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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/3/2026 has been entered.
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 1-3, 7-11, 15-24 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-3 and 21-24, 7-8, 9-11, 15-17, 18-20 are directed to a system, a method and a computer program product of ML model training. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 9 and 18
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level and also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0020]-[0034] etc., Fig. 1. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 9, 18 these claims
This limitation recites using one or more neural networks as a tool to perform an abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
MPEP § 2106.05(f): Mere Instructions to Apply an Exception. Do the additional element(s) amount to merely the words “apply it” (or an equivalent)
or are mere instructions to implement an abstract idea or other exception on a computer? (Yes)
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 9 and 18: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “fine-tune…” “fine-tune…”, etc. . These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 10
Claim 2, 10 merely recite other additional elements that define training and fine-tuning the model automatically which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 7, 11, 15, 19-20
Claim 3, 7, 11, 15, 19-20 merely recite other additional elements that define the private data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8, 16, 17
Claim 8, 16-17 merely recite other additional elements that define the model is fine-tuned using public data and private data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 21
Claim 21 merely recite other additional elements that fine-tuning the model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 22
Claim 22 merely recite other additional elements that defining trained and stabilized ML model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 23
Claim 23 merely recite other additional elements that defining performing fine-tuning of the model by using the privacy-preserving trainer module which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 24
Claim 24 merely recite other additional elements that defining the privacy-preserving trainer module which performs generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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, 9 and 18 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims 1, 9 and 18 recite “fine-tune the machine learning model using anonymized private data to generate a warm- start model state for encrypted training; and fine-tune the machine learning model using encrypted private data based on the warm- start model state.” But there is no warm- start model state described at the specification, please further amend the claims to help move forward the prosecution.
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, 9 and 18 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 pre-AIA the applicant regards as the invention. The claims 1, 9 and 18 recite “fine-tune the machine learning model using anonymized private data to generate a warm- start model state for encrypted training; and fine-tune the machine learning model using encrypted private data based on the warm- start model state.” The claims are indefinite for failing to particularly point out and distinctly claim the subject matter since there is no warm- start model state described at the specification. For the purpose of examination, the claims will be given BRI. Please further amend the claims to help move forward the prosecution.
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.
Claims 1-2, 8-10, 16-18, 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Fridental et al. (Fridental) US 2019/0325318 in view of Jeuk et al. (Jeuk) US 2021/0392049 and Khavronin et al. (Khavronin) US 2022/0188700
In regard to claim 1, Fridental disclose A system, comprising a processor to: ([0022][0038]-[0043] a system with a processor)
train and stabilize a machine learning model using public data; ([0016]-[0017] [0056]-[0058][0060][0067] train a ML model using public data and updating the learner engine and repeat the process until convergence)
after training and stabilizing the machine learning model using the public data, ([0016]-[0017] [0056]-[0067] after the ML model is converged, the process can return to the seed learning, that is fine-tuning the ML model with additional data)
fine-tune the machine learning model using the anonymized private data; and
fine-tune the machine learning model using the encrypted private data based on the warm- start model state. ([0056]-[0067] fine-tune the ML model with private data and the process is repetitive with additional private data)
But Fridental fail to explicitly disclose “using anonymized private data, using encrypted private data.”
Jeuk disclose using anonymized private data, using encrypted private data. ([0067] [0071] training the ML using anonymized private data and using encrypted private data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Jeuk‘s method of ML training into Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Jeuk‘s method of ML training using anonymized and encrypted private data would help to provide more kinds of training data into Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more kinds of training data in training ML model would help to improve accuracy of ML training.
But Fridental and Jeuk fail to explicitly disclose “to generate a warm-start model state for encrypted training; and fine-tune the machine learning model based on the warm- start model state.”
Khavronin disclose to generate a warm-start model state for encrypted training; and fine-tune the machine learning model based on the warm- start model state. ([0022] [0034]-[0035] train the model and the model generate a training result, such as parameter set, such as performance level and based on the results, the model is retrained to hopefully improve the model performance. Note: please use functional language to help move forward the prosecution, please further define the warm-start model state, but it seems the warm-start model state is not disclosed at the specification, call to discuss if necessary)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Khavronin‘s method of ML training into Jeuk and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Khavronin‘s tuning the ML model based on criteria would help to provide more model training control into Jeuk and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing ML model training control would help to improve ML model performance.
