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
2. 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
3. The information disclosure statement (IDS) submitted on 12/10/2024, 01/21/2025 and 07/30/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. 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.
7. Claim(s) 1- 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Noda et al. (US 2023/0245308 A1) in view of Wang et al. (US 2021/0166383 A1).
8. With reference to claim 1, Noda teaches A medical image display apparatus comprising a hardware processor that acquires a series of medical images including a plurality of medical images, (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “The medical image processing apparatus 30 executes various kinds of processing to be described later using the medical image collected by the medical image diagnosis apparatus 10. For example, the medical image processing apparatus 30 includes an input interface 31, a display 32, a memory 33, and a processing circuit 34, as shown in FIG. 1.” [0026] “In the medical image processing apparatus 30 shown in FIG. 1, each processing function is stored in the memory 33 in a form of a program executable by a computer. The processing circuit 34 is a processor that reads out the program from the memory 33 and executes it, thereby implementing the function corresponding to each program.” [0035]) Noda also teaches performs image analysis on the plurality of medical images to extract a plurality of medical images each including a candidate region, (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the processing circuit 34 reads out a program corresponding to the setting function 34c from the memory 33 and executes it, thereby setting a plurality of candidate regions on a medical image as the candidates of a region of interest to be set on the medical image. Also, for example, the processing circuit 34 reads out a program corresponding to the analysis function 34d from the memory 33 and executes it, thereby executing quantitative analysis concerning the composition of the object P for each of the plurality of candidate regions. In addition, for example, the processing circuit 34 reads out a program corresponding to the output function 34e from the memory 33 and executes it, thereby outputting a plurality of analysis results corresponding to the plurality of candidate regions. Also, for example, the processing circuit 34 reads out a program corresponding to the accepting function 34f from the memory 33 and executes it, thereby accepting, from the user who has referred to the analysis results, an operation of selecting one of the plurality of candidate regions as a region of interest.” [0034] “the medical image processing apparatus 30 sets a region of interest on an X-ray image collected by the medical image diagnosis apparatus 10 and executes quantitative analysis according to the region of interest.” [0038]) Noda further teaches for at least two medical images among the extracted plurality of medical images each including the candidate region, calculates information on the candidate region included in each of the at least two medical images, (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the medical image processing apparatus 30 sets a region of interest on an X-ray image collected by the medical image diagnosis apparatus 10 and executes quantitative analysis according to the region of interest. … the medical image processing apparatus 30 performs bone mineral density measurement as the quantitative analysis, and calculates a Bone Mineral Density (BMD). In this case, in the medical image diagnosis apparatus 10, using a plurality of X-ray energies, an X-ray image corresponding to each X-ray energy is collected. For example, the medical image diagnosis apparatus 10 executes dual energy collection and collects a first X-ray image corresponding to a first X-ray energy and a second X-ray image corresponding to a second X-ray energy.” [0038-0039] “the processing circuit 34 sets a plurality of candidate regions (step S102). Next, the processing circuit 34 executes quantitative analysis (step S103). For example, the processing circuit 34 performs bone mineral density (BMD) measurement for each of the plurality of candidate regions.” [0088] “The setting function 34c sets a plurality of candidate regions on the medical image as the candidates of a region of interest to be set on the medical image. The analysis function 34d executes quantitative analysis concerning the composition of the object P for each of the plurality of candidate regions.” [0091]) Noda teaches based on the information on each of the at least two medical images from each of which the information has been calculated, controls display of information indicating presence of the candidate region about each of the plurality of medical images each including the candidate region. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the processing circuit 34 reads out a program corresponding to the output function 34e from the memory 33 and executes it, thereby outputting a plurality of analysis results corresponding to the plurality of candidate regions. Also, for example, the processing circuit 34 reads out a program corresponding to the accepting function 34f from the memory 33 and executes it, thereby accepting, from the user who has referred to the analysis results, an operation of selecting one of the plurality of candidate regions as a region of interest.” [0034] “the output function 34e displays, on the display 32, the bone density image obtained by the obtaining function 34b, and the user performs an operation of drawing the outline of the candidate region while referring to the bone density image.” [0055] “The output function 34e outputs the analysis result of the analysis function 34d. The output function 34e displays a plurality of analysis results and a plurality of candidate regions in association with each other. For example, the output function 34e displays, on the display 32, the plurality of candidate regions R31 to R33 and calculated BMDs in association with each other.” [0062])
