CTNF 18/371,596 CTNF 94446 Detailed Action Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-17 are pending for examination. Claims 1, 6, and 17 are independent. Claim Objections 07-29-01 AIA Claim 11 objected to because of the following informalities: Claim 11 line 22 is missing a semicolon at the end. Claim 11 line 23 ends with a period instead of a semicolon . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 1-17 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. 07-34-05 AIA Claim 1 recites the limitation " the one respective updated weight " in line 17 . There is insufficient antecedent basis for this limitation in the claim. 07-34-05 AIA Claim 1 recites the limitation " the selected client " in line 18 . There is insufficient antecedent basis for this limitation in the claim. Independent claims 6 and 11 are also rejected for the same reasons above. Dependent claims 2-5, 7-10, 12-17 do not resolve the 112(b) and are also rejected. Claims 2-4, 7-9, 12-14 describe mathematical formulas without a description of what the variables represent. Claims 5, 10, and 15-17 depend on the claims above and do not resolve the 112(b) rejection, therefore they also rejected under 112(b) Double Patenting 08-33 AIA 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. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-35 Claim 1-5 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-5 of co-pending Application No. 18371594 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitations in Co-pending application 18371594 are substantially similar as highlighted in the table below. The difference appears in the instant application 18371596 when describing "selecting one activation output from one of the each client", which is taught by Ainsworth et al. (" GIT Re-Basin: Merging Models Modulo Permutation Symmetries ") in Section 3.1 when describing activation matching and selecting model A. It would be obvious for one of ordinary skill in the art to have combined the similar limitations taught in co-pending application (18371594) with Ainsworth. Doing so can select model activations to match and learn similar features to accomplish similar task between models . This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. 08-30 AIA A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co. , 151 U.S. 186 (1894); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert , 245 F.2d 467, 114 USPQ 330 (CCPA 1957). A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101. 08-32 Claim 6-17 provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claim 6-17 of copending Application No. 18371594 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented. Instant application: 18371596 Copending Application: 18371594 Claim 1: A method of training neural networks with federated learning, the method comprising: sending at least a portion of a server-maintained machine learning model from a server to a plurality of clients, yielding a plurality of local machine learning models; at each of the plurality of clients, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; updating respective weights for each of the plurality of local machine learning models; evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; receiving at the server, the activation outputs from each client; selecting one activation output from one of the each client ; permuting the activation outputs according to the one respective updated weight to match a dimension of the selected client to obtain a matrix; sending, by the server, the matrix to each client; permuting, by each client, the plurality of local machine learning models based on the matrix to obtain permuted weights; receiving, by the server, the permuted weights; aggregating, at the server, the permuted weights from each of the plurality of local machine learning models to obtain an aggregated server-maintained machine learning model; transferring the aggregated permuted weights to each of the plurality of clients; and updating each of the plurality of local machine learning models with the aggregated permuted weights. Claim 1: A method of training neural networks with federated learning, the method comprising: sending at least a portion of a server-maintained machine learning model from a server to a plurality of clients, yielding a plurality of local machine learning models; at each of the plurality of clients, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; updating respective weights for each of the plurality of local machine learning models based on the respective cross entropy loss ; evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; receiving, at the server, the activation outputs from each client; permuting, at the server , the activation outputs according to the one respective updated weights to match a dimension of the selected client to obtain a matrix; sending, by the server, the matrix to each client; permuting, by each client, the plurality of local machine learning models based on the matrix to obtain permuted weights; receiving, by the server, the permuted weights; aggregating, at the server, the permuted weights from each of the plurality of local machine learning models to obtain an aggregated server-maintained machine learning model with aggregated permuted weights ; transferring the aggregated permuted weights to each of the plurality of clients; and updating each of the plurality of local machine learning models with the aggregated permuted weights. Claim 6: A system of training neural networks with federated learning, the system comprising: memory storing instructions; and at least one processor that, when executing the instructions stored in the memory, collectively perform: sending at least a portion of a server-maintained machine learning model from a server to a plurality of clients, yielding a plurality of local machine learning models; at each of the plurality of clients, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; updating respective weights for each of the plurality of local machine learning models; evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; receiving at the server, the activation outputs from each client; selecting one activation output from one of the each client; permuting the activation outputs according to the one respective updated weight to match a dimension of the selected client to obtain a matrix; sending, by the server, the matrix to each client permuting, by each client, the plurality of local machine learning models based on the matrix to obtain permuted weights. receiving, by the server, the permuted weights; aggregating, at the server, the permuted weights from each of the plurality of local machine learning models to obtain an aggregated server-maintained machine learning model; transferring the aggregated permuted weights to each of the plurality of clients; and updating each of the plurality of local machine learning models with the aggregated permuted weights. Claim 6: A system of training neural networks with federated learning, the system comprising: memory storing instructions; and at least one processor that, when executing the instructions stored in the memory, collectively perform: sending at least a portion of a server-maintained machine learning model from a server to a plurality of clients, yielding a plurality of local machine learning models; at each of the plurality of clients, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; updating respective weights for each of the plurality of local machine learning models; evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; receiving at the server, the activation outputs from each client; selecting one activation output from one of the clients; permuting the activation outputs according to the one respective updated weight of the one of the clients to match a dimension of the selected client to obtain a matrix; sending, by the server, the matrix to each client; permuting, by each client, the plurality of local machine learning models based on the matrix to obtain permuted weights; receiving, by the server, the permuted weights; aggregating, at the server, the permuted weights from each of the plurality of local machine learning models to obtain an aggregated server-maintained machine learning model; transferring the aggregated permuted weights to each of the plurality of clients; and updating each of the plurality of local machine learning models with the aggregated permuted weights. Claim 11: A robotic system operated by a neural network comprising: memory storing instructions; and at least one processor that, when executing the instructions stored in the memory, collectively train the neural networks with federated learning by: sending at least a portion of a server-maintained machine learning model from a server to a plurality of clients, yielding a plurality of local machine learning models; at each of the plurality of clients, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; updating respective weights for each of the plurality of local machine learning models; evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; receiving at the server, the activation outputs from each client; selecting one activation output from one of the each client; permuting the activation outputs according to the one respective updated weight to match a dimension of the selected client to obtain a matrix; sending, by the server, the matrix to each client permuting, by each client, the plurality of local machine learning models based on the matrix to obtain permuted weights. receiving, by the server, the permuted weights; aggregating, at the server, the permuted weights from each of the plurality of local machine learning models to obtain an aggregated server-maintained machine learning model; transferring the aggregated permuted weights to each of the plurality of clients; and updating each of the plurality of local machine learning models with the aggregated permuted weights. Claim 11: A robotic system operated by a neural network comprising: memory storing instructions; and at least one processor that, when executing the