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 .
Notice to Applicants
This action is in response to the Application filed on 09/25/2024.
Claims 1-11 are pending.
Priority
The present Application claims priority to JP-2023-169858 with a priority filing date of 09/29/2023, which is acknowledged.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 09/25/2024 has been fully considered by the examiner.
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 examiner notes that claims 1-9 recite a “control unit”, but they also specify that it includes at least one processor and at least one memory, which modifies it with sufficient structure to avoid invoking 112(f).
Claim 10 also specifies that the control unit is part of an OCT image processing device, which also modifies it with sufficient structure to avoid 112(f).
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 1-2 and 9-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Analysis for claim 1 is provided in the following. Claim 1 is reproduced in the following (annotation added):
An OCT image processing device that processes data of an OCT image of a living tissue that is taken by an OCT device, the OCT image processing device comprising: a control unit having at least one processor and at least one memory storing a computer program code, the computer program code, when executed by the at least one processor, causing the control unit to perform:
an image acquisition step of acquiring the OCT image taken by the OCT device;
a normalization step of performing a normalization process on the OCT image acquired at the image acquisition step to generate a normalized image from the OCT image;
and a medical information acquisition step of acquiring medical information output by a mathematical model by inputting the normalized image generated at the normalization step into the mathematical model that has been trained by a machine learning algorithm using a plurality of training images that are OCT images,
wherein at the normalization process, the computer program code causes the control unit to generate the normalized image by approximating at least one of characteristics of the OCT image to statistical information of characteristics of the plurality of training images.
Step 1: Does the claim belong to one of the statutory categories? Claim 1 is directed to a machine, which is a statutory category of invention (YES).
Step 2A Prong One: Does the claim recite a judicial exception? Steps d and e are regarded as reciting abstract ideas, specifically mental processes that can be practically performed in the human mind.
Step d recites inputting a normalized image into a trained mathematical model and acquiring medical information output by said model. The broadest reasonable interpretation of “medical information” includes many embodiments described in the specification, and can include, for example, general diagnosis results (see paragraph 0014 of the originally-filed specification). The claim does not give any specific details on the mathematical model, other than that it is generally trained by a machine learning algorithm on a plurality of OCT images, which is common to all mathematical models as disclosed. Therefore, considering that the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer (see MPEP 2106.04(a)(2).III), a human can mentally perform the same function as the general mathematical model, i.e. generally determining diagnosis information from an OCT image.
Step e states that the image normalization process includes approximating at least one characteristic of the OCT image to statistical information of characteristics of training images, which can be mentally performed in the human mind by any comparison of the two characteristic values (YES).
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? Step a recites a computerized system at a high level of generality, and that the OCT image depicts a living tissue, neither of which integrate into a practical application. Step b recites acquiring the OCT image, which amounts to mere data gathering. Part c recites performing a normalization process on the OCT image; although step e recites that the normalization step includes the approximation step, it is unclear from the claim exactly how the approximation achieves the normalized image as claimed (NO).
Step 2B: Does the claim as a whole amount to significantly more than the recited exception? The claim as a whole recites a computerized system at a high level of generality and data gathering. The claim recites a normalization step that is vaguely tied to an approximation step that is performable in the human mind. The claim finally recites using a generalized trained model to obtain medical information, such as diagnosis information, which is also practically performable in the human mind (NO). Claim 1 is not eligible.
Similar analysis is applicable to independent claims 10 and 11. Claims 10 and 11 are not eligible.
Claim 2 recites that additional information is attached to the OCT image, and approximating this additional information to the statistical information of the training images, which is still practically performable in the human mind. Claim 2 is not eligible.
Claims 3 and 4 recite specific image modification steps, i.e. adding a background area to the image, in response to the approximation of the characteristics, i.e. if the imaged range of the image is narrower than the statistical imaged range of the training images. Thus, the approximation step and medical information determination step are integrated into a practical application and claims 3 and 4 are eligible.
