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 .
Status of Claims
Pending Claims: 1, 7, 8, 12, 16 and 20 – 25.
Canceled Claims: 2 – 6, 9 – 11 13 – 15 and 17 – 19.
Amended Claims: 1, 7, 8, 12 and 16.
New Claims: 20 – 25
Response to Arguments
Applicant's arguments filed January 21, 2025 with respect to claims 1, 7, 8, 12, 16 and 20 – 25 have been considered but are moot because the new grounds of rejection necessitated by applicant’s amendment.
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.
Claims 1, 12, 16 and 20 – 22 are rejected under 35 U.S.C 103 as being unpatentable over Shiba Patent Application Publication No. WO-2020026535-A1 (hereinafter Shiba) in view of Li US Patent Application Publication No. US-20200134825-A1 (hereinafter Li) and further in view of Wang Patent Application No. WO-2017020580-A1 (hereinafter Wang).
Regarding claim 1, Shiba discloses a medical image processing device that is configured to process data of a three-dimensional image of a biological tissue, the medical image processing device comprising a controller configured to: acquire, as an image acquisition step (Shiba in [Page – 14, Paragraph – 4] discloses, “the CPU 23 acquires a three-dimensional tomographic image of the tissue of the subject's eye (the fundus in the first embodiment) (S11)).
Shiba doesn’t disclose about the following limitation as further recited in the claim.
Wang discloses a three-dimensional image of a tissue including a plurality of two-dimensional images which have a first two-dimensional image including a first tissue image area and a first non-tissue image area; extract, as an extraction step, the first tissue image area from the first two-dimensional image (Wang in [Page – 5; Last Paragraph] discloses, “the method of three-dimensional reconstruction comprising: S101. drawing the edges of the medical tissue in a slice image whose edges are clear in a set of slice images of the medical tissue. Where a slice image refers to a two-dimensional sequence of digital tomography images within a human body and its internal organs obtained using a computer imaging device” wherein capturing image of different internal organ equates to capturing tissue and non-tissue image. Wang in [Page – 3; Paragraph - 5] discloses about extracting, “extracting an equivalent surface of the rendered first slice image and the second slice image for spatially adjacent first slice images and second slice images, generating a display panel comprising: a display panel having a first surface and a second surface”).
It would have been obvious to one with one having an ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Wang into the system of Shiba because it would allow the system to observe structure across multiple slices and improve detection accuracy of structure.
Shiba and Wang in the combination doesn’t disclose about the following limitation as further recited in the claim.
Li discloses acquire, as a first structure detection step, a detection result of a specific structure of the tissue in the extracted first tissue image area by inputting the first tissue image area into a mathematical model that is trained by a machine learning algorithm to output a detection result of a specific structure that is shown in an image input into the mathematical model (Li in [0043] discloses, “As a multi-stage extraction method, for example, the structure extraction unit 230 first applies a learning model learned by multi-scale learning data to a plane obtained from image data, thereby obtaining a structure included in the plane. Subsequently, the structure extraction unit 230 generates an image obtained by cutting out a region including the detected structure from the plane, and applies the learning model again”).
It would have been obvious to one with one having an ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Li into the system of Shiba in view of Wang because by applying the mathematical model only to a specific region instead of the entire dataset the system reduces the amount of data need to be processed.
Summary of Citations (Wang)
[Page – 3; Paragraph - 5]; “extracting an equivalent surface of the rendered first slice image and the second slice image for spatially adjacent first slice images and second slice images, generating a display panel comprising: a display panel having a first surface and a second surface”.
[Page – 5; Last Paragraph]; “the method of three-dimensional reconstruction comprising: S101. drawing the edges of the medical tissue in a slice image whose edges are clear in a set of slice images of the medical tissue. Where a slice image refers to a two-dimensional sequence of digital tomography images within a human body and its internal organs obtained using a computer imaging device”.
Summary of Citations (Shiba)
[Page – 3, Paragraph – 1]; “FIG. 3 is a diagram schematically showing a relationship between a two-dimensional tomographic image 40 input to a mathematical model and one-dimensional areas A1 to AN in the two-dimensional tomographic image 40”.