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Noda does not explicitly teach lesion candidate and lesion information. This is what Wang teaches (“a target medical image of a lesion to be detected is obtained.” [0033] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
9. With reference to claim 2, Noda teaches an operation part to specify a medical image to be displayed among the series of medical images; and a display that displays a detection mark as the information indicating the presence. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the output function 34e displays, on the display 32, the bone density image obtained by the obtaining function 34b, and the user performs an operation of drawing the outline of the candidate region while referring to the bone density image. In this case, the setting function 34c may set the region drawn by the user to a candidate region. The output function 34e displays a plurality of candidate regions on the display 32, and the user performs an operation of adjusting the displayed candidate regions. In this case, the setting function 34c sets the regions adjusted by the user to new candidate regions.” [0055] “the output function 34e may detect an abnormal value candidate from the plurality of analysis results. For example, in FIG. 8, the BMD corresponding to the candidate region R44 set at the measurement date/time T3 has a remarkably high value. In this case, the output function 34e detects the BMD for (R44, T3) as an abnormal value candidate.” [0076])
10. With reference to claim 3, Noda teaches the plurality of medical images each including the candidate region includes at least a first medical image including a first candidate region and a second medical image including a second candidate region, and wherein based on at least first information on the first candidate region and second information on the second candidate region, the hardware processor controls display of at least information indicating presence of the first candidate region about the first medical image. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the processing circuit 34 reads out a program corresponding to the setting function 34c from the memory 33 and executes it, thereby setting a plurality of candidate regions on a medical image as the candidates of a region of interest to be set on the medical image … In the medical image processing apparatus 30 shown in FIG. 1, each processing function is stored in the memory 33 in a form of a program executable by a computer. The processing circuit 34 is a processor that reads out the program from the memory 33 and executes it, thereby implementing the function corresponding to each program.” [0034-0035] “in the medical image diagnosis apparatus 10, using a plurality of X-ray energies, an X-ray image corresponding to each X-ray energy is collected. For example, the medical image diagnosis apparatus 10 executes dual energy collection and collects a first X-ray image corresponding to a first X-ray energy and a second X-ray image corresponding to a second X-ray energy.” [0039] “the setting function 34c sets a region R21 of interest corresponding to vertebrae, as shown in FIG. 3A. In addition, the setting function 34c sets a region R22 of interest corresponding to the horizontal projections of the vertebrae and bones other than the vertebrae, as shown in FIG. 3B.” [0044] “the output function 34e displays, on the display 32, the bone density image obtained by the obtaining function 34b, and the user performs an operation of drawing the outline of the candidate region while referring to the bone density image. In this case, the setting function 34c may set the region drawn by the user to a candidate region. The output function 34e displays a plurality of candidate regions on the display 32, and the user performs an operation of adjusting the displayed candidate regions.” [0055])
Noda does not explicitly teach lesion candidate and lesion information. This is what Wang teaches (“a target medical image of a lesion to be detected is obtained.” [0033] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
11. With reference to claim 4, Noda teaches based on the information on each of the at least two medical images from each of which the information has been calculated, the hardware processor controls display of the information indicating the presence of the candidate region about each of the plurality of medical images each including the candidate region. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the processing circuit 34 reads out a program corresponding to the output function 34e from the memory 33 and executes it, thereby outputting a plurality of analysis results corresponding to the plurality of candidate regions. Also, for example, the processing circuit 34 reads out a program corresponding to the accepting function 34f from the memory 33 and executes it, thereby accepting, from the user who has referred to the analysis results, an operation of selecting one of the plurality of candidate regions as a region of interest. …In the medical image processing apparatus 30 shown in FIG. 1, each processing function is stored in the memory 33 in a form of a program executable by a computer. The processing circuit 34 is a processor that reads out the program from the memory 33 and executes it, thereby implementing the function corresponding to each program.” [0034-0035] “the output function 34e displays, on the display 32, the bone density image obtained by the obtaining function 34b, and the user performs an operation of drawing the outline of the candidate region while referring to the bone density image.” [0055] “The output function 34e outputs the analysis result of the analysis function 34d. The output function 34e displays a plurality of analysis results and a plurality of candidate regions in association with each other. For example, the output function 34e displays, on the display 32, the plurality of candidate regions R31 to R33 and calculated BMDs in association with each other.” [0062])
Noda does not explicitly teach lesion candidate and lesion information. This is what Wang teaches (“a target medical image of a lesion to be detected is obtained.” [0033] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
