DETAILED ACTION
This non-final rejection is responsive to communication filed July 25, 2024. 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 25, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 9 and 19 are 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. Claims 9 and 19 recite “receiving m node features from the edge nodes in the set of edge nodes, and m<<M, where M is a total number of nodes in the network.” However, it is unclear as to what is meant by “m<<M”. For example, it is unclear as to whether the symbol “< <” means less than or something else. 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.
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’).
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);
using the datapoints to train a model (i.e. training a neural network) (paragraphs 8, 84, 97, and 106); and
after the model has been trained, applying the model to the network (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 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 in the specified class has been identified by application of the SH model to the network, collecting respective data points and features from each of those edge nodes of the specified class; and building a final dataset that comprises the edge nodes of the specified class, and their associated data points and features, that were identified by application of the SH model to the network.
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 nodes (labels, attributes, connections of nodes) in the specified class (i.e. target node type) that were identified by the applying of the 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 in the specified class has been identified by application of the SH model to the network, collecting respective data points 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 of the specified class, and their associated data points and features, that were identified by application of the SH model to the network (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.
With respect to claims 2 and 12, Fu in view of Murai teaches wherein when the threshold number of the 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 of the specified class has been reached (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 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 teaches wherein applying the SH model to the network comprises applying the SH model to less than the entire 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 teaches wherein for purposes of applying the 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 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 teaches wherein the edge nodes in the final dataset are discovered without requiring application of the SH model to the entire 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 teaches wherein the 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 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<<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 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).
Contact Information
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