In regard to claim 2, Fridental and Jeuk, Khavronin disclose The system of claim 1,
Fridental disclose wherein the training and fine-tuning is automatically executed without intermediate interaction with a user. (Fig.1 [0060]-[0067] training and fine-tuning is executed by the processor without user interaction)
In regard to claim 8, Fridental and Jeuk, Khavronin disclose The system of claim 1,
Fridental disclose wherein the machine learning model is fine-tuned using public data in addition to the private data. ([0056]-[0067] fine-tune the ML model with public data and private data)
But Fridental and Khavronin fail to explicitly disclose “the anonymized private data, the encrypted private data.”
Jeuk disclose the anonymized private data, the encrypted private data. ([0067] [0071] training the ML using anonymized private data and using encrypted private data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Jeuk‘s method of ML training into Khavronin and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Jeuk‘s method of ML training using anonymized and encrypted private data would help to provide more kinds of training data into Khavronin and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more kinds of training data in training ML model would help to improve accuracy of ML training.
In regard to claims 9-10, 16, claims 9-10, 16 are method claims corresponding to the system claims 1-2, 8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-2, 8.
In regard to claim 17, Fridental and Jeuk, Khavronin disclose The computer-implemented method of claim 9,
Fridental disclose wherein training and fine-tuning the machine learning model comprises training using only user data comprising the public data and the private data. ([0041][0048 [0067] [0071] training and fine-tuning the ML using user annotated public data and a private dataset of a client)
In regard to claim 18, claim 18 is a computer program product claim corresponding to the system claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 22, Fridental and Jeuk, Khavronin disclose The system of claim 1,
Fridental disclose wherein the machine learning model is trained and stabilized using public data until convergence according to a performance criterion. ([0016]-[0017] [0056]-[0058][0060][0067] train a ML model using public data and updating the learner engine and repeat the process until convergence according to a quality target or quantity value)
In regard to claim 23, Fridental and Jeuk, Khavronin disclose The system of claim 1,
But Fridental and Jeuk fail to explicitly disclose “wherein the processor executes a privacy-preserving trainer module configured to perform the fine-tuning of the machine learning model.”
Khavronin disclose disclose wherein the processor executes a privacy-preserving trainer module configured to perform the fine-tuning of the machine learning model. ([0218]-[0224] trainer node configured to perform the fine tuning of the ML model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Khavronin‘s method of ML training into Jeuk and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Khavronin‘s tuning the ML model with trainer node would help to provide more model training control into Jeuk and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing ML model training control with trainer node would help to improve ML model performance.
In regard to claim 24, Fridental and Jeuk, Khavronin disclose The system of claim 23,
But Fridental and Khavronin fail to explicitly disclose “wherein the privacy-preserving trainer module comprises: an anonymized private data fine-tuner sub-module; and an encrypted private data fine-tuner sub-module.”
Jeuk disclose wherein the privacy-preserving trainer module comprises: an anonymized private data fine-tuner sub-module; and an encrypted private data fine-tuner sub-module. ([0030][0071] the engine can have subcomponents, Note: please use functional language to describe the invention.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Jeuk‘s method of ML training into Khavronin and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Jeuk‘s method of ML training using anonymized and encrypted private data would help to provide more kinds of training data into Khavronin and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more kinds of training data in training ML model would help to improve accuracy of ML training.
Claims 3, 11, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fridental et al. (Fridental) US 2019/0325318 and Jeuk et al. (Jeuk) US 2021/0392049, Khavronin et al. (Khavronin) US 2022/0188700 as applied to claim 1, further in view of Choudhury et al. (Choudhury) US 2021/0150269 and Liu et al. (Liu) US 2021/0312334
In regard to claim 3, Fridental and Jeuk, Khavronin disclose The system of claim 1,
But Fridental and Jeuk, Khavronin fail to explicitly disclose “wherein the private data is anonymized using a technique selected from the group consisting of k-anonymity, masking,”
Choudhury disclose wherein the private data is anonymized using a technique selected from the group consisting of k-anonymity, masking, and differential privacy. (([0005] [0023]-[0025] [0040]-[0055] data is anonymized using k-anonymity, masking and differential privacy)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Choudhury‘s method of ML training into Khavronin, Jeuk and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Choudhury‘s ML training using anonymized private data would help to provide more kinds of training data into Khavronin, Jeuk and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more kinds of training data in training ML model would help to improve accuracy of ML training.