12. With reference to claim 5, Noda teaches based on the information on each of the at least two medical images from each of which the information has been calculated, the hardware processor controls display of the information indicating the presence of the candidate region about each of the plurality of medical images each including the candidate region by making a display form of at least one piece of the information indicating the presence different from a display form of another piece of the information indicating the presence, among pieces of the information indicating the presence of the candidate region about the plurality of medical images each including the candidate region. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the display 32 displays a Graphical User Interface (GUI) configured to accept various kinds of instructions or settings from the user via the input interface 31. For example, the display 32 is a liquid crystal display or a Cathode Ray Tube (CRT) display. The display 32 may be of a desktop type or may be formed by a tablet terminal capable of wirelessly communicating with the main body of the medical image processing apparatus 30.” [0028] “the processing circuit 34 reads out a program corresponding to the output function 34e from the memory 33 and executes it, thereby outputting a plurality of analysis results corresponding to the plurality of candidate regions. Also, for example, the processing circuit 34 reads out a program corresponding to the accepting function 34f from the memory 33 and executes it, thereby accepting, from the user who has referred to the analysis results, an operation of selecting one of the plurality of candidate regions as a region of interest. …In the medical image processing apparatus 30 shown in FIG. 1, each processing function is stored in the memory 33 in a form of a program executable by a computer. The processing circuit 34 is a processor that reads out the program from the memory 33 and executes it, thereby implementing the function corresponding to each program.” [0034-0035] “the output function 34e displays, on the display 32, the bone density image obtained by the obtaining function 34b, and the user performs an operation of drawing the outline of the candidate region while referring to the bone density image.” [0055] “The output function 34e outputs the analysis result of the analysis function 34d. The output function 34e displays a plurality of analysis results and a plurality of candidate regions in association with each other. For example, the output function 34e displays, on the display 32, the plurality of candidate regions R31 to R33 and calculated BMDs in association with each other.” [0062] “the output function 34e may control a projector and project the graph shown in FIG. 6. Alternatively, for example, the output function 34e may transmit the graph shown in FIG. 6 to another apparatus, and the display of the other apparatus may perform display to the user. Alternatively, for example, the output function 34e may output the graph shown in FIG. 6 from a printer and provide it to the user. … The output function 34e may display the BMD calculated based on the candidate region R31, the BMD calculated based on the candidate region R32, and the BMD calculated based on the candidate region R33 as a table or a text in association with the candidate region R31, the candidate region R32, and the candidate region R33, respectively. Also, the output function 34e may further display an image representing a candidate region in association with an analysis result. As an example, the output function 34e displays an image representing the candidate region R31 on the graph in linkage with the plot of the BMD calculated based on the candidate region R31, as shown in FIG. 7. Similarly, the output function 34e displays an image representing the candidate region R32 on the graph in linkage with the plot of the BMD calculated based on the candidate region R32. Similarly, the output function 34e displays an image representing the candidate region R33 on the graph in linkage with the plot of the BMD calculated based on the candidate region R33.” [0069-0071])
Noda does not explicitly teach lesion candidate and lesion information. This is what Wang teaches (“a target medical image of a lesion to be detected is obtained.” [0033] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
13. With reference to claim 6, Noda teaches based on the information on each of the at least two medical images from each of which the information has been calculated, the hardware processor controls display of the information indicating the presence of the candidate region about each of the plurality of medical images each including the candidate region by not displaying at least one piece among pieces of the information indicating the presence of the candidate region about the plurality of medical images each including the candidate region. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the processing circuit 34 reads out a program corresponding to the output function 34e from the memory 33 and executes it, thereby outputting a plurality of analysis results corresponding to the plurality of candidate regions. Also, for example, the processing circuit 34 reads out a program corresponding to the accepting function 34f from the memory 33 and executes it, thereby accepting, from the user who has referred to the analysis results, an operation of selecting one of the plurality of candidate regions as a region of interest. …In the medical image processing apparatus 30 shown in FIG. 1, each processing function is stored in the memory 33 in a form of a program executable by a computer. The processing circuit 34 is a processor that reads out the program from the memory 33 and executes it, thereby implementing the function corresponding to each program.” [0034-0035] “the output function 34e displays, on the display 32, the bone density image obtained by the obtaining function 34b, and the user performs an operation of drawing the outline of the candidate region while referring to the bone density image.” [0055] “The output function 34e outputs the analysis result of the analysis function 34d. The output function 34e displays a plurality of analysis results and a plurality of candidate regions in association with each other. For example, the output function 34e displays, on the display 32, the plurality of candidate regions R31 to R33 and calculated BMDs in association with each other.” [0062] “the accepting function 34f accepts, as the operation of selecting a region of interest, an operation of designating a position on the graph shown in FIG. 6 or a position on characters “R31”, “R32”, or “R33. Also, the accepting function 34f accepts, as the operation of selecting a region of interest, an operation of designating a position on the bone density image shown in FIG. 5. Furthermore, the accepting function 34f accepts, as the operation of selecting a region of interest, an operation of designating a position on an explanatory note shown in FIG. 5. The output function 34e outputs an analysis result corresponding to the selected region of interest. For example, if the candidate region R32 is selected as a region of interest, the output function 34e displays, on the display 32, a report in which the BMD of the candidate region R32 is written.” [0066-0067])
Noda does not explicitly teach lesion candidate and lesion information. This is what Wang teaches (“a target medical image of a lesion to be detected is obtained.” [0033] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
14. With reference to claim 7, Noda teaches the hardware processor makes the display form of the at least one piece of the information indicating the presence of the candidate region and the display form of the another piece thereof by controlling at least one of a size, a color and a design of a mark indicating a position of the candidate region. (“the display 32 displays a Graphical User Interface (GUI) configured to accept various kinds of instructions or settings from the user via the input interface 31. For example, the display 32 is a liquid crystal display or a Cathode Ray Tube (CRT) display. The display 32 may be of a desktop type or may be formed by a tablet terminal capable of wirelessly communicating with the main body of the medical image processing apparatus 30.” [0028] “the processing circuit 34 reads out a program corresponding to the output function 34e from the memory 33 and executes it, thereby outputting a plurality of analysis results corresponding to the plurality of candidate regions. Also, for example, the processing circuit 34 reads out a program corresponding to the accepting function 34f from the memory 33 and executes it, thereby accepting, from the user who has referred to the analysis results, an operation of selecting one of the plurality of candidate regions as a region of interest. …In the medical image processing apparatus 30 shown in FIG. 1, each processing function is stored in the memory 33 in a form of a program executable by a computer. The processing circuit 34 is a processor that reads out the program from the memory 33 and executes it, thereby implementing the function corresponding to each program.” [0034-0035] “the output function 34e displays, on the display 32, the bone density image obtained by the obtaining function 34b, and the user performs an operation of drawing the outline of the candidate region while referring to the bone density image. … the setting function 34c sets a candidate region by enlarging or reducing a region output from the learned model as the estimation result of a region of interest. As an example, the setting function 34c sets a region output from the learned model as the estimation result of a region of interest to the candidate region R32, sets a region obtained by reducing the candidate region R32 to the candidate region R31, and sets a region obtained by enlarging the candidate region R32 to the candidate region R33. … if “110%” is selected, the setting function 34c enlarges the region output from the learned model as the estimation result of a region of interest to the size “110%” while maintaining the shape and the center position.” [0055-0057] “The output function 34e outputs the analysis result of the analysis function 34d. The output function 34e displays a plurality of analysis results and a plurality of candidate regions in association with each other. For example, the output function 34e displays, on the display 32, the plurality of candidate regions R31 to R33 and calculated BMDs in association with each other.” [0062] “the output function 34e may control a projector and project the graph shown in FIG. 6. Alternatively, for example, the output function 34e may transmit the graph shown in FIG. 6 to another apparatus, and the display of the other apparatus may perform display to the user. Alternatively, for example, the output function 34e may output the graph shown in FIG. 6 from a printer and provide it to the user. … The output function 34e may display the BMD calculated based on the candidate region R31, the BMD calculated based on the candidate region R32, and the BMD calculated based on the candidate region R33 as a table or a text in association with the candidate region R31, the candidate region R32, and the candidate region R33, respectively. Also, the output function 34e may further display an image representing a candidate region in association with an analysis result. As an example, the output function 34e displays an image representing the candidate region R31 on the graph in linkage with the plot of the BMD calculated based on the candidate region R31, as shown in FIG. 7. Similarly, the output function 34e displays an image representing the candidate region R32 on the graph in linkage with the plot of the BMD calculated based on the candidate region R32. Similarly, the output function 34e displays an image representing the candidate region R33 on the graph in linkage with the plot of the BMD calculated based on the candidate region R33.” [0069-0071] “the output function 34e may color a portion where the bone density largely changes to highlight that portion.” [0081])
