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 .
Response to Remarks
The Office Action has been made issued in response to amendment filed July 29, 2026. Claims 1-3, 6-17 and 20-24 are pending. Applicant’s arguments have been carefully and respectfully considered in light of the instant amendment, and are not persuasive. Accordingly, this action has been made FINAL.
Claim Objection
Applicant has amended the claims to suggested by the Examiner. Therefore, the objection is being withdrawn.
Regarding the 101 rejection:
On pages 7-8, Applicant states that “Applicant's Specification describes that maintaining separate parameter sets for multiple feed-forward blocks may increase model size, storage requirements, and memory consumption, and that reusing parameters across the feed-forward blocks may improve parameter efficiency while maintaining model performance. See, e.g., Specification, paras. [0005] and [0028]-[0029]. The amended claim 1 expressly recites the mechanism that delivers that improvement separate feed-forward blocks having linear layers that use the same parameter set”, Additionally, the Appeals Review Panel vacated a patent eligible subject matter rejection in Ex parte Desjardins, Appeal 2024-000567 (ARP 2025), where the claims reflected an improvement in how a machine-learning model operated within a system context. Similar to Desjardins, amended claim 1 is directed to an improvement in the architecture and operation of an ML model. Reusing the same linear-layer parameter set across multiple feed-forward blocks reduces the number of separate parameter sets required to implement the model and thereby improves model-size and resource efficiency. Thus, the improvement resides in how the ML model itself is structured and executed, rather than in merely applying a mathematical algorithm to data.
First, the examiner finds the amendment does not overcome the 101 rejection because the claims are directed to a mere manipulation of the input data using mathematical formulation and does not involve transformation of the data and thus fall under the concept of “mathematical concepts”. The claimed invention does not reflects a practical application wherein, nor include a particular trained machine learning model. Moreover, the data being processed does not represent real world data, but merely input data. Therefore, the examiner finds the amendment does not reflect a practical application and improvement and the Applicants’ argument are not persuasive and thus the 101 rejection has not been withdrawn.
Second, as to the Appeals Review Panel vacated a patent eligible subject matter rejection in Ex parte Desjardins, Appeal 2024-000567 (ARP 2025), being similar to Applicant’s current claims, the Examiner disagrees. Desjardins invention was directed to training the machine learning model and included detail regarding the training of the network and more important the improvement of the system. Applicant current claims have neither and therefore dissimilar fact pattern. Therefore, the Examiner has not withdrawn the rejection.
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 1-3, 6-17 and 20-24 are rejected under 35 U.S.C. 101
Regarding independent claim 1 and dependent claims 2-3, 6-14 and 21-24
Step 1 Analysis: Claim 1 is directed to an apparatus, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, “process the input data using a first feed-forward block, and wherein the first feed-forward block includes one or more linear layers having including a first set of parameters” and process the intermediate data using a second feed-forward block, and wherein the second feed- forward block includes one or more linear layers having set of parameters that is the same as the set of parameters associated with the first feed-forward block”, as drafted, are elements that, under broadest reasonable interpretation, covers “mathematical concepts” grouping of abstract ideas.
The limitations of “process the input data to generate intermediate data, the first ML block including a first set of parameters” and “process the intermediate data to generate processed data, the second ML block including a second feed-forward block, and wherein the second feed- forward block includes one or more linear layers having set of parameters that is the same as the set of parameters associated with the first feed-forward block” is a mere manipulation of the input data using mathematical formulation and does not involve transformation of the data and thus fall under the concept of “mathematical concepts”.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: The judicial exception is not integrated into a practical application. Particularly, the claim recites the following additional limitations:
“one or more memories; and one or more processors coupled to the one or more memories”
“a machine learning model”, “a first machine learning (ML)” and “a second ML block”
The additional elements of “one or more memories” and “one or more processors coupled to the one or more memories” are recited at a high level of generality, i.e., as a generic processor and memory performing a generic computer function of processing data. This generic processor and memory limitations are no more than mere instructions to apply the exception using generic computer components.
The additional elements of “a machine learning model”, “a first machine learning (ML)” and “a second ML block” are recited at a high level of generality and is therefore a mere generic machine learning model. Note that the first and second block are sub models within the machine learning model. First, the machine learning model is not trained and thus the claim does not recite any details of the training of the machine learning model. Second, the advantage or improvement for implementing the machine learning model is not claimed.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
The claim element of “receiving input data” and “output the processed data” impart additional elements, the additional elements merely constitute pre-solution and post-solution activating involving receiving of input data and outputting the processed data. Such extra-solution activities do not integrate the abstract idea into a practical application. Please see MPEP §2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
In view of the of the foregoing, the additional step does not integrate the abstract idea into a practical application.
