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 .
Response to Arguments
Applicant's arguments and amendments filed 3/13/2026 have been fully considered but they are not persuasive.
Applicant has amended the claims to recite “the set of distributions including a plurality of different distributions”. The Examiner notes that the amendment still reads on the rejection of record. The amendment does not describe how the distributions are different. Bello discloses that the training mechanism is iterative using Algorithm 1 on Page 5. Page 4 in Section 4 states that while a single distribution is used, multiple graphs are drawn using the single distribution. Therefore, since multiple distributions are iteratively applied (1st, 2nd, 3rd and nth distributions), “different” distributions are used to generate multiple distributions of graphs. The Examiner recommends further describing how the distributions are different.
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 27 is 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 27 recites “and/or” which fails to indicate if the claim should be interpreted using the “and” or the “or” claim recitation. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over Tian et al. (U.S. Patent Application Publication 2022/0026221) in view of Bello et al. (Neural Combinatorial Optimization with Reinforcement Learning, 2017).
Referring to claim 1, Tian discloses a computer-implemented method for training a pointer network model (see the bottom of Paragraph 0043) for performing a task having a target distribution (see Paragraphs 0041-0043) using a processor and a memory (see Figures 1a and 2a).
While Tian teaches a NCO model uses a pointer network to address the TSP problem (see Paragraph 0002 and 0085) in addition to Tian’s invention using a machine learning model in the form of a pointer network (see the bottom of Paragraph 0043), Tian fails to teach the details of training the NCO model.
Bello discloses training a neural combinatorial optimization (NCO) model for performing a task having a target distribution (see the Abstract and the Fourth Paragraph of Section 1 on Page 1), the method comprising:
meta-training the NCO model to learn an efficient heuristic on a set of distributions (see Page 7, Table 1 for indicating that the RL pretraining-Active Search training method, which includes learning on the training data and further note the RL pretraining section on Page 7).
fine-tuning the meta-trained NCO model to specialize a learned heuristic for the target distribution (see the bottom of Page 7 for the RL-pretraining-Active Search process which initializes the model parameters from a pretrained RL model and run the Active Search process, which is the refining process defined in Table 1 at the top of Page 7).
Referring to claim 5, Bello discloses that the NCO model is a graph-based model, and wherein the target distribution is a target graph distribution defined by at least one parameter (see the Abstract for comparing learning of network parameters on a set of training graphs against learning them on individual test graphs).
Referring to claim 6, Bello discloses that the target graph distribution is defined by a plurality of parameters (see the Abstract and the rejection of claim 5).
Referring to claim 7, Bello discloses that the plurality of parameters comprises one or more of number of modes, number of nodes, or scale (see the 1st Paragraph of Section 1 on Page 1 and the Abstract for analyzing a number of nodes to find the optimal combination of nodes).
Referring to claim 8, Bello discloses said meta-training uses a reinforcement learning method (see the Abstract and the 3rd Paragraph of Section 1 on Page 1).
Referring to claim 9, Bello discloses that said reinforcement learning method uses an attention-based model (see the 3rd Paragraph of Section 3 on Page 3 for following the attention model from Bahdanau et al. 2015).
Referring to claim 10, Bello discloses that said meta-training uses a supervised learning method (see the 2nd Paragraph of Section 5.1 on Page 7 for using supervised learning).
Referring to claim 11, Bello discloses all of the limitations of claim 10, but fails to teach that said supervised learning method uses a Graph Convolutional Network (GCN)-based model.
The Examiner takes Official Notice that a supervised learning method uses a GCN-based model.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to modify the supervised learning method, as taught by Tian and Bello, using the GCN-based model, as taught by the Examiner’s statement of Official Notice, for the purpose of excelling at capturing complex relationships in graph-structed data by aggregating information from neighboring nodes, making them effective for tasks like node classification and graph classifications.
Referring to claim 12, Bello discloses that said meta-trained model is defined by learned meta-parameters (see the 4th Paragraph in Section 5.1 on Pages 7-8 for the RL pretraining combined with active search using the learned meta-parameters from the RL pretraining and using those meta-parameters to define the active search).
Referring to claim 22, Bello discloses that the NCO model is configured to heuristically solve a traveling salesman problem (see Abstract on Page 1).
Referring to claim 23, see the rejection of claim 1 and further note that Tian discloses that the computer implemented method provides a salutation to a combinatorial optimization program (see Paragraph 0003 for determining the best route to solve a traveling salesman program).
Tian also discloses receiving, by a processor, a request to perform a CO task, the request including input data (see Paragraph 0012).
Tian also discloses outputting the CO solution (see the Abstract).
Bellow discloses processing the input data using a fine-tuned NCO model to determine a CO solution (see the bottom of Page 7 and the top of Page 8 for the RL pretraining-Active Search).
Referring to claim 24, see the rejection of claim 1.
Allowable Subject Matter
Claims 2-4 and 13-21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 25-26 are allowed. Reasons for allowance will be issued upon the entire instant application being in condition for allowance.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON P SALCE whose telephone number is (571)272-7301. The examiner can normally be reached 5:30am-10:00pm M-F (Flex Schedule).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Flynn can be reached at 571-272-1915. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jason Salce/Senior Examiner, Art Unit 2421
Jason P Salce
Senior Examiner
Art Unit 2421
April 20, 2026
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