instructions stored in the memory, collectively train the neural networks with federated learning by: sending at least a portion of a server-maintained machine learning model from a server to a plurality of clients, yielding a plurality of local machine learning models; at each of the plurality of clients, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; updating respective weights for each of the plurality of local machine learning models; evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; receiving at the server, the activation outputs from each client; selecting one activation output from one of the clients; permuting the activation outputs according to the one respective updated weight of the one of the clients to match a dimension of the selected client to obtain a matrix; sending, by the server, the matrix to each client; permuting, by each client, the plurality of local machine learning models based on the matrix to obtain permuted weights; receiving, by the server, the permuted weights; aggregating, at the server, the permuted weights from each of the plurality of local machine learning models to obtain an aggregated server-maintained machine learning model; transferring the aggregated permuted weights to each of the plurality of clients; and updating each of the plurality of local machine learning models with the aggregated permuted weights. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mozaffari et al. (US 20240064016 A1, hereinafter "Mozaffari") in view of Qi et al. (US 20240330705 A1, hereinafter "Qi") and Ainsworth et al. (“ GIT Re-Basin: Merging Models Modulo Permutation Symmetries ”, hereinafter “Ainsworth”) . Regarding Claim 1 Mozaffari discloses: A method of training neural networks with federated learning ([Abstract, Para 0086, and Fig 6]) , the method comprising: sending at least a portion of a server-maintained machine learning model from a server to a plurality of clients, yielding a plurality of local machine learning models; ([Para 0086, Fig 4, and Fig 6] describes a federation server 610 may maintain a federated machine learning model 612 and distribute a current version of the machine learning model 612 to local models 620, 630, and 640 (i.e. clients).) at each of the plurality of clients, training the plurality of local machine learning models with locally-stored data that is stored locally at that respective client, ([Para 0087, Fig 4, and Fig 6] describes training the local models using local training data sets.) updating respective weights for each of the plurality of local machine learning models; ([Para 0041, 0091 Fig 4, and Fig 6-7] describes local updates to the parameters of the machine model according to applied local training data.) transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; ([Para 0017 0022, 0076, Fig 4, and Fig 6-7] describes clients encrypt their updates then send them to the server.) receiving at the server, the activation outputs from each client; ([Para 0017 0022, 0076, Fig 4, and Fig 6-7] describes sever receiving clients encrypted updates.) permuting the activation outputs according to the one respective updated weight to match a dimension of the selected client to obtain a matrix ; ([Para 0022-0025, 0050- 0056, Fig 4 and Fig 6-7] describes each client and intra model parameter and parameter permutation for federated learning.) sending, by the server, the matrix to each client; ([Para 0044 0050-0056, 0094, Fig 4 and Fig 6-7] describes the encoded parameters may be provided to the aggregation server using one or more Private Information Retrieval (PIR) queries.) permuting, by each client, the plurality of local machine learning models based on the matrix to obtain permuted weights; ([Para 0022-0025, 0050-0056, Fig 4 and Fig 6-7] describes each client and intra model parameter and parameter permutation for federated learning.) receiving, by the server, the permuted weights; ([Para 0044 0050-0056, 0094, Fig 4 and Fig 6-7] describes the encoded parameters may be provided to the aggregation server using one or more Private Information Retrieval (PIR) queries.) aggregating, at the server, the permuted weights from each of the plurality of local machine learning models to obtain an aggregated server-maintained machine learning model; ([Para 0044 0050-0056, 0067, 0086-0088, 0094, Fig 4-7] describes the aggregation server aggregating model parameter updates.) transferring the aggregated permuted weights to each of the plurality of clients; ([Para 0044 0050-0056, 0067, 0086-0088, 0094, Fig 4-7] describes the aggregation server aggregates the updates, and sends the aggregated update to the clients.) and updating each of the plurality of local machine learning models with the aggregated permuted weights. ([Para 0044 0050-0056, 0067, 0086-0088, 0094, Fig 4- 7] describes each client can decrypt the encrypted global parameters received from the server and updates its local copy of the global machine learning model.) Mozaffari does not explicitly disclose: wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; However, Qi