Notice of Pre-AIA or AIA Status
1. 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 § 112
2. 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.
3. Claims 9-11 are rejected under 35 U.S.C. 112(b) 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.
Regarding claim 9, the claim recites in part a limitation for “responsive to determining that the candidate weight is above the neighbor selection threshold, modifying, by the neighbor-decisioning component, the set of neighbor edge nodes to a) exclude the particular neighbor edge node from the set of neighbor edge nodes and b) include the additional neighbor node in the set of neighbor edge nodes.” The bolded language in the limitation constitutes a conditional limitation under MPEP 2111.04 (II) and hence has the effect of rendering the claim vague and indefinite. Specifically, because it is not clear to the Examiner (based on a careful review of claim 9 and claim 8 from which it depends) whether the claim actually requires the condition to be met, it is then unclear whether the limitation as a whole, and particularly the consequence of the condition as recited (i.e., the “modifying” step), is required.
Claims 10-11 depend from claim 9 without otherwise curing its deficiency as noted here. Hence, for that reason, they too are rejected under the same rationale.
Claim Rejections - 35 USC § 102
4. 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.
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office Action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-2, 5-9, 12-15, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2021/0357800 (“Sharma”).
Regarding claim 1, SHARMA teaches A system for generating a federated partial-data aggregation model used by an edge node in a decentralized edge computing network (per [0004]-[0008], the FIGs. and hence the invention are expressly directed to “a system for distributed decentralized machine learning model training”, where the model training is performed via a peer-to-peer network of distributed nodes per [0015]-[0018], which can be lent towards a version of invention where federated learning is used to distribute the model to edge nodes ([0013]), and where the model sharing between peers/nodes can be based on a compressed copy per [0056] (i.e., lending itself to partial data aggregation, with aggregation more generally described in [0036])), the system comprising:
a first edge node in the decentralized edge computing network, the first edge node configured to communicate with a second edge node in the decentralized edge computing network (FIGs. 1 and 4, and [0015]-[0018], as noted above, teach a decentralized network comprising peers/nodes that are directly connected via edges),
the first edge node comprising:
a model-generation component configured for:
determining a first parameter for a first model of the first edge node (FIG. 3’s steps 302 and 304 discussing the receipt of an initial model and initial parameters, i.e., the receipt and subsequent processing constitutes possession, use, and hence “determining”, by a particular node in the network) ...
Sharma’s first edge node further comprising
receiving, from the second edge node, a second parameter for the first model, wherein the second parameter is included in a second model of the second edge node (FIG. 3 step 310 discussing receipt of model and parameters from another node in the network);
modifying the first model to include a first weight for the first parameter and a second weight for the second parameter (FIG. 3’s step 312 is a model aggregation step that incorporates the other node’s model and parameters (per step 310) expressly “with local model” of the receiving node, hence it reasons that aggregating here is understood to feature model information from both models being aggregated); and
training the first model based on data received by the first edge node, wherein training the first model includes modifying one or more of the first weight or the second weight (FIG. 3 step 304, which iteratively can follow the previously discussed steps based on the looping arrow going from step 318 back to step 304 for example, and where the Examiner understands step 304 to be a training step for the model and would be understood to be modify its weights, which at this time could be a weight from either the node’s prior model or the node’s prior model as subject to aggregation using weights from the model received from the other node),
wherein the first edge node applies the trained first model to additional data received by the first edge node (FIG. 3’s step 314, which is part of an iterative loop as shown in the FIG. to perform after instances of both additional model training and both local data updating).
Regarding claim 2, Sharma teaches the system of claim 1, the first edge node further comprising a neighbor-decisioning component configured for:
identifying a set of neighbor edge nodes for the first edge node, wherein the set of neighbor edge nodes includes the second edge node (FIG. 3 step 306) ...
