DETAILED ACTION
This nonfinal office action is responsive to the amendment filed on January 6, 2026. Claims 1-18 are pending. Claims 1, 7, and 13 are independent.
The claim rejection of claim 5 under 35 USC §112(b) is withdrawn in light of applicant’s amendment.
Claim rejections of claims 3-4, 9-10, and 15-16 under 35 USC §101 are maintained. See sections Claim Rejections – 35 USC §101 and Response to Arguments below.
Claim rejections under 35 USC §103 have been updated in light of applicant’s amendment. See sections Claim Rejections – 35 USC §103 and Response to Arguments below.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 6, 2026 has been entered.
Claim Objections
Claim 14 is objected to because of the following informalities: There is an extra period at the end of the claim. Appropriate correction is required.
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 3-4, 9-10, and 15-16 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Regarding claim 3:
Claim 3, which is dependent on claim 1, recites a method and therefore falls within the statutory category of a process. The claim also recites that the one score is based on an aggregation statistic of a score output from all of the second models. The foregoing can practically be performed in the human mind. For instance, a person is capable of looking at a list of scores and calculating an average, total, median, etc. Therefore, these limitations fall within the "mental processes" grouping of abstract ideas.
The judicial exception is not integrated into a practical application. The claim recites receiving data, outputting a corresponding score, and outputting one score for all models which is nothing more than insignificant extra-solution activity. The claim also recites a computing device, training a first model using recursive model stacking, training a revised model based on the output of all the first models, training a second model based on a corresponding output score of all the revised models which is nothing more than mere instructions to apply the judicial exception using a generic computer. The claim as a whole, looking at the additional elements individually and in combination does not integrate the judicial exception into a practical application. Therefore, the claim is directed to an abstract idea.
The claim does not recite additional elements that amount to significantly more than the judicial exception. In particular, claim 3 recites receiving data, outputting a corresponding score, and outputting which is nothing more than insignificant extra-solution activity. Receiving and transmitting (outputting) data is well-understood, routine, conventional activities (buySAFE, Inv. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096), see MPEP 2106.05(d). The claim also recites a computing device, training a first model using recursive model stacking, training a revised model based on the output of all the first models, training a second model based on a corresponding output score of all the revised models which is nothing more than mere instructions to apply the judicial exception using a generic computer. The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception. The claim is not patent eligible.
Regarding claim 4:
Claim 4 further elaborates on the aggregation statistic of claim 3. The claim also recites that the aggregation statistic is an average. The foregoing can practically be performed in the human mind. For instance, a person is capable of looking at a list of scores and calculating an average. Therefore, these limitations fall within the "mental processes" grouping of abstract ideas. There are no additional elements recited.
Regarding claim 9:
Claim 9, which is dependent on claim 7, recites a system and therefore falls within the statutory category of a machine. The claim also recites that the one score is based on an aggregation statistic of a score output from all of the second models. The foregoing can practically be performed in the human mind. For instance, a person is capable of looking at a list of scores and calculating an average, total, median, etc. Therefore, these limitations fall within the "mental processes" grouping of abstract ideas.
The judicial exception is not integrated into a practical application. The claim recites a processor, a server, training a first model using recursive model stacking, training a revised model based on the output of all the first models, training a second model based on a corresponding output score of all the revised models which is nothing more than mere instructions to apply the judicial exception using a generic computer. The claim also recites receiving data, outputting a corresponding score, and outputting one score for all models which is nothing more than insignificant extra-solution activity. The claim as a whole, looking at the additional elements individually and in combination does not integrate the judicial exception into a practical application. Therefore, the claim is directed to an abstract idea.
The claim does not recite additional elements that amount to significantly more than the judicial exception. In particular, claim 9 recites a processor, a server, training a first model using recursive model stacking, training a revised model based on the output of all the first models, training a second model based on a corresponding output score of all the revised models which is nothing more than mere instructions to apply the judicial exception using a generic computer. The claim also recites receiving data, outputting a corresponding score, and outputting one score for all models which is nothing more than insignificant extra-solution activity. Receiving and transmitting (outputting) data is well-understood, routine, conventional activities (buySAFE, Inv. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096), see MPEP 2106.05(d). The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception. The claim is not patent eligible.
Regarding claim 10:
Claim 10 further elaborates on the aggregation statistic of claim 9. The claim also recites that the aggregation statistic is an average. The foregoing can practically be performed in the human mind. For instance, a person is capable of looking at a list of scores and calculating an average. Therefore, these limitations fall within the "mental processes" grouping of abstract ideas. There are no additional elements recited.
