DETAILED ACTION
This action is in response to claims filed 21 November 2023 for application 18515317 filed 21 November 2023. Currently claims 1-10 are pending.
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 § 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 1-10 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.
Claim 1 recites
w
∈
R
Q
, however, w and Q are not defined by the claim. Claim 4 recites
w
∈
R
Q
,
h
-
k
, and pk, are all not defined in the claim. Appropriate correction is required. Claims 2-10 are rejected due to their dependency on claim 1.
Claim Rejections - 35 USC § 103
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ni et al. (Enabling Ubiquitous Non-Orthogonal Multiple Access and Pervasive Federated Learning via STAR-RIS) in view of Cao et al. (Transmission Power Control for Over-the-Air Federated Averaging at Network Edge).
Regarding claim 1, Ni discloses:
A semi-federated learning (semiFL) method based on a next-generation multiple access (NGMA) technology, comprising the following steps:
S1: reporting, by users, state information to a base station (BS), wherein the state information comprises instantaneous channel state information (CSI)
(“We assume that the perfect channel state information (CSI) of all channels is available at the BS.” P2 §II.A ¶1)
S2: after receiving the state information, classifying, by the BS, the users into communication-centric centralized learning (CL) users and computing-centric FL users based on computing capabilities of local devices of the users, and broadcasting a classification result to all users after classification (P2 §II.A ¶1 and Fig 1, hybrid users NOMA and AirFL users, users are indexed);
S3: training, by each FL user, a local model through a local data set based on a global model w obtained in a previous round, and computing a local gradient gk (p2 Fig 1 local model and equation (6) gradient); and
preparing, by each CL user, a local data set Dn to be uploaded to the BS (Fig 1 local data);
S4: encoding the local data set of each CL user into a communication symbol {sn}, processing the gradient of the local model trained by each FL user into a computation symbol {sk}, and sending, by all users, the information bearing symbols of the users to the BS by using the NGMA technology in combination with a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) (p2 §II.A ¶3-5 and eq (2), NOMA is an NGMA technology and STAR-RIS are used to encode and superimpose signals from the two types of users);
S5: receiving, by the BS, a superimposed signal from the CL users and the FL users, decoding the local data sets from the CL users to perform centralized training and obtain an average gradient, aggregating the gradients of the local models from the FL users, and aggregating a global model by using the obtained gradients (p2 §II.A ¶5-6 signals are decoded and global gradients are used to update the global model);
S6: after each round of communication is completed, broadcasting, by the BS, the latest global model w∈RQ to all FL users for gradient computation in a next round (“Then, the newly obtained global model w(t+1)∈RQ will be Broadcasted to all AirFL users for the next training round” p2 §II.A ¶7); and
S7: repeating the foregoing steps until convergence or a maximum quantity of rounds of communication is reached (“To reveal the impacts of the transmit power, fading channel and wireless noise on the convergence upper bound (a.k.a. optimality gap) of AirFL, we provide theoretical analysis of the learning performance after T communication rounds.” p2 §II.A ¶8).
Ni discloses frequency information p2 §II.A ¶3 but does not explicitly disclose: and available central processing unit (CPU) frequency state information.
Cao teaches: available central processing unit (CPU) frequency state information (“Furthermore, the computation latency at each outer iteration corresponds to that required for the K devices to implement Ω local training epochs. Let c denote the central processing unit (CPU) cycles for computing each sample and f denote the CPU frequency.” P7 §V.A ¶2).
Ni and Cao are in the same field of endeavor of federated learning and are analogous. Ni discloses a hybrid system for different types of users/devices using NGMA and STAR-RIS methods. Cao teaches the consideration of device CPU frequency in order to minimize latency. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known NGMA, STAR-RIS federated learning system of Ni to utilize the known minimization using CPU frequency as taught by Cao for federated learning to yield predictable results of reduced latency.
Regarding claim 2, Ni discloses: The semiFL method based on the NGMA technology according to claim 1, wherein the NGMA technology in S4 provides services for all users in a same frequency band in a non-orthogonal manner such that all users are allowed to communicate in parallel on a same time-frequency resource (“At the t-th communication round, both NOMA users and AirFL users transmit simultaneously over the same time frequency resource” p2 §II.A ¶3, NOMA is non-orthogonal multiple access).
Regarding claim 3, Ni discloses: The semiFL method based on the NGMA technology according to claim 1, wherein the STAR-RIS deployed in S4 modifies an amplitude and a phase of an incident signal to reshape a wireless transmission environment and adjust channel gains of different users (p2 §II.A ¶1-4 STAR-RIS is used to phase shift and modify power/noise of signals of multiple users and then channel gains can be adjusted to get a processing order).
Allowable Subject Matter
Claims 4-10 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 as well as overcoming all outstanding rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ni et al. (Over-the-Air Federated Learning and Non-Orthogonal Multiple Access Unified by Reconfigurable Intelligent Surface)(hereinafter “Ni2”) discloses similar subject matter to Ni and teaches similar but not anticipatory equations to claims 4 and 5.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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