[Page – 14, Paragraph – 4]; “the CPU 23 acquires a three-dimensional tomographic image of the tissue of the subject's eye (the fundus in the first embodiment) (S11). The three-dimensional tomographic image is captured by the ophthalmologic image capturing device 11B and acquired by the ophthalmologic image processing device 21”.
[Page – 14, Paragraph – 5]; “The CPU 23 extracts a T-th (the initial value of T is “1”) two-dimensional tomographic image from a plurality of two-dimensional tomographic images constituting the acquired three-dimensional tomographic image (S12). FIG. 6 shows an example of the two-dimensional tomographic image 40”.
Summary of Citations (Li)
Paragraph [0043]; “As a multi-stage extraction method, for example, the structure extraction unit 230 first applies a learning model learned by multi-scale learning data to a plane obtained from image data, thereby obtaining a structure included in the plane. Subsequently, the structure extraction unit 230 generates an image obtained by cutting out a region including the detected structure from the plane, and applies the learning model again”.
Regarding claim 12, is a non-transitory computer readable storage medium claim
corresponds to apparatus claim 1. Therefore, the rejection analysis of claim 1 is applied in claim 12.
Regarding claim 16, method claim 16 corresponds to apparatus claim 1. Therefore, the rejection analysis and motivation to combine of claim 1 is applicable to claim 16.
Regarding claim 20, Wang in the combination discloses the medical image processing device according to claim 1, wherein the plurality of two-dimensional images further includes a second two-dimensional image including a second tissue image area and a second non-tissue image area, the controller is further configured to: extract the second tissue image area from the second two-dimensional image (Wang in [Page – 18, Paragraph – 4] discloses, “extracting the equivalent surfaces of spatially adjacent slice images in the filled slice image two and the third slice image to generate a display panel, respectively; splicing the display panel generated from the slice image one and the slice image two with the equivalent surfaces of the display panel generated from the slice image two and the third slice image to obtain a three-dimensional image of the medical tissue. Alternatively, the edges of said medical tissue in each slice image in a set of slice images of said medical tissue that are clearly defined by all edges”. Furthermore, Wang in [Page – 5; Last Paragraph] discloses about capturing image of different internal organ which equates to capturing tissue and non-tissue image); and acquire a detection result of the specific structure of the tissue in the extracted second tissue image area by inputting the second tissue image area into the mathematical model (Wang in [Page – 10; Last Paragraph] discloses, “A three dimensional visualization result of the structure information corresponding to the spatial information of the tissue of the slice image in the human body can be accurately and rapidly presented by spatially connecting the two-dimensional slice image through a three-dimensional reconstruction method by extracting an equivalent surface of the two-dimensional slice image”. Additionally, Wang in [0002] discloses about a mathematical model).
Summary of Citations (Wang)
Paragraph [0002]; “Three-dimensional reconstruction refers to the creation of mathematical models for three-dimensional objects suitable for computer representation and processing”.
[Page – 5; Last Paragraph]; “the method of three-dimensional reconstruction comprising: S101. drawing the edges of the medical tissue in a slice image whose edges are clear in a set of slice images of the medical tissue. Where a slice image refers to a two-dimensional sequence of digital tomography images within a human body and its internal organs obtained using a computer imaging device”.
[Page – 10; Last Paragraph]; “A three dimensional visualization result of the structure information corresponding to the spatial information of the tissue of the slice image in the human body can be accurately and rapidly presented by spatially connecting the two-dimensional slice image through a three-dimensional reconstruction method by extracting an equivalent surface of the two-dimensional slice image”.
[Page – 18, Paragraph – 4&5]; “extracting the equivalent surfaces of spatially adjacent slice images in the filled slice image two and the third slice image to generate a display panel, respectively; splicing the display panel generated from the slice image one and the slice image two with the equivalent surfaces of the display panel generated from the slice image two and the third slice image to obtain a three-dimensional image of the medical tissue. Alternatively, the edges of said medical tissue in each slice image in a set of slice images of said medical tissue that are clearly defined by all edges”.
