DETAILED ACTION
This office action is responsive to the above identified application filed 2/28/2024. The application contains claims 1-10, all examined and rejected.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The Information Disclosure Statement with references submitted 2/28/2024, has been considered and entered into the file.
Claim Objections
Claim 9 objected to because of the following informalities: claim recite “for a” instead of “for a”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 disclose the building of an AI model. However, the claim recite the usage of a model of the last round. But in the first round there is no global model of a last round to pull from the blockchain. Dependent claim inherit the deficiency.
Claims 1-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 disclose “obtaining, by the model trainer and the model verifier, global models of a last round from a blockchain, respectively“. The language used in the claim especially the term respectively sounds like each of the model trainer and the model verifier are obtaining a different global model. Dependent claim inherit the deficiency.
Claims 1-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 disclose “obtaining, by the model trainer and the model verifier, global models“ and “to obtain a global model of the current round”. The presented claim is contradicting as it disclose that at end of the round there is a single global model, however upon the retrieval of data related to the previous round the system is retrieving multiple “Global Models” instead of the single “master model” that is used for federated learning. Dependent claim inherit the deficiency.
Claims 1-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 disclose “allocating participants in a training process proportionally into three categories: a model trainer, a model verifier and a model uploader” and “obtaining, by the model trainer and the model verifier”. The claim is vague as the claim create three categories to allocate participants into. However, later the claims associate actions with the categories and a category cannot perform an action. Dependent claim inherit the deficiency.
Claims 2-3 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 disclose “allocating participants in a training process proportionally into three categories: a model trainer, a model verifier and a model uploader” and claim 2 disclose “ striving, by the model uploader”. The claims associate actions with the categories and a category cannot perform an action. Dependent claim inherit the deficiency.
Claims 2-3 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim disclose terms that are not defined in the specifications and claims “PoS”. Dependent claim inherit the deficiency.
Claims 6-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim disclose terms that are not defined in the claim as the term
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. Dependent claim inherit the deficiency.
Claims 6-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention
Claim 6 recites the limitation "its private key", “signing
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”. There is insufficient antecedent basis for this limitation in the claim. Dependent claim inherit the deficiency.
Claims 7-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention
Claim 7 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim disclose terms that are not defined in the claim as the term
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. . Dependent claim inherit the deficiency.
Claims 8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention
Claim 8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim disclose that the first limitation approves any trainer model whose accuracy is greater or equal to the verifier accuracy. Therefore the remaining models in group Trest are the models that failed (i.e. trainer accuracy less that average accuracy). The second limitation of the claim is calculating the Avg by subtracting smaller trainer accuracy from average accuracy, which means that the calculated Avg is always positive. Finally the claim is adding a judgment condition that check if the subtraction of verification accuracy from test accuracy (negative value) is bigger or equal to the Avg (positive value), which will never occur.
Claim 8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention
Claim 8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim disclose terms that are not defined in the claim as the claim presented formulas terms .
Claim 9-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention
Claim 9 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim disclose terms that are not defined in the claim as the term
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,. Dependent claim inherit the deficiency.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claim 1 is directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-10 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) 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. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. 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 amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claim is determined to include process as in independent Claim 1 and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“randomly allocating participants in a training process proportionally into three categories: a model trainer, a model verifier and a model uploader, prior to start of each round of training of the original AI model”, “checking, partial models generated by the model trainer through using partial models generated locally”, “aggregating, by the model uploader, all partial models passing the checking of the model verifier to obtain a global model of the current round” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
“A blockchain-based AI model training method, comprising: building an original AI model according to features of data sets“ (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h))
during each round of training, obtaining, by the model trainer and the model verifier, global models of a last round from a blockchain (insignificant extra-solution activity, MPEP 2106.05(g));
“training the global models through using local data sets to generate respective partial models of a current round”, “model verifier” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f))
“packing the global model, checking results and all the partial models of the current round to the blockchain” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“A blockchain-based AI model training method, comprising: building an original AI model according to features of data sets“ (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
during each round of training, obtaining, by the model trainer and the model verifier, global models of a last round from a blockchain (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
“training the global models through using local data sets to generate respective partial models of a current round”, “model verifier” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f));
“packing the global model, checking results and all the partial models of the current round to the blockchain” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “ striving, by the model uploader, for a right of uploading models to the blockchain through using a PoS consensus algorithm after packing data, wherein a model uploader who has acquired the right of uploading models to the blockchain packs data to the blockchain” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “wherein if two or more model uploaders all acquire the right of uploading models to the blockchain at a same time, a bifurcation problem is solved according to a credit reward of each model uploader saved in the blockchain, and a block packed by a model uploader with a high credit reward is selected as a legal block” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “when the data sets are image data, the original AI model uses a convolutional neural network, and the convolutional neural network comprises three convolution layers and two full connected layers” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “prior to the start of each round of training, the participants are allocated with a proportion relationship: T>V>M, wherein T is the model trainer, V is the model verifier, and M is the model uploader” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose “wherein during each round of training, an execution process of the model trainer comprises: downloading, by a model trainer ti, a global model Gj-1 of the last round from the blockchain (insignificant extra-solution activity, MPEP 2106.05(g) and well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), performing a training, with the global model Gj-1 of the last round as a starting point of the training, through using local training sets, to obtain a local partial model Lt i j, signing txt i j by using its private key Kt i pri (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), and sending txt i j to a model verifier (insignificant extra-solution activity, MPEP 2106.05(g) and well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), wherein the partial model Lt i j and a credit reward of the model trainer ti are encapsulated in Lt i j” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 disclose “ wherein during each round of training, an execution process of the model verifier comprises:
“receiving, by the model verifier, txt i j sent by the model trainer” (insignificant extra-solution activity, MPEP 2106.05(g) and well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), verifying txt i j through using a public key Kt i pub of the model trainer ti (mental process), if the verifying fails, discarding txt i j, and if the verifying passes (mental process), executing following steps:
downloading, by the model verifier vk, the global model Gj-1 of the last round from the blockchain (insignificant extra-solution activity, MPEP 2106.05(g) and well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), and performing a training, with the global model Gj-1 of the last round as a starting point of the training, through using local training sets to obtain a local partial model Lv k j (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f));
calculating, through using local testing sets, accuracies of the local partial model Lt i j sent by the model trainer and the local partial model Lv k j trained by the model verifier, respectively, so as to obtain an accuracy A(Ljti)testvk of the partial model trained by the model trainer and an accuracy A(Ljvk)testvk of the partial model trained by the model verifier (mental process, mathematical concept);
checking the partial model trained by the model trainer by voting, according to the accuracies of two models (mental process, mathematical concept);
encrypting, by the model verifier vk, txv k j with its own private key Kv k pri after finishing checking (mental process), and then sending txv k j to the model uploader (insignificant extra-solution activity, MPEP 2106.05(g) and well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), wherein voting results, the partial model trained by the model trainer, a credit reward of the model verifier and the credit reward of the model trainer are encapsulated in txv k j. (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 8 disclose “
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” (mental process, mathematical concept) )). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 9 disclose “receiving, by a model uploader mp, txv k j sent by the model verifier” (insignificant extra-solution activity, MPEP 2106.05(g) and well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), verifying with a public key Kv k pub of the model verifier vk, and discarding txv k j if the verifying fails (mental process);
counting, by each model uploader mp, votes of all the model verifiers fora partial model Lt j trained by the model trainer (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), and calculating votes of each partial model Lt i j (mental process, mathematical concept); if a number of legal partial models trained by the model trainer is greater than or equal to a number of illegal partial models, aggregating all the legal partial models, otherwise, doing nothing (mental process, mathematical concept); packing, by the model uploader mp, the global model, voting results and all the partial models of the current round into a block blockm p j (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)) )). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 10 disclose “
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” (mental process, mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 2-18,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1-9 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus” Published 2021, disclosed in IDS submitted on 28/2/2024 [hereinafter D1].
With regard to Claim 1,
D1 disclose a blockchain-based AI model training method (Abstract, “we propose a blockchain-based decentralized FL frame work, termed VBFL, by exploiting two mechanisms in a blockchained architecture”), comprising:
building an original AI model according to features of data sets (P. 7, Experimenal result, “Each device in both Vanilla FL and VBFL adopted FedAvg and the MNIST CNN(McMahan et al. 2017) network structure, with 5 local training epochs per communication round, learning rate 0.01, and batch size 10”, P. 3, Operations of VBFL, ¶2, “A local model update by worker w in communication round Rj, denoted as Lw j , is generated by performing lo cal learning on the global model constructed in round Rj−1, denoted as Gj−1”);
randomly allocating participants in a training process proportionally into three categories:
a model trainer, a model verifier and a model uploader, prior to start of each round of training of the original AI model (P1-2, “In VBFL, at every communication round, each device possessing its local dataset is randomly appointed as: (i) a machine learning worker updating its local model, (ii) a model validator examining and voting on the legitimacy of the received local model updates, or (iii) a blockchain miner1 attempting to incorporate voting results with the corresponding local models to be stored in the next consensual block”, P. 3, Operations of VBFL, “In each round, each device d ∈ D is assigned to one of the following roles: worker w ∈ W, validator v ∈ V, and miner m ∈ M, where |W|+|V|+|M| = |D|, and performs its function according to the role assigned”, P. 4, Operations of VBFL, “After a d constructs Gj, d concludes Rj and enters Rj+1. The role of each d is reassigned in Rj+1 based on the role switching policy”);
during each round of training, obtaining, by the model trainer and the model verifier, global models of a last round from a blockchain, respectively, and training the global models through using local data sets to generate respective partial models of a current round (P. 3, Operations of VBFL, ¶2, “A local model update by worker w in communication round Rj, denoted as Lw j , is generated by performing local learning on the global model constructed in round Rj−1, denoted as Gj−1”, P. 4, Col. 1, “Each m would append this legitimate block to its own blockchain, and request its associated w and v to down load this legitimate block to append to their blockchains as well”, Col. 2, ¶1, “After a d being m, v or w appends a block, d processes the appended block by performing the following two tasks: (1) compute Gj by using those model updates with the number of Positive votes no less than the number of Negative votes”, P. 4, Col. 1, ¶1, , “The voting mechanism requires each v to perform one-epoch of local model update by using its own local training set trainv”, P. 6, “our proposed validator voting mechanism requires a validator v in VBFL to perform one-epoch of local learning to get Av(Lv j(1))”);
checking, by the model verifier, partial models generated by the model trainer through using partial models generated locally (P. 6, “After v receives a Lw j (n), v evaluates Lw j (n) on its test set testv to get Av(Lw j (n)), and calculates vad = Av(Lv j(1)) − Av(Lw j (n)) to compare it with validator threshold vhv j. If vad > vhv j, v considers Lw j (n) to be potentially distorted and w to be likely malicious, and generates a Negative vote for Lw j (n). Otherwise, a Positive vote for Lw j (n) is generated, Algorithm 1);
aggregating, by the model uploader, all partial models passing the checking of the model verifier to obtain a global model of the current round, and packing the global model, checking results and all the partial models of the current round to the blockchain (Operations of VBFL, P. 4, Col. 1, ¶2, “For all the extracted vtv(Lw j ), m would aggregate the voting results that vote on the same Lw j by each v ∈ V into vtm,V(Lw j ). Then, all the aggregated voting results {vtm,V(Lw j )}, for all w ∈ W, are put inside of a privately constructed candidate block blockm”, Col. 2, “(1) compute Gj by using those model updates with the number of Positive votes no less than the number of Negative votes”, P. 2, PoS-inspired Consensus, “to create legitimate blocks recording local model updates with their corresponding voting results”, P. 8, Conclusion and Future Work “a communication-efficient, FL-dedicated PoS consensus mechanism to select the block mined by the miner that has made the primary contribution to the learning process for extracting the positively voted local model updates to construct the global model”).
With regard to Claim 2,
D1 disclose the blockchain-based AI model training method according to claim 1, further comprising: striving, by the model uploader, for a right of uploading models to the blockchain through using a PoS consensus algorithm after packing data (P. 2, PoS-inspired Consensus, “The consensus mechanism of VBFL is inspired by proof-of-stake (PoS) (King and Nadal 2012), which allows the device serving the role of miner that has made the most learning contributions among the assigned miners in each communication round (i.e., the device that has been mostly cumulatively re warded among the devices that are assigned to miners) to create the legitimate block recording local model up dates with their corresponding voting results”, P. 4, Operations of VBFL, “Miner m then mines its own candidate block according to the VBFL-PoS consensus by hashing the entire block content and signing the hash by its private key”), wherein a model uploader who has acquired the right of uploading models to the blockchain packs data to the blockchain (VBFL-PoS Miner Selection, P. 6-7, “VBFL-PoS dictates selecting the block mined by the miner possessing the highest stake among M, as the miner with the highest stake has made the most contributions to the learning process among M, and therefore it is regarded as the most trustworthy and the least probable to obstruct the learning process. The block selected by VBFL PoS is called the legitimate block and denoted as blockj in Rj. The miner producing blockj is called the winning-miner in Rj”, P. 4, “Under VBFL-PoS, only this legitimate block can be used to extract the rewards record and the voting results with the corresponding model updates”, P. 4, Col. 1, “Each m would append this legitimate block to its own blockchain).
