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 .
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.
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 1-8, 9-17, 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 these claims recite
train and stabilize a machine learning model using public data; fine-tune the machine learning model using anonymized private data; and fine-tune the machine learning model using encrypted private data.
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 the machine learning model using anonymized private data; and fine-tune the machine learning model using encrypted private data. 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.
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 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
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)
fine-tune the machine learning model using the anonymized private data; and
fine-tune the machine learning model using the encrypted private data. ([0056][0067] fine-tune the ML model with private data. Note: please do not use intended use language since there is not much patent weight, use functional language to help move forward the prosecution)
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.
In regard to claim 2, Fridental and Jeuk 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 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 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 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.
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 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.
Claims 3, 5-6, 11, 13-14, 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 as applied to claim 1, further in view of Choudhury et al. (Choudhury) US 2021/0150269
In regard to claim 3, Fridental and Jeuk disclose The system of claim 1,
But Fridental and Jeuk fail to explicitly disclose “wherein the private data is anonymized using k-anonymity.”
Choudhury disclose wherein the private data is anonymized using k-anonymity. ([0044]-[0053] data is anonymized using k-anonymity)
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 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 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 5, Fridental and Jeuk disclose The system of claim 1,
But Fridental and Jeuk fail to explicitly disclose “wherein the private data is anonymized using masking.”
Choudhury disclose wherein the private data is anonymized using masking. ([0040]-[0054] data is anonymized by masked)
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 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 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 6, Fridental and Jeuk disclose The system of claim 1,
But Fridental and Jeuk fail to explicitly disclose “wherein the private data is anonymized using differential privacy.”
Choudhury disclose wherein the private data is anonymized using differential privacy. ([0005] [0023]-[0025] data is anonymized by 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 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 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 claims 11, 13-14, claims 11, 13-14 are method claims corresponding to the system claims 3, 5-6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3, 5-6.
In regard to claim 19, claim 19 is a computer program product claim corresponding to the system claims 3, 4, 5, or 6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3,4, 5 or 6.
Claims 4, 7, 12, 15, 19-20 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 as applied to claim 1, further in view of Liu et al. (Liu) US 2021/0312334
In regard to claim 4, Fridental and Jeuk disclose The system of claim 1,
But Fridental and Jeuk 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 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 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 7, Fridental and Jeuk disclose The system of claim 1,
But Fridental and Jeuk 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 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 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 claims 12, 15, claims 12, 15 are method claims corresponding to the system claims 4, 7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 4, 7.
In regard to claim 19, claim 19 is a computer program product claim corresponding to the system claims 3, 4, 5, or 6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 3,4, 5 or 6.
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.
Response to Arguments
Applicant's arguments filed on 2/18/2026 regarding to claims 1-20 have been fully considered but they are not persuasive.
With respect to 35 USC § 101 rejection regarding to claims 1-20, please see the updated rejection above. Therefore, the applicant’s argument is not persuasive.
With respect to claim 1, the applicant argues that Fridental and Jeuk fail to disclose “a training pipeline that (i) train and stabilize a machine learning model using public data; (ii) fine-tune the machine learning model using anonymized private data; and (iii) fine-tune the machine learning model using encrypted private data.” The examiner respectfully disagrees. Fridental disclose at [0055]-[0060] etc. training and stabilizing a model with public data until converge and fine-tune the model with additional private data to perform supplemental seed learning, with repeating stages. There are least 3 steps in the initial training and fine-tuning processes, since fine-tuning can be done multiple times with various private data. Further using public data, using anonymized private data, using encrypted private data which are intended use language and there is not much patent weight. As long as Jeuk provides more data set to fine-tune the ML model which is the well known to the people with the skill would do to train the ML with various data set as disclosed at Fridental at [0043] “performs data generation to generate and/or synthesize, for example based on the extracted characterizing attributes of the received private data, more data with similar characteristics. For example, the provided system may extract from private image data characterizing attributes of the data that enable the provided system to generate more synthesized images with similar attributes. By the additional data, the provided system may update and/or train its learner engine.” [0057] “repeating self-learning 114 may include, at any stage, and returning to a seed learning 111a, for fine-tuning with additional data” and [0060] “In some embodiments, processor 10 performs initial learning 111 based on public data of a task domain related to the target task of engine 110. Engine 110 may be configured to perform supplemental seed learning 111a by fine-tuning the weights of engine 110, optionally according to a specific private dataset.” Fridental perform supplemental seed learning by fine-tuning with a specific private dataset based on a target task. Therefore the combination of Fridental and Jeuk disclose the recited claim. Therefore, the applicant’s argument is not persuasive.
