DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 16 September 2025.
Claims 1-20 are pending. Claims 1, 8, and 15 are independent claims.
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 6-7, 13-14, and 20 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.
With respect to claims 6, 13, and 20, the applicant recites “computing the AUC based on calculating
∫
Z
-
=
-
∞
+
∞
1
-
P
+
z
-
p
-
z
-
d
z
-
, wherein
p
-
represents the PDF associated with the distribution of negative samples in the user data and
P
+
represents the CDF associated with the distribution of positive samples in the user data (lines 2-4).”
However, it is noted that several variables within the equation are undefined. For example, the claim does not define “
z
-
“ or “
d
z
-
“. For this reason, the examiner is unable to determine the scope of the equation, and similarly the claim.
Dependent claims 7 and 14 fail to cure the deficiencies of claims 6 and 13, respectively. Therefore, claims 7 and 14 are rejected under similar rationale.
Claim Rejections - 35 USC § 103
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 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.
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 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Reddi et al (Adaptive Federate Optimization, September 2021, hereafter Reddi) and further in view of Sharma Mittal et al. (US 12417411, filed 2 August 2021, hereafter Sharma Mittal), and further in view of Taherzadeh Boroujeni et al. (US 2022/0101130, published 31 March 2022, hereafter Taherzadeh Boroujeni) and further in view of Marzban et al. (US 2023/0246753, filed 28 July 2022, hereafter Marzban) and further in view of Spoliansky et al. (US 2022/0237502, published 28 July 2022, hereafter Spoliansky).
As per independent claim 1, Reddi discloses a method for determining a performance classifier, the method comprising:
training a first machine learning model and a second machine learning model by aggregating updates to the first machine learning model and the second machine learning model received from a plurality of client computing devices, wherein the first machine learning model is updated by each of the plurality of client computing devices based on samples in user data accessible only the plurality of client computing devices, (page 1: Here, a plurality of models are trained using a federated learning paradigm. Each client device receives a global model from a central server. This model is trained across multiple iterations and optimized at each client. The training and optimizing at a first client is the first machine learning model; the training and optimizing at a second client is the second machine learning model. The results of the training of each model is then provided back to the central server for optimization of the global model at the server)
performing an integration-based computation of the area under the receiver operating characteristic curve of the classifier using the data received from different learning models (pages 1-3: Here, each of the client models is received at a server and server optimizes the combination of the trained first and second models. Further, the server performs this optimization using a FedAvg method that minimizes the loss function of the summation of data based on the data distribution and variance (density) functions)
Reddi fails to specifically disclose:
wherein the second machine learning model is updated based on samples in the user data accessible only by the plurality of client computing devices
estimating a cumulative distribution function (CDF) associate with a distribution of the positively labeled samples in the user data using the trained first machine learning model
estimating a probability density function (PDF) associated with a distribution of the negatively labeled samples in the user data using the trained second machine learning model
performing an integration-based computation of the area under the receiver operating characteristic curve of the classifier using the PDF and CDF
determining the performance of the classifier based on the integration-based computation of the AUC without accessing the user data
However, Sharma Mittal, which is analogous to the claimed invention because it is directed toward federating learning at client devices, discloses wherein the second machine learning model is updated based on samples in the user data accessible only by the plurality of client computing devices (claim 1: Here, a plurality of models may be trained at each client and provided via a federated learning environment). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Sharma Mittal with Rossi, with a reasonable expectation of success, as it would have allowed for training and updating multiple models at a client in a federated learning environment (Sharma Mittal: claim 1).
Further, Taherzadeh Boroujeni, which is analogous to the claimed invention because it is directed toward using cumulative distribution and density functions in federated learning, discloses:
estimating a cumulative distribution function (CDF) associated with a distribution of the positively labeled samples in the user data using the trained machine learning model (paragraph 0081: Here, a cumulative distribution function is used to adjust the distribution of the probability based on a distribution of first components of the vectors)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Taherzadeh Boroujeni with Rossi-Sharma Mittal, with a reasonable expectation of success, as it would have allowed identifying the distribution of vectors in order to improve the federated learning model (Taherzadeh Boroujeni: paragraph 0081).
Further, Marzban, which is analogous to the claimed invention because it is directed toward using probability density functions in federated learning, discloses:
estimating a probability density function (PDF) associated with a distribution of the negatively labeled samples in the user data using the trained second machine learning model (paragraph 0065: Here, a probability density function describes the probability of the value of a variable falling in a range. In this instance, the examiner interprets the range as being the range of negative numbers)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Marzban with Rossi-Sharma Mittal-Taherzadeh Boroujeni, with a reasonable expectation of success, as it would have allowed identifying the a convergence of the probabilities associated with the model based upon the probability of a variable falling in a range increasing (Marzban: paragraph 0065).