With regard to Claim 3,
D1 disclose the blockchain-based AI model training method according to claim 2, wherein if two or more model uploaders all acquire the right of uploading models to the blockchain at a same time, a bifurcation problem is solved according to a credit reward of each model uploader saved in the blockchain, and a block packed by a model uploader with a high credit reward is selected as a legal block (The claim disclose a contingent clauses “wherein” and conditional term “if”; therefore the claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. Under the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (MPEP 2111.04)).
With regard to Claim 4,
D1 disclose the blockchain-based AI model training method according to claim 1, wherein when the data sets are image data, the original AI model uses a convolutional neural network, and the convolutional neural network comprises three convolution layers and two full connected layers (The claim disclose a contingent clauses “wherein” and conditional term “if”; therefore the claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. Under the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (MPEP 2111.04)).
With regard to Claim 5,
D1 disclose the blockchain-based AI model training method according to claim 1, wherein, prior to the start of each round of training, the participants are allocated with a proportion relationship: T>V>M, wherein T is the model trainer, V is the model verifier, and M is the model uploader (VBFL Validator Voting Mechanism, P. 4, “Although the role of each d was reassigned in a new communication round, to focus on evaluating the effectiveness, we enforced the same role-combination to devices as 12 w’s, 5 v’s and 3 m’s in each round to guarantee that there was enough number of W and V”, P. 7, Effectiveness of VBFL’s Validation Mechanism, “We manually assigned an arbitrary combination of 12 w’s, 5 v’s and 3 m’s for all three VBFL executions across all 100 rounds”, P. 7, Operations of VBFL “where |W| + |V| + |M| = |D|”).
With regard to Claim 6,
D1 disclose the blockchain-based AI model training method according to claim 1, wherein during each round of training, an execution process of the model trainer comprises: downloading, by a model trainer
t
ⅈ
, a global model Gj-1 of the last round from the blockchain (Operations of VBFL, P. 4, “Each m would append this legitimate block to its own blockchain, and request its associated w and v to download this legitimate block to append to their blockchains as well”, Col. 2, “After a d being m, v or w appends a block, d processes the appended block by performing the following two tasks: (1) compute Gj by using those model updates with the number of Positive votes no less than the number of Negative votes”), performing a training, with the global model Gj-1 of the last round as a starting point of the training, through using local training sets, to obtain a local partial model
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(Operations of VBFL, P. 4, “A local model update by worker w in communication round Rj , denoted as Lwj, is generated by performing local learning on the global model constructed in round Rj-1, denoted as Gj-1”, Table 1: List of Notations, “Global model constructed at the end of round Rj”), signing
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by using its private key
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and sending
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to a model verifier (Operations of VBFL, P. 4, “Worker w then encapsulates both Lwj and rw j in a worker-transaction txwj signed by w’s private key, and sends txwj to a randomly associated validator”, “The id of a device is its public key, which is used to verify the signatures of transactions or blocks generated by the device”), wherein the partial model
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and a credit reward of the model trainer
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are encapsulated in
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(Operations of VBFL, P. 4, “In addition, worker w computes its expected rewards rw j for learning Lwj by following the reward mechanism of VBFL-PoS. Worker w then encapsulates both Lwj and rwj in a worker-transaction txwj signed by w’s private key”, Table 1: List of Notations, “Worker-transaction containing Lwj initiated and signed by worker w in Rj”, “Rewards to device d in round Rj”).