Regarding claim 15:
Claim 15, which is dependent on claim 13, recites a non-transitory computer program product and therefore falls within the statutory category of a manufacture. The claim also recites that the one score is based on an aggregation statistic of a score output from all of the second models. The foregoing can practically be performed in the human mind. For instance, a person is capable of looking at a list of scores and calculating an average, total, median, etc. Therefore, these limitations fall within the "mental processes" grouping of abstract ideas.
The judicial exception is not integrated into a practical application. The claim recites a non-transitory computer program product, a server, training a first model using recursive model stacking, training a revised model based on the output of all the first models, training a second model based on a corresponding output score of all the revised models which is nothing more than mere instructions to apply the judicial exception using a generic computer. The claim also recites receiving data, outputting a corresponding score, and outputting one score for all models which is nothing more than insignificant extra-solution activity. The claim as a whole, looking at the additional elements individually and in combination does not integrate the judicial exception into a practical application. Therefore, the claim is directed to an abstract idea.
The claim does not recite additional elements that amount to significantly more than the judicial exception. In particular, claim 15 recites a non-transitory computer program product, a server, training a first model using recursive model stacking, training a revised model based on the output of all the first models, training a second model based on a corresponding output score of all the revised models which is nothing more than mere instructions to apply the judicial exception using a generic computer. The claim also recites receiving data, outputting a corresponding score, and outputting one score for all models which is nothing more than insignificant extra-solution activity. Receiving and transmitting (outputting) data is well-understood, routine, conventional activities (buySAFE, Inv. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096), see MPEP 2106.05(d). The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception. The claim is not patent eligible.
Regarding claim 16:
Claim 16 further elaborates on the aggregation statistic of claim 15. The claim also recites that the aggregation statistic is an average. The foregoing can practically be performed in the human mind. For instance, a person is capable of looking at a list of scores and calculating an average. Therefore, these limitations fall within the "mental processes" grouping of abstract ideas. There are no additional elements recited.
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wink et al. (An Approach for Peer-to-Peer Federated Learning), hereinafter Wink, in view of Pothula et al. (US20210174257), hereinafter Pothula.
Regarding claim 1, Wink teaches:
A method of creating models with a plurality of entities, wherein the plurality of entities is at least three (Wink, page 152, column 1, paragraph 3: “Figure 1 shows an abstract overview of the iterative peer to-peer model training process with three participants Peer1, Peer2 and Peer3 as an example.” – Wherein peers are analogous to the entities.)
training, by the computing device, a first model for each of the plurality of entities based on the received data, wherein each first model is trained using recursive model stacking and each first model outputs a corresponding score; (Wink, page 152, column 1, paragraph 3: “In the first training round, each peer (including the initiator Peer1) trains its own respective copy of v0 locally. Local training steps are conducted by using local sample datasets only, which the given participant wants to keep secret.” And column 2, paragraph 3: “To partition a given weight value
w
of a given neural network model into
n
parts, the participant
P
e
e
r
i
(as owner of
w
) first generates
n
positive random numbers
r
n
1
,
⋯
,
r
n
n
. These values will be divided by their sum, such that
p
r
n
i
=
r
n
i
∑
k
=
1
n
r
n
k
,
i
∈
1
,
⋯
,
n
. The resulting percentage distribution is then used to generate normalized
w
1
,
⋯
w
n
partitions of
w
, where
w
i
=
w
*
p
r
n
i
,
i
∈
1
,
⋯
,
n
. For example, assume the weight
w
=
19
has to be partitioned in three parts. The owner of
w
will first generate three random numbers, such as
r
n
1
=
9
,
r
n
2
=
18
,
and
r
n
3
=
30
leading to the percentage factors of
p
r
n
1
=
0.16
,
p
r
n
2
=
0.32
,
and
p
r
n
3
=
0.52
,
respectively. The resulting parts of
w
are thus
w
1
=
3
,
w
2
=
6
,
and
w
3
=
10
(see also in Table I).” – v0 being trained locally is analogous to the first model for each of the entities being trained using recursive model stacking, see, e.g., fig. 1. The percentage factors for each peer is analogous to the score that’s being output by each model.)
training, by the computing device, a revised model for each of the plurality of entities based on the output of all of the first models, wherein each revised model is trained using the recursive model stacking; (Wink, page 152, column 2, paragraph 1: “In the next phase, participants collaboratively conduct Secure Average Computation (SAC) steps to determine the average of corresponding model weight values in v0 , v0 and v0 . During that process they do not exchange any model weight values among each other. As a result of SAC, all participants will possess identical updated model instances v1 at their local premises.” – The secure average computation is analogous to the revised model being based on the output of all of the first model, as seen in Fig. 1, after exchanging the secure average computation the model is further trained for each peer.)
