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
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record on file.
Information Disclosure Statement(s)
The Information disclosure statements (IDS) filed on January 03rd, 2024 and March 29th, 2024 have been acknowledged and considered by the examiner.
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 1-4 and 6-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tom Brosch et. al. (“US 2020/0410691 A1” hereinafter as “Brosch”).
Regarding claim 1, Brosch discloses a medical image processing device that processes tomographic image data of a living tissue ([0109] discloses the field of relevant radiation therapy for this invention includes tomography images of organs [living tissue]), the device comprising: a control unit that includes at least one processor and at least one memory storing computer program code, the computer program code, when executed by the at least one processor, causing the at least one processor to ([0003] discloses the method for computer processing which includes the use of a computer, which has a processor to execute instructions stored in a memory to carry out the invention ): acquire a tomographic image in which a layer of the living tissue appears ([0036] disclose the invention includes identifying surface [layer] of the surface element of the organ); perform a tilt-reduction process on the acquired tomographic image to reduce a tilt of the layer of the living tissue with respect to a main direction ([0036] discloses the training unit is to displace a surface element and tilt a surface element for modifying the surface element [to reduce the deviation/tilt according to 0093], wherein the deviation is a distance from the respective distance to actual distance and the distance is determined based on a direction according to [0015] which is analogous to the main direction as claimed); and acquire medical data by inputting, into a mathematical model, a tilt-reduced image that is the tomographic image on which the tilt-reduction process was performed ([0092] discloses the image is being input image into the machine learning model to calculate the deviation/tile to modify the surface element to reduce the deviation/tile, using convolutional neural network [mathematical model]), wherein the mathematical model is trained by a machine learning algorithm to output medical data by processing an input image ([0093] discloses the neural network is trained using machine learning on the medical image).
Regarding claim 2, Brosch discloses the medical image processing device according to claim 1, wherein when an image is input, the mathematical model is configured to output, as the medical data, data of a high-quality image with improved quality with respect to the input image (the mathematical model as discussed previously being the convolutional neural network is to output images with confidence value is higher that the deviation is smaller [hence indicates higher quality image output compared to the input image]).
Regarding claim 3, Brosch discloses the medical image processing device according to claim 1, wherein the at least one processor is further caused to restore an arrangement of the medical data to an original arrangement of the medical data prior to the tilt-reduction process being performed by performing ([0107] discloses generation of training data by displacing and tile the surface elements by known amounts for generating the modified training surface models for training, using back propagation, therefore, it can be understood as the training data being generated prior to performing modifying and reducing the tile by the neural network, the synthesizing of training image restore the images into original tiled images for training purpose, therefore, it can be understood as restoring an arrangement to original tiled arrangement by a known amount; since the displacement here is being known being acquired from [0106]), on the acquired medical data output from the mathematical model, an opposite process to the tilt-reduction process (the process as discussed as disclosed in [0107] is an opposite process to the tile reduction process using back propagation).
Regarding claim 4, Brosch discloses the medical image processing device according to claim 1, wherein the at least one processor is further caused to: extract, from the tomographic image, an image region in which the tissue appears ([0015] discloses the organ region is extracted from the tomographic image); and input, into the mathematical model, the image region of the tomographic image on which the tilt-reduction process was performed ([0015-0016] discloses the surface element is obtained, which is used to perform the tilt reduction process of [0092]).
Regarding claim 6, Brosch discloses a non-transitory, computer readable storage medium storing a medical image processing program executed by a medical image processing device that processes tomographic image data of a living tissue, the program, when executed by at least one processor of the medical image processing device, causing the at least one processor to ([0003] discloses the method for computer processing which includes the use of a computer, which has a processor to execute instructions stored in a memory or a RAM/ROM [non-transitory storage medium] to carry out the invention): acquire a tomographic image in which a layer of the living tissue appears ([0036] disclose the invention includes identifying surface [layer] of the surface element of the organ); perform a tilt-reduction process on the acquired tomographic image to reduce a tilt of the layer of the living tissue with respect to a main direction ([0036] discloses the training unit is to displace a surface element and tilt a surface element for modifying the surface element [to reduce the deviation/tilt according to 0093], wherein the deviation is a distance from the respective distance to actual distance and the distance is determined based on a direction according to [0015] which is analogous to the main direction as claimed); and acquire medical data by inputting, into a mathematical model, a tilt-reduced image that is the tomographic image on which the tilt-reduction process was performed ([0092] discloses the image is being input image into the machine learning model to calculate the deviation/tile to modify the surface element to reduce the deviation/tile, using convolutional neural network [mathematical model]), wherein the mathematical model is trained by a machine learning algorithm to output medical data by processing an input image ([0093] discloses the neural network is trained using machine learning on the medical image).
