DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 7/27/2026 for application number 18/468,481.
Claims 1-20 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 Objections
Claims 9 and 19 are objected to because of the following informalities: Claims 9 and 19 appears to contain extraneous language of “, and” at the end of the claim. Appropriate correction is required.
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.
Claim(s) 1-3, 6-7, 11-13, and 16-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nori et al. (US 2020/0005148 A1).
In reference to claim 1, Nori discloses a processing system, comprising: at least one memory having executable instructions stored thereon; and one or more processors configured to execute the executable instructions (fig. 9) to cause the processing system to: extract, using a first neural network block, a plurality of first features from a received input (“transformer” performs transformation on input data to produce transformed output, which are features, para. 0055-56; the “transformers” comprise parts, or a block, of a neural network, para. 0034); identify, using a gating neural network and the plurality of first features, a second neural network block from a plurality of second neural network blocks to use in processing the received input (router module processes the transformed output to determine which next transformer to send output to, para. 0055-56; router may be CNN, para. 0033), each neural network in the plurality of second neural network blocks configured to process data from a cluster of domains in a universe of domains (see para. 0053: the blocks process a cluster, like images of “man-made objects,” from a domain of all images); and process the received input using the identified second neural network block from the plurality of second neural network blocks (second block in turn processes the input, para. 0055-56).
In reference to claim 2, Nori discloses the processing system of Claim 1, wherein to process the received input using the identified second neural network block, the one or more processors are configured to cause the processing system to: extract a plurality of second features from the first features using the identified second neural network block; identify, using a gating neural network associated with the identified second neural network block, a third neural network from a plurality of third neural networks to use in processing the received input, each neural network in the plurality of third neural networks being trained to perform a specified task; and process the received input using the identified third neural network from the plurality of third neural networks (above process repeats for going from a second to third block when the third block is not an “inference” block, see para. 0055-56 and step 816 in fig. 8 and structure in fig. 7).
In reference to claim 3, Nori discloses the processing system of Claim 1, wherein to extract the plurality of first features from the received input, the one or more processors are configured to cause the processing system to: extract features usable by the gating neural network to identify the second neural network block from the plurality of second neural network blocks to process the received input, and extract features usable by the second neural network block from the plurality of second neural network blocks to generate an inference from the received input (the transformed output, or features, are used both by the router module to determine where to send the output, and by the second neural network block the output it sent to for making an inference, para. 0055-56 and steps 810, 812, and 814 in fig. 8).
In reference to claim 6, Nori discloses the processing system of Claim 1, wherein the first neural network block and the plurality of second neural network blocks comprise jointly trained neural network blocks (refinement process, which is a type of training, can be joint over whole tree, para. 0051).
In reference to claim 7, Nori discloses the processing system of Claim 1, wherein the plurality of second neural network blocks comprise image processing neural networks trained to classify images from one of a plurality of image data domains (image classification, para. 0052-53).
In reference to claim 11, this claim is directed to a method associated with the system claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 12, this claim is directed to a method associated with the system claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 13, this claim is directed to a method associated with the system claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a method associated with the system claimed in claim 6 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a method associated with the system claimed in claim 7 and is therefore rejected under a similar rationale.
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) 4, 8, 14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nori et al. (US 2020/0005148 A1) as applied to claims 1 and 11 above, and further in view of Pardeshi et al. (US 12,026,626 B1).
In reference to claim 4, Nori does not explicitly teach the processing system of Claim 3, wherein the second neural network block comprises a feature extractor block and a task head configured to generate the inference based on features extracted by the feature extractor block.
Pardeshi teaches the processing system of Claim 3, wherein the second neural network block comprises a feature extractor block and a task head configured to generate the inference based on features extracted by the feature extractor block (prediction blocks include both feature extraction and inference generation, see col. 7, line 33 – col. 8, line 16).
It would have been obvious to one of ordinary skill in art, having the teachings of Nori and Pardeshi before the earliest effective filing date, to modify the prediction of Nori to include the prediction blocks of Pardeshi.
One of ordinary skill in the art would have been motivated to modify the prediction of Nori to include the prediction blocks of Pardeshi because it helps make more flexible classification networks that can accommodate new classes without full retraining (Pardeshi, col. 3, lines 54-67).
In reference to claim 8, Nori does not explicitly teach the processing system of Claim 7, wherein the processor is further configured to cause the processing system to output an output of the image processing neural networks to an autonomous vehicle control system to trigger generation of one or more control signals to control an autonomous vehicle based on the output of the image processing neural networks.
