DETAILED ACTION
This office action is in response to the application filed on 07/11/2023. Claims 1-20 are pending and are 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 .
Priority
Receipt is acknowledged of certified copies 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 reference(s) listed on the Information Disclosure Statement(s) submitted on 07/11/2023 has/have been considered by the examiner (see attached PTO-1449).
Claim Objections
Claim 6 is objected to because of the following informalities:
Regarding Claim 6, line 2 recites, “the artificial neural network model” (i.e., lacking clear antecedent basis). For purposes of examination, the limitation will be reasonably interpreted as - - the neural network model - - .
Examiner respectfully requests from Applicant verification and requires appropriate correction regarding this matter.
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.
Claims 1-2, 4, 6-14, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al., US Patent Applicant Publication No.: 2024/0265240 A1, hereby Zhang. Although the invention is not identically disclosed or described as set forth in 35 U.S.C. 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 designer having ordinary skill in the art to which the claimed invention pertains, the invention is not patentable.
Zhang discloses the invention substantially as claimed. Regarding Claims 1, 9, and 13, Zhang discloses a processor-implemented method and an electronic device (Figs. 1-5, 8, 15-16, and 18), comprising:
“initializing a neural network model with arbitrary values using a random seed (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], and [0451]-[0453], further disclosing training; see also Figs. 15-16, and 18; see also Fig. 8);
training the neural network model based on the arbitrary values (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], and [0455]-[0469], further disclosing training; see also Figs. 15-16, and 18; see also Fig. 8);
determining a number of coats and respective densities of the coats (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], and [0455]-[0469], further disclosing the claimed number of coats (weights/parameters) and respective densities (e.g., subgroup of total parameters in group as in [0453]); see also Figs. 15-16, and 18; see also Fig. 8);
learning respective scores of parameters of the neural network model based on the number of coats and the respective densities of the coats (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; see also Figs. 15-16, and 18; see also Fig. 8);
determining mask information for determining the parameters of the neural network model to be comprised in each of the coats based on the scores (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; see also Figs. 15-16, and 18; see also Fig. 8); and
generating a bitstream based on the number of coats, the respective densities of the coats, the mask information, and the random seed (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on the claimed number of coats, densities, mask information, and random initialization; see also Fig. 8).”
Accordingly, before the effective filing date, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhang, to modify the claimed neural network processing method/device to include an additional embodiment, such as a score/importance ordering list associated with densities (total groups in subgroup). The motivation for doing so would have been to create the advantage of reducing the bitrate/bitstream overhead (see Zhang, Figs. 1-5, 8, 15-16, and 18, [0392], [0451]-[0453], and [0455]).
Regarding Claim 11, Zhang discloses each and every feature of Claims 1, 9, and 13, as outlined above, and further discloses a processor-implemented method (Figs. 1-5, 8, 15-16, and 18), comprising:
“receiving a bitstream; obtaining a random seed, a number of coats, respective densities of the coats, and mask information by decoding the received bitstream; initializing a neural network model using the random seed; determining a number of coats and respective densities of coats to be used in the neural network model based on constraints on an amount of computation (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on the claimed number of coats, densities, mask information, random initialization, and scale information; see also Fig. 8);
based on the determined number of coats and the determined respective densities of the coats, determining scale information corresponding to the determined coats (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization, including scale information; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating the claimed parameters based on the scale and mask information; see also Fig. 8); and
generating parameters of the neural network model based on the scale information and the mask information (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization, including scale information; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating the claimed parameters based on the scale and mask information; see also Fig. 8).”
The motivation that was utilized in Claims 1, 9, and 13 applies equally as well here.
Regarding Claims 2 and 14, Zhang discloses:
“determining scale information corresponding to each of the coats based on the number of coats and the respective densities of the coats, wherein the mask information is determined based on the determined scale information (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization, including scale information; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on the claimed number of coats, densities, mask information, random initialization, and scale information; see also Fig. 8).”
The motivation that was utilized in Claims 1, 9, 11, and 13 applies equally as well here.
Regarding Claims 4 and 16, Zhang discloses:
“wherein the determining of the number of coats comprises determining the number of coats based on a transmission bitrate constraint (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization, including scale information; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on target bitrate/bitrate control/bit budget, the claimed number of coats, densities, mask information, random initialization, and scale information; see also Fig. 8).”
The motivation that was utilized in Claims 1, 9, 11, and 13 applies equally as well here.
Regarding Claims 6 and 18, Zhang discloses:
“wherein the training of the neural network model comprises training the neural network model such that an output of the artificial neural network model is in a form of an output of a classifier network (Fig. 8, [0255], [0258], and [0301]; Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization, including scale information; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on the claimed number of coats, densities, mask information, random initialization, and scale information; see also Fig. 8).”
The motivation that was utilized in Claims 1, 9, 11, and 13 applies equally as well here.
Regarding Claims 7, 12, and 19, Zhang discloses:
“wherein the training of the neural network model comprises training the neural network model to output frame information of a frame corresponding to a predetermined point in time, and the frame information comprises probability information of a probability that each of a plurality of pixels comprised in the frame belongs to a class corresponding to a pixel value (Fig. 8, [0232], [0255], [0258], and [0301]; Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization, including scale information; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on the claimed number of coats, densities, mask information, random initialization, and scale information; see also Fig. 8).”
The motivation that was utilized in Claims 1, 9, 11, and 13 applies equally as well here.
Regarding Claims 8 and 20, Zhang discloses:
“wherein the initializing of the neural network model comprises initializing the parameters of the neural network model to a predetermined value or distribution (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on the claimed number of coats, densities, mask information, random initialization, and scale information; see also Fig. 8).”
The motivation that was utilized in Claims 1, 9, 11, and 13 applies equally as well here.
Regarding Claim 10, Zhang discloses:
“wherein the coats comprise binary masks of weights of the neural network model (Figs. 1-5, [0381]-[0382], [0400], [0402], [0404]-[0407], disclosing a randomly initialized NN and values determined based on random initialization; [0392], [0430]-[0436], [0451]-[0453], [0455]-[0469], and [0480]-[0483], further disclosing the claimed number of coats (weights/parameters), respective densities (e.g., subgroup of total parameters in group as in [0453]), an importance mask(s) ([0456], binary mask), and an importance ordering list; Figs. 15-16, and 18 and [0514]-[0518], [0531]-[0535], and [0539]-[0540], disclosing generating a bitstream based on the claimed number of coats, densities, mask information, and random initialization; see also Fig. 8).”
Allowable Subject Matter
Claims 3, 5, 15, and 17 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:
Examiner notes that multiple references cited disclose artificial neural networks. For example, the following references show similar features in the claims, although not relied upon: Aytekin (US 2022/0164995 A1), Figs. 6-7.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHLEEN M WALSH whose telephone number is (571)270-0423. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Kelley can be reached at (571) 272-7331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATHLEEN M WALSH/Primary Examiner, Art Unit 2482