With regard to Claim 7,
D1 disclose the blockchain-based AI model training method according to claim 6, wherein during each round of training, an execution process of the model verifier comprises:
receiving, by the model verifier,
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sent by the model trainer, verifying
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through using a public key
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of the model trainer
t
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, if the verifying fails, discarding
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, and if the verifying passes (Operations of VBFL, P. 3, “Each validator v then obtains txwj
from all of its associated workers, and broadcasts txwj to all the other validators”, “Validator v receives a verification-reward, rv-veri j , by verifying the signature of one worker-transaction txwj. If the signature of transaction txwj is verified, v would extract Lwj from txwj and cast a vote on Lwj”, “The id of a device is its public key, which is used to verify the signatures of transactions or blocks generated by the device”), executing following steps:
downloading, by the model verifier
v
k
, the global model Gj-1of the last round from the blockchain, and performing a training, with the global model Gj-1 of the last round as a starting point of the training, through using local training sets to obtain a local partial model
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(Operations of VBFL, P. 4, “request its associated w and v to download this legitimate block to append to their blockchains as well”, VBFL Validator Voting Mechanism, “one possible solution is to get rid of Av(Gj1), and let validator v first perform one-epoch of legitimate local learning and calculate Av(Lvj (1)) as a proxy evaluation of Aw(Lwj (1))”, P. 4, Col. 1, ¶1, , “The voting mechanism requires each v to perform one-epoch of local model update by using its own local training set trainv”);
calculating, through using local testing sets, accuracies of the local partial model
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sent by the model trainer and the local partial model
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trained by the model verifier, respectively, so as to obtain an accuracy
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of the partial model trained by the model trainer and an accuracy
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of the partial model trained by the model verifier (P. 6, “After v receives a Lwj(n), v evaluates Lwj(n) on its test set testv to get Av(Lwj(n)), and calculates vad = Av(Lvj(1)) Av(Lwj(n)) to compare it with validator threshold Vhvj”, Table 1: List of Notations, “Private test set of device d”, “Accuracy of Ldj evaluated by validator v using test set testv in Rj”;
checking the partial model trained by the model trainer by voting, according to the accuracies of two models (P. 6, Algorithm 1: ValidateByVoting Mechanism, steps 4-7);
encrypting, by the model verifier
v
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with its own private key
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after finishing checking, and then sending
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to the model uploader, wherein voting results, the partial model trained by the model trainer, a credit reward of the model verifier and the credit reward of the model trainer are encapsulated in
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(
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, Table 1: List of Notations, “Validator-transaction containing txwj and vtv(Lwj) initiated by v in Rj”).
With regard to Claim 8,
D1 disclose the blockchain-based AI model training method according to claim 7, wherein the checking the partial model of the model trainer by voting comprises:
if the accuracy of the partial model trained by the model trainer is not lower than that of the partial model trained by the model verifier, that is,
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directly judging that the partial model
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trained by the model trainer is legal, and voting "Agree" (P. 6, Algorithm 1: ValidateByVoting Mechanism, steps 4-7), VBFL Validator Voting Mechanism, P. 6, “If vad > vhvj , v considers Lwj(n) to be potentially distorted and w to be likely malicious, and generates a Negative vote for Lwj(n). Otherwise, a Positive vote for Lwj(n) is generated”, P. 5, “v may subjectively choose a threshold value, denoted as vhvj, as a tolerance of an accuracy-drop measurement between Av(Gj1) and Av(Lwj)”, P. 7, Experimental Results “VBFL PoS 0/20 vh1.00: VBFL-PoS with all 20 legitimate devices and fixed validator-threshold 1.0”);
otherwise, except for the legal partial model, denoting a remaining partial model
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trained by the model trainer as Trest, and calculating a weighted accuracy difference according to following formula;
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judging a difference between an accuracy of all the remaining partial model t trained by the model trainer and the accuracy of the partial model trained by the model verifier and the weighted accuracy difference, wherein t E Trest, a judgment condition is as follows:
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if the above judgment condition is met, voting "Agree"; otherwise, judging that the partial model is illegal, and voting "Disagree" (limitation is met by meeting the first “if” branch limitation as the rest of the claim method may never occur See MPEP 2111.04, “contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met”).
