DETAILED ACTION
Status of the Claims
1. This action is in reply to the Response to Election/Restriction filed dated March 19, 2026.
2. By the Applicant’s Response, they have made an Election of Invention I, corresponding to Claims 1-4 and have newly added Claims 21-28, without traverse, for further examination.
3. Claims 5-20 have been canceled.
4. Claims 21-28 are newly added.
Notice of Pre-AIA or AIA Status
5. 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 § 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.
6. Claims 25-28 are rejected under 35 U.S.C. §101 because, in order to comply with §101, a computer program product claim must recite that the computer program product comprises a non-transitory computer readable medium having program instructions (or code) embodied thereon and said instructions are configured to control a computer to perform specific functional steps. The claim must then recite the specific functional steps performed by execution of the instructions contained on the computer-readable medium by the computer, rather than reciting the code or software itself (i.e. software per se is not patentable). A computer program product, when properly claimed, describes the method steps performed when executed by a computer system, not the code or software itself.
The preamble for a computer program product has to state that (1) the product is stored on a non-transitory computer-readable medium (which is present), (2) the product can be executed on a computer (which is not clearly present present) and (3) when executed the product causes the computer to perform a method (which is not clearly present) where the further claim limitations are written as method steps. It is the actual the method being performed by the computer which is patentable, rather than the software itself.
Here, as the computer program is recited to be “adapted to be loaded and executed”, it is not clear if it is the stored instructions being executed or if there is some intermediate step being undertaken. Appropriate correction is required.
Claim Rejections - 35 USC § 102/103
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 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.
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.
7. Claim(s) 1-4 and 21-28 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Jakkam Reddi et al. (US PG Pub. 2021/0073639) (“Jakkam”)
Regarding Claim 1, Jakkam discloses the following:
A data processing method, performed by a service device, the method comprising:
acquiring local model parameters corresponding to N local recognition models in an rth synchronization period; (See Jakkam Fig. 2, paras 96-97 – providing by the client device a local model based on one or more local data examples)
the N local recognition models being respectively trained by different clients, each client comprising sample data for training an associated local recognition model, both N and r being positive integers greater than 1, and N denoting a quantity of the clients; (See Jakkam Fig. 2, paras 96-98 – plurality of local model updates provided by a plurality of client devices, each having a plurality of unevenly distributed data examples; no client device includes a representative sample of the overall distribution of data and the number of client devices may exceed the number of data examples on any one client device)
performing parameter fusion on the local model parameters respectively corresponding to the N local recognition models to obtain a target global model corresponding to the rth synchronization period; (See Jakkam Fig. 2, paras 98-99 – decode each received local model or local update and perform adaptive optimization or adaptive update process; determining a global model based at least in part on the received local model updates provided by the plurality of client devices)
acquiring a historical global model corresponding to an r-1th synchronization period; the historical global model being generated based on local model parameters respectively uploaded by N clients in the r-1th synchronization period; (See Jakkam Fig. 2, paras 100-107 – method can include providing the global model to each client device and can include receiving the global model, in some implementations, the local update can be determined by retraining or otherwise updating the global model based on locally stored training data [historical global model is updated]; any number of iterations of local and global updates can be performed)
determining global federated momentum corresponding to the rth synchronization period according to the historical global model and the target global model; the global federated momentum indicating training directions of the N local recognition models; and (See Jakkam Fig. 2, paras 102-103 – the local update may be determined using stochastic model descent techniques to determine a direction in which to adjust one or more parameters of the loss function and in some implementations a step size associated with the local update determination can be determined at least in part on a number of data examples stored on the client device) The local update can be determined using a linear term that forces each client device to update the parameters of the loss function in the same direction,)
transmitting the global federated momentum of the N clients, the N clients respectively updating the associated local recognition models according to the global federated momentum. (See Jakkam Fig. 2, paras 102-107 – the local model update can be determined by performing adaptive or non-adaptive optimization and each client device is forced to update parameters; local model updates are received by the server from a plurality of client devices)
Regarding Claim 21, this claim recites substantially similar limitations as those seen in Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above.
Regarding Claim 25, this claim recites substantially similar limitations as those seen in Claims 1 and 21 and as to those limitations is rejected for the same basis and reasons as disclosed above.
Regarding Claims 2, 22, and 26, these substantially similar claims recite the limitations of Claims 1, 21 and 25 and as to those limitations are rejected for the same basis and reasons as disclosed above. Further, Jakkam discloses the following:
wherein the sample data is multimedia sample data, the multimedia sample data comprising an object of a target object type; and (See Jakkum paras 109-110 – data sets can be image datasets, text datasets, or any suitable dataset; datasets can be partitioned and each dataset can have their own set of clients) the method further comprises:
acquiring, in response to the N local recognition models reaching a training termination condition, the local recognition models reaching the training terminal condition as object recognition models; (See Jakkam paras 81-85 – data can generate a recognition output or prediction output; in some cases, the image processing task can be image classification, where the output is a set of scores with each score corresponding to a different object class and representing the likelihood that the one or more images depict an object belonging to the object class [object recognition model])
the object recognition models being configured to recognize the object of the target object type in the multimedia sample data. (See Jakkum paras 81-86 – the image processing task can be image segmentation, where the image processing output defines, for each pixel in the one or more images, a respective likelihood for each category in a predetermined set of categories which can be foreground and background or can be object classes [recognizes the object in the sample data]
Regarding Claims 3, 23 and 27, these substantially similar claims recite the limitations of Claims 1, 21 and 25 and as to those limitations are rejected for the same basis and reasons as disclosed above. Further, Jakkum discloses the following:
wherein the determining global federated momentum corresponding to the rth synchronization period according to the historical global model and the target global model comprises:
acquiring training learning rates of the N local recognition models in the rth synchronization period and a model parameter difference between the target global model and the historical global model; and (See Jakkam Fig. 2, paras 59-61, 102-103, 105-108, 116-118)
determining a ratio of the model parameter difference to the training learning rates as the global federated momentum. (See Jakkam paras 59-61; 116-119 – training rates are tuned and momentum parameter)
Regarding Claims 4, 24 and 28, these substantially similar claims recite the limitations of Claims 1, 21 and 25 and as to those limitations are rejected for the same basis and reasons as disclosed above. Further, Jakkam discloses the following:
wherein the performing parameter fusions on the local model parameters respectively correspond to the N local recognition models to obtain a target global model corresponding to the rth synchronization period comprises:
acquiring M local model parameters from the local model parameters respectively corresponding to the N local recognition models; M being a positive integer less than N; (See Jakkam Fig. 2, paras 98-99)
acquiring training influence weights respectively corresponding to the M local model parameters; and (See Jakkam Fig. 2, paras 59, 98-99, 110-112 – weighted average of client outputs and updates
performing weighted summation on the training influence weights and the M local model parameters to obtain a fusion model parameter, and determining a model carrying the fusion model parameter as the target global model. (See Jakkam Abstract and paras 59-61, 95-99, 105-107, Cl.2 )
Conclusion
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/AMBREEN A. ALLADIN/Primary Examiner, Art Unit 3691 May 31, 2026
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