The applicant secondly argues that Fridental and Jeuk fail to disclose “fine-tuning with both Anonymized and Encrypted data”. The examiner respectfully disagrees. The examiner do not rely Fridental to disclose Anonymized and Encrypted data. Jeuk disclose both anonymized and Encrypted data at spec. [0067] [0071] “Additionally, in some embodiments, the machine-learning engine 112 may include one or more integrated data security components 324 and/or data privacy components 326. For example, security components 324 and/or private components may include functionalities for training/testing machine-learning models on anonymized data sets, and/or using encryption on data to train/test machine-learning models.” The combination of Fridental and Jeuk together disclose the fine-tuning the ML model using Anonymized and Encrypted data. Therefore the combination of Fridental and Jeuk disclose the recited claim. Therefore, the applicant’s argument is not persuasive.
The applicant makes a third arguments stating “Failure to Establish a prima Facie Rejection.” The examiner respectfully disagrees. Jeuk provides more data set to fine-tune the ML model which is the well known to the people with the skill would do as disclosed at Fridental at [0043] “performs data generation to generate and/or synthesize, for example based on the extracted characterizing attributes of the received private data, more data with similar characteristics. For example, the provided system may extract from private image data characterizing attributes of the data that enable the provided system to generate more synthesized images with similar attributes. By the additional data, the provided system may update and/or train its learner engine.” [0057] “repeating self-learning 114 may include, at any stage, and returning to a seed learning 111a, for fine-tuning with additional data” and [0060] “In some embodiments, processor 10 performs initial learning 111 based on public data of a task domain related to the target task of engine 110. Engine 110 may be configured to perform supplemental seed learning 111a by fine-tuning the weights of engine 110, optionally according to a specific private dataset.” Fridental perform supplemental seed learning by fine-tuning with additional data. This is the well known to the people with the skill would do to train the ML with various data set. 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. Therefore the combination of Fridental and Jeuk disclose the recited claim. Therefore, the applicant’s argument is not persuasive.
The applicant makes further arguments stating that Fridental and Jeuk fail to disclose “Synergistic Effect of the claims” The examiner respectfully disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e. Synergistic Effect of the claims) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, the applicant’s argument is not persuasive.
The applicant makes further arguments stating “Improper Combination” The examiner respectfully disagrees. Jeuk provides more data set to fine-tune the ML model which is the well known to the people with the skill would do to train the ML with various data set as disclosed at Fridental at [0043] “performs data generation to generate and/or synthesize, for example based on the extracted characterizing attributes of the received private data, more data with similar characteristics. For example, the provided system may extract from private image data characterizing attributes of the data that enable the provided system to generate more synthesized images with similar attributes. By the additional data, the provided system may update and/or train its learner engine.” [0057] “repeating self-learning 114 may include, at any stage, and returning to a seed learning 111a, for fine-tuning with additional data” and [0060] “In some embodiments, processor 10 performs initial learning 111 based on public data of a task domain related to the target task of engine 110. Engine 110 may be configured to perform supplemental seed learning 111a by fine-tuning the weights of engine 110, optionally according to a specific private dataset.” Fridental perform supplemental seed learning with additional data. This is the well known to the people with the skill would do to train the ML with various data set. 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. Therefore the combination of Fridental and Jeuk disclose the recited claim. Therefore, the applicant’s argument is not persuasive.
The applicant makes further arguments stating “Combination Made in Hindsight” The examiner respectfully disagrees. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Jeuk provides more data set to fine-tune the ML model which is the well known to the people with the skill would do to train the ML with various data set as disclosed at Fridental at [0043] “performs data generation to generate and/or synthesize, for example based on the extracted characterizing attributes of the received private data, more data with similar characteristics. For example, the provided system may extract from private image data characterizing attributes of the data that enable the provided system to generate more synthesized images with similar attributes. By the additional data, the provided system may update and/or train its learner engine.” [0057] “repeating self-learning 114 may include, at any stage, and returning to a seed learning 111a, for fine-tuning with additional data” and [0060] “In some embodiments, processor 10 performs initial learning 111 based on public data of a task domain related to the target task of engine 110. Engine 110 may be configured to perform supplemental seed learning 111a by fine-tuning the weights of engine 110, optionally according to a specific private dataset.” Fridental perform supplemental seed learning with additional data. This is the well known to the people with the skill would do to train the ML with various data set. 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. Therefore the combination of Fridental and Jeuk disclose the recited claim. Therefore, the applicant’s argument is not persuasive.
Applicant’s argument that independent claims 8, 15, with similar elements as claim 1, and thus are allowable, is not persuasive, since claim * has been shown to be rejected 1.
Applicant's arguments that remaining dependent claims of 1, 8, 15 are allowable since they directly or indirectly dependent upon one of the independent claims 1, 8, 15 is not persuasive, since the independent claim 1, 8, 15 have been shown/explained to be rejected.
The examiner would like to suggest (1) further specify the steps in the training (2) how the data is used to fine-tune the ML model to reach what goal, etc. to help move forward the prosecution, etc.
Conclusion
THIS ACTION IS MADE FINAL. 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 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.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights