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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. § 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-046288, filed on 03/23/2023.
Election/Restrictions
In re response to Election/Restriction, Election was made without traverse in the reply filed on 05/28/2026. Therefore, claims 1–14 and 19 are pending in the instant application.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an image acquiring unit configured to acquire,” “an information acquiring unit configured to acquire,” “a determining unit configured to determine” in claim 14; and “wherein the receiver receives” in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification (e.g., See Applicant’s Spec. ¶82. He discloses that an image estimating apparatus comprises an image acquiring unit, an information acquiring unit, a determining unit and a receiver.) as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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)(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.
Claims 1, 5, 8, 10, 12–14 and 19 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Yang et al. (U.S. 12,266,089 B2).
Regarding claim 1, Yang discloses an image processing method comprising:
a first step of acquiring a first image and first image information about an imaging condition (Per Fig. 3, Yang’s image feature information obtainer 232 discloses image feature information based on a first image 231. Yang col. 9 lines 32–51. The image feature information obtainer 232 may obtain, based on the first image 231, the image feature information of the first image 231 related to an image quality degradation.)
a second step of generating a second image by enhancing the first image using a quantized machine learning model, (Per Fig. 2, Yang’s decoder 230 discloses a second image 238 after analyzing the first image 231. Ibid. col. 8 lines 59–67. [p]rocess the image quality degradation corresponding to the identified image quality degradation type by using a neural network (i.e., the first DNN) to obtain a second image 238 with reduced image quality.)
wherein in the second step, either the first image information (Per Fig. 7 at step S715, Yang discloses a second image 238 by computing a value of degradation quality, which would be less than a preset threshold. Ibid. col. 13 line 50 – col. 14 line 3. [w]hen a value indicating a degree of the most dominant image quality degradation type is less than the preset threshold, ...the second image 238 in which the image quality degradations are finally reduced may be obtained.)
Regarding claim 14, Yang discloses an image processing apparatus comprising:
one or more memories configured to store instructions; and (Fig. 2, 230 an AI decoder)
at least one processor executing the instructions causing the image processing apparatus to: (Fig. 2, 210 a receiver)
an image acquiring unit configured to acquire a first image; an information acquiring unit configured to acquire first image information about an imaging condition (Per Fig. 3, Yang’s image feature information obtainer 232 discloses image feature information based on a first image 231. Yang col. 9 lines 32–51. The image feature information obtainer 232 may obtain, based on the first image 231, the image feature information of the first image 231 related to an image quality degradation.)
an image processing unit configured to generate a second image by enhancing the first image using a quantized machine learning model; and (Per Fig. 2, Yang’s decoder 230 discloses a second image 238 after analyzing the first image 231. Ibid. col. 8 lines 59–67. [p]rocess the image quality degradation corresponding to the identified image quality degradation type by using a neural network (i.e., the first DNN) to obtain a second image 238 with reduced image quality.)
a determining unit configured to determine whether to use either the first image information (Per Fig. 7 at step S715, Yang discloses a second image 238 by computing a value of degradation quality, which would be less than a preset threshold. Ibid. col. 13 line 50 – col. 14 line 3. [w]hen a value indicating a degree of the most dominant image quality degradation type is less than the preset threshold, ...the second image 238 in which the image quality degradations are finally reduced may be obtained.)
Regarding claim 5, Yang discloses the image processing method, wherein the first image information is information about at least one of an image compression rate, a sharpness intensity, and a noise reduction intensity (a noise reduction intensity construed as image quality degradation) during development corresponding to the development condition. (Per Fig. 3, Yang’s image quality degradation processor 236 discloses image quality degradation is reduced in his neural network model. Yang col. 10 lines 20–29. The image quality degradation processor 236 may use the first DNN 237 corresponding to the NN setting information of the first DNN 237 obtained through the DNN setting information obtainer 235, to obtain the second image 238 in which the image quality degradation is reduced.)
Regarding claim 8, Yang discloses the image processing method, wherein the second image information includes a fixed value based on the first threshold or a fixed value that does not depend on the first image information. (Per Fig. 7, Yang discloses a second image 238 superseding the first image 231. In this process, he determines whether a value of degradation type is less than the preset threshold. Yang col. 13 lines 50 – col. 14 line 3. [w]hen a value indicating a degree of the most dominant image quality degradation type is less than the preset threshold, the image processing using the first DNN 237 may be no longer performed and bypassed,)
Regarding claim 10, it has been rejected in the same manner as claim 8.
Regarding claim 12, it has been rejected in the same manner as claim 5.
Regarding claim 13, Yang discloses a non-transitory computer-readable storage medium storing a program that causes a computer to execute the image processing method. (Per Fig. 2, Yang’s receiver 210 comprises a memory. Yang col. 8 lines 15–26. [t]he dedicated processor may include a memory for implementing embodiments of the disclosure or a memory processing unit for using an external memory.)
Regarding claim 19, Yang discloses an image processing system comprising:
a processor communicable with the image processing apparatus, (Fig. 1, 130 a receiver)
wherein the processor includes a transmitter configured to transmit a request for causing the image processing apparatus to execute processing for a captured image, (Fig. 1, 120 a transmitter)
wherein the image processing apparatus includes a receiver and an image processing unit, (Fig. 1, 100 a server)
wherein the receiver receives the request transmitted by the transmitter, and wherein the image processing unit executes the processing for the captured image according to the request. (Per Fig. 1, Yang’s system discloses how an original image 105 is processed based on a request. Yang col. 7 lines 43–58. In the case where the original image 105 is an encoded image, the server 100, upon a download request by the terminal 150 with respect to the image,)
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.
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.
Claim 2 is rejected under 35 U.S.C. § 103 as being unpatentable over Yang in view of Toshiaki (JP H11187288 A).
Regarding claim 2, Yang fails to specifically disclose the image processing method, wherein in a case where the value relating to the first image information is equal to or smaller than the first threshold, the second step generates the second image by enhancing the first image using the first image information, and wherein in a case where the value relating to the first image information is larger than the first threshold, the second step generates the second image by enhancing the first image using the second image information.
In related art, Toshiaki discloses the image processing method, wherein in a case where the value relating to the first image information is equal to or smaller than the first threshold, the second step generates the second image by enhancing the first image using the first image information, and (Per Fig. 1, Toshiaki evaluates whether minimum values are greater or equal to a threshold between two images. Toshiaki Spec. ¶19. [i]f the difference between the calculated maximum and minimum values exceeds (or is greater than or equal to) the threshold, the pixel value of that pixel is left as the actual pixel value.)
wherein in a case where the value relating to the first image information is larger than the first threshold, the second step generates the second image by enhancing the first image using the second image information. (Toshiaki discloses a second image after calculates the difference value of the two images. Ibid. ¶26. The noise level calculation means 6 generates a difference image by taking the difference between two images stored in the image 1 storage area 101 and the image 2 storage area 102 of the input image storage means 1, and further calculates the standard deviation of the difference image.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Toshiaki into the teachings of Yang to improve image quality comparing a threshold value and a difference value between two images. Ibid. ¶28.
Claims 3–4 are rejected under 35 U.S.C. § 103 as being unpatentable over Yang in view of Kano (U.S. 11,228,701 B2).
Regarding claim 3, Yang fails to specifically disclose the image processing method, wherein the first image information is information about at least one of a type, an F-number, a focal length, an object distance, and an optical characteristic for each image height of a lens apparatus that was used for imaging corresponding to the imaging condition.
In related art, Kano discloses the image processing method, wherein the first image information is information about at least one of a type, an F-number, a focal length, an object distance, and an optical characteristic for each image height of a lens apparatus that was used for imaging corresponding to the imaging condition. (Per Fig. 1, Kano discloses focus adjustment to calculate an object distance. Kano col. 4 lines 7–20. In order to perform focus adjustment according to an object distance, a lens position of a focus lens 101b is controlled by an autofocus (AF) mechanism or a manually-operated manual focus mechanism,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kano into the teachings of Yang to prevent optical characteristics that cause deterioration of image quality. Ibid. col. 1 lines 26–43.
Regarding claim 4, Yang fails to specifically disclose the image processing method, wherein the first image information is information about at least one of a type, sensor sensitivity, a shutter speed, and an imaging mode of an image pickup apparatus that was used for imaging corresponding to the imaging condition.
In related art, Kano discloses the image processing method, wherein the first image information is information about at least one of a type, sensor sensitivity, a shutter speed, and an imaging mode of an image pickup apparatus that was used for imaging corresponding to the imaging condition. (Per Fig. 1, Kano discloses a F-number in terms of shooting state setting. Kano col. 4 lines 7–20. An opening diameter of a diaphragm 101a is controlled as an F-number shooting state setting.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kano into the teachings of Yang to prevent optical characteristics that cause deterioration of image quality. Ibid. col. 1 lines 26–43.
Claims 6–7 and 11 are rejected under 35 § U.S.C. 103 as being unpatentable over Yang in view of Zhang (CN 113869517 A).
Regarding claim 6, Yang fails to specifically disclose the image processing method, wherein the number of bits precision for a weight for at least one layer of the machine learning model is not more than twice the number of bits precision of the first image.
In related art, Zhang discloses the image processing method, wherein the number of bits precision for a weight for at least one layer of the machine learning model is not more than twice the number of bits precision of the first image. (Zhang discloses bit register with a length twice of operation bits. Zhang Spec. ¶94. As long as the result of the multiplication-accumulation operation is subsequently stored in a register with a length twice that of the storage and operation bits, such a quantization bit length is within the protection scope of this invention.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Zhang into the teachings of Yang such that parameter quantization is applied to accelerate deep learning models. Ibid. ¶4.
Regarding claim 7, it has been rejected in the same manner as claim 6.
Regarding claim 11, Yang fails to specifically disclose the image processing method, wherein the first threshold is determined based on a quantization error in a case where the machine learning model is quantized.
In related art, Zhang discloses the image processing method, wherein the first threshold is determined based on a quantization error in a case where the machine learning model is quantized. (Zhang’s deep learning model quantizes an input matrix which is equalized with a same number of bits. Zhang Spec. ¶108. The quantization scheme for the input matrix is the same as that for the deep learning model, that is, each matrix unit value is quantized into the same number of bits as the weight unit value of each model layer,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Zhang into the teachings of Yang such that parameter quantization is applied to accelerate deep learning models. Ibid. ¶4.
Claim 9 is rejected under 35 U.S.C. § 103 as being unpatentable over Yang in view of Zhou (CN 115761531 A).
Regarding claim 9, Yang fails to specifically disclose the method, wherein the second step generates the second image by enhancing the first image using a second threshold based on the first image information and the first threshold.
In related art, Zhou discloses the method, wherein the second step generates the second image by enhancing the first image using a second threshold based on the first image information and the first threshold. (Zhou discloses a second threshold processing a first image to determine whether pixel change rate causes a noise reduction. Zhou Spec. ¶138. [d]etermines whether the pixel change rate is within a preset first threshold range, a preset second threshold range, or a preset third threshold range, and then performs corresponding low pixel change rate noise reduction,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Zhou into the teachings of Yang to improve a recognition of a first denoised image information. Ibid. ¶13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhu (U.S. 12,211,185 B2) discloses a computer-implemented image processing method obtaining a pair of training samples.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-16:00 (EST).
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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