Wang, as modified by Huang, teaches the method of claim 4, as described above.
Wang further teaches the method wherein generating the surrogate histogram distribution further comprises:
Generating the surrogate histogram using the participant epsilon value to define a bin size of the histogram distribution; Wang further teaches generating a surrogate histogram distribution (e.g., training date bin sizes) based on the participant epsilon values (see Wang, Sec. II.B, described supra, claim 1).
Generating a gamma value used to generate a quantile sketch given the participant epsilon value; Wang further teaches generating a gamma value to generate a quantile sketch based on the participant epsilon values for each participant system (see Wang, Sec. II.B, described supra, claim 1).
For each sample in the data set of the participant system, identifying a corresponding bin index; Wang further teaches identifying a corresponding bin index for each sample (see Wang, Sec. II.B, described supra, claim 1).
For each sample in the data set of the participant system, determining a relative percentile; Wang further teaches determining a relative percentile for each sample (see Wang, Sec. II.B, described supra, claim 1) .
With respect to dependent claim 6, Wang, as modified by Huang, teaches the method of claim 1, as described above.
Wang further teaches the method wherein transmitting the surrogate histogram distribution further comprises:
Compressing a payload of the surrogate histogram distribution to generate a compressed histogram distribution; Wang further teaches compressing the payload for transmission to the server (see Wang, Sec. II.B, described supra, claim 1).
Transmitting the compressed histogram distribution to the federated learning aggregator; Wang further teaches compressing the payload for transmission to the server (see Wang, Sec. II.B, described supra, claim 1).
With respect to dependent claim 7, Wang, as modified by Huang, teaches the method of claim 1, as described above.
Wang further teaches the method the method further comprising:
Determining, by the federated learning aggregator, a new participant system has been added to the set of participant systems; Wang further teaches determining that a new participant system has been added (see Wang, Sec. II.B, described supra, claim 1).
Generating a new participant epsilon value for the new participant system, the new participant epsilon value generated independently of the participant systems of the set of participant systems; Wang further teaches generating a new participant epsilon value for the newly added participant system (see Wang, Sec. II.B, described supra, claim 1).
Receiving a new surrogate data set for the new participant system; Wang further teaches receiving a new surrogate data set for the new participant system (see Wang, Sec. II.B, described supra, claim 1).
Independent claim 8, and its respective dependent claims 9-14, recite a system, comprising: one or more processors; and a computer-readable storage medium, coupled to the one or more processors, storing program instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising the method of independent claim 1, and its respective dependent claims 2-7. Accordingly, independent claim 8, and its respective dependent claims 9-14, are rejected under the same rationales used to reject independent claim 1, and its respective dependent claims 2-7, which are incorporated herein.
Independent claim 15, and its respective dependent claims 16-20, recite a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions being executable by one or more processors to cause the one or more processors to perform operations comprising the method of independent claim 1, and its respective dependent claims 2-5 and 7. Accordingly, independent claim 15, and its respective dependent claims 16-20, are rejected under the same rationales used to reject independent claim 1, and its respective dependent claims 2-5 and 7, which are incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure. See PTO-892.
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ERIC J. BYCER whose telephone number is (571) 270-3741. The Examiner can normally be reached Monday - Thursday 9am-6pm, and alternate Fridays 9am-5pm.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, Applicants are encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, MATT ELL can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC J. BYCER/
Primary Examiner
Art Unit 2141