Regarding claim 21, is a non-transitory computer readable storage medium claim
corresponds to apparatus claim 20. Therefore, the rejection analysis and motivation to combine of claim 20 is applied in claim 21.
Regarding claim 22, method claim 22 corresponds to apparatus claim 20. Therefore, the rejection analysis and motivation to combine of claim 20 is applicable to claim 22.
Claims 7, 8, 23 and 24 are rejected under 35 U.S.C 103 as being unpatentable over Shiba in view of Li and Wang and further in view of Iwase US Patent Application Publication No. US-20210158525-A1 (hereinafter Iwase).
Regarding claim 7, Li in the combination discloses the medical image processing device according to claim 1, and in the first structure detection step, input, into the mathematical model (Li in Paragraph [0059] discloses, “f or multi-stage extraction, the structure detection unit 232 cuts out the region of the structure detected in step S303 from the plane and inputs the region again as an input image of the learning model (S304 and S305)).
Shiba, Li and Wang in the combination doesn’t disclose about the following limitation as further recited in the claim.
Iwase discloses the controller is further configured to: align, as a two-dimensional image internal alignment step, tissue images between a plurality of rows of pixels that constitute each of the plurality of two-dimensional images (Iwase in [0130] discloses, “the tomographic images and boundary images for a plurality of arbitrary XZ cross-sections in the XY surface can be learned in advance ... the volume data in which alignment among a plurality of adjacent tomographic images is performed can be used for training data”. Moreover, Iwase in [0103] discloses that the topographic image is 2D); the first tissue image area having a rectangular shape that is subject to the two-dimensional image internal alignment step and the extraction step (Iwase in [0191] discloses, “the square region size at the time of learning can be made constant by normalizing the scanning range (imaging angle of view) and the scanning density (number of A scans) to align the image size”. Additionally, Iwase in [0424] discloses, “The analyzing unit 2806 can, for example, calculate changes in the shape of tissue included in the retina layers or a layer thickness or the like by the image analysis processing”. ).
It would have been obvious to one with one having an ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Iwase into the system of Shiba in view of Li and Wang because by inputting the model an aligned and standardized region the system increases reliability in detecting boundaries and tissue structure.
Summary of Citations (Li)
Paragraph [0059]; “f or multi-stage extraction, the structure detection unit 232 cuts out the region of the structure detected in step S303 from the plane and inputs the region again as an input image of the learning model (S304 and S305)”.
Summary of Citations (Iwase)
Paragraph [0103]; “performing the A scan in an arbitrary transverse direction (main scanning direction) a plurality of times to obtain two-dimensional tomographic information in the transverse direction and the depth direction of the eye to be examined is called the B scan”.
Paragraph [0130]; “the tomographic images and boundary images for a plurality of arbitrary XZ cross-sections in the XY surface can be learned in advance, so as to be able to correspond to cross-sections imaged by various different scan patterns, such as raster scan and radial scan. For example, when using the data of tomographic images, etc., in which the retina is three-dimensionally imaged by raster scan, the volume data in which alignment among a plurality of adjacent tomographic images is performed can be used for training data”.
Paragraph [0191]; “the square region size at the time of learning can be made constant by normalizing the scanning range (imaging angle of view) and the scanning density (number of A scans) to align the image size”.
Paragraph [0424]; “The analyzing unit 2806 can, for example, calculate changes in the shape of tissue included in the retina layers or a layer thickness or the like by the image analysis processing”.
Regarding claim 8, the combination of Shiba, Wang and Li as a whole teaches claim 1 but fails to teach the further limitations as recited in claim 8. Iwase teaches claim 8 for the same grounds of rejection and motivation established in the Non-Final Office Action of 09/11/2025.
Regarding claim 23, is a non-transitory computer readable storage medium claim
corresponds to apparatus claim 7. Therefore, the rejection analysis and motivation to combine of claim 7 is applied in claim 23.
Regarding claim 24, method claim 24 corresponds to apparatus claim 7. Therefore, the rejection analysis and motivation to combine of claim 7 is applicable to claim 24.
