DETAILED ACTION
The present Office action is in response to the amendments filed on 12 MAY 2026.
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 .
Response to Amendment
Claims 1, 5, and 7-12 have been amended. No claims have been canceled or added. The title has been amended to successfully overcome the title objection and the claims have been amended to successfully obviate the 35 U.S.C. § 112(f) invocation. Claims 1-14 are pending and herein examined.
Response to Arguments
Applicant's arguments filed 12 MAY 2026 have been fully considered but they are not persuasive.
With regard to claim 1, rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Publication No. 2022/0335633 A1 (hereinafter “Iwase”) in view of U.S. Publication No. 2021/0158525 A1 (hereinafter “Mizobe”), Applicant alleges the following:
“As best understood by Applicant, Iwase merely discloses measuring retinal layer thickness and producing a retinal layer thickness map. However, the retinal layer thickness map of Iwase merely includes the retinal layer thickness measurement results without discerning whether the retinal layer is normal or abnormal.” (Remarks, p. 2.)
The Examiner recognizes Iwase does not expressly state the retinal layer thickness measurement is “abnormal.” However, as per the rejection, Iwase describes analyzing with an image diagnosis for the layer thickness of the retinal layer. See Iwase, ¶ [0283]. Iwase further describes how tomographic imaging are, “useful for more accurately diagnose diseases.” See Iwase, ¶ [0003]. Therefore, while Iwase does not describe any particular diagnosis, it is clear Iwase’s disclosure describes a diagnostic apparatus, which is intending to diagnose diseases such as those resulting from a retinal layer thickness abnormality.
“While the Office Action relies on Iwase to reject “the information regarding the elongation state” of independent claim 1, the information of Iwase is not used as criteria, conditions, or input information for analyzing abnormalities in retinal thickness.” (Remarks, pp. 2-3.)
The Examiner respectfully disagrees. Iwase describes the need to correct for distorted proportions in the tomographic images containing the retinal thickness for more accurately analyzing the tomographic images containing the retinal thickness. See Iwase, ¶¶ [0236-0253] and FIGS. 17A-17B. Furthermore, the claim does not require any particularities as to how the analyzes is “based on the information regarding the elongation state.” Under the broadest reasonable interpretation, because the tomographic image is corrected using the elongation state, then the analyses is “based on the information regarding the elongation state.”
“However, Mizobe does not disclose obtaining information regarding and elongation state of the eyeball, for example, an ocular axial length, a visual acuity, a refractive power, and an eyeball shape, and analyzing an abnormality of the retinal layer thickness based on the information.” (Remarks, p. 3.)
The Examiner respectfully disagrees for two reasons. First, the rejection relies on showing tomographic imaging, such as described in Iwase’s disclosure, can be used for detecting abnormalities in the thickness of the retinal layer. See Mizobe, ¶¶ [0659-0663]. Second, Mizobe captures a plurality of information pertaining to the eye. For instance, ¶ [0237] discloses, “the predetermined shape characteristics, such as the height (thickness), the width, the area, and the Cup/Disc ratio of each region related to the eye to be examined, can be measured.” Mizobe additionally discloses measurements of the eye are based on refractions and differences in ocular axial lengths of the eyes to be examined. See Mizobe, ¶¶ [0097-0098].
“While Mizobe discloses detecting disease and abnormal areas, detection of disease or abnormal areas does not necessarily indicate the analysis of “an abnormality in the thickness of the retinal layer,” as recited in independent claim 1. For example, disease or abnormal areas may include imaging findings that are not directly synonymous with retinal thickness, such as hemorrhage, neovascularization, white spots, and drusen.” (Remarks, p. 3.)
The Examiner respectfully disagrees. Applicant’s argument appears to stem from Mizobe, ¶ [0228], which describes, “[a]s for the macular area, in order to recognize, for example, the thickness of the entire retina due to bleeding, neovascularization, etc., […].” Said paragraph determines thickness of the entire retina based on changes, for instance, ¶ [0444] states, “[t]he analyzing unit 2806, for example, calculates changes in the layer thickness or tissue shape or the like that are visualized in the input image by the image analysis processing.” That is to say, the learning model is labeling changes in the layer thickness to make labels for evaluation. Lastly, the Examiner notes the claim does not require detecting of disease or abnormal areas, only the analyzing thereof. The claim only requires “output information regarding the abnormality in the thickness of the retinal layer.” Such an output can be a mere label, a diagnosis, any indication of a non-expected thickness value, a region-of-interest identifier, etc. The rejection clearly establishes Mizobe’s disclosure identifying a kind of disease or an abnormal site of an eye to be examined (¶ [0659]) and providing labels and ROIs for further analysis (¶ [0450]).
