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 .
Status of Claims
Claims 1, 4, 7-8, 11, 14-15, and 18 are pending and are examined herein.
Claims 1, 4, 7-8, 11, 14-15, and 18 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Claims 1, 4, 7-8, 11, 14-15, and 18 are rejected under 35 USC 103.
Response to Arguments/Amendments
On page 6, applicant argues that claim 1 is not a mere mental process because it includes technical features that require specific computing technologies and improve the performance of computer systems.
The computer system can prevent overfitting for domains of the training model by regularizing data sets of multiple domains. In addition, the computer system can implement the training model by using even a single neural network, that is, without adding another neural network, because the computer system implements the training model based on information shared between data sets of multiple domains.
Response: Examiner respectfully disagrees because the claims are directed to training data processing that includes data extraction. The feature of using a single model for multiple domains, such as domain adaptation or transfer learning, is not recited in the claims.
Furthermore, an implemented training model can have further improved performance because the computer system reinforces the complexity of feature data to be extracted from each of data sets when regularizing the data sets. That is, a problem in that the feature data extracted from the data sets is simplified when the data sets are regularized can be prevented. In addition, the human mind is not equipped to reinforce complexity of data domains by using a decaying BSP algorithm.
Response: Examiner respectfully disagrees because the arguments are directed to the potential results of performing the BSP algorithm. Paragraphs [0011]-[0013] of the published specification sets forth the improvement in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art); therefore, the claims do not appear to recite an improvement to the technology, see MPEP 2106.05(a). It is noted that the decaying BSP algorithm is directed to a series of calculations. A human can perform the series of calculations with the aide of pen and paper.
On page 8, applicant argues that one of ordinary skill in the art would not be motivated to combine Kang with Zhang and Zhang does not teach the use of a decaying BSP algorithm.
Response: It is noted that the amendment to specify a decaying BSP changes the scope of the previously presented claims and a new grounds of rejection is issued to address the amended claims. Applicant’s arguments against the Zhang reference are moot in view of the new grounds of rejection necessitated by amendment.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/10/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101 - Abstract Idea
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, 4, 7, 8, 11, 14, 15 and 18 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 Analysis
According to the first part of the analysis, in the instant case Claims 1, 4 and 7 are directed to a method, Claims 8, 11, and 14 are directed to an apparatus, and Claims 15 and 18 are directed to a non-transitory medium that stores the method; consequently, these claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2 Analysis (Combined Step 2A Prong 1-2 and Step 2B Analysis)
Claim 1 includes the following recitation of an abstract idea:
regularizing data sets of a plurality of domains (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
extracting information shared between the regularized data sets (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
wherein the regularizing of the data sets comprises extracting, from each of the data sets, feature data to be inputted to the neural network (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
wherein the extracting of the shared information comprises extracting the shared information based on the feature data (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
wherein the regularizing of the data sets reinforces complexity of the feature data to be extracted from each of the data sets by using a decaying batch spectral penalization (BSP) algorithm (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. In addition, the algorithm itself includes a series of calculation steps.)
Claim 1 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
implementing a training model by performing training based on the extracted shared information (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
transferring the training model to a target domain (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
wherein the extracting of the shared information extracts the shared information by encoding the regularized data sets over a single neural network (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Claim 1 does not reflect an improvement to computer technology or any other technology. The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 4 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 4 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
the neural network is a convolution neural network (CNN) (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Claim 4 does not reflect an improvement to computer technology or any other technology. The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 7 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 7 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
performs adversarial training through a single discriminator (This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Claim 7 does not reflect an improvement to computer technology or any other technology. The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 8 recites at least the abstract idea identified above in Claim 1.
Claim 8 recites the following additional elements aside from those described in Claim 1 which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
A computer system comprising: a memory; and a processor connected to the memory and configured to execute at least one instruction stored in the memory, wherein the processor is configured s (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 8 does not reflect an improvement to computer technology or any other technology. The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claims 11 and 14 recite at least the abstract idea identified above in the claim upon which they depend. Claim 11 and 14 recite substantially similar subject matter to Claims 4 and 7, respectively, and are rejected with the same rationale, mutatis mutandis. Claims 11 and 14 do not reflect an improvement to computer technology or any other technology. The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 15 recites at least the abstract idea identified above in Claim 1.
