DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice of Pre-AIA or AIA Status
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 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.
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 statements (IDS) submitted on 8/7/2025 and 1/29/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claim 1, claim cites a method for processing at least one image of a retina of an eye, the method comprising the steps of: “processing a first image of the at least one image using a noise reduction algorithm based on machine learning to generate a de-noised image of the retina, …and combining a second image of the at least one image with the de- noised image to generate at least one hybrid image of the retina ….”. The cited claiming terms referring to calculation models and parameters of claimed method/steps are directed to an abstract idea as it require no actual selection components/devices but rather requires steps of calculations, which can be performed by a human, on an optimization choice and calculations. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim does not recite any configurations/components of structures and compositions involving in practicing the method and means for performing the light steering. Thus, the claim does not amount to anything significantly more than the abstract idea, and is not patent eligible. Alice Corp., 134 S. Ct. at 2357, 110 USPQ2d at 1981 (See MPEP § 2106).
Claims 11 and 12 have same issues as that of claim 1 above for operation steps.
Claims 2-10, 13-16 and 17-20 are rejected as containing deficiencies of respective claims through their dependency from respective claims 1 and 11-12.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claim 1, cited terms of “a first image of the at least one image” (line 4) and “a second image of the at least one image” (line 8) are vague and renders the claims indefinite. Claim cites “at least one image of a retina of an eye” (line 1-2), that is, may having one image of a retina of an eye. In case of “one image of a retina of an eye”, terms of “a first image of the one image (of a retina)” and “a second image of the one image (of a retina)” becomes ambiguous. Further, a relation between the “first image” and “second image” is missing, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
Claims 2-10 are rejected as containing the deficiencies of claim 1 through their dependency from claim 1.
Regarding claim 11, the “…the at least one image…” (line 5) is indefinite and lacks antecedent, as nowhere in claim 11 specifies “at least one image”. Further, a relation between the “first image” and “second image” is missing, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
Claims 13-16 are rejected as containing the deficiencies of claim 11 through their dependency from claim 11.
Regarding claim 12, the “…the at least one image…” (line 6) is indefinite and lacks antecedent, as nowhere in claim 12 specifies “at least one image”. Further, a relation between the “first image” and “second image” is missing, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
Claims 17-20 are rejected as containing the deficiencies of claim 12 through their dependency from claim 12.
Therefore proper amendments are required in order to clarify the scopes of the claims and overcome the rejections.
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)(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 1-2, 4-13, 15-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Osamu et al (JP 2023076615, English translation attached) .
Regarding Claim 1, Osamu teaches a computer-implemented method of processing at least one image of a retina of an eye acquired by an ophthalmic imaging device, wherein the at least one image shows a texture of the retina (abstract; figs. 1-5), the method comprising:
processing a first image of the at least one image using a noise reduction algorithm based on machine learning to generate a de-noised image of the retina, wherein the texture of the retina shown in the first image is at least partially removed by the noise reduction algorithm to generate the de-noised image (abstract; 1-7, an image quality improvement part for generating a second image (--de-noised image) processed with at least one of the noise reduction and contrast enhancement as compared to a first image of an eye to be examined from the first image using a learned model; (¶[0010], line 1-6, includes an image quality improvement unit that uses a trained model to generate a second image from a first image of the eye under examination, in which at least one of noise reduction and contrast enhancement has been performed compared to the first image, and a display control unit that switches the first image and the second image and displays them side by side or superimposed on each other in a display unit); and
combining a second image of the at least one image with the de- noised image to generate at least one hybrid image of the retina which shows more of the texture of the retina than the de-noised image (abstract; 1-7, a display control unit for controlling a display unit to display the first image (--second image) and the second image (--de-noised image) while switching, arranging, or superposing the images; page 9, claim 16, wherein the display control unit sets transparency for at least one of the first image and the second image, and causes the display unit to display the first image and the second image superimposed on each other).
Regarding Claim 2, Osamu teaches the computer-implemented method of Claim 1, wherein the at least one hybrid image of the retina is generated by combining the second image with the de-noised image using respective weightings for the second image and the de-noised image (¶[0099], line 1-11, when displaying images before and after image quality enhancement processing superimposed, the display control unit 250 can set the transparency (--weightings) of at least one of the images before and after image quality enhancement processing, and display the images before and after image quality enhancement processing superimposed on the display unit 270; page 9, claim 16, wherein the display control unit sets transparency (--weightings) for at least one of the first image and the second image, and causes the display unit to display the first image and the second image superimposed on each other).
Regarding Claim 4, Osamu teaches the computer-implemented method of Claim 2, further comprising receiving a setting indication from a user for setting the weightings, and setting the weightings using the setting indication (¶[0057], line 1-6, the display control unit 250 can display information entered by the user on the display unit 270; ¶[0094], line 1-8, a pop-up menu 620 is displayed that allows the user to select whether or not to perform image quality enhancement processing; ¶[0099], line 1-11, when displaying images before and after image quality enhancement processing superimposed, the display control unit 250 can set the transparency (--weightings) of at least one of the images before and after image quality enhancement processing, and display the images before and after image quality enhancement processing superimposed on the display unit 270).
Regarding Claim 5, Osamu teaches the computer-implemented method of Claim 2, further comprising generating a control signal for a display device to display the at least one hybrid image (¶[0057], line 1-6, the display control unit 250 can display information entered by the user on the display unit 270; ¶[0094], line 1-8, a pop-up menu 620 is displayed that allows the user to select whether or not to perform image quality enhancement processing).
Regarding Claim 6, Osamu teaches the computer-implemented method of Claim 5, wherein a plurality of hybrid images is generated by combining the second image with the de-noised image using different respective weightings, and wherein a plurality of control signals are generated for the display device to display the plurality of hybrid images (fig. 4, 301, 302; ¶[0099], line 1-11, when displaying images before and after image quality enhancement processing superimposed, the display control unit 250 can set the transparency (--weightings) of at least one of the images before and after image quality enhancement processing, and display the images before and after image quality enhancement processing superimposed on the display unit 270).
Regarding Claim 7, Osamu teaches the computer-implemented method of Claim 5, further comprising receiving an update indication from a user for updating the weightings, and updating the weightings using the update indication (¶[0057], line 1-6, the display control unit 250 can display information entered by the user on the display unit 270; ¶[0094], line 1-8, a pop-up menu 620 is displayed that allows the user to select whether or not to perform image quality enhancement processing).
Regarding Claim 8, Osamu teaches the computer-implemented method of Claim 1, wherein the noise reduction algorithm is based on a convolutional neural network (¶[0074], line 1-5, as an example of a trained model according to this embodiment, a convolutional neural network (CNN) that performs image quality improvement processing on input tomographic images…).
Regarding Claim 9, Osamu teaches the computer-implemented method of Claim 1, wherein the at least one image of the retina of the eye is at least one fundus autofluorescence image of the retina of the eye (¶[0265], line 1-6, Frontal images include, for example, frontal images of the fundus, frontal images of the anterior segment, fluorescently scanned fundus images, and En-Face images….).
Regarding Claim 10, Osamu teaches the computer-implemented method of Claim 1, wherein the second image is the same as the first image (fig. 3, 301, 302).
Regarding Claim 11, Osamu teaches a non-transitory computer-readable storage medium storing a computer program comprising computer-readable instructions which, when executed by a processor, cause the processor to perform a set of operations (abstract; figs. 1-5), the set of operations comprising:
processing a first image of the at least one image using a noise reduction algorithm based on machine learning to generate a de-noised image of the retina, wherein the texture of the retina shown in the first image is at least partially removed by the noise reduction algorithm to generate the de-noised image (abstract; 1-7, an image quality improvement part for generating a second image (--de-noised image) processed with at least one of the noise reduction and contrast enhancement as compared to a first image of an eye to be examined from the first image using a learned model; (¶[0010], line 1-6, includes an image quality improvement unit that uses a trained model to generate a second image from a first image of the eye under examination, in which at least one of noise reduction and contrast enhancement has been performed compared to the first image, and a display control unit that switches the first image and the second image and displays them side by side or superimposed on each other in a display unit); and
combining a second image of the at least one image with the de-noised image to generate at least one hybrid image of the retina that shows more of the texture of the retina than the de-noised image (abstract; 1-7, a display control unit for controlling a display unit to display the first image (--second image) and the second image (--de-noised image) while switching, arranging, or superposing the images; page 9, claim 16, wherein the display control unit sets transparency for at least one of the first image and the second image, and causes the display unit to display the first image and the second image superimposed on each other).
Regarding Claim 12, Osamu teaches a data processing apparatus, comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the data processing apparatus to perform a set of operations (abstract; figs. 1-5), the set of operations comprising:
processing a first image of the at least one image using a noise reduction algorithm based on machine learning to generate a de-noised image of the retina, wherein the texture of the retina shown in the first image is at least partially removed by the noise reduction algorithm to generate the de-noised image (abstract; 1-7, an image quality improvement part for generating a second image (--de-noised image) processed with at least one of the noise reduction and contrast enhancement as compared to a first image of an eye to be examined from the first image using a learned model; (¶[0010], line 1-6, includes an image quality improvement unit that uses a trained model to generate a second image from a first image of the eye under examination, in which at least one of noise reduction and contrast enhancement has been performed compared to the first image, and a display control unit that switches the first image and the second image and displays them side by side or superimposed on each other in a display unit); and
combining a second image of the at least one image with the de-noised image to generate at least one hybrid image of the retina that shows more of the texture of the retina than the de-noised image (abstract; 1-7, a display control unit for controlling a display unit to display the first image (--second image) and the second image (--de-noised image) while switching, arranging, or superposing the images; page 9, claim 16, wherein the display control unit sets transparency for at least one of the first image and the second image, and causes the display unit to display the first image and the second image superimposed on each other).
Regarding Claim 13, Osamu teaches the non-transitory computer-readable storage medium of Claim 11, wherein the at least one hybrid image of the retina is generated by combining the second image with the de-noised image using respective weightings for the second image and the de-noised image (¶[0099], line 1-11, when displaying images before and after image quality enhancement processing superimposed, the display control unit 250 can set the transparency (--weightings) of at least one of the images before and after image quality enhancement processing, and display the images before and after image quality enhancement processing superimposed on the display unit 270; page 9, claim 16, wherein the display control unit sets transparency (--weightings) for at least one of the first image and the second image, and causes the display unit to display the first image and the second image superimposed on each other).
Regarding Claim 15, Osamu teaches the non-transitory computer-readable storage medium of Claim 13, wherein the set of operations further comprises generating a control signal for a display device to display the at least one hybrid image (¶[0057], line 1-6, the display control unit 250 can display information entered by the user on the display unit 270; ¶[0094], line 1-8, a pop-up menu 620 is displayed that allows the user to select whether or not to perform image quality enhancement processing).
Regarding Claim 16, Osamu teaches the non-transitory computer-readable storage medium of Claim 15, wherein a plurality of hybrid images is generated by combining the second image with the de-noised image using different respective weightings, and wherein a plurality of control signals are generated for the display device to display the plurality of hybrid images (fig. 4, 301, 302; ¶[0099], line 1-11, when displaying images before and after image quality enhancement processing superimposed, the display control unit 250 can set the transparency (--weightings) of at least one of the images before and after image quality enhancement processing, and display the images before and after image quality enhancement processing superimposed on the display unit 270).
Regarding Claim 17, Osamu teaches the data processing apparatus of Claim 12, wherein the at least one hybrid image of the retina is generated by combining the second image with the de-noised image using respective weightings for the second image and the de-noised image (¶[0099], line 1-11, when displaying images before and after image quality enhancement processing superimposed, the display control unit 250 can set the transparency (--weightings) of at least one of the images before and after image quality enhancement processing, and display the images before and after image quality enhancement processing superimposed on the display unit 270; page 9, claim 16, wherein the display control unit sets transparency (--weightings) for at least one of the first image and the second image, and causes the display unit to display the first image and the second image superimposed on each other).
Regarding Claim 19, Osamu teaches the data processing apparatus of Claim 17, wherein the set of operations further comprises generating a control signal for a display device to display the at least one hybrid image (¶[0057], line 1-6, the display control unit 250 can display information entered by the user on the display unit 270; ¶[0094], line 1-8, a pop-up menu 620 is displayed that allows the user to select whether or not to perform image quality enhancement processing).
Regarding Claim 20, Osamu teaches the data processing apparatus of Claim 19, wherein a plurality of hybrid images is generated by combining the second image with the de-noised image using different respective weightings, and wherein a plurality of control signals are generated for the display device to display the plurality of hybrid images (fig. 4, 301, 302; ¶[0099], line 1-11, when displaying images before and after image quality enhancement processing superimposed, the display control unit 250 can set the transparency (--weightings) of at least one of the images before and after image quality enhancement processing, and display the images before and after image quality enhancement processing superimposed on the display unit 270).
Allowable Subject Matter
Claims 3, 14 and 18 are rejected 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 in cases the 101/112 rejections of the independent claim is overcome by proper amendments.
The following is an examiner’s statement of reasons for allowance: The prior art taken singularly or in combination fails to anticipate or fairly suggest the limitations of the independent claims, in such a manner that a rejection under 35 U.S.C. 102 or 103 would be proper.
In regard to claims 3, 14 and 18, the prior art taken either singly or in combination fails to anticipate or fairly suggest an apparatus/method further comprise wherein the at least one hybrid image of the retina is generated by using the weightings to calculate one of a weighted sum or a weighted average of the second image and the de-noised image..
Examiner’s Note
Regarding the references, the Examiner cites particular figures, paragraphs, columns and line numbers in the reference(s), as applied to the claims above. Although the particular citations are representative teachings and are applied to specific limitations within the claims, other passages, internally cited references, and figures may also apply. In preparing a response, it is respectfully requested that the Applicant fully consider the references, in their entirety, as potentially disclosing or teaching all or part of the claimed invention, as well as fully consider the context of the passage as taught by the reference(s) or as disclosed by the Examiner.
Conclusion
Any inquiry concerning this communication or earlier communication from the examiner should be directed to Jie Lei whose telephone number is (571) 272 7231. The examiner can normally be reached on Mon.-Thurs. 8:00 am to 5:30 pm.
If attempts to reach the examiner by the telephone are unsuccessful, the examiner's supervisor, Stephone Allen can be reached on (571) 272 2434. The Fax number for the organization where this application is assigned is (571) 273 8300.
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/JIE LEI/Primary Examiner, Art Unit 2872
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