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 .
Claims 1-10 are pending for examination. Claims 1, 8, and 10 are independent.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 8, and 10 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 6 of copending Application No. 18/203,950 in view of Wang et al. ("Federated Learning with Matched Averaging", hereinafter "Wang"). The corresponding application (18/203,950) discloses similar limitations, shown in the table below, to the instant application (18/204,031). The Corresponding application 18/203,950 describes calculating a global parameter with both a local parameter and weight. Under broadest reasonable interpretation, a local parameter and weight are synonymous. The limitations for the instant application 18/204,031 further describe a similarity calculation which is taught by Wang in Section 2.1-2.2 and Algorithm 1. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the similar limitations taught in the corresponding application (18/203,950) with the method for calculating similarity taught by Wang. Doing so evaluates similarities between local parameters across different client apparatuses.
This is a provisional nonstatutory double patenting rejection.
Instant Application: 18/204,031
Corresponding Application: 18/203,950
Claim 1: A server apparatus comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
receive, from a plurality of client apparatuses that perform federated learning of a neural network model having multiplex branches capable of performing different operations on a common input and thereby learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches, the local model parameters corresponding to each of the branches;
calculate a degree of similarity between the local model parameters corresponding to each of the branches, received from different client apparatuses;
calculate a parameter of a global model based on the local model parameter selected based on a result of calculation by the similarity degree calculating unit; and
transmit the parameter calculated by the parameter calculating unit to the client apparatus.
Claim 1: A server apparatus comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
receive, from each of a plurality of client apparatuses performing federated learning of a neural network model having multiplex branches capable of performing different operations on a common input, a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches;
calculate a parameter of a global model based on the local model parameter and the weight received by the receiving unit; and
transmit the parameter calculated by the calculating unit to the client apparatuses.
Claim 8: A calculation method by an information processing apparatus, the method comprising:
receiving, from a plurality of client apparatuses that perform federated learning of a neural network model having multiplex branches capable of performing different operations on a common input and thereby learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches, the local model parameters corresponding to each of the branches;
calculating a degree of similarity between the local model parameters corresponding to each of the branches, received from different client apparatuses;
calculating a parameter of a global model based on the local model parameter selected based on a result of the calculating; and
transmitting the calculated parameter to the client apparatus.
Claim 6: A calculation method by an information processing apparatus, the calculation method comprising:
receiving, from each of a plurality of client apparatuses performing federated learning of a neural network model having multiplex branches capable of performing different operations on a common input, a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches;
calculating a parameter of a global model based on the received local model parameter and weight; and
transmitting the calculated parameter to the client apparatuses.
Claim 10: A non-transitory computer-readable recording medium having a program recorded thereon, the program comprising instructions for causing an information processing apparatus to realize process to:
receive, from a plurality of client apparatuses that perform federated learning of a neural network model having multiplex branches capable of performing different operations on a common input and thereby learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches, the local model parameters corresponding to each of the branches;
calculate a degree of similarity between the local model parameters corresponding to each of the branches, received from different client apparatuses;
calculate a parameter of a global model based on the local model parameter selected based on a result of the calculation; and
transmit the calculated parameter to the client apparatus.
Claim 1: A server apparatus comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
receive, from each of a plurality of client apparatuses performing federated learning of a neural network model having multiplex branches capable of performing different operations on a common input, a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches;
calculate a parameter of a global model based on the local model parameter and the weight received by the receiving unit; and
transmit the parameter calculated by the calculating unit to the client apparatuses.
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.
Claim 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 recites the limitation "the similarity degree calculating unit" in line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the parameter calculating unit" in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites the limitation "the permutating unit" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites “calculate a parameter of a global model based on the local model parameter selected based on a result of calculation by the similarity degree calculating unit” in lines 11-12. It is unclear what constitutes “the local model parameters selected” since the claim does not specifically disclose selecting any local model parameter. The claims never disclose selecting a local model parameter in some manner or tied to any calculation.
Independent claims 8 and 10 also recites a similar limitation and are also rejected under 112(b).
Dependent claims 2-7, and 9 do not resolve the 112(b) rejection from independent claims 1 and 8 and are also rejected under 112(b).
For purpose of examination, examiner interprets the selection as being based on the calculating a degree of similarity.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7 are directed to an apparatus, claims 8-9 are directed to a method, and claim 10 is directed to a non-transitory computer-readable recording medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
calculate a degree of similarity between the local model parameters corresponding to each of the branches, received from different client apparatuses; (This step for calculating similarity is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
calculate a parameter of a global model based on the local model parameter selected based on a result of calculation by the similarity degree calculating unit; (This step for calculating is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A server apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: (The server apparatus comprising memory and processor is understood to be generic computer elements - See MPEP 2106.05(f).
receive, from a plurality of client apparatuses that perform federated learning of a neural network model having multiplex branches capable of performing different operations on a common input and thereby learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches, the local model parameters corresponding to each of the branches; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
transmit the parameter calculated by the parameter calculating unit to the client apparatus. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A server apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: (The server apparatus comprising memory and processor is understood to be generic computer elements - See MPEP 2106.05(f).
receive, from a plurality of client apparatuses that perform federated learning of a neural network model having multiplex branches capable of performing different operations on a common input and thereby learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches, the local model parameters corresponding to each of the branches; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
transmit the parameter calculated by the parameter calculating unit to the client apparatus. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 8: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A calculation method by an information processing apparatus, the method comprising:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 10: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A non-transitory computer-readable recording medium having a program recorded thereon, the program comprising instructions for causing an information processing apparatus to realize process to:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 2 and 9
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2:
by repeatedly executing a process of calculating the degree of similarity between the local model parameters received from two client apparatuses, calculate the degrees of similarity between the local model parameters received from the plurality of client apparatuses. (This step is directed to performing repetitive calculations, is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
2B:
by repeatedly executing a process of calculating the degree of similarity between the local model parameters received from two client apparatuses, calculate the degrees of similarity between the local model parameters received from the plurality of client apparatuses. (This step is directed to performing repetitive calculations, which is understood to be well understood, routine and conventional activity as identified by the court (MPEP2106.05(d)(ll)(ii)))
Regarding Claim 3
2A Prong 1:
calculate the degree of similarity between the local model parameter corresponding to each of the branches received from a first client apparatus among the plurality of client apparatuses and the local model parameter corresponding to each of the branches received from a second client apparatus different from the first client apparatus, and thereafter calculate the degree of similarity between the local model parameter corresponding to each of the branches received from the second client apparatus and the local model parameter corresponding to each of the branches received from a third client apparatus different from the second client apparatus. (This step for calculating is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e., evaluation).)
2A Prong 2: The claim does not recite any additional elements.
Regarding Claim 4
2A Prong 1:
select the branches corresponding to the respective client apparatuses so as to combine the branches with highest similarity degree based on the result of calculation by the similarity degree calculating unit. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2: The claim does not recite any additional elements.
Regarding Claim 5
2A Prong 1:
permutate the branches based on the result of calculation by the similarity degree calculating unit; (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).) and
select the branches to be a parameter calculation target based on a result of permutation by the permutating unit. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).)
2A Prong 2: The claim does not recite any additional elements.
Regarding Claim 6
2A Prong 1:
permutate the branches so as to combine the branches with highest similarity degree. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: The claim does not recite any additional elements.
Regarding Claim 7
2A Prong 1:
calculate the parameter of the global model by calculating an average value of the branches with a same sequential number after permutation by the permutating unit. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: The claim does not recite any additional elements.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. ("Federated Learning with Matched Averaging", hereinafter "Wang") in view of Huang et al. ("Heterogeneous Federated Learning Through Multi-Branch Network", hereinafter "Huang").
Regarding Claim 1
Wang discloses: receive, from a plurality of client apparatuses that perform federated learning of a neural network model
calculate a degree of similarity between the local model parameters corresponding to each of the branches, received from different client apparatuses; ([Section 2.1-2.2 and Algorithm 1] describes calculating a similarity function between weights corresponding to each layers received from J clients (i.e. different clients) for matching.)
calculate a parameter of a global model based on the local model parameter selected based on a result of calculation by the similarity degree calculating unit ([Section 2.1-2.3 and Algorithm 1] describes calculating a global weight based on the matched local weights (i.e. based on similarity function).); and
transmit the parameter calculated by the parameter calculating unit to the client apparatus. ([Section 2.1-2.3 and Algorithm 1] describes broadcasting the global weights to clients to train consecutive layers.)