“But page 9 of the Office Action fails to cite anything in Iwase and Mizobe that suggests [Examiner’s motivation] reasoning. In Iwase, the information of the elongation state of the eyeball is merely used in shape correction and is not used in the analysis of retinal layer thickness abnormalities. In Mizobe, no analysis based on the information of the elongation state of the eyeball is disclosed. Therefore, Applicant submits that the idea for the combination came from reading Applicant’s claims and specification.” (Remarks, p. 4.)
The Examiner respectfully disagrees for the reasons stated above. There is no particular recitation as to how the information of the elongation state of the eyeball is used as part of the analysis, only that it is “based” thereon. The tomographic images being taken inconsideration of the elongation state of the eyeball and also modified using the elongation state of the eyeball is sufficient to meet the “based on” criteria. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
“Respectfully, this inappropriately splits apart claim language that must be read together. That is, “analyze … the thickness of the retinal layer based on the information regarding the elongation state and the data including the information regarding thickness of the retinal layer” and “analyze an abnormality in the thickness of the retinal layer” cannot be split up and separately mapped to different references without the other. (Remarks, pp. 4-5.)
The Examiner respectfully disagrees. Applicant’s characterization of the rejection is incorrect. First, Mizobe does not analyze an abnormality in the thickness of the retinal layer in a vacuum. Any information used for the information of the data including the information regarding the thickness of the retinal layer constitutes as said information, as the result pertains to the abnormality in the thickness of the retinal layer. It is noted the claim is broader than what Applicant argues, because the claim does not set forth any type of information, just what can be indicated/extrapolated therefrom. For instance, the image itself serves as “information regarding a thickness of a retinal layer of the eyeball.” Second, as explained above, the correction of the tomographic image meets the limitation of “based on the information regarding the elongation state” and because both Iwase and Mizobe utilize a tomographic image for obtaining “information regarding the thickness of the retinal layer,” then there is no need for redundantly showing Mizobe providing a second information regarding the elongation state. Lastly, Applicant’s arguments merely allege an inappropriately split limitation with the rationale, “[limitations] cannot be split and separately mapped to different references without the other.” However, there is no articulation regarding why they cannot be addressed in the way presented by the Examiner. The claimed invention as a whole is considered in the rejection and is reiterated below.
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.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2022/0335633 A1 (hereinafter “Iwase”) in view of U.S. Publication No. 2021/0158525 A1 (hereinafter “Mizobe”).
Regarding claim 1, Iwase discloses an information processing apparatus (FIG. 1, controlling apparatus 200) comprising:
at least one processor ([0316], “computer has one or a plurality of processors or circuits”); and
a memory storing instructions ([0007], “computer-readable medium storing a program”) thereon which, when executed by the at least one processor, causes the information processing apparatus ([0316], “a computer of the system or the apparatus reads and executes the program”) to:
acquire information regarding an elongation state of an eyeball to be analyzed ([0086], “The signal processing unit 190 performs generation of an image, analysis of the generated image, generation of visualized information of an analysis result, etc., based on signals output from the differential detector 129, the APD 152, and the anterior ocular segment camera 165, respectively.” [0262], “the thickness and state of the entire retina, and the shape of the retina can be more accurately grasped with single-time imaging.” FIG. 3A depicts imaging including the shape of the eyeball. [0239], “the ocular axial length of the eye to be examined:” e.g., elongation of the eye);
acquire data including information regarding a thickness of a retinal layer of the eyeball ([0086], “The signal processing unit 190 performs generation of an image, analysis of the generated image, generation of visualized information of an analysis result, etc., based on signals output from the differential detector 129, the APD 152, and the anterior ocular segment camera 165, respectively.” [0262], “the thickness and state of the entire retina, and the shape of the retina can be more accurately grasped with single-time imaging.” [0271], “The analyzing unit 1908 can, for example, measure the curvature of a boundary line and the thickness of the retina”); and