Claim 15 recites the following additional elements aside from those described in Claim 1 which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
A non-transitory computer-readable storage medium for storing one or more programs to execute a method (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).) Claim 15 does not reflect an improvement to computer technology or any other technology. The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
Claim 18 recite at least the abstract idea identified above in the claim upon which they depend. Claims 18 recite substantially similar subject matter to Claim 4, and is rejected with the same rationale, mutatis mutandis. Claim 18 does not reflect an improvement to computer technology or any other technology. The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and does not amount to significantly more than the identified judicial exception.
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, 4, 7, 8, 11, 14, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over “Kang” (CN 202011209104 A, Multi-domain Co-adaptive Training Method Of Cervical Cancer TCT Slice Positive Cell Detection Model, 2020) in view of “A Kernel Perspective for Regularizing Deep Neural Networks” (hereinafter Bietti), and further in view of “Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation” (hereinafter Chen).
Regarding Claim 1, Kang teaches
data sets of a plurality of domains (Kang, Contents of Invention recites “obtaining the digital pathological image of the first cervical cancer TCT slice as the source domain image; obtaining the digital pathological image of the second cervical cancer TCT slice as the target domain image; the target domain image and the source domain image size are the same.”)
extracting information shared between the data sets (Kang, Contents of Invention states “Using K-Mean clustering method to the first area candidate frame and the second area candidate frame the central point of the feature clustering, after clustering to obtain the first candidate group characteristic and a second candidate group feature.”)
implementing a training model by performing training based on the extracted shared information (Kang, Contents of Invention states “Using K-Mean clustering method to the first area candidate frame and the second area candidate frame the central point of the feature clustering, after clustering to obtain the first candidate group characteristic and a second candidate group feature.” Kang, S40, candidate group characteristic domain discrimination recites “Specifically, the first candidate group feature and a second candidate group feature inputting the discriminator to judge the feature.” Kang, Claim 1 recites “Cervical cancer TCT slice positive cell detection model multi-domain co-adaptive training method…the positive cell detection model comprises an encoder, an area generating network, a discriminator and a classifier…” The group features are feed into a component of the model, the discriminator, and is used in a claim centering around a training method.)
transferring the training model to a target domain (Kang, Summary of Invention states “Therefore, the model obtained by training the single scene (source domain) is applied to the scene (target domain) often due to noise distribution, data deviation and other influence caused by the model performance is greatly influenced, the test effect is strong.”)
wherein the extracting of the shared information extracts the shared information by encoding the…data sets over a single neural network (Kang, S20, domain unchanged feature extraction states “it is worth noting that the source domain image and the target domain image adopt the same encoder to extract the depth characteristic; the encoder parameter is shared. the encoder is a residual network, composed of convolution layer, batch standardization layer, activation layer and a pool layer” Examiner interprets a residual network composed of different layers to satisfy the limitation of being a single neural network.)
wherein the extracting of the shared information comprises (Kang, S20, domain unchanged feature extraction - “it is worth noting that the source domain image and the target domain image adopt the same encoder to extract the depth characteristic...the encoder is a residual network…” Examiner interprets the depth characteristic to be feature data which is performed before the encoder, a neural network, takes the data.) extracting the shared information based on the feature data (Kang, Contents of Invention states “Using K-Mean clustering method to the first area candidate frame and the second area candidate frame the central point of the feature clustering, after clustering to obtain the first candidate group characteristic and a second candidate group feature.”)
Kang does not appear to explicitly teach
regularizing data sets;
wherein the regularizing of the data sets comprises extracting, from each of the data sets, feature data to be inputted to the neural network; and
wherein the regularizing of the data sets reinforces complexity of the feature data to be extracted from each of the data sets by using a decaying batch spectral penalization (BSP) algorithm.
However, Bietti, directed to analogous art of regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS, see Abstract) that perform empirically best in the context of generalization from small image and biological datasets, by providing a tighter control of the RKHS norm (Summary of the contributions, p. 2), teaches
regularizing data sets (see Abstract, “propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space”);
wherein the regularizing of the data sets comprises extracting, from each of the data sets, feature data to be inputted to the neural network (Section 2.1, “Kernel methods consist of mapping data living in a set X to a RKHS H associated to a positive definite kernel K through a mapping function
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, and then learning simple machine learning models in H”); and
wherein the regularizing of the data sets reinforces complexity of the feature data (Section 3.1 Guarantees on adversarial generalization, “our bound uses the complexity of the original class and leverages smoothness properties of functions to derive the margin bound”) to be extracted from each of the data sets by using a decaying spectral norm algorithm (Section 4. Experiments, “we consider the SN projection strategy with decaying τ , as well as the SN penalty (10)).