Claims 5-8, similarly to claims 3-4 above, recite specific image modification steps, i.e. extracting sub-areas and/or modifying the tilt of the images, in response to the approximation of the characteristics, i.e. if the imaged range of the image is larger than the statistical imaged range of the training images. Thus, the approximation step and medical information determination step are integrated into a practical application and claims 3 and 4 are eligible.
Claim 9 recites that the normalization process includes a similar approximation, but only on statistical data representing at least part of a tissue-imaged area of the image, which is still practically performable in the human mind. Claim 9 is not eligible.
To overcome the above 101 rejections, the examiner suggests amending step e in claim 1 above to instead read:
“wherein at the normalization process, the computer program code causes the control unit to generate the normalized image by approximating an imaged range of the OCT image in the depth direction to a statistical imaged range in the depth direction of the plurality of training images by modifying the OCT image.”
and similar language to match the last step of independent claims 10 and 11.
This amendment would address the issue raised in Step 2A Prong Two above by clarifying how the recited approximation achieves the image normalization, generically to eligible claims 3-8, and thus would integrate into a practical application by reflecting the improvements discussed in at least paragraphs 0012-0013 of the originally-filed specification. This amendment would find support from at least steps S1-S5 of figure 3 and paragraphs 0067-0073 of the originally-filed specification.
Alternatively, the examiner suggests rewriting any of the above eligible claims (3-8) in independent form.
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.
(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, 3, 5-7, and 9-11 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Kawczynski et al. (U.S. Publ. US-2022/0230300-A1).
Regarding claim 1, Kawczynski discloses an OCT image processing device that processes data of an OCT image (see figure 13, network of computing systems 1300 and paragraphs 0188-0195) of a living tissue that is taken by an OCT device, the OCT image processing device comprising (see figure 13, eye imaging systems 1305 and paragraph 0188, where the imaging systems can collect OCT images of eyes):
a control unit having at least one processor and at least one memory storing a computer program code, the computer program code, when executed by the at least one processor, causing the control unit to perform (see figure 13, machine-learning model system 1315 and paragraph 0190, where the system can include one or more processors and memories):
an image acquisition step of acquiring the OCT image taken by the OCT device (see paragraphs 0040-0049, where input image data including OCT images of eyes can be obtained for processing or training; see figure 1, step 110 and paragraph 0110, where the image is obtained);
a normalization step of performing a normalization process on the OCT image acquired at the image acquisition step to generate a normalized image from the OCT image (see paragraphs 0050-0055, where the input image can be preprocessed by various methods; paragraph 0055 particularly states that machine-learning models can be configured to receive images of a "particular size", and that the image can be cropped and/or padded to this particular size, which is a form of normalization);
and a medical information acquisition step of acquiring medical information output by a mathematical model by inputting the normalized image generated at the normalization step into the mathematical model (see figure 1, steps 115-120 and paragraphs 0111-0112, where the image is input to a trained machine-learning/mathematical model that outputs a predicted visual acuity/medical information of the eye in the image)
that has been trained by a machine learning algorithm using a plurality of training images that are OCT images (see figure 1, step 105 and paragraphs 0078-0084, 0106, where the model is first trained on a set of OCT images),
wherein at the normalization process, the computer program code causes the control unit to generate the normalized image by approximating at least one of characteristics of the OCT image to statistical information of characteristics of the plurality of training images (paragraphs 0056-0059 specify that the training data can also be preprocessed using the above methods of cited paragraphs 0050-0055; thus, in the case in which the model is configured to receive images of a "particular size", these training images would also be of the same particular size, and the padding/cropping of paragraph 0055 would then modify the input images to match/approximate their sizes/characteristics to the same sizes of the training data).
Regarding claim 3, Kawczynski discloses wherein at the normalization process, the computer program code further causes the control unit to generate the normalized image by adding a background area to the OCT image acquired at the image acquisition step in a depth direction of the living tissue when an imaged range of the OCT image in the depth direction is narrower than a statistical imaged range of the plurality of training images in the depth direction (see paragraph 0055, where the normalization to match the predetermined size can include padding the image, which would naturally occur when the OCT image is too small / has a narrower imaging range; padding the image reads on adding a background area).
