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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/08/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 2 is objected to because of the following informalities:
In claim 2, line 1, --the—should be inserted before “unlabeled”
In claim 2, line 2, “a first” should be changed to –the first—
In claim 2, line 2, --the—should be inserted before “parameters”
In claim 2, line 5, “an” should be changed to –a--
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.
Claims 1, 5-7, 9 and 10 are rejected under 35 U.S.C. 102a1 as being anticipated by CN111126481A, hereinafter “CN’481.” Note that all citations below to CN’481 are in relation to the supplied English translation of CN’481.
As per claim 1, CN’481 discloses a model generation method, wherein the method comprising: constructing a convolutional neural network model for image classification (see page 6, middle of page and page 8, classification of vehicles, e.g. type and color), and dividing the convolutional neural network model into N modules in sequence (see page 8, as broadly claimed N=2 modules - a pre-training module 204 and a fine-tuning module 206) each of the modules includes multiple adjacent layers in the neural network model (bottom of page 8, CNN and parallel sub-convolution layers), and N is an integer greater than 1 (in this case “2”); based on unlabeled training data, training a first module to an (N-1)-th module to obtain parameters and models of the first to (N-1)-th modules (the pre-training module is trained on unlabeled images); cascading the trained first to (N-1)-th modules with an N-th module (the “N-th” module being the fine-tuning module), and using labeled training data to train the cascaded N modules to obtain the parameters and models of the modules (labeled data is used for the fine-tuning module 206 and then the judgement module 208 decides whether the overall model is complete, or needs further iterative training, see middle of page 8).
As per claim 5, CN’481 discloses the model generation method according to claim 1, wherein after cascading the trained first to (N-1)-th modules with an N-th module, and using the labeled training data to train the cascaded N modules, to obtain the parameters and models of the modules, the method further comprises: converting the parameters and models of the modules into a format for running on the controller (see page 11, terminal device 10 with processor 100, memory 101).
As per claim 6, CN’481 discloses the model generation method according to claim 1, wherein the constructing a convolutional neural network model for image classification includes: based on the attributes of the image to be classified and the system parameters of the controller, generating a convolutional neural network model for classifying the images to be classified (see page 9, bottom of page).
As per claim 7, CN’481 discloses the image classification method, wherein it is applied to a controller, the method includes: obtaining a convolutional neural network model for classifying the images to be classified, the convolutional neural network model is generated based on the model generation method according to claim 1; using the obtained convolutional neural network model to classify the images to be classified (see bottom pf page 9).
As per claim 9, CN’481 discloses a controller, wherein it is used for executing a model generation method according to claim 1 (see bottom of page 9).
As per claim 10, CN’481 discloses an electronic device, wherein it includes: a controller according to claim 9 and a memory communicatively connected with the controller (see bottom of page 9).
Claim Rejections - 35 USC § 103
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.
Claims 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over CN’481 in view of CN114492723A, hereinafter “CN’723. Note that all citations below to CN’481 and CN’723 are in relation to the respectively supplied English translations of CN’481 and CN’723.
CN’481 discloses a CNN training process that trains a pre-training module on unlabeled images, while a second, fine-tuned module is trained on labeled images. Furthermore, CN’481 discloses running multiple modules included in the obtained convolutional neural network model in parallel in multiple threads or processors of the controller to classify the images to be classified (see CN’481 at the bottom of page 1 to the top of page 2).
However, CN’481 fails to disclose the memory occupied by the parameters of the module corresponding to the multi-layer structure model is less than the on-chip storage of the controller running the convolutional neural network model.
On the other hand, and in the same field of endeavor as CN’481, CN’723 discloses a CNN training methos for classifying images where on the bottom of page 1 CN’723 states “According to the solution of the embodiment of the present application, different parts of the parameters of the neural network model can be stored in multiple accelerators, and the first accelerator can obtain the required parameters from other devices and complete the forward calculation of the neural network model. The required memory is much smaller than the memory required for storing the complete neural network model, which reduces the storage pressure of the first accelerator and avoids failure to train the neural network model due to insufficient memory of the first accelerator.”
Therefore, it would have been obvious before the effective filing date of the claimed invention to have implemented the multiple accelerators of the neural network taught by CN’723 into the neural network model taught by CN’481 since doing this would avoid failure in training the model due to insufficient memory being available.
Allowable Subject Matter
Claims 2 and 3 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The coted prior art is indicative of the state of the art surrounding the training of neural network models via the use of both labeled images and unlabeled images.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID OMETZ whose telephone number is (571)272-7593. The examiner can normally be reached M-F, 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DAVID OMETZ
Primary Examiner
Art Unit 2672
/DAVID OMETZ/Primary Examiner, Art Unit 2672
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