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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/23/2024 was filed and is being considered by the examiner.
Claim Objections
Claims 6, 15, and 24 are objected to because of the following informalities:
The recalculation step in each claim recites “to generate a recalculated integer set of parameters”. The use of “integer” appears to be an error, and was meant to recite “recalculated integrated set of parameters”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-27 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as based on a disclosure which is not enabling. The disclosure does not enable one of ordinary skill in the art to practice the invention without the validation step of claim 1, 10, and 19, which is/are critical or essential to the practice of the invention but not included in the claim(s). See In re Mayhew, 527 F.2d 1229, 188 USPQ 356 (CCPA 1976).
In regard to claim 1, 10, and 19, the applicant recites “determining, by the server, whether the set of parameters from each of the client devices in the subset of multiple client devices are valid according to a Central Limit Theorem”. This is similarity claimed in claims 10 and 19. However, the applicant must disclose how the validation according to the CLT is done—this is a critical aspect of the invention that must be claimed.
Claims 1-5, 9-14, 18-23, and 27 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as based on a disclosure which is not enabling. The disclosure does not enable one of ordinary skill in the art to practice the invention without the steps leading to the recalculated integrated set of parameters in claim 6, 15, and 24, which is/are critical or essential to the practice of the invention but not included in the claim(s). See In re Mayhew, 527 F.2d 1229, 188 USPQ 356 (CCPA 1976).
In regard to claim 1, 10, and 19, the applicant disclosed federated learning with feedback, however no correction of the model occurs in the independent claims. This only occurs in the steps provided by claim 6, 15 and 24. These steps are critical to the practice of the invention, and must be included in the independent claims. Appropriate correction is required.
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 9, 18, and 27 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.
In regard to claim 9, 18, and 27, the applicant recites iterative updating of the model of claim 1, 10, and 19. However, in these independent claims, all that is happening is a validation parameter is determined and feedback is sent to the clients. Nothing changes about the server model, so there is nothing to iteratively update. All that would happen is a repeat of the same steps with no changes. The Examiner does not believe this is the intention of the applicant, and as such believes these claims are indefinite. Appropriate correction is required.
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.
Claim(s) 1-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Blanchard et al (NPL: 2017) in view of Pappu (US 2016/0380826 A1).
In regard to claim 1, 10, and 19, Blanchard et al disclose a robust federated machine learning system with validation, having a computer-implemented method for federated learning in a network including a server and multiple client devices, comprising:
receiving, by the server, a set of parameters of a local machine-learning model from each client device in a subset of the multiple client devices;
combining, by the server, the set of parameters from each of the client devices in the subset to generate an integrated set of parameters;
determining, by the server, whether the set of parameters from each of the client devices in the subset of multiple client devices are valid;
calculating, by the server, a parameter difference between the integrated set of parameters and the set of parameters for each client device in the subset of the multiple client devices; and
sending, by the server, feedback to each client device in the subset of multiple client devices, the feedback being based on a comparison of the parameter difference of each client device in the subset of the multiple client devices to a first threshold value. (Please see full paper.)
The Examiner further takes official notice that this part of the claim is known robust federated learning.
Blanchard et al fail to disclose determining, by the server, whether the set of parameters from each of the client devices in the subset of multiple client devices are valid according to a Central Limit Theorem.
Pappu teaches a validation method using central limit theorem. (See at least [0085])
The Examiner further takes notice that z-score validation is notoriously old and well-known in computer validation.
It would have been obvious to one of ordinary skill in the art at the time of filing to update the robust federated learning model of Blanchard et al using the validation step of Pappu in order to improve the validation of the federated learning.
In regard to claim 2, 11, and 20, in combination, the combination of Blanchard et al and Pappu disclose removing, by the server, an invalid set of parameters from the integrated set of parameters, the invalid set of parameters being determined to be invalid using the Central Limit Theorem.
In regard to claim 3, 12, and 21, in combination, the combination of Blanchard et al and Pappu disclose calculating, by the server, the first threshold value using a normal distribution based on the Central Limit Theorem; and comparing, by the server, the parameter difference to the first threshold value. (This is a part of normal robust federated learning validation—basing it on a z-score is obvious in view of Pappu.)
In regard to claim 4, 13, and 22, in combination, the combination of Blanchard et al and Pappu disclose the feedback includes the integrated set of parameters, a parameter difference normal distribution, and the parameter difference when the parameter difference is less than or equal to the first threshold value.
In regard to claim 5, 14, and 23, in combination, the combination of Blanchard et al and Pappu disclose the feedback is proportional to a standard deviation of the normal distribution.
In regard to claim 6, 9, 15, 18, 24, and 27 Blanchard et al disclose when the parameter difference is greater than the first threshold value, further comprising:
recording, by the server, a number of times the parameter difference for each of the client devices in the subset is greater than the first threshold value;
sending, by the server, the parameter difference to each of the client devices in the subset;
removing, by the server, the set of parameters for each of the client devices in the subset when the number of times is greater than a second threshold value; recalculating, by the server, the integrated set of parameters to generate a recalculated integer set of parameters; and
recalculating, by the server, a recalculated parameter difference between the recalculated integrated set of parameters and the set of parameters for each client device in the subset of the multiple client devices,
and as recited in claim 9, 18, and 27,
storing a global machine-learning model at the server; and
iteratively updating, by the server, the global machine-learning model based on the set of parameters received from each of the client devices in the subset of the multiple client devices.
In regard to claim 7, 16, and 25 Blanchard et al disclose setting, by the server, a particular client device as an outlier client device if the number of times is greater than the second threshold value; and
removing, by the server, the outlier client device from the subset.
Moreover, the Examiner takes notice that blacklisting is a known practice in validation.
In regard to claim 8, 17, and 26, in combination, the combination of Blanchard et al and Pappu disclose the setting includes determining the outlier client device based on the normal distribution and the first threshold value.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sprague et al (US 2020/0334524 A1) disclose edge learning.
Hechtman et al (US 2020/0125949 A1) disclose training neural networks.
Sheller et al (US 2019/0042937 A1) disclose federated learning.
Julian et al (US 2015/0324686 A1) disclose distributed model learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER E DUNAY whose telephone number is (571)270-1222. The examiner can normally be reached 7:00 am - 6:00 pm.
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/CHRISTOPHER E DUNAY/Primary Examiner, Art Unit 2875