Regarding claim 5, Kawczynski discloses wherein the computer program code further causes the control unit to generate the normalized image by extracting a part of an area in a depth direction of the living tissue from the OCT image acquired at the image acquisition step when an imaged range of the OCT image in the depth direction is larger than a statistical imaged range of the plurality of training images in the depth direction (see paragraph 0055, where the normalization to match the predetermined size can include cropping the image, which would naturally occur when the OCT image is too large / has a larger imaging range; cropping the image reads on extracting a part of the image).
Regarding claim 6, Kawczynski discloses wherein the computer program code further causes the control unit to, at the normalization process, extract at least a part of a tissue-imaged area of the OCT image where the living tissue appears in the OCT image when extracting the part of the area in the depth direction from the OCT image (see figure 1, steps 115-120 and paragraphs 0111-0112, where the image is input to a trained machine-learning/mathematical model that outputs a predicted visual acuity/medical information of the eye in the image; thus, the cropping of paragraph 0055 would necessarily be limited to include part of the tissue-imaged area for the model to properly function).
Regarding claim 7, Kawczynski discloses wherein a layer of the living tissue appears in the OCT image (see paragraphs 0040-0049, where input image data including OCT images of eyes can be obtained for processing or training, thus at least one layer of living tissue must be present in the OCT image),
and the computer program code further causes the control unit to, when extracting the part of the area in the depth direction from the OCT image at the normalization process: reduce a tilt of the layer of the living tissue appearing in the OCT image (see paragraphs 0050-0055, where the preprocessing/normalization steps can include flattening processes that reduce the curvature of the imaged structures and thus effectively reduce the tilt of the OCT image);
and extract at least the part of the tissue-imaged area with the tilt of the layer of the living tissue being reduced (paragraph 0052 specifies that the image volumes can be cropped to pixels above and below the tissue layer of interest).
Regarding claim 9, Kawczynski discloses wherein the computer program code further causes the control unit to, at the normalization process: calculate statistical information of characteristics of at least a part of a tissue-imaged area where the living tissue appears in the OCT image acquired at the image acquisition step (determining the dimensions of the OCT image for the cropping/padding in paragraph 0055 would read on calculating the statistical information of at least a part of the tissue-imaged area, as the dimensions represent the size of at least part of the tissue-imaged area; alternatively, determining the curvature of the image features in paragraph 0052 before the flattening process would also read on this process);
and generate the normalized image by approximating the calculated statistical information of the characteristics of the tissue-imaged area to statistical information of characteristics of a tissue-imaged area for the plurality of training images (see paragraph 0055, where the image is cropped/padded to match/approximate the dimensions to the same dimensions used for the training data; alternatively, the flattening process of paragraphs 0052-0055 would also read on matching/approximating the curvature of the features to the same curvature achieved in the normalization of the training data).
Regarding claim 10, Kawczynski discloses a non-transitory, computer readable, tangible storage medium storing an OCT image processing program executed by an OCT image processing device (see paragraphs 0241-0242) that processes data of an OCT image of a living tissue taken by an OCT device (see figure 13, eye imaging systems 1305 and paragraph 0188, where the imaging systems can collect OCT images of eyes),
the OCT image processing program, when executed by a control unit of the OCT image processing device, causing the OCT image processing device to perform (see paragraphs 0241-0242).
The remainder of claim 10 recites steps identical to those of claim 1. Therefore, Kawczynski anticipates claim 10 as applied to claim 1 above.
Regarding claim 11, Kawczynski discloses an OCT image processing method for processing data of an OCT image (see figure 1) of a living tissue that is taken by an OCT device, the OCT image processing method comprising (see figure 13, eye imaging systems 1305 and paragraph 0188, where the imaging systems can collect OCT images of eyes).