Noda does not explicitly teach lesion candidate. This is what Wang teaches (“a target medical image of a lesion to be detected is obtained.” [0033] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
15. With reference to claim 8, Noda does not explicitly teach the lesion information is information on at least one of a risk degree of a lesion, a certainty factor of the lesion, a size of the lesion, a type of the lesion, a mode of a change in the lesion between past and present, and anatomical structure information on the lesion, calculated from the lesion candidate region. This is what Wang teaches (“a target medical image of a lesion to be detected is obtained.” [0033] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
16. With reference to claim 9, Noda does not explicitly teach the lesion information is lesion information on a thoracic node, and is information on at least one of a size of a lesion, a difference in the lesion between past and present, a type of the lesion, and a site of the lesion. This is what Wang teaches (“first the server may obtain the target medical image of the lesion to be detected. The target medical image may be an Optical Coherence Tomography (OCT) image, a Computerized Tomography (CT) image, etc.” [0034] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Wang teaches the OCT image and CT image can be any part / information of medical image in the body. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
17. With reference to claim 10, Noda does not explicitly teach the lesion information is lesion information on a fracture, and is information on at least one of a site of the fracture and a degree of the fracture. This is what Wang teaches (“first the server may obtain the target medical image of the lesion to be detected. The target medical image may be an Optical Coherence Tomography (OCT) image, a Computerized Tomography (CT) image, etc.” [0034] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Wang teaches the OCT image and CT image can be any part / information of medical image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
18. With reference to claim 11, Noda does not explicitly teach the lesion information is lesion information on a head, and is information on at least one of a type of a lesion and a site of the lesion. This is what Wang teaches (“first the server may obtain the target medical image of the lesion to be detected. The target medical image may be an Optical Coherence Tomography (OCT) image, a Computerized Tomography (CT) image, etc.” [0034] “the target medical image is input into a pre-trained deep learning model to obtain a target sequence output from the deep learning model, each element in the target sequence being a first confidence corresponding to each preset lesion type, the first confidence representing a probability that the target medical image belongs to a corresponding preset lesion type, the deep learning model being obtained by pre-training a medical image sample corresponding to each preset lesion type, and each medical image sample being marked with a lesion type included in the image.” [0036]) Wang teaches the OCT image and CT image can be any part / information of medical image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang into Noda, in order to realize the detection and localization of a lesion in a medical image with a small marking workload.
19. With reference to claim 12, Noda teaches the series of medical images is a plurality of consecutive tomographic images at different slice positions. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the setting function 34c sets a candidate region R31, a candidate region R32, and a candidate region R33 as the candidates of a region of interest corresponding to the vertebra, as shown in FIG. 5.” [0048] “X-ray images such as a bone density image and a mammography image have been described as examples of a medical image. However, the embodiment is not limited to this. For example, application to quantitative analysis using a medical image such as an X-ray CT (Computed Tomography) image, a PET (Positron Emission computed Tomography) image, an SPECT (Single Photon Emission Computed Tomography) image, an ultrasonic image, or an MR (Magnetic Resonance) image is possible.” [0108])
20. With reference to claim 13, Noda teaches the series of medical images is a plurality of temporally consecutive medical images. (“The medical image diagnosis apparatus 10 is an apparatus that performs imaging of an object P and collects a medical image. If the medical image diagnosis apparatus 10 is an X-ray diagnosis apparatus, the medical image diagnosis apparatus 10 irradiates the object P with X-rays and detects the X-rays transmitted through the object P, thereby collecting X-ray images.” [0024] “the setting function 34c inputs a bone density image collected from the object P at the measurement date/time T1 to a learned model and sets a region output from the learned model to the candidate region R41. In addition, the setting function 34c inputs a bone density image collected from the object P at the measurement date/time T2 to the learned model and sets a region output from the learned model to the candidate region R41. Also, the setting function 34c inputs a bone density image collected from the object P at the measurement date/time T3 to the learned model and sets a region output from the learned model to the candidate region R41. That is, the setting function 34c sets the region output from the learned model to the candidate region R41 at each measurement date/time. As an example, the setting function 34c executes morphology processing for a region output from a first learned model, thereby setting the candidate region R42 at each measurement date/time. Also, the setting function 34c sets a region output from a second learned model different from the first learned model to the candidate region R43 at each measurement date/time. … the output function 34e displays a three-axis graph formed from an axis representing the type (ROI TYPE) of the candidate region, an axis representing the value