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 2-14 do not provide elements that overcome the deficiencies of the independent claim 1.
Claim 2 recites in part “wherein the intermediate data generated by the first ML block is processed by a third ML block before being processed by the second ML block “ do not overcome the rejection of the parent claim 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 3 recites in part “wherein the first ML block and second ML block comprise a same type of ML block “do not overcome the rejection of the parent claim 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 6 recites in part “wherein the feed-forward block is a part of at least one of a transformer block, a conformer block, or a convnext block” do not overcome the rejection of the parent claim 4 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 7 recites in part “” do not overcome the rejection of the parent claim 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 8 recites in part “herein the adapter block includes one or more linear layers” do not overcome the rejection of the parent claim 7 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 9 recites in part “wherein parameters of the one or more linear layers of a first adapter block associated with the first ML block differ from parameters of the one or more linear layers of a second adapter block associated with the second ML block” do not overcome the rejection of the parent claim 8 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 10 recites in part “” do not overcome the rejection of the parent claim 8 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 11 recites in part “wherein the adapter block is trained with the first ML block and the second ML block” do not overcome the rejection of the parent claim 7 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 12 recites in part “wherein the adapter block, the first ML block, and the second ML block are trained using on-device training” do not overcome the rejection of the parent claim 7 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 13 recites in part “wherein the input data comprises image data.
” do not overcome the rejection of the parent claim 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 14 recites in part “further comprising one or more cameras configured to capture the image data” do not overcome the rejection of the parent claim 14 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 21 recites in part “wherein the third ML block includes a third feed-forward block having one or more linear layers, and wherein the one or more linear layers of the third feed- forward block have a set of parameters that is the same as the set of parameters associated with the first feed-forward block” do not overcome the rejection of the parent claim 14 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 22 recites in part “wherein the one or more processors are configured to, during training of the machine learning model, adjust the set of parameters for the first feed- forward block and the second feed-forward block together.” do not overcome the rejection of the parent claim 14 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 23 recites in part “wherein the set of parameters is randomly initialized before the first feed-forward block and the second feed-forward block are trained together” do not overcome the rejection of the parent claim 14 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 24 recites in part “wherein a conformer block includes the first feed-forward block and the second feed-forward block, the conformer block further including a self-attention block and a convolution block positioned between the first feed-forward block and the second feed-forward block.” do not overcome the rejection of the parent claim 14 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding independent claim 15 and dependent claims 16-20
Step 1 Analysis: Claim 15 is directed to a method, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 15 recites, in part, “processing the input data and “processing the intermediate data as drafted, are elements that, under broadest reasonable interpretation, covers “mathematical concepts” grouping of abstract ideas.
The limitations of “process the input data to generate intermediate data, the first ML block including a first set of parameters” and “process the intermediate data to generate processed data, including a second set of parameters matching the first set of parameters” is a mere manipulation of the input data using mathematical formulation and does not involve transformation of the data and thus fall under the concept of “mathematical concepts”.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: The judicial exception is not integrated into a practical application. Particularly, the claim recites the following additional limitations:
“a machine learning model”, “a first machine learning (ML)” and “a second ML block”
Note that the first and second blocks are sub models within the machine learning model. The additional element includes generic machine learning model recited at high level of generality without limiting further, in details, on how machine learning model is trained to arrive at such output, these additional elements are recited as a mere attempt to implement the abstract ideas/judicial exceptions using generic machine learning models. Furthermore, the advantage or improvement for implementing the machine learning model is not claimed.
The claim element of “receiving input data” and “output the processed data” impart additional elements, the additional elements merely constitute pre-solution and post-solution activating involving receiving of input data and outputting the processed data. Such extra-solution activities do not integrate the abstract idea into a practical application. Please see MPEP §2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
In view of the of the foregoing, the additional step does not integrate the abstract idea into a practical application.
For all of the foregoing reasons, claim 15 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 16-20 do not provide elements that overcome the deficiencies of the independent claim 15.
Claim 16 recites in part “wherein the intermediate data generated by the first ML block is processed by a third ML block before being processed by the second ML block “ do not overcome the rejection of the parent claim 15 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 17 recites in part “wherein the first ML block and second ML block comprise a same type of ML block “do not overcome the rejection of the parent claim 15 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 18 recites in part “wherein the first ML block and second ML block comprise a feed-forward block” do not overcome the rejection of the parent claim 17 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 19 recites in part “wherein the first set of parameters and second set of parameters comprise parameters for at least one linear layer of the feed-forward block” do not overcome the rejection of the parent claim 18 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 20 recites in part “wherein the feed-forward block is a part of at least one of a transformer block, a conformer block, or a convnext block” do not overcome the rejection of the parent claim 18 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns, can be reached on (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673
September 24, 2026
