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 Interpretation
Claim(s) 1-20 do not use “means for” (or “step for”) language, or generic placeholders for "means” coupled with functional language without recitation of sufficient structure for carrying out the claimed functions and therefore do not invoke 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 11-19 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. [claims 1 and 11]
Claim 1 recites an optimizing method of distributed training, comprising: training a local model by using one of a plurality of sample sets and a global parameter of a global model to generate a local parameter of the local model; and determining at least one deviation parameter among the local parameter of a plurality of local models trained by the sample sets, wherein a distribution of the at least one deviation parameter is far from a distribution of other local parameters, and the local parameter of the local models is used to update the global parameter of the global model.
Claim 11 recites a master computing apparatus, comprising: a memory, storing a code; and a processor, coupled to the memory and loading the code to execute: obtaining a local parameter corresponding to a plurality of sample sets, wherein the local parameter corresponding to each of the sample set are generated by training a local model using one of the sample sets and a global parameter of a global model; and determining at least one deviation parameter among the local parameter of a plurality of local models trained by the sample sets, wherein a distribution of the at least one deviation parameter is far from a distribution of other local parameters, and the local parameter of the local models is used to update the global parameter of the global model.
These claims are directed to an abstract idea of determining parameters which may fall within mental processes.
This judicial exception is not integrated into a practical application because the claim as a whole does not amount to more than simply determining a deviation parameter from among local parameter(s). The determination of the deviation parameter is not used for improving the functioning of a computer or other technology as no operations are performed using the determined parameter. Instead the parameter is simply determined as a deviation parameter.
While the specification describes identifying contaminated data and taking appropriate action to “improve the accuracy” of a trained model (e.g. Paragraph 0008, 0053), the claims as written are not directed to this improvement as no action is taken once the deviation parameter is determined.
See the 05 December 2025 memorandum entitled “Advance notice of change to the MPEP in light of Ex Parte Desjardins”:
“Indeed, the Ex Parte Desjardins decision analyzed eligibility in terms of whether the claims were directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field under longstanding Federal Circuit precedent in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). See also MPEP §§ 2106.04(d)(l) and 2106.05(a)”
“Second, if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., that the claim includes the components or steps of the invention that provide the improvement described in the specification.”
“After the examiner has consulted the specification and determined that the disclosed
invention improves technology or a technical field, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp.,838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology or a technical field (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim “as a whole,” in other words, the claim should be evaluated “as an ordered combination, without ignoring the requirements of the individual steps.” When performing this evaluation, examiners should be “careful to avoid oversimplifying the claims” by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100. See also Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential) (“Examiners and panels should not evaluate claims at such a high level of generality” that potentially meaningful technical limitations are dismissed without adequate explanation).
An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration. When evaluating a claim as a whole, examiners should not dismiss additional elements as mere “generic computer components” without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer system. See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025) (Appeals Review Panel Decision).”
Since the claim does not include the necessary steps/structures which provide the improvement described in the specification (i.e. the claims do not take the necessary “appropriate action” needed to improve the accuracy of the trained model), the claims are not integrated into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 11 recites additional elements including a memory, storing a code and a processor, coupled to the memory and loading the code. These elements are directed to generic computer components to perform the method.
Additionally, the presence of these elements does not confer a technologic improvement to a technological problem, as the recited steps do not result in the improvement described in the specification.
As previously noted, merely determining a deviation parameter from a local parameter(s) does not result in improved accuracy for the trained model as the necessary “appropriate action” described in the specification is not taken.
Therefore, the additional elements do not amount to significantly more and do not confer a technological improvement to a technical problem.
For these reasons, claims 1 and 11 are directed to non-statutory subject matter.
Note that in contrast to claims 1 and 11, dependent claims 9 and 19 recite deleting the at least one deviation parameter from the local parameter of the local models trained by the sample sets and updating the global parameter of the global model with other local parameters other than the at least one deviation parameter.
This operation would result in improved accuracy of the global model as described by the specification, and therefore integrates the abstract idea into a practical application. Therefore, claims 10 and 20 are considered to be directed to statutory subject matter.[claims 2-8 and 12-18]
Claims 2-8 and 12-18 recite additional steps which further limit the determination of the deviation parameter, but like claims 1 and 11 above are directed to an abstract idea without significantly more and do not integrate the abstract idea into a practical application.
Specifically, the steps recited in claims 2-8 and 12-18 do not take the necessary “appropriate action” described in the specification and thus do not confer a technological improvement to a technical problem.