But Fridental and Jeuk, Khavronin, Choudhury fail to explicitly disclose “wherein the private data is anonymized using blurring.”
Liu disclose wherein the private data is anonymized using blurring. ([0008]-[0009] [0042]-[0044] blurring the data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Liu‘s method of ML training into Khavronin, Choudhury, Jeuk and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Liu‘s ML training using anonymized private data would help to provide more kinds of training data into Khavronin, Choudhury, Jeuk and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more kinds of training data in training ML model would help to improve accuracy of ML training.
In regard to claim 11, claim 11 is a method claim corresponding to the system claim 3 above and, therefore, is rejected for the same reasons set forth in the rejections of claims 3.
In regard to claim 19, claim 19 is a computer program product claim corresponding to the system claims 3 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3.
Claims 7, 15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fridental et al. (Fridental) US 2019/0325318 and Jeuk et al. (Jeuk) US 2021/0392049, Khavronin et al. (Khavronin) US 2022/0188700 as applied to claim 1, further in view of Liu et al. (Liu) US 2021/0312334
In regard to claim 7, Fridental and Jeuk, Khavronin disclose The system of claim 1,
But Fridental and Jeuk, Khavronin fail to explicitly disclose “wherein the private data is encrypted using homomorphic encryption.”
Liu disclose wherein the private data is encrypted using homomorphic encryption. ([0058]-[0067] encrypting the data using homomorphic encryption)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Liu‘s method of ML training into Khavronin, Jeuk and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Liu‘s ML training using encrypted private data would help to provide more kinds of training data into Khavronin, Jeuk and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more kinds of training data in training ML model would help to improve accuracy of ML training.
In regard to claim 15, claim 15 is a method claim corresponding to the system claim 7 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 7.
In regard to claim 20, claim is 20 a computer program product claim corresponding to the system claim 7 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 7.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Fridental et al. (Fridental) US 2019/0325318 and Jeuk et al. (Jeuk) US 2021/0392049, Khavronin et al. (Khavronin) US 2022/0188700 as applied to claim 1, further in view of Sarpatwar et al. (Sarpatwar) US 2021/0397988
In regard to claim 21, Fridental and Jeuk, Khavronin disclose The system of claim 1,
But Fridental and Jeuk, Khavronin fail to explicitly disclose “wherein the fine-tuning the machine learning model using encrypted private data is subject to computational constraints imposed by homomorphic encryption.”
Sarpatwar disclose wherein the fine-tuning the machine learning model using encrypted private data is subject to computational constraints imposed by homomorphic encryption. ([0006] [0074]-[0084] training the ML model is limited by the HE computational constraints)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Sarpatwar ‘s method of ML model training into Khavronin, Jeuk and Fridental’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Sarpatwar’s ML model training with resource constraints would help to provide more ML model training control into Khavronin, Jeuk and Fridental’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing ML model training resource constraints would help to balance the accuracy of ML training and the resource usage.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 filed on 6/3/2026 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20230088897 A1 2023-03-23 WANG et al.
HETEROGENEOUS PROCESSING SYSTEM FOR FEDERATED LEARNING AND PRIVACY-PRESERVING COMPUTATION
WANG et al disclose A heterogeneous processing system for federated learning and privacy-preserving computation, including: a serial subsystem configured for distributing processing tasks and configuration information of processing tasks, the processing task indicating performing an operation corresponding to computing mode on one or more operands; and a parallel subsystem configured for, based on the configuration information, selectively obtaining at least one operand of the one or more operands from an intermediate result section on the parallel subsystem while obtaining remaining operand(s) of the one or more operands with respect to the at least one operand from the serial subsystem, and performing the operation on the operands obtained based on the configuration information… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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, Jennifer Welch can be reached at 571-272-7212. 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.
XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights