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 .
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 2-3, and 5-6 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.
Regarding claim 2, which recites the limitation, “… receiving an initial value of the artificial intelligence model from the center…” It is unclear what the initial value of the artificial intelligence model represents.
Regarding claim 3, which recites the limitation, “… wherein the predetermined criterion is determined in consideration of a communication cost between the center and the local domain”. It is unclear to the examiner, and the claim does not define what is considered as the communication cost.
Regarding claim 5, which recites the limitation, “… wherein the information about the data includes a ratio of data used for learning among data obtained in the local domain and number of number data for the learning” it is unclear where the claimed “a number of data used for the learning” is obtained. Is the number of data used for the learning the total number or amount of data obtained?
Regarding claim 6, which recites the limitation, “… its own local model” It is unclear what “its” refers back to in the claim.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-6 is/are rejected under 35 U.S.C. 102(a1) as being anticipated by Gautham Krishna et al., (U.S. Pub. No. 2024/0095539-A1; herein referenced as “Krishna”).
As per claim 1, Krishna teaches a learning method using user-centric artificial intelligence, the learning method comprising: (a) executing learning using data in a plurality of local domains (figs. 1-5B; [0041], [0065-0066]); (b) building a global model using the learning result in the plurality of local domains ([0041], [0071]; “The local users 104 may train their individual models locally with their own data. The results of such local training may then be reported back to central computing device 102, which may pool the results and update the global model”); and (c ) transmitting the global model to the local domain, additionally executing learning, and then transmitting the global model to a center (fig. 2; fig. 3A-5B and [0041]; transmitting the global ML model to local computing device 1 and local computing device 2 ).
As per claim 2, Krishna teaches wherein the step (a) includes: standardizing an artificial intelligence model in the plurality of local domains ([0004], [0015], [0017-0018]); receiving an initial value of the artificial intelligence model form the center, and executing the leaning using some data according to a predetermined criterion among acquired data (abstract, [0067], [0073-0074], 0087], and fig. 2-3A-3B; “ML model probabilities values”).
As per claim 4, Krishna teaches wherein the step (b) includes building the global model using information about data, learning results received from the plurality of local domains (fig. 1-5B).
As per claim 5, Krishna teaches wherein the information about the data includes a ratio of data used for learning among data obtained in the local domain and a number of data used for the learning (table 1 and at least claim 8).
As per claim 6, Krishna teaches wherein the step (c ) includes transmitting the global model to a new local domain (fig. 2; local computing device 3), and the new local domain receives the global model (fig. 2-5B) and compares the global model with its own local model ([0072] and fig.2).
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gautham Krishna et al., (U.S. Pub. No. 2024/0095539-A1; herein referenced as “Krishna”) in view of Shaloudegi et al., (U.S. Pub. No. 2021/0365841 A1).
Regarding claim 3, Krishna teaches everything as claimed above, see claim 2. Krishna does not explicitly disclose wherein the predetermined criterion is determined in consideration of a communication cost between the center and the local domain.
However, Shaloudegi teaches wherein the predetermined criterion is determined in consideration of a communication cost between the center and the local domain ([0045-0046]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Shaloudegi with Krishna to improve a federated learning method and system that may provide reduced communication costs and/or improved fairness among client nodes, compared to common FL approaches (e.g., federated averaging).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hu et al., (U.S. Pub. No. 2025/0209383 A1), “Systems and Methods For Federated Learning”
Butt et al., (U.S. Pub. No. 2024/0152768 A1), “Efficient Federated-Learning Model Training In Wireless Communication System”
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA PRINCE whose telephone number is (571)270-1821. The examiner can normally be reached M-F 7:30-3:30 P.M..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jamie Atala can be reached at 571-272-7384. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JESSICA PRINCE
Examiner
Art Unit 2486
/JESSICA M PRINCE/ Primary Examiner, Art Unit 2486
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