Therefore, the additional elements do not amount to significantly more and do not confer a technological improvement to a technical problem.[claims 9 and 19]
Claims 9 and 19 recite “alerting the at least one deviation parameter”. This limitation amounts of insignificant extra solution activity, e.g. outputting. Additionally, merely outputting an alert does not result in improved accuracy for the trained model as the necessary “appropriate action” described in the specification is not taken.
Therefore, the additional elements do not amount to significantly more and do not confer a technological improvement to a technical problem.
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, 9-11, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(1) or 102(a)(2) as being anticipated by Yu et al. (US 2022/0269977 A1).[claim 1]
Regarding claim 1, Yu discloses an optimizing method of distributed training, comprising: training a local model by using one of a plurality of sample sets and a global parameter of a global model to generate a local parameter of the local model (Figure 1; Paragraphs 0016-0021; LOCAL TRAINED ML MODEL 116/126/136 trained using datasets and a global “same initial model”); and determining at least one deviation parameter among the local parameter of a plurality of local models trained by the sample sets, wherein a distribution of the at least one deviation parameter is far from a distribution of other local parameters, and the local parameter of the local models is used to update the global parameter of the global model (Paragraphs 0023-0024, 0065-0066; determining outliers which may be discarded and not use for updating the global parameters of the global model based on distance value and standard deviation).[claim 9]
Regarding claim 9, Yu discloses alerting the at least one deviation parameter (Paragraph 0065-0066; once identified, the method of Yu proceeds to filter out outliers by a malicious participant identification/filtration engine; note that some manner of signal/alert/indication must be provided to cause the filtration to occur for a deviation parameter once identified, additionally the claim as written does not define the particular manner in which the alert is generated or output).[claim 10]
Regarding claim 7, Yu discloses deleting the at least one deviation parameter from the local parameter of the local models trained by the sample sets (Paragraph 0065-0066); and updating the global parameter of the global model with other local parameters other than the at least one deviation parameter (Figure 3; updating federated global model using local parameters other than the filtered outliers).[claims 11, 19 and 20]
Regarding claims 11, 19 and 20, see the rejection of claims 1, 9 and 10 above and note that Yu further discloses implementing the claimed method using a master computing apparatus, comprising: a memory, storing a code; and a processor, coupled to the memory and loading the code to execute the method as claimed (Paragraphs 0046-0053, 0095-0096).
Subject Matter Not Taught by the Prior Art
Claims 2-8 and 12-18 contain subject matter not taught by the prior art, but cannot be considered allowable due to the above 35 USC 101 rejections.[claims 2-6 and 12-16]
Regarding claims 2-6 and 12-16, the prior art does not teach or reasonably suggest wherein the local parameter of each of the local models comprises a local correction parameter, the global parameter comprises a global correction parameter, the global correction parameter is obtained by a weighted operation based on the local correction parameter, and the step of determining the at least one deviation parameter among the local parameter of the local models trained by the sample sets comprises: determining the at least one deviation parameter according to an operation weight used by the local correction parameter of each of the local models in the weighted operation.
While the prior art teaches determining at least one deviation parameter which is far from a distribution of other local parameters (see e.g. Yu as discussed above), the prior art does not teach or reasonably suggest performing the determination according to an operation weight used by the local correction parameter of each of the local models in the weighted operation as recited in these claims.[claims 7, 8, 17 and 18]
Regarding claims 7, 8, 17 and 18, the prior art does not teach or reasonably suggest wherein the local parameter of each of the local models comprises a local correction parameter, the global parameter comprises a global correction parameter, and the step of determining the at least one deviation parameter among the local parameter of the local models trained by the sample sets comprises: determining whether the local correction parameter of each of the local models is the at least one deviation parameter through an error detection model.
While the prior art teaches determining at least one deviation parameter which is far from a distribution of other local parameters (see e.g. Yu as discussed above), the prior art does not teach or reasonably suggest determining whether the local correction parameter of each of the local models is the at least one deviation parameter through an error detection model as recited in these claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ezrielev et al. US 12,481,793 B2
Mathews et al. US 2025/0175483 A1
Sun et al. US 2024/0320514 A1
Sharad et al. US 11,836,643 B2
Asghari et al. US 2023/0281472 A1
Son et al. US 2023/0125436 A1
Karame et al. US 2021/0051169 A1
McMahan et al. US 2017/0109322 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J HENN whose telephone number is (571)272-7310. The examiner can normally be reached Monday-Friday ~10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Twyler Haskins can be reached at (571) 272-7406. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Timothy J Henn/Primary Examiner, Art Unit 2639