The remainder of claim 11 recites steps identical to those of claim 1. Therefore, Kawczynski anticipates claim 11 as applied to claim 1 above.
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Kawczynski et al. (U.S. Publ. US-2022/0230300-A1) in view of Lu et al. (U.S. Publ. US-2021/0287365-A1).
Regarding claim 2, Kawczynski fails to disclose the limitations of claim 2. More specifically, Kawczynski doesn't appear to use attached additional information to determine the characteristics of the input OCT image, such as tilt or image dimensions, but instead only appears to manually determine them via image processing in paragraphs 0050-0055; thus, Kawczynski merely fails to disclose that, before the normalization to the characteristics of the training data, the characteristics of the input image are determined from additional data, instead of manual image processing.
Pertaining to the same field of endeavor, Lu discloses wherein additional information indicating the characteristics of the OCT image is attached to the OCT image acquired at the image acquisition step, at the normalization process, the computer program code causes the control unit to: refer to the additional information; and generate the normalized image by approximating at least one of the characteristics of the OCT image indicated by the additional information to the statistical information of the characteristics of the plurality of training images (see paragraph 0213, where input medical images are normalized according to the corresponding DICOM tag / additional information describing the image format).
Kawczynski and Lu are considered analogous art, as they are both directed to deep learning models for tomographic image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Lu into Kawczynski by using additional information to determine the input image characteristics to be normalized because doing so accounts for the variation in image format across different scanner models and manufacturers (see Lu paragraph 0213).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kawczynski et al. (U.S. Publ. US-2022/0230300-A1) in view of Chen et al. (U.S. Publ. US-2024/0233212-A1).
Regarding claim 4, Kawczynski fails to disclose the limitations of claim 4.
Pertaining to the same field of endeavor, Chen discloses wherein the computer program code further causes the control unit to: when the normalized image was generated by adding the background area to the OCT image in the depth direction at the normalization process and the medical information was output by the mathematical model by inputting the normalized image into the mathematical model, perform a removal step of removing information on an area corresponding to the added background area from the medical information (see paragraphs 0004 and 0024-0025, where an input image can be padded to achieve a specific image size required by the model, then the corresponding output image is cropped to remove said padded/background area).
Kawczynski and Chen are considered analogous art, as they are both directed to deep learning models for medical image analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Kawczynski by cropping the padded portions of the output image / medical information because doing so ensures that the output image is the same size as input image (see Chen paragraphs 0024-0025).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kawczynski et al. (U.S. Publ. US-2022/0230300-A1) in view of Hu et al. (U.S. Publ. US-2017/0132793-A1).
Regarding claim 8, Kawczynski fails to disclose the limitations of claim 8.
Pertaining to the same field of endeavor, Hu discloses wherein the computer program code further causes the control unit to: when the normalized image was generated by extracting the at least the part of the tissue-imaged area with the tilt of the layer of the living tissue being reduced and the medical information was output by the mathematical model by inputting the normalized image into the mathematical model, perform a restoration step of restoring an arrangement of the medical information to an original arrangement of the OCT image prior to the tilt of the layer of the living tissue being reduced (first see figure 16, step S141 and paragraphs 0092-0093, where a region of interest of an OCT image is first flattened/tilt-reduced; then see figure 16, steps S142-S145 and paragraphs 0094-0097, where segmentation operations are performed on the flattened image; finally see figure 16, step S146 and paragraphs 0098, 0103-0104, where the output segmented image is finally unflattened/restored to its original configuration/tilt).
Kawczynski and Hu are considered analogous art, as they are both directed to image processing of OCT images of eyes. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Hu into Kawczynski by restoring the output image to the original input image’s tilt because doing so converts the segmentations in the flattened image to those of the unaltered original image (see Hu paragraph 0098).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS JOHN HELCO whose telephone number is (703)756-5539. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached at telephone number 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS JOHN HELCO/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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