analyze ([0280], “The analysis map 1915 is a map image indicating the analysis results generated by the analyzing unit 1908. The map image may be, for example, the above-described curvature map, a layer thickness map, or a blood vessel density map.” Note, the analyzing unit 1908 performs image processing to determine the shape of the eye and the thickness of the retinal layer and then the data is used for outputting the analysis map used in diagnosis. [0271], “The analyzing unit 1908 performs image analysis processing on tomographic images, motion contrast images, and three-dimensional data. The analyzing unit 1908 can, for example, measure the curvature of a boundary line and the thickness of the retina from a tomographic image and a three-dimensional tomographic image.” [0283], “the analysis of the curvature radius and the layer thickness of the retinal layer in image diagnosis”),
wherein the analysis includes a trained model configured to use, as input, at least the data including the information regarding the thickness of the retinal layer to output information regarding ([0290], “The generator intends to generate data similar to training data, and the discriminator discriminates whether data comes from training data or from generated models. In this case, learning is performed for a generation model such that, when a tomographic image of an arbitrary cross section position of three-dimensional data built from radial scan data, as illustrated by the tomographic image 911 in FIG. 9C, is input, a tomographic image as if it is actually imaged is generated. Accordingly, the data generating unit 1904 may generate, by using the learned model, three-dimensional data directly from a plurality of tomographic images obtained by radial scan.” Note, the tomographical image includes image data of the thickness of the retinal layer imaged).
Iwase fails to expressly disclose an abnormality in the thickness of the retinal layer. Although Iwase discloses tomographical images are used for diagnosing diseases in the prior-art, there is no express disclosure of diagnosing an abnormality related to the thickness of the retinal layer. See Iwase, [0003].
However, Mizobe teaches an abnormality in the thickness of the retinal layer ([0659], “the image processing apparatus 20, 80, 2800 or 4400 may identify the kind of disease or an abnormal site of an eye to be examined from an image using a separately prepared learned model” [0382], “when an image depicting the retina layers obtained by the imaging of the OCT that uses the posterior ocular segment as the imaging target is input to the learned model trained with the second training data, the learned model outputs the region label image for the retina layers depicted in the image.” [0663], “cause analysis results such as the thickness of a desired layer”).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have analyzed an abnormality, as taught by Mizobe ([0659]), in Iwase’s invention. One would have been motivated to modify Iwase’s invention, by incorporating Mizobe’s invention, to accurately process tomographical images of an eye even when imaging diseased retina layers (Mizobe: [0008]).
Regarding claim 2, Iwase and Mizobe disclose every limitation of claim 1, as outlined above. Additionally, Iwase discloses wherein the trained model is configured to further use, as input, the information regarding the elongation state ([0256], “the three-dimensional data using the tomographic images after the actual shape modification can be input to the learned model.” Note, the shape modification of the tomographical image includes information of the ocular axial length, which is the elongation state. See FIG. 16, step S1604 and FIG. 17 with accompanying paragraphs).
Regarding claim 3, Iwase and Mizobe disclose every limitation of claim 1, as outlined above. Additionally, Iwase discloses wherein the information regarding the elongation state includes one or a combination of two or more of scalar values that each represent any one of an ocular axial length, a visual acuity, eyeball refraction data, or shape of the eyeball ([0239], “a distance pvl from the pivot point P1 to a retina surface Re, corresponding to the ocular axial length of the eye.” FIG. 16, step S1604 describes “perform actual shape modification” and calculating a refraction index, ocular axial length, and shape of the eyeball, see section Actual Shape Modification at [0236]).