Finally, Spoliansky, which is analogous to the claimed invention because it is directed toward computing the performance of a classifier, discloses determining the performance of the classifier based on the integration-based computation of the AUC without accessing the user data (paragraphs 0058-0060: Here, the model is evaluated based on a performance value. The performance value is determined by calculating the value under the performance curve. This may include calculating the area under the entire curve or a portion of the curve). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Spoliansky with Rossi-Sharma Mittal-Taherzadeh Boroujeni-Marzban, with a reasonable expectation of success, as it would have allowed for evaluating a classification model to make improvements (Spoliansky: paragraph 0003).
As per dependent claim 2, Rossi, Sharma Mittal, Taherzadeh Boroujeni, Marzban, and Spoliansky disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Rossi discloses wherein the training the first machine learning model and the second machine learning model comprises receiving the updates in a plurality of rounds from the plurality of client computing devices (page 1: Here, federated learning uses multiple client devices where data is never shared with the other clients).
As per dependent claim 3, Rossi, Sharma Mittal, Taherzadeh Boroujeni, Marzban, and Spoliansky disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Rossi discloses distributing the first machine learning model and the second machine learning model to the plurality of client computing devices in a first round of the plurality of rounds, wherein each of the plurality of client computing devices generates a first round of updates to at least one of the first machine learning model or the second machine learning model using locally stored user data (page 1: Here, federated optimization method use local client updates where the model is updated multiple times locally before communicating these trained models to the server).
As per dependent claim 4, Rossi, Sharma Mittal, Taherzadeh Boroujeni, Marzban, and Spoliansky disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Rossi discloses preparing the first machine learning model and the second machine learning model for a second round of the plurality of rounds based on the first round of updates (page 1: Here, multiple rounds of learning is performed at each client, where the state is maintained between each round of learning).
As per dependent claim 5, Rossi, Sharma Mittal, Taherzadeh Boroujeni, Marzban, and Spoliansky disclose the limitations similar to those in claim 4, and the same rejection is incorporated herein. Rossi discloses wherein the first round of updates is received from a first subset of the plurality of client computing devices, and wherein a second round of updates is received from a second subset of the plurality of client computing devices (page 3: Here, for each round of multiple rounds, the global model is provided to the client. The clients then optimize the model and provided their results to the server to update and optimize the global model).
With respect to claim 8, the applicant recites the limitations substantially similar to those in claim 1. The rejection of claim 1 is incorporated herein by reference.
Further, Taherzadeh Boroujeni discloses at least one processor (paragraph 0008) and at least one memory comprising computer-readable instructions that upon execution by the at least one processor cause the processor cause the computing device to perform operations (paragraph 0010).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Taherzadeh Boroujeni with Rossi- Taherzadeh Boroujeni-Marzban, with a reasonable expectation of success, as it would have allowed for implementing the method in a computer system.
With respect to claims 9-12, the applicant discloses the limitations substantially similar to those in claims 2-5, respectively. Claims 9-12 are similarly rejected.
With respect to claim 15, the applicant recites the limitations substantially similar to those in claim 1. The rejection of claim 1 is incorporated herein by reference.
Further, Taherzadeh Boroujeni discloses a non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution by the at least one processor cause the processor cause the computing device to perform operations (paragraph 0010).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Taherzadeh Boroujeni with Rossi- Taherzadeh Boroujeni-Marzban, with a reasonable expectation of success, as it would have allowed for implementing the method in a computer-readable medium.
With respect to claims 16-19, the applicant discloses the limitations substantially similar to those in claims 2-5, respectively. Claims 16-19 are similarly rejected.
Response to Arguments
Applicant's arguments filed with respect to the rejection of claims 6-7, 13-14, and 20 under 35 USC 112(b) have been fully considered but they are not persuasive.
The applicant argues that the claims are not indefinite because z- is used as the integration and is made clear by “the inclusion of the differential dz-, which unambiguously identifies z- as the variable of integration” (pages 8-9). The applicant further argues that dz- is the differential (page 9). Finally, the applicant argues that a person of ordinary skill in the art would recognize this, and thus the claims are not indefinite. The examiner agrees.
However, the examiner notes that the claims do not recite z- and dz-. Instead, the claims recite “
z
-
“ or “
d
z
-
“. Based on the applicant’s arguments, the examiner recommends amending the claims to recite “z-“ and not “
z
-
“; the examiner recommends amending the claims to recite “dz-“ and not “
d
z
-
“. Such an amendment would overcome the rejection of claims under 35 USC 112(b).
Applicant’s arguments with respect to the rejection of claims under 35 USC 101 have been fully considered and are persuasive. The rejection has been withdrawn.
Applicant’s arguments with respect to the rejection of claims under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Rossi, Sharma Mittal, Taherzadeh Boroujeni, Marzban, and Spoliansky.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bhuyan et al. (Multi-Model Federated Learning, 13 January 2022): Discloses a federated learning system in which multiple unrelated models are trained simultaneously at clients (Abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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