DETAILED ACTION
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 Interpretation
Claim 11 is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they are all method claims.
Claims 1-10 and 12 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the recitations of “memory”, “processor” and “instructions” provide sufficient structure to perform all claimed limitations.
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, 7 and 11-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Osogawa (U.S. Pat. App. Pub. No. 2020/0209333 A1, referred as Osogawa hereinafter).
Regarding claim 1 as a representative claim Osogawa teaches an information processing apparatus configured to reduce noise in an image using a trained neural network, the information processing apparatus comprising (see figure 1: processing circuitry 127 performs denoised function 1279 by performing noise reduction as described in para. [0068]), wherein circuitry 127 employs a neural network descried in para. [0069]):
at least one processor and at least one memory having instructions stored thereon that, when executed by the at least one processor, cause the at least one processor and the at least one memory to cooperate to (see para. [0079]: program, processor/circuitry 127, storage):
adjust a black floating of an input image so as to be closer to a black floating of an image used at a time of training of the neural network (see para. [0073]: image GSI; para. [0068: performing noise reduction on image GSI; para. [0084]: black floating reduction process and DNN); and
perform inference processing on a noise-reduced image of the adjusted image, using the neural network trained to suppress the black floating (see para. [0073]: image II; para. [0084]: image II).
Claims 11-12 are also rejected for the same reasons as set forth in claim 1 above because each of these claim recites similar claim limitations called for in claim 1.
Regarding claim 7, Osogawa further teaches wherein the at least one processor and the at least one memory further cooperate to calculate the black floating of the input image at a predetermined timing (see para. [0046]: circuitry 127 performs the black floating reduction process after reading complex image CI1).
Allowable Subject Matter
Claims 2-6 and 8-10 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 2, the cited prior art does not teach or suggest claim limitations “calculate the black floating of the input image from a difference value between a black floating amount obtained from an optical black region of the input image and a black floating suppression amount of the neural network. is allowable”.
Regarding claim 3, the cited prior art does not teach or suggest claim limitations “calculate the black floating of the input image from a difference value between a black floating amount obtained from an image captured in a light shielded state and a black floating suppression amount of the neural network.”
Regarding claim 8, the cited prior art does not teach or suggest claim limitations “wherein the predetermined timing is a timing at a regular interval from a start of the inference processing, or a timing at which the black floating exceeds a predetermined threshold value.”
Claims 4-6, 9-10 are allowable as being dependent from claim 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sato (U.S. Pat. App. Pub. No. 2022/0345606 A1) teaches noise reduction (para. [0082]) and DNN (para. [0223]).
Yoneda et al. (U.S. Pat. App. Pub. No. 2011/0304330 A1) teaches noise reduction (para. [0085]).
Takano et al. (U.S. Pat. App. Pub. No. 2005/0185837 A1) teaches noise reduction (para. [0305]) and neural network (para. [0415]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY M DANG whose telephone number is (571)272-7389. The examiner can normally be reached Monday to Friday from 7:00AM to 3:00PM. 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, Amandeep Saini can be reached at 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DMD
1/2026
/DUY M DANG/Primary Examiner, Art Unit 2662