Pardeshi teaches the processing system of Claim 7, wherein the processor is further configured to cause the processing system to output an output of the image processing neural networks to an autonomous vehicle control system to trigger generation of one or more control signals to control an autonomous vehicle based on the output of the image processing neural networks (col. 20, lines 29-44).
It would have been obvious to one of ordinary skill in art, having the teachings of Nori and Pardeshi before the earliest effective filing date, to modify the image recognition of Nori to include the vehicle control of Pardeshi.
One of ordinary skill in the art would have been motivated to modify the image recognition of Nori to include the vehicle control of Pardeshi because it would allow the neural network of Nori to perform more functions, like vehicle control.
In reference to claim 14, this claim is directed to a method associated with the system claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a method associated with the system claimed in claim 8 and is therefore rejected under a similar rationale.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nori et al. (US 2020/0005148 A1) as applied to claims 1 and 11 above, and further in view of Liu et al., Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition (NPL [U], see Notice of References Cited).
In reference to claim 5, Nori does not explicitly teach the processing system of Claim 1, wherein the gating neural network comprises a clustering neural network trained to classify an input into one of a plurality of data domains based on similarities to data in different data domains in a multi-domain data set.
Liu teaches the processing system of Claim 1, wherein the gating neural network comprises a clustering neural network trained to classify an input into one of a plurality of data domains based on similarities to data in different data domains in a multi-domain data set (see pages 104-07 and 111-12: tasks are hierarchically clustered with similar data so that during inference, a task is routed to the right model).
It would have been obvious to one of ordinary skill in art, having the teachings of Nori and Liu before the earliest effective filing date, to modify the gating network of Nori to include the clustering of Pardeshi.
One of ordinary skill in the art would have been motivated to modify the gating network of Nori to include the clustering of Pardeshi because it helps automatically group actions to task-specific models without depending on manual human grouping (Pardeshi, page 102-03).
In reference to claim 15, this claim is directed to a method associated with the system claimed in claim 5 and is therefore rejected under a similar rationale.
Claim(s) 9-10 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nori et al. (US 2020/0005148 A1) as applied to claims 1 and 11 above, and further in view of Xue et al., RAPHAEL:Text-to-Image Generation via Large Mixture of Diffusion Paths (NPL [V], see Notice of References Cited).
In reference to claim 9, Nori does not explicitly teach processing system of Claim 1, wherein the plurality of second neural networks comprise generative artificial intelligence models trained to generate responses to prompts in one of a plurality of task-specific domains, and.
Xue teaches the processing system of Claim 1, wherein the plurality of second neural networks comprise generative artificial intelligence models trained to generate responses to prompts in one of a plurality of task-specific domains (see pages 3-6, and fig. 3 in particular: mixture of experts model is trained to generate images responsive to a prompt, wherein different experts, or neural network blocks, are designed space-specific for depicting a concept at a region from the prompt), and (see objection above).
It would have been obvious to one of ordinary skill in art, having the teachings of Nori and Pardeshi before the earliest effective filing date, to modify the network of Nori to include the image generation of Xue.
One of ordinary skill in the art would have been motivated to modify the network of Nori to include the image generation of Xue because it would allow the efficient MoE of Nori to perform more than only classification tasks, such as the generative tasks of Xue.
In reference to claim 10, Nori does not explicitly teach the processing system of Claim 1, wherein the plurality of second neural network blocks comprise generative artificial intelligence models trained to generate responses to prompts in one of a plurality of data-specific domains.
Xue teaches the processing system of Claim 1, wherein the plurality of second neural network blocks comprise generative artificial intelligence models trained to generate responses to prompts in one of a plurality of data-specific domains (see pages 3-6, and fig. 3 in particular: mixture of experts model is trained to generate images responsive to a prompt, wherein different experts, or neural network blocks, are designed to paint particular concepts at different timesteps, which is a content-specific domain).
It would have been obvious to one of ordinary skill in art, having the teachings of Nori and Pardeshi before the earliest effective filing date, to modify the network of Nori to include the image generation of Xue.
One of ordinary skill in the art would have been motivated to modify the network of Nori to include the image generation of Xue because it would allow the efficient MoE of Nori to perform more than only classification tasks, such as the generative tasks of Xue.
In reference to claim 19, this claim is directed to a method associated with the system claimed in claim 9 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a method associated with the system claimed in claim 10 and is therefore rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yang et al., NPL [W] which teaches multitask learning and routing strategies, and Cheng et al. (US 20210232919 A1) which teaches modular neural networks with dynamic routing for multi-task learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