Sharma further teaching:
determining, for a particular neighbor edge node in the set of neighbor edge nodes, that a particular weight associated with the particular neighbor edge node is below a neighbor selection threshold (node similarity matrix, e.g. per FIG. 2 and its related discussion, defines weights for edges between nodes, and a threshold for the weight is used to determine whether an edge exists between two nodes based on whether the similarity is sufficient ([0017], [0031], [0056]: “threshold”));
determining an additional set of candidate neighbor nodes for the first edge node, wherein at least one of the candidate neighbor nodes in the additional set of candidate neighbor nodes is an additional neighbor node of the particular neighbor edge node ([0031] discussing neighbor determination including considering up to “k degrees of separation”, in which case a neighbor of a neighbor may be considered or eligible for model transfer/sharing in accordance with the taught invention);
receiving a candidate weight associated with the additional neighbor node and responsive to determining that the candidate weight is above the neighbor selection threshold, modifying the set of neighbor edge nodes to a) exclude the particular neighbor edge node from the set of neighbor edge nodes and b) include the additional neighbor node in the set of neighbor edge nodes ([0034] discussing model sharing/transfer between nodes that is in part based on degrees of separation considerations as discussed above, and where such a sharing/transfer is predicated on whether the similarity between the two nodes is sufficient per [0017], [0031], and [0056] as was previously discussed).
Regarding claim 5, Sharma teaches the system of claim 1, wherein the data on which the first model is trained is received by the first edge node at a timestep prior to receiving the second parameter from the second edge node (FIG. 3’s step 302 precedes step 310, and would be understood in relation to the step flow of that FIG. 3 to be of an earlier time/order and hence reads on an earlier “timestep” as recited).
Regarding claim 6, Sharma teaches the system of claim 1, wherein the model-generation component is further configured for modifying the first model based on an aggregation of the first parameter and the second parameter (FIG. 3’s step 312 is understood to encompass the creation of a new model at a particular node based on its own original parameters and parameters it may have received from other nodes, e.g. as part of step 310).
Regarding claim 7, Sharma teaches wherein the model-generation component is further configured for stabilizing the first parameter and the second parameter during the modifying of the one or more of the first weight or the second weight (FIG. 3’s step 312 involves a weighting process based on similarity that the Examiner equates with a modification of weight and a “stabilizing” as recited).
Regarding claim 8, the claim includes the same or similar limitations as claim 1 discussed above, and is therefore rejected under the same rationale.
Regarding claim 9, the claim includes the same or similar limitations as claim 2 discussed above, and is therefore rejected under the same rationale.
Regarding claim 12, the claim includes the same or similar limitations as claim 6 discussed above, and is therefore rejected under the same rationale.
Regarding claim 13, the claim includes the same or similar limitations as claim 7 discussed above, and is therefore rejected under the same rationale.
Regarding claim 14, the claim includes the same or similar limitations as claim 1 discussed above, and is therefore rejected under the same rationale. The Examiner notes that the claim is actually broader than 1. Also, the claim specifically recites in part “a non-transitory computer-readable medium” which is taught per Sharma’s FIG. 5 element 512.
Regarding claim 15, the claim includes the same or similar limitations as claim 2 discussed above, and is therefore rejected under the same rationale.
Regarding claim 18, the claim includes the same or similar limitations as claim 5 discussed above, and is therefore rejected under the same rationale.
Regarding claim 19, the claim includes the same or similar limitations as claim 6 discussed above, and is therefore rejected under the same rationale.
Regarding claim 20, the claim includes the same or similar limitations as claim 7 discussed above, and is therefore rejected under the same rationale.
Allowable Subject Matter
7. Claims 3-4, 10-11, and 16-17 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.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure:
US-20250013872 A1
US-12632729 B2
US-20210383197 A1
CN-116468114 A
CN-116683970 A
CN-109543077 A
Non-Patent Literature “Gossip training for deep learning”
Non-Patent Literature “Gossip Learning with Linear Models on Fully Distributed Data”
Non-Patent Literature “An Overview of Self-Organizing Networks (SON) in Wireless Networks”
Non-Patent Literature “Communication-Efficient Edge AI: Algorithms and Systems”
Non-Patent Literature “The Parameter-Less Self-Organizing Map algorithm”
Non-Patent Literature “Gossip Learning: Off the Beaten Path”
Non-Patent Literature “Decentralized learning works: An empirical comparison of gossip learning and federated learning”
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHOURJO DASGUPTA whose telephone number is (571)272-7207. The examiner can normally be reached M-F 8am-5pm CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144
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