training, by the computing device, a second model for each of the plurality of entities based on the corresponding output score of all of the revised models, wherein each second model is trained using the recursive model stacking; (Wink, page 152, column 2, paragraph 1: “If it is not yet the case, they may start another training round and further re-train their local v1 instances while using again their respective local training datasets as input. It may take in total r rounds until the model vr will converge, i.e., hit the desired quality threshold, so that the training process can terminate.” – The local training repeating indicates that after each training round the secure average computation is performed and then the model at the peer is further trained. Thus, v1 is analogous to the revised model and v2 would be analogous to the second model.)
outputting one score, by the computing device, based on the output of all of the second models. (Wink, page 152, column 2, paragraph 1: “If it is not yet the case, they may start another training round and further re-train their local v1 instances while using again their respective local training datasets as input. It may take in total r rounds until the model vr will converge, i.e., hit the desired quality threshold, so that the training process can terminate.” – The retraining occurring again indicates that after v2, e.g., the second models, the process then conducts the secure average computation again and, therefore, outputs the one score based on the output of all the second models using the same process as noted above.)
Wink does not explicitly teach:
receiving, by a computing device, data from a plurality of entities,
However, Pothula teaches:
receiving, by a computing device, data from a plurality of entities, (Pothula, paragraph 0026: “In some embodiments, datasets 14 may be obtained from various entities (e.g. enterprises) and may include entity events (e.g., those involving the entity, like where the entity acted upon or was acted upon by a system being modeled, like a customer) and non-event attributes related to those events (e.g., exogenous events, like the weather, news events, holidays, etc.).”)
Pothula is considered analogous to the claimed invention as it is in the same field of endeavor, machine learning. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified Wink, which already teaches training machine learning models for a plurality of entities but does not explicitly teach receiving data from the plurality of entities, to include the teachings of Pothula which does teach receiving data from the plurality of entities where the system may control the amount of data that is shared in order to provide the appropriate level of security and protections. (Pothula, paragraph 0027)
Regarding claim 2, Wink and Pothula teach the computerized-method of claim 1, as cited above.
Wink further teaches:
training a third model for each of the plurality of entities based on the one score that is output for all models and wherein the third model for each of the plurality of entities is trained using recursive model stacking. (Wink, page 152, column 2, paragraph 1: “If it is not yet the case, they may start another training round and further re-train their local v1 instances while using again their respective local training datasets as input. It may take in total r rounds until the model vr will converge, i.e., hit the desired quality threshold, so that the training process can terminate.” And Fig. 1 – The process repeating a total of r rounds indicates that after conducting the secure average computation using the output of v2, e.g., the second model, then v3 would be trained which is analogous to the third model for each peer based on the score output that is taught by Wink in claim 1 above.)
Regarding claim 3, Wink and Pothula teach the computerized-method of claim 1, as cited above.
Wink further teaches:
the one score is based on an aggregation statistic of a score output from all of the second models for each of the plurality of entities. (Wink, page 153, column 1, last paragraph: “In the next step, Peeri determines the subtotal psi by summing up wii and the wji values that it received from the other n-1 peers. The locally computed subtotal psi will than be sent to all other participants. Upon receiving the subtotals from all peers, Peeri is able to compute S as the sum of subtotals and can calculate the average Avg=S/n finally.” – The average is analogous to an aggregation statistic of a score output.)
Regarding claim 4, Wink and Pothula teach the computerized-method of claim 3, as cited above.
Wink further teaches:
the aggregation statistic is an average. (Wink, page 153, column 2, first paragraph: “Upon receiving the subtotals from all peers, Peeri is able to compute S as the sum of subtotals and can calculate the average Avg=S/n finally.”)
Regarding claim 5, Wink and Pothula teach the computerized-method of claim 2, as cited above.
Wink further teaches:
training of the model, the second model and the third model is performed in a pipeline. (Wink, Fig. 1 – The figure shows that the training flows from the first model to the second to the third etc., therefore the training is performed in a pipeline.)
Regarding claim 6, Wink and Pothula teach the computerized-method of claim 1, as cited above.
Wink further teaches:
wherein the recursive model stacking is recursive federated learning. (Wink, page 152, column 1, paragraph 2: “Thus, the most basic requirement for a federated peer-to-peer ML system is that collaboratively trained models should have at least the same quality (prediction accuracy) as if a central server had been involved in the training.” And Fig. 1 – The federated peer-to-peer ML system is analogous to recursive federated learning. Fig. 1 shows that the learning method is recursive model stacking.)