With regard to Claim 9,
D1 disclose the blockchain-based AI model training method according to claim 7, wherein an execution process of a model uploader comprises:
receiving, by a model uploader mP,
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sent by the model verifier, verifying
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with a public key
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of the model verifier
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and discarding
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if the verifying fails (Operations of VBFL, P. 3, Col. 1, “Each m then receives txvj (Lwj) from its associated v, and broadcasts txvj (Lwj) to all the other miners. By doing so, each m 2M would have txvj (Lwj), for all
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”, “Miner m receives a verification-reward, rm-verij , by verifying the signature of one txvj(Lwj).”, P. 4, col. 2, “The id of a device is its public key, which is used to verify the signatures of transactions or blocks generated by the device.”);
counting, by each model uploader mP, votes of all the model verifiers for a partial model
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trained by the model trainer, and calculating votes of each partial model
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(Operations of VBFL, P. 4, “For all the extracted vtv(Lwj), m would aggregate the voting results that vote on the same Lwj by each
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into vtm;V(Lwj).”, “Suppose all the validators have voted on both Lw1j and Lw2j , and vtv1 (Lw1j ) = vtv3 (Lw1j ) = Positive, vtv2 (Lw1j ) = Negative. Suppose there are two miners M = fm1;m2g. After m1 and m2 aggregate the voting results, both vtm1;V(Lw1j ) and vtm2;V(Lw1j ) would be equal to {2 Positive; 1 Negative} … {0 Positive; 3 Negative}”, P. 3, Table 1, “Aggregated voting results from V on Lwj, collected by m”);
if a number of legal partial models trained by the model trainer is greater than or equal to a number of illegal partial models, aggregating all the legal partial models, otherwise, doing nothing (Operations of VBFL, P. 4, Col. 2, “compute Gj by using those model updates with the number of Positive votes no less than the number of Negative votes”, “assume the legitimate block was mined by m2 as it possesses more stake than m1, then each d would receive vtm2;V(Lw1j ) = f2Positive; 1Negativeg and vtm2;V(Lw2j ) = {0Positive; 3Negativeg). In this case, only Lw1j would be used for the construction of Gj”, Conclusion and Future Work , P. 7, “extracting the positively voted local model updates to construct the global model”;
packing, by the model uploader mP, the global model, voting results and all the partial models of the current round into block
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(P. 4,
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, P. 2, PoS-inspired Consensus, “to create the legitimate block recording local model updates with their corresponding voting results”, Table 1,
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).
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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over “Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus” Published 2021, disclosed in IDS submitted on 28/2/2024 [hereinafter D1] in view of “Communication-Efficient Learning of Deep Networks from Decentralized Data” Published on 2017 [hereinafter D2].
With regard to Claim 10,
D1 teach the blockchain-based AI model training method according to claim 9., wherein a formula for aggregating all the legal partial models (P. 7, Experimental Results, “Each device in both Vanilla FL and VBFL adopted FedAvg and the MNIST CNN(McMahan et al. 2017) network structure”, P. 4, Col. 2, “compute Gj by using those model updates with the number of Positive votes no less than the number of Negative votes”), wherein G is a global model generated in a j-th round of training;
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is a number of training sets of the model trainer
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; train_total is a total number of training sets of all legal model trainers; and is a partial model trained by
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in the j-th round of training (P. 4, “Global model constructed at the end of round Rj”, “Locally updated model by device d in round Rj”, “Private training set of device d”).
D1 does not explicitly teach that the FedAvg formula
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D2 teach
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(P. 4, Col. 1, ¶3,
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… We term this approach FederatedAveraging (or FedAvg), denominator n represent total samples across all participant nodes).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of Federated learning model aggregation specifically combing local models into a single global model. Thus, 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 D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
D1 explicitly disclose (P. 7, Experiment Results) that its device “VBFL adopted FedAvg and the MNIST CNN(McMahan et al. 2017) network structure” . However D1 relies on the citation rather than presenting the formula for the FedAvg aggregation step. One of ordinary skill in the art would be motivated to modify D1 to include the actual formula in order to implement the FedAvg step expressly required by D1 and would naturally and necessarily look to cited D2 reference to obtain the standard formula. This is simply Combining prior art elements according to known methods to yield predictable results, and the use of known technique to improve similar devices (methods, or products) in the same way specially that the reference D1 call for the exact technique by name and citation (MPEP 2143).
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. Malicious
US Patent Application Publication No. 20210406782 filed by NAKAYAMA et al. that disclose an adaptive learning framework is capable of achieving transition from static AI to adaptive AI. The potential AI applications need to adapt a number of distributed devices generating a huge amount of data as well as continuous and adaptive learning frameworks. The adaptive AI basically supports continuous learning and prevents machine learning models from getting drifted or outdated. The system is designed to adapt dynamic AI models that are constantly updated at the distributed edge side and aggregate the updated models from distributed learning environments.
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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