Claim 25 is rejected under 35 U.S.C 103 as being unpatentable over Wang in view of Li.
Regarding claim 25, Wang discloses a medical image processing device that is configured to process data of a three- dimensional image of a biological tissue, the medical image processing device comprising a controller configured to: acquire a three-dimensional image of a tissue including a plurality of two- dimensional images that have a first two-dimensional image including a first tissue image area in which a first tissue appears and a second tissue image area in which a second tissue adjacent to the first tissue appears; extract the first tissue image area from the first two-dimensional image; (Wang in [Page – 5; Last Paragraph] discloses about three dimensional image of tissue including plurality of 2D image, “the method of three-dimensional reconstruction comprising: S101. drawing the edges of the medical tissue in a slice image whose edges are clear in a set of slice images of the medical tissue. Where a slice image refers to a two-dimensional sequence of digital tomography images within a human body and its internal organs obtained using a computer imaging device”. Additionally, Wang in [Page – 3; Paragraph - 5] second tissue adjacent to the first tissue appears, “extracting an equivalent surface of the rendered first slice image and the second slice image for spatially adjacent first slice images and second slice images, generating a display panel comprising: a display panel having a first surface and a second surface”); input the first tissue image area into a mathematical model, which is trained by a machine learning algorithm to output a detection result of a specific structure of the tissue, to acquire a first specific structure in the first tissue image area; and acquire a second specific structure in the second tissue image area based on the first specific structure acquired from the (Wang in [Page – 11; Paragraph – 3] discloses, “render a three-dimensional visualization result corresponding to the actual structural information and spatial information of the slice image tissue in the human body, Addressing the bottlenecks in which conventional three-dimensional reconstruction methods fail to quickly and accurately process human tissue from presenting intermittent display or plaque slice images provides important assistance to physicians in understanding the structural information of such tissue and the relationship between tissue and surrounding tissue” wherein determining surrounding tissue structure and structure of multiple slice image tissue implies to determining structure of first tissue and specific structure in the second tissue image area based on the first specific structure.
Wang doesn’t disclose about the following limitation as further recited in the claim.
Li discloses machine learning algorithm Li in [0043] discloses, “As a multi-stage extraction method, for example, the structure extraction unit 230 first applies a learning model learned by multi-scale learning data to a plane obtained from image data, thereby obtaining a structure included in the plane. Subsequently, the structure extraction unit 230 generates an image obtained by cutting out a region including the detected structure from the plane, and applies the learning model again”).
It would have been obvious to one with one having an ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Li into the system of Wang because it would the system to perform more precise and reliable detection of tissue structure.
Summary of Citations (Wang)
[Page – 3; Paragraph - 5]; “extracting an equivalent surface of the rendered first slice image and the second slice image for spatially adjacent first slice images and second slice images, generating a display panel comprising: a display panel having a first surface and a second surface”.
[Page – 5; Last Paragraph]; “the method of three-dimensional reconstruction comprising: S101. drawing the edges of the medical tissue in a slice image whose edges are clear in a set of slice images of the medical tissue. Where a slice image refers to a two-dimensional sequence of digital tomography images within a human body and its internal organs obtained using a computer imaging device”.
[Page – 11; Paragraph – 3]; “render a three-dimensional visualization result corresponding to the actual structural information and spatial information of the slice image tissue in the human body, Addressing the bottlenecks in which conventional three-dimensional reconstruction methods fail to quickly and accurately process human tissue from presenting intermittent display or plaque slice images provides important assistance to physicians in understanding the structural information of such tissue and the relationship between tissue and surrounding tissue”.
Summary of Citations (Li)
Paragraph [0043]; “As a multi-stage extraction method, for example, the structure extraction unit 230 first applies a learning model learned by multi-scale learning data to a plane obtained from image data, thereby obtaining a structure included in the plane. Subsequently, the structure extraction unit 230 generates an image obtained by cutting out a region including the detected structure from the plane, and applies the learning model again”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684. The examiner can normally be reached M-F 8 am - 5 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on (571)272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
04/26/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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