Regarding claim 7, Brosch discloses a medical image processing method implemented by a medical image processing device that processes tomographic image data of a living tissue, the method comprising: ([0003] discloses the method for computer processing which includes the use of a computer, which has a processor to execute instructions stored in a memory to carry out the invention): acquiring a tomographic image in which a layer of the living tissue appears ([0036] disclose the invention includes identifying surface [layer] of the surface element of the organ); performing a tilt-reduction process on the acquired tomographic image to reduce a tilt of the layer of the living tissue with respect to a main direction ([0036] discloses the training unit is to displace a surface element and tilt a surface element for modifying the surface element [to reduce the deviation/tilt according to 0093], wherein the deviation is a distance from the respective distance to actual distance and the distance is determined based on a direction according to [0015] which is analogous to the main direction as claimed); and acquiring medical data by inputting, into a mathematical model, a tilt-reduced image that is the tomographic image on which the tilt-reduction process was performed ([0092] discloses the image is being input image into the machine learning model to calculate the deviation/tile to modify the surface element to reduce the deviation/tile, using convolutional neural network [mathematical model]), wherein the mathematical model is trained by a machine learning algorithm to output medical data by processing an input image ([0093] discloses the neural network is trained using machine learning on the medical image).
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 5 is rejected under 35 U.S.C. 103 as being unpatentable over Tom Brosch et. al. (“US 2020/0410691 A1” hereinafter as “Brosch”) in view of Terry Fritz Helmuth Fritz (Foreign Patent Document “CN 1765330 A” English translation hereinafter as “Fritz”).
Regarding claim 5, Brosch discloses the medical image processing device according to claim 1 (as discussed above in claim 1).
However, Brosch does not explicitly disclose wherein the tomographic image is formed of a plurality of small regions each extending in a direction intersecting the main direction, and the at least one processor is further caused to align positions of the plurality of small regions with each other by moving the plurality of small regions in the direction intersecting the main direction.
In the same field of tissue tomography image tilt correction (page 2, 4th to the last par., and page 3, 2nd par., Fritz) Fritz discloses wherein the tomographic image is formed of a plurality of small regions each extending in a direction intersecting the main direction (page 3, 2nd par., disclose the image for processing includes tomography image, moreover, the image includes different directions according to page 3, 4th par., with different axes and a longitudinal direction extend from the horizontal axis, moreover, the image includes these axes and different regions corresponding to the directions as disclosed in page 3, 4th par., and page 5, 2nd par.), and the at least one processor is further caused to align positions of the plurality of small regions with each other by moving the plurality of small regions in the direction intersecting the main direction (the correction of the tilt of the organ layer based on the image according to page 3, 4th to the last par., includes correcting the mismatch of the image with reference axial direction by matching the image frame according to the reference axial direction by moving the regions or the axes and different corresponding directions and regions such as disclosed in page 16, 3rd to the last par.; the directions crossing each other according to page 3, 2nd to the last par.).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Brosch to perform obtaining of a tomographic image of a living tissue, wherein the tomographic image is formed of a plurality of small regions each extending in a direction intersecting the main direction, and the at least one processor is further caused to align positions of the plurality of small regions with each other by moving the plurality of small regions in the direction intersecting the main direction as taught by Fritz to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to process tissue image correctly with reduced nose and tilt effectively based on directions and intersecting (abstract, and page 2, 4th to the last par., Fritz)
Pertinent Prior Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Annan Li et. al. (“Automated Basal Cell Carcinoma Detection in High-Definition Optical Coherence Tomography, August 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society”) discloses processing of tomographic image (abstract) using deep learning on surface feature extraction of the tissue (Fig. 1) based on image normalization (section II.B) by performing flattening of the surface of the skin surface using the neural network (fig. 1).
Conclusion
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/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673