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to modify Kang in view of Bietti because the combined strategies based on both upper and lower bounds performs empirically best in the context of generalization from small image and biological datasets, by providing a tighter control of the RKHS norm (see Summary of the contributions).
Although Bietti teaches decaying spectral norm algorithm, Bietti does not teach a batch spectral penalization algorithm.
However, Chen teaches Batch Spectral Penalization (BSP) for adversarial domain adaptation, a general approach to penalizing the largest singular values so that other eigenvectors can be relatively strengthened to boost the feature discriminability (see Abstract and Section 4.1).
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to further modify the combination of Kang and Bietti to include the teachings of Chen because this significantly improves upon representative adversarial domain adaptation methods to yield state of the art results (see Abstract of Chen).
Regarding Claim 4, the rejection of Claim 1 is incorporated herein. Furthermore, Kang teaches
the neural network is a convolution neural network (CNN) (Kang, S20, domain unchanged feature extraction – “the encoder is a residual network, composed of convolution layer, batch standardization layer, activation layer and a pool layer” Examiner sees a neural network having a convolution layer as being classified as a convolution neural network).
Regarding Claim 7, the rejection of Claim 1 is incorporated herein. Furthermore, Kang teaches
performs adversarial training through a single discriminator (CNN) (Kang, S40, candidate group characteristic domain discrimination recites “Specifically, the first candidate group feature and a second candidate group feature inputting the discriminator to judge the feature” Contents of the invention also recites “in the training process, the encoder, region generating network, arbiter and classifier by continuously iterating parameter updating…” Examiner recognizes that having these components like a discriminator, encoder and performing training in this way is adversarial learning. A person of ordinary skill in the art would be able to recognize this. It also is not stated where the discriminator contains multiple. However, the way it is written implies that there is one with language such as ‘a’ yet there could be more. The broadest reasonable interruption of this language would cover a limitation that limited the discriminator to be singular.)
Claims 8, 11, and 14 recite a system which performs substantially similar steps as listed by the method in Claims 1, 4, and 7, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Kang teaches
A computer system comprising: a memory; and a processor connected to the memory and configured to execute at least one instruction stored in the memory, wherein the processor is configured (Kang, S50, Optimal model selection recites “For purposes of illustration and description, the above description has been given. In addition, the description is not intended to limit the embodiments of the present application to the form disclosed herein. Although a plurality of exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations thereof, modifications, changes, addiations and subcombinations.” Kang, Abstract states “The invention claims a cervical cancer TCT slice cell detection model multi-domain co-adaptive training method, using the source domain image and the target domain image to train the detection model…” A person of ordinary skill in the art would recognize that one would need generic computer equipment in order to create the model and train it. This would include memory and a processor to perform the method and create the model.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Kang and Bietti in view of Chen as described above with respect to Claim 1.
Claims 15 and 18 recite a system which performs substantially similar steps as listed by the method in Claims 1 and 4, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Kang teaches
A non-transitory computer-readable storage medium for storing one or more programs to execute a method (Kang, S50, Optimal model selection recites “For purposes of illustration and description, the above description has been given. In addition, the description is not intended to limit the embodiments of the present application to the form disclosed herein. Although a plurality of exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations thereof, modifications, changes, addiations and subcombinations.” Kang, Abstract states “The invention claims a cervical cancer TCT slice cell detection model multi-domain co-adaptive training method, using the source domain image and the target domain image to train the detection model…” A person of ordinary skill in the art would recognize that one would need generic computer equipment in order to create the model and train it. This would include computer readable memory to store the instructions of the learning method and model.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Kang and Bietti in view of Chen as described above with respect to Claim 1.
Conclusion
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 LI B ZHEN whose telephone number is (571)272-3768. The examiner can normally be reached M-F, 7:30a-4p.
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/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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