Regarding claim 7, claim 7 has all the same limitations of claim 1 which are taught by Wink and Pothula, see claim 1 above.
Wink does not explicitly teach:
at least one processor configured to:
However, Pothula further teaches:
at least one processor configured to: (Pothula, paragraph 0008: “Some aspects include a system, including one or more processors, and memory storing instructions that, when executed by the processors, cause the processors to effectuate operations of the above-mentioned process.”)
Regarding claim 8, Wink and Pothula teach the system of claim 7, as cited above. Claim 8 additionally has the same limitations of claim 2 which are taught by Wink and Pothula – see claim 2 above.
Regarding claim 9, Wink and Pothula teach the system of claim 7, as cited above. Claim 9 additionally has the same limitations of claim 3 which are taught by Wink and Pothula – see claim 3 above.
Regarding claim 10, Wink and Pothula teach the system of claim 9, as cited above. Claim 10 additionally has the same limitations of claim 4 which are taught by Wink and Pothula – see claim 4 above.
Regarding claim 11, Wink and Pothula teach the system of claim 8, as cited above. Claim 11 additionally has the same limitations of claim 5 which are taught by Wink and Pothula – see claim 5 above.
Regarding claim 12, Wink and Pothula teach the system of claim 7, as cited above. Claim 12 additionally has the same limitations of claim 6 which are taught by Wink and Pothula – see claim 6 above.
Regarding claim 13, claim 13 has all the same limitations of claim 1 which are taught by Wink and Pothula, see claim 1 above.
Wink does not explicitly teach:
A non-transitory computer program product comprising instruction which, when the program is executed cause the computer to:
However, Pothula further teaches:
A non-transitory computer program product comprising instruction which, when the program is executed cause the computer to: (Pothula, paragraph 0115: “Instructions or other program code to provide the functionality described herein may be stored on a tangible, non-transitory computer readable media.”)
Regarding claim 14, Wink and Pothula teach the non-transitory computer program product of claim 13, as cited above. Claim 14 additionally has the same limitations of claim 2 which are taught by Wink and Pothula – see claim 2 above.
Regarding claim 15, Wink and Pothula teach the non-transitory computer program product of claim 13, as cited above. Claim 15 additionally has the same limitations of claim 3 which are taught by Wink and Pothula – see claim 3 above.
Regarding claim 16, Wink and Pothula teach the non-transitory computer program product of claim 15, as cited above. Claim 16 additionally has the same limitations of claim 4 which are taught by Wink and Pothula - see claim 4 above.
Regarding claim 17, Wink and Pothula teach the non-transitory computer program product of claim 14, as cited above. Claim 17 additionally has the same limitations of claim 5 which are taught by Wink and Pothula – see claim 5 above.
Regarding claim 18, Wink and Pothula teach the non-transitory computer program product of claim 13, as cited above. Claim 18 additionally has the same limitations of claim 6 which are taught by Wink and Pothula – see claim 6 above.
Response to Arguments
Applicant's arguments regarding claim rejections under 35 USC §101 have been fully considered but they are not persuasive. In particular, applicant argues on page 11 of Applicant’s Remarks that “amended claim 1 now recites additional limitations that when taken together with claim 3 (e.g., all of claim 1 limitations plus claim 3 limitations) amount to significantly more.” However, applicant does not explain how the limitations of amended claim 1 amounts to significantly more. The addition of training the revised models is merely adding another generic training limitation and would not amount to significantly more than the other two training limitations as they are all recited at such a high level of generality they are mere instructions to apply the judicial exception, especially given that the final outcome of claim 1 is to output the one score which claim 3 further clarifies as being based on an aggregation statistic which can be practically performed in the human mind. The same rationale applies to dependent claim 4 as well as claims 9-10 and 15-16.
Therefore claim rejections under 35 USC §101 of claims 3-4, 9-10, and 15-16 are maintained. See section Claim Rejections – 35 USC §101 above.
Applicant’s arguments, on pages 7-9, with respect to rejection of claims 1-18 under 35 USC §103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wink, which does teach the revised model based on the output of all first models and a second model based on the output of all revised models. See section Claim Rejections – 35 USC §103 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fusco et al. (US20220207349)
Butler et al. (US11276023)
Kang et al. (FedMVT: Semi-supervised Vertical Federated Learning with MultiView Training)
McMahan et al. (Federated Learning of Deep Networks using Model Averaging)
Saha and Ahmad (Federated Transfer Learning: concept and applications)
Sharma et al. (Secure and Efficient Federated Transfer Learning)
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/J.C.M./ Examiner, Art Unit 2144
/TAMARA T KYLE/ Supervisory Patent Examiner, Art Unit 2144
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