Regarding claim 4, Iwase and Mizobe disclose every limitation of claim 1, as outlined above. Additionally, Iwase discloses wherein the data including the information regarding the thickness of the retinal layer includes at least any one selected from the group consisting of an optical coherence tomographic image, a map image in which information indicating the thickness of the retinal layer is projected onto a plane along a fundus of an eye, retinal layer segmentation data, an image of the eyeball photographed by a magnetic resonance imaging (MRI) apparatus, and an image of the eyeball photographed by a computer tomography (CT) apparatus ([0003], “a retinal layer can be observed in three dimensions by using a tomographic image imaging apparatus of eyes, such as an OCT apparatus using the optical coherence tomography (OCT).” [0054], “an optical coherence tomography apparatus (OCT apparatus) that images a subject body.” [0057], “the OCT apparatus includes a tomographic image obtaining unit that obtains a tomographic image of the subject body.” [0262], “the thickness and state of the entire retina, and the shape of the retina can be more accurately grasped with single-time imaging.” FIG. 3B is a tomographic image and FIG. 3C is a fundus image).
Regarding claim 5, Iwase and Mizobe disclose every limitation of claim 1, as outlined above. Additionally, Mizobe discloses wherein a result obtained through the analysis includes at least any one selected from the group consisting of a map image indicating a degree of abnormality in the thickness of the retinal layer, a true or false value indicating presence or absence of a disease, a scalar value indicating a possibility of having a disease, and thickness data of the retinal layer expected to be obtained when the thickness of the retinal layer is normal ([0664], “An analysis result may be displayed using an analysis map, or using sectors which indicate statistical values corresponding to respective divided regions.” [0355], “specify the disease of a subject, or to observe the degree of the disease.” [0155], “The thickness graph 712 of the retina is a graph that illustrates the thickness of the retina derived from the boundaries 715, 716. Additionally, the thickness map 702 represents the thickness of the retina derived from the boundaries 715, 716 in a color map. Note that, in FIG. 7, although the color information corresponding to the thickness map 702 is not illustrated for description, practically, the thickness map 702 can display the thickness of the retina corresponding to each coordinate in the SLO image 701 according to a corresponding color map”). The same motivation of claim 1 applies equally as well to claim 5.
Regarding claim 6, Iwase and Mizobe disclose every limitation of claim 5, as outlined above. Additionally, Mizobe discloses wherein the disease includes at least any one selected from the group consisting of glaucoma, posterior staphyloma, retinal detachment, diabetic retinopathy, retinal choroidal atrophy, macular hemorrhage, myopic traction maculopathy, and myopic choroidal neovascularization ([0669], “diagnosis results such as results relating to glaucoma or age-related macular degeneration.” [0174], “peripapillary chorioretinal atrophy.” [0228], “recognize, for example, the thickness of the entire retina due to bleeding, neovascularization, etc., the defect in photoreceptor related to eyesight, or the thinning of the choroid coat due to pathological myopia.” [0663], “the value (distribution) of a parameter relating to a region including at least one abnormal site such as drusen, a neovascular site, leucoma (hard exudates), pseudodrusen or the like may be displayed as an analysis result”). The same motivation of claim 1 applies equally as well to claim 6.
Regarding claim 7, Iwase and Mizobe disclose every limitation of claim 1, as outlined above. Additionally, Iwase discloses wherein the instructions, when executed by the processor, further cause the information processing apparatus to perform control for displaying a result of the analysis performed (FIG. 18, display S1806. [0277], “In step S1806, the controlling unit 191 displays, on the display unit 192, tomographic images, motion contrast images, three-dimensional data, and various map images serving as analysis results”).
Regarding claim 8, Iwase and Mizobe disclose every limitation of claim 7, as outlined above. Additionally, Iwase discloses wherein the instructions, when executed by the processor, further cause the information processing apparatus to perform control for simultaneously displaying the result of the analysis and the information regarding the thickness of the retinal layer (FIG. 19A depicts simultaneously displaying multiple images, including results of the analysis and a layer thickness map. [0280], “The analysis map 1915 is a map image indicating the analysis results generated by the analyzing unit 1908. The map image may be, for example, the above-described curvature map, a layer thickness map”).
Regarding claim 9, Iwase and Mizobe disclose every limitation of claim 8, as outlined above. Additionally, Iwase discloses wherein the instructions, when executed by the processor, further cause the information processing apparatus to switch a display method for the information regarding the thickness of the retinal layer based on the information regarding the elongation state (FIG. 19A, checkboxes Real Shape 1916 and Real Scale 1917. [0278], “The check box 1916 is the indication of a selecting unit for selecting whether or not to apply the actual shape modification processing for reducing the scanning distortion by the image modifying unit 1907. Additionally, the check box 1917 is the indication of a selecting unit for selecting whether or not to apply the aspect ratio distortion modification processing by the controlling unit 191.” Note, the modifications are based on the elongation state, as described in the section Actual Shape Modification starting in [0236]).
Regarding claim 10, Iwase and Mizobe disclose every limitation of claim 1, as outlined above. Additionally, Mizobe discloses wherein the analysis includes a plurality of the trained models, and selects and uses at least one trained model from the plurality of the trained models based on the elongation state ([0241], “when the first processing unit 822 includes a plurality of learned models, the selecting unit 1524 can select a learned model used for the detection processing by the first processing unit 822, based on the imaging conditions and the learned content related to the learned models of the first processing unit 822.” [0659], “the image processing apparatus 20, 80, 2800 or 4400 can automatically select a learned model to be used in the aforementioned processing based on the kind of disease or the abnormal site that was identified using the separately prepared learned model.” [0704] describes a first learned model based on the analysis result such as an analysis map, which would be based on the size of the eyeball, as per the rejection of claim 1). The same motivation of claim 1 applies equally as well to claim 10.
Regarding claim 11, Iwase and Mizobe disclose every limitation of claim 10, as outlined above. Additionally, Mizobe discloses wherein information used for training the plurality of the trained models includes training information regarding the elongation state of the eyeball, and wherein the instructions, when executed by the processor, further cause the information processing apparatus to acquire, from each of the plurality of the trained models, distribution information regarding the elongation state included in the training information regarding the elongation state of the eyeball, and to select at least one trained model based on the distribution information and the information regarding the elongation state of the eyeball to be analyzed ([0010], “wherein the learned model has been obtained by using training data including data indicating at least one layer of a plurality of layers in a tomographic image of an eye to be examined.” [0128], “The training data for the machine learning model according to the present example includes pairs of one or more input data and ground truth. Specifically, a tomographic image 401 obtained by the OCT is listed as input data, and a boundary image 402 in which the boundaries of the retina layers are specified for the tomographic image is listed as ground truth.” [0241], “The selecting unit 1524 selects image processing performed on a tomographic image, based on the imaging conditions obtained by the obtaining unit 21 and the learned contents (training data) related to the learned model of the first processing unit 822 […] Additionally, when the first processing unit 822 includes a plurality of learned models, the selecting unit 1524 can select a learned model used for the detection processing by the first processing unit 822, based on the imaging conditions and the learned content related to the learned models of the first processing unit 822.” Note, the input tomographic image includes information on the elongation state and [0718] describes the learned models having distinct distribution values for parameters). The same motivation of claim 1 applies equally as well to claim 11.
Regarding claim 12, Iwase and Mizobe disclose every limitation of claim 1, as outlined above. Additionally, Iwase discloses wherein the instructions, when executed by the processor, further cause the information processing apparatus to correct, based on the information regarding the elongation state, the information regarding the abnormality in the thickness of the retinal layer which has been output from the trained model (FIG. 19A, checkboxes Real Shape 1916 and Real Scale 1917. [0278], “The check box 1916 is the indication of a selecting unit for selecting whether or not to apply the actual shape modification processing for reducing the scanning distortion by the image modifying unit 1907. Additionally, the check box 1917 is the indication of a selecting unit for selecting whether or not to apply the aspect ratio distortion modification processing by the controlling unit 191.” Note, the modifications (e.g., corrections) are based on the elongation state, as described in the section Actual Shape Modification starting in [0236]. [0280] describes the output including a layer thickness map).
Regarding claim 13, the limitations are the same as those in claim 1; however, written in process form instead of machine form. Therefore, the same rationale of claim 1 applies equally as well to claim 13.
Regarding claim 14, Iwase and Mizobe disclose every limitation of claim 13, as outlined above. Additionally, Iwase discloses a non-transitory storage medium having stored thereon a program for causing a computer to execute the information processing method of claim 13 ([0007], “a computer-readable medium storing a program.” [0316], “a computer of the system or the apparatus reads and executes the program”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2014/0085606 A1 – Determines a thickness of the retina and an abnormality associated with a large thickness of the retina. See ¶ [0068].
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STUART D BENNETT/Examiner, Art Unit 2481
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