DETAILED ACTION
This final rejection is responsive to communication filed April 30, 2026. Claims 1, 2, 4, 5, 7-9, 11, 12, 14, 15 and 17-19 are currently amended. Claims 1-20 are pending in this application.
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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al. (US 2024/0273364 A1) (‘Fu’) in view of Murai et al. (“Selective Harvesting over Networks” – from IDS) (‘Murai’), and further in view of Yogerst et al. (US 2025/0328753 A1) (‘Yogerst’).
With respect to claims 1 and 11, Fu teaches a method and a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
receiving respective sets of node features (i.e. application scenario features) from each edge node in a set of edge nodes of a network (i.e. edge devices in a system) (paragraph 71-72);
identifying those edge nodes in the set of edge nodes that contain datapoints corresponding to a specified class of edge nodes (i.e. edge devices in class group) (paragraphs 9 and 78-80);
training a model using the datapoints associated with the identified edge nodes (i.e. training a neural network) (paragraphs 8, 84, 97, and 106); and
applying the trained model to the network to identify additional edge nodes in the specified class (i.e. deploying trained neural network; also using K adjacent edge devices for iterative training) (paragraphs 115-116 and 120).
Fu does not explicitly teach training and applying a selective harvesting (SH) model, wherein the applying is constrained by a budget of k queries; collecting datapoints from the identified edge nodes and the identified additional edge nodes in the specified class that were identified by the applying of the SH model to the network; when a threshold number of the edge nodes and the identified additional edge nodes in the specified class has been identified by application of the SH model to the network, collecting respective datapoints and features from each of those edge nodes of the specified class; and building a final dataset that comprises the identified edge nodes and the additional edge nodes of the specified class identified by application of the trained SH model to the network, and their associated datapoints and features.
Murai teaches training (page 2, section 1, paragraphs 3-4; page 5, section 3; page 7, section 3.2, paragraph 1) and applying a selective harvesting (SH) model, wherein the applying is constrained by a budget of k queries (pages 8-9, section 4; page 14, Algorithm 1 along with explanation below; page 16, section 6.2, paragraphs 1-3);
collecting datapoints from identified and additional nodes (labels, attributes, connections of nodes and neighboring nodes) in the specified class (i.e. target node type) that were identified by the applying of the trained SH model to the network (abstract, page 2, section 1, paragraphs 2 and 4; page 5, paragraphs 1-4); when a threshold number of the nodes and additional nodes in the specified class has been identified by application of the SH model to the network, collecting respective datapoints and features from each of those nodes of the specified class (page 14, Algorithm 1 along with explanation below; page 16, section 6.2, paragraphs 1-3, page 17); and
building a final dataset that comprises the nodes and additional nodes (neighboring nodes) of the specified class identified by application of the trained SH model to the network, and their associated datapoints and features (targets found, which include node attributes) (page 2, section 1, paragraph 2; page 5, Table 1 and paragraphs 1 and 4; pages 16-17, section 6.2).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified the model of Fu to be a selective harvesting model as taught by Murai to enable discovery of the largest number of target (edge) nodes given a fixed budget and network that is not fully observed (Murai, abstract and conclusion), thereby improving the incremental training set of edge device data in Fu.
Further regarding claims 1 and 11, Fu in view of Murai does not explicitly teach building a final dataset for training a machine-learning model. (The examiner also notes that this limitation is worded as an intended use statement. However, in an effort to practice compact prosecution, the examiner is applying prior art.)
Yogerst teaches building a final dataset (from newly labeled training data/samples) for training a machine-learning model (i.e. training another/downstream model) (paragraphs 3, 5, 15, 51, 76, and 85).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have further modified Fu in view of Murai to build a final dataset for training a machine-learning model as taught by Yogerst because active learning systems routinely use newly acquired labeled samples to improve subsequent model training. Further, generating labeled training data that may be used to train another artificial intelligence model to autonomously label new training examples improves accuracy and precision (Yogerst, paragraph 3) and reduces the amount of time needed to prepare and develop training data for further downstream tasks (Yogerst, paragraph 94).
With respect to claims 2 and 12, Fu in view of Murai and Yogerst teaches wherein when the threshold number of the edge nodes and the identified additional edge nodes of the specified class has not been reached, retraining the SH model with new data, and applying the retrained SH model to the network until the threshold number of edge nodes and the identified additional edge nodes of the specified class has been reached (Murai, page 14, Algorithm 1 along with explanation below; page 16, section 6.2, paragraphs 1-3, page 17).
With respect to claims 3 and 13, Fu in view of Murai and Yogerst teaches wherein the budget of k queries specifies a number of times that the network will be queried to identify edge nodes in the specified class (Fu teaches edge nodes in specified class, paragraphs 9 and 78-80)(Murai, abstract; page 2, section 1, paragraphs 1-2; page 8, section 4, paragraph 1; page 14, Algorithm 1).
With respect to claims 4 and 14, Fu in view of Murai and Yogerst teaches wherein applying the trained SH model to the network comprises applying the trained SH model to less than entirety of the network (Murai, abstract, page 2, section 1, paragraphs 2-3; page 23, section 9).
With respect to claims 5 and 15, Fu in view of Murai and Yogerst teaches wherein for purposes of applying the trained SH model to the network, the network is modeled as a partially observed graph (Murai, abstract, page 2, section 1, paragraphs 2-3; page 23, section 9).
With respect to claims 6 and 16, Fu in view of Murai and Yogerst teaches wherein the edge nodes in the final dataset all share a common domain (i.e. nodes of a target class) (Murai, page 21, section 7, paragraph 1) .
With respect to claims 7 and 17, Fu in view of Murai and Yogerst teaches wherein the edge nodes in the final dataset are discovered without requiring application of the trained SH model to entirety of the network (Murai, abstract, page 2, section 1, paragraphs 2-3; page 23, section 9).
With respect to claims 8 and 18, Fu in view of Murai and Yogerst teaches wherein the trained SH model comprises a D3TS algorithm (Murai, abstract, pages 12-14, sections 5, 5.1, and 5.2; page 16, section 6.2).
With respect to claims 9 and 19, Fu in view of Murai and Yogerst teaches wherein receiving respective sets of node features comprises receiving m node features from the edge nodes in the set of edge nodes, and m is less than M, where M is a total number of nodes in the network (Fu, paragraph 71-72; Murai, page 2, section 1, paragraphs 2-3; page 5, paragraph 4; page 15, Tables 3 and 4; page 16, paragraph 2)
With respect to claims 10 and 20, Fu in view of Murai and Yogerst teaches wherein the respective sets of node features each comprise one or more representative datapoints collected by the node from which the set of node features was received (Fu, paragraph 69, 71-72; Murai, page 2, section 1, paragraphs 2-3; page 5, paragraph 4; page 15, Tables 3 and 4; page 16, paragraph 2).
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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/ALICIA M WILLOUGHBY/ Primary Examiner, Art Unit 2156 June 27, 2026
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