discloses in the same field of endeavor: wherein training at each client includes determining a respective cross entropy loss for each of the plurality of local machine learning models; ([Para 0004, 0022, 0026-0028, 0057, 0114 and Fig 4-7] describes determining, by a client computing system, a model update based at least in part on a SoftMax loss.) updating respective weights for each of the plurality of local machine learning models; ([Para 0004, 0022, 0026-0029, 0057, 0114 and Fig 4-7] describes local updated and training local models by updating the parameters. ) evaluating the updated respective weights on a common dataset at each layer of the local machine learning models to obtain activation outputs for each layer; ( [Para 0004, 0022, 0026-0029, 0057, 0114 and Fig 4-7] describes local updates determined by training and evaluating local models with a loss functions or backpropagation. ) transferring the activation outputs from each client to the server without transferring the locally-stored data of the clients; ([Para 0037 0042 0059 0092 and Fig 4-7]) receiving at the server, the activation outputs from each client; ([Para 0092, 0096, and Fig 4-7] describes receiving (e.g., by a server computing system comprising one or more computing devices) one or more class sets from one or more client computing systems.) It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Machine learning models with sampled softmax disclosed by Qi into the method of Federated Parameter Permutation disclosed by Mozaffari to utilize cross entropy loss. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Machine learning models with sampled softmax disclosed by Qi as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to optimize model parameters with respect to a SoftMax objective. Mozaffari in view of Qi does not explicitly disclose: selecting one activation output from one of the each client; permuting the activation outputs according to the one respective updated weight to match a dimension of the selected client to obtain a matrix; However, Ainsworth discloses in the same field of endeavor: selecting one activation output from one of the each client; ([Section 3.1 Algorithm 1, and Appendix] describes activation matching, with equation 1 selecting a model A as a reference and model B permuted to match model A.) permuting the activation outputs according to the one respective updated weight to match a dimension of the selected client to obtain a matrix; ([Section 3.1 Algorithm 1, and Appendix] describes activation matching, with equation 1 selecting a model A as a reference and model B permuted to match model A.) It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Permutation Symmetries disclosed by Ainsworth into the method of Mozaffari in view of Qi to utilize activation matching. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Permutation Symmetries disclosed by Ainsworth as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to further align models. Regarding Claim 6 Mozaffari in view of Qi and Ainsworth discloses: A system of training neural networks with federated learning, the system comprising: memory storing instructions; and at least one processor that, when executing the instructions stored in the memory, ([Fig 1], Qi) collectively perform: (Claim 6 is a system claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground) Regarding Claim 11 Mozaffari in view of Qi and Ainsworth discloses: A robotic system operated by a neural network comprising: memory storing instructions; and at least one processor that, when executing the instructions stored in the memory, collectively train the neural networks with federated learning ([Fig 1], Qi) by: (Claim 6 is a system claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground) Regarding Claim 2 Mozaffari in view of Qi and Ainsworth discloses: The method of claim 1, wherein the permuting according to the respective updated weights is according to: W l ~ = P W l , B l = P B l ([Section 2], Ainsworth describes permutation of a weights space and the same formula at the end of page 2. [Section 3.1] Ainsworth also discloses a similar function.) Regarding Claim 3 Mozaffari in view of Qi and Ainsworth discloses: The method of claim 2, wherein the permuting to obtain the matrix is according to P = a r g m i n ∑ i = 1 n | | θ A - P θ B | | ([Section 3.1-3.2, Algorithm 1, and Appendix] Ainsworth describes permutation matrices and similar formulas (e.g. equation 1 and algorithm 1).) Regarding Claim 4 Mozaffari in view of Qi and Ainsworth discloses: The method of claim 3, wherein each layer of the matrix is according to P = a r g m i n ∑ i = 1 n | | θ A - P θ B | | ([Section 3.1-3.2, Algorithm 1, and Appendix] Ainsworth describes permutation matrices for each layer l and similar formulas (e.g. equation 1 and algorithm 1).) Regarding Claim 5 Mozaffari in view of Qi and Ainsworth discloses: The method of claim 4, wherein the selecting is a random or pseudorandom selection. ([Section 3.1-3.2, Algorithm 1, Algorithm 3, and Appendix] Ainsworth describes activations for l th layer selection based on random permutation.) Regarding claim 14 Mozaffari in view of Qi and Ainsworth discloses: The robotic system of claim 13, wherein the permutation is performed on an activation for a layer with Z l = [ z l , 1 T , . . . , z l , n T ] , wherein the permutation at each layer is performed according to P l = a r g min P l ϵ S d l + 1 ∑ i = 1 n | | Z l + 1 , i ( A ) - P l Z l + 1 , i ( B ) | | ([Section 3.1-3.2, Algorithm 1, and Appendix] Ainsworth describes permutation matrices for each layer l activations for all n training data points, and similar formulas (e.g. equation 1 and algorithm 1).) Regarding Claim 15 Mozaffari in view of Qi and Ainsworth discloses: The robotic system of claim 14, further comprising selecting respective updated weights from each client via a random or pseudorandom selection. ([Section 3.1-3.2, Algorithm 1, Algorithm 3, and Appendix] Ainsworth describes activations for l th layer selection based on random permutation.) Regarding Claim 7 (Claim 7 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.) Regarding Claim12 (Claim 12 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.) Regarding Claim 8 (Claim 8 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.) Regarding Claim 13 (Claim 13 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.) Regarding Claim 9 (Claim 9 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.) Regarding Claim 10 (Claim 10 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.) 07-21-aia AIA Claim (s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mozaffari in view of Qi, Ainsworth, and Kim et al. (US 20240223407 A1, hererinafter "Kim") . Regarding Claim 16 Mozaffari in view of Qi, and Ainsworth disclose: The robotic system of claim 15, Mozaffari in view of Qi, and Ainsworth does not explicitly disclose: wherein the robotic system is an autonomous driving vehicle. However, Kim discloses in the same field of endeavor: wherein the robotic system is an autonomous driving vehicle. ([Para 0372-0374 and Fig 37] describes an autonomous driving system.) It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the system for Federated Learning disclosed by Kim into the method of Mozaffari in view of Qi, and Ainsworth to execute on autonomous driving vehicle system. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Federated Learning disclosed by Kim as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to perform autonomous driving operations. Regarding Claim 17 Mozaffari in view of Qi, Ainsworth, and Kim disclose: The robotic system of claim 15, wherein the robotic system is a medical system. ([Para 0384 and Fig 32], Kim describes a medical robot.) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Li et al. (US 20250005375 A1) describes Federated learning and training across diverse models. Bukharev et al. (US 20210326698 A1) describes training models with heterogeneous data . Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TEWODROS E MENGISTU/Examiner, Art Unit 2127 Application/Control Number: 18/371,596 Page 2 Art Unit: 2127 Application/Control Number: 18/371,596 Page 3 Art Unit: 2127 Application/Control Number: 18/371,596 Page 4 Art Unit: 2127 Application/Control Number: 18/371,596 Page 5 Art Unit: 2127 Application/Control Number: 18/371,596 Page 6 Art Unit: 2127 Application/Control Number: 18/371,596 Page 7 Art Unit: 2127 Application/Control Number: 18/371,596 Page 8 Art Unit: 2127 Application/Control Number: 18/371,596 Page 9 Art Unit: 2127 Application/Control Number: 18/371,596 Page 10 Art Unit: 2127 Application/Control Number: 18/371,596 Page 11 Art Unit: 2127 Application/Control Number: 18/371,596 Page 12 Art Unit: 2127 Application/Control Number: 18/371,596 Page 13 Art Unit: 2127 Application/Control Number: 18/371,596 Page 14 Art Unit: 2127 Application/Control Number: 18/371,596 Page 15 Art Unit: 2127 Application/Control Number: 18/371,596 Page 16 Art Unit: 2127 Application/Control Number: 18/371,596 Page 17 Art Unit: 2127 Application/Control Number: 18/371,596 Page 18 Art Unit: 2127 Application/Control Number: 18/371,596 Page 19 Art Unit: 2127 Application/Control Number: 18/371,596 Page 20 Art Unit: 2127 Application/Control Number: 18/371,596 Page 21 Art Unit: 2127 Application/Control Number: 18/371,596 Page 22 Art Unit: 2127 Application/Control Number: 18/371,596 Page 23 Art Unit: 2127 Application/Control Number: 18/371,596 Page 24 Art Unit: 2127 Application/Control Number: 18/371,596 Page 25 Art Unit: 2127 Application/Control Number: 18/371,596 Page 26 Art Unit: 2127 Application/Control Number: 18/371,596 Page 27 Art Unit: 2127 Application/Control Number: 18/371,596 Page 28 Art Unit: 2127 Application/Control Number: 18/371,596 Page 29 Art Unit: 2127 Application/Control Number: 18/371,596 Page 30 Art Unit: 2127 Application/Control Number: 18/371,596 Page 31 Art Unit: 2127 Application/Control Number: 18/371,596 Page 32 Art Unit: 2127 Application/Control Number: 18/371,596 Page 33 Art Unit: 2127 Application/Control Number: 18/371,596 Page 34 Art Unit: 2127
Prosecution Insights