DETAILED ACTION
This action is responsive to communications filed on November 27, 2023. This action is made Non-Final.
Claims 1-20 are pending in the case.
Claims 1, 8, and 15 are independent claims.
Claims 1 and 8 are rejected.
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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS(s)) submitted on 11/27/2023 is/are in compliance with the provisions of 37 C.F.R. 1.97. Accordingly, the IDS(s) is/are being considered by the examiner.
Claim Rejections - 35 USC § 102
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)(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 and 8 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hong, US Publication 2024/0202592 (“Hong”).
Claim 1:
Hong discloses a system for acceleration of deep-learning computing with edge-terminal collaboration, the system comprising at least one terminal device and at least one edge server (Fig. 1, 2), wherein the terminal device is configured to:
when being present in a service coverage of at least one edge server, determine an inter-layer partitioning and/or an intra-layer partitioning policy for a deep learning model based on a first configuration information related to the terminal device and second configuration information related to the edge server (see Fig. 6; para. 0010 - changing a splitting execution type of the machine learning model by using one of the edge device and the service server, based on a resource status thereof; para. 0055 - performing an arithmetic operation on the machine learning model on the basis of the determined splitting execution type may be a step of performing an arithmetic operation on some of all layers included in the machine learning model by using the edge device 100 and performing an arithmetic operation on the other layers by using the service server; para. 0068 - service server 200 or 300 may analyze resource status information about the edge device 100 and a resource status of the service server 200 or 300 to determine whether changing of a splitting execution type is needed.), and
the edge server is configured to:
execute the inter-layer partitioning and/or intra layer partitioning policy for the deep learning model in response to an inference request message, so as to implement collaborative inference (see Fig. 1, 2; para. 0026 - difficult to execute all AI services, provided by the cloud server 300, in the edge device 100; para. 0027 - Therefore, in an embodiment of the present invention, as illustrated in FIG. 2, a number of layers included in deep learning may be split; para. 0028 - whereby the edge device 100 may use an Al service; para. 0029 - When the edge device 100 starts the Al service, a splitting execution type illustrated in FIG. 2 may be determined; para. 0055 - performing an arithmetic operation on the machine learning model on the basis of the determined splitting execution type may be a step of performing an arithmetic operation on some of all layers included in the machine learning model by using the edge device 100 and performing an arithmetic operation on the other layers by using the service server; para. 0070 - when the service server 200 or 300 receives a response message corresponding to the request message from the edge device 100, at least one of the edge device 100 and the service server 200 or 300 may perform an arithmetic operation on the machine learning model, based on the changed splitting execution type. That is, at least one of the edge device 100 and the service server 200 or 300 may perform an arithmetic operation on a changed layer section, based on the changed splitting execution type.).
Claim(s) 8:
Claim(s) 8 correspond to Claim 1, and thus, Hong discloses the limitations of claim(s) 8 as well.
Allowable Subject Matter
Claims 2-7 and 9-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 15-20 are allowed.
Note
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Saguil, Darren, and Akramul Azim. "A layer-partitioning approach for faster execution of neural network-based embedded applications in edge networks." IEEE Access 8 (2020): 59456-59469;
Mohammed, Thaha, et al. "Distributed inference acceleration with adaptive DNN partitioning and offloading." IEEE INFOCOM 2020-IEEE conference on computer communications. IEEE, 2020;
Matsubara, Yoshitomo, et al. "Distilled split deep neural networks for edge-assisted real-time systems." Proceedings of the 2019 Workshop on Hot Topics in Video Analytics and Intelligent Edges. 2019.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T McIntosh whose telephone number is (571)270-7790. The examiner can normally be reached M-Th 8:00am-5:30pm.
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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.
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/ANDREW T MCINTOSH/Primary Examiner, Art Unit 2144
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