DETAILED ACTION
Remarks
This Office Action is in response to the application 18/583605 filed on 21 February 2024.
Claims 1-20 have been examined.
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 § 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 10-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
As to claims 10-19, the claim(s) recite(s) a “neural network.” Under the broadest reasonable interpretation, the term “neural network” refers to an algorithm/process, i.e. software1. Furthermore, Applicant’s specification describes the claimed neural network as a “software architecture” (see para. 0099-0103 of Applicant’s published specification, and see Applicant’s Fig. 5). In addition, Applicant's specification states the following: “Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components.” (see para. 0107 of Applicant’s published disclosure, emphasis added). Therefore, the claim(s) encompass software per se. “Software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment” and is not patent eligible subject matter. MPEP 2106.03. As such, the claim(s) are not limited to statutory subject matter and are therefore non-statutory. In order to overcome the software per se rejection(s), the Examiner recommends amending the claim(s) to recite a processor coupled to a memory.
Allowable Subject Matter
Claims 1-9 and 20 are allowed.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to asymmetrically distributed convolution-attention neural networks.
a. Golden et al.; “AUTOMATED SEGMENTATION UTILIZING FULLY CONVOLUTIONAL NETWORKS”; U.S. PGPub. No. 20180218502 A1.
Teaches a convolutional neural network that has an asymmetrical architecture optimized for speed (see abstract and para. 0287).
b. Paszke, Adam, et al. "Enet: A deep neural network architecture for real-time semantic segmentation." arXiv preprint arXiv:1606.02147 (2016).
Teaches a convolutional neural network that has an asymmetrical architecture optimized for fast inference and high accuracy (see Section 1 “Introduction” and Section 3 “Network architecture”).
c. Zhang, Xiaofeng, et al. "Asymmetric cross-attention hierarchical network based on CNN and transformer for bitemporal remote sensing images change detection." IEEE Transactions on Geoscience and Remote Sensing 61 (2023): 1-15..
Teaches the Asymmetric Cross-attention Hierarchical Network (ACAHNet) (see abstract and Section II(A) “Overall Architecture” and Fig. 3) that provides faster inference speed (see Section III(E) “Comparative Experiements”).
d. Chen et al.; “DIABETIC RETINOPATHY DETECTION USING MACHINE LEARNING”; U.S. PGPub. No. 20220301153 A1.
Teaches a neural network with asymmetrical convolutional layers (see para. 0057-0058).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UMAR MIAN whose telephone number is (571)270-3970. The examiner can normally be reached Monday to Friday, 10 am to 6:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on (571) 272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Umar Mian/
Primary Examiner, Art Unit 2163
1 See the following:
“neural network.” FOLDOC: Free On-Line Dictionary of Computing. Accessed 1 Aug 2026 from https://foldoc.org/neural+network
“A neural network is a processing device, either an algorithm, or actual hardware.”
“What is a Neural Network?” Accessed 1 Aug 2026 from https://aws.amazon.com/what-is/neural-network/
“A neural network is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning (ML) process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.” (emphasis added)
“What is a Neural Network?” Accessed 1 Aug 2026 from https://www.ibm.com/think/topics/neural-networks
“Neural networks are among the most influential algorithms in modern machine learning and artificial intelligence (AI).” (emphasis added)
Prosecution Insights