Wang does not explicitly disclose: A server apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: a neural network model having multiplex branches capable of performing different operations on a common input and thereby learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches.
However, Huang discloses in the same field of endeavor: A server apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: ([Section 3.1.- 3.2.] disclose a server.)
receive, from a plurality of client apparatuses that perform federated learning of a neural network model having multiplex branches capable of performing different operations on a common input and thereby learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches, the local model parameters corresponding to each of the branches; ([Section 3.1 Algorithm 1-2 Fig 1] describes a multi-branch neural network framework that receives model parameters from clients to perform aggregating MFedAvg.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the multi-branch network disclosed by Huang into the method of Federated matched averaging disclosed by Wang to receive local model parameters from a multi-branch neural network. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of multi-branch networks disclosed by Huang as all the references are in the field of Federated learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to evaluate a multi-branch model into a federated learning paradigm and perform branch-wise averaging-based aggregation.
Regarding Claim 8
Wang in view of Huang discloses: A calculation method by an information processing apparatus ([Section Experiments], Wang describes a computing environment.), the method comprising: (Claim 8 is a method claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 10
Wang in view of Huang discloses: A non-transitory computer-readable recording medium having a program recorded thereon ([Section Experiments], Wang describes a computing environment.), the program comprising instructions for causing an information processing apparatus to realize process to: (Claim 8 is a method claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 2
Wang in view of Huang discloses: The server apparatus according to Claim 1, wherein the processor is configured to execute the instructions to by repeatedly executing a process of calculating the degree of similarity between the local model parameters received from two client apparatuses, calculate the degrees of similarity between the local model parameters received from the plurality of client apparatuses. ([Section 2.1-2.3 and Algorithm 1], Wang describes repeatedly calculating the similarity function within a while loop.)
Regarding Claim 3
Wang in view of Huang discloses: The server apparatus according to Claim 1, wherein the processor is configured to execute the instructions to
calculate the degree of similarity between the local model parameter corresponding to each of the branches received from a first client apparatus among the plurality of client apparatuses and the local model parameter corresponding to each of the branches received from a second client apparatus different from the first client apparatus, and thereafter calculate the degree of similarity between the local model parameter corresponding to each of the branches received from the second client apparatus and the local model parameter corresponding to each of the branches received from a third client apparatus different from the second client apparatus. ([Section 2.1-2.3 and Algorithm 1], Wang describes calculating the similarity across different clients j.)
Regarding Claim 4
Wang in view of Huang discloses: The server apparatus according to Claim 1, wherein the processor is configured to execute the instructions to select the branches corresponding to the respective client apparatuses so as to combine the branches with highest similarity degree based on the result of calculation by the similarity degree calculating unit. ([Section 2.1-2.3 and Algorithm 1], Wang describes maximum bipartite matching.)
Regarding Claim 5
Wang in view of Huang discloses: The server apparatus according to Claim 1, wherein the processor is configured to execute the instructions to permutate the branches based on the result of calculation by the similarity degree calculating unit; ([Section 2.1-2.3 and Algorithm 1], Wang describes finding permutations for layers based on the matching.) and
select the branches to be a parameter calculation target based on a result of permutation by the permutating unit. ([Section 2.1-2.3 and Algorithm 1], Wang describes calculating a global weight based on the matched/permutated local weights.)
Regarding Claim 6
Wang in view of Huang discloses: The server apparatus according to Claim 5, wherein the processor is configured to execute the instructions to permutate the branches so as to combine the branches with highest similarity degree. ([Section 2.1-2.3 and Algorithm 1], Wang describes maximum bipartite matching and permutating.)
Regarding Claim 7
Wang in view of Huang discloses: The server apparatus according to Claim 5, wherein the processor is configured to execute the instructions to calculate the parameter of the global model by calculating an average value of the branches with a same sequential number after permutation by the permutating unit. ([Section 2.1-2.3 and Algorithm 1], Wang describes calculating a federated matched average after permutation.)
Regarding Claim 9
(Claim 9 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Li et al. (US 20250094822 A1, hereinafter "Li") describes Federated Learning a parallel layers (Para 0104). Xu et al. (CN114386570A) describes Federated matching and Multi-branch Neural networks.
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/TEWODROS E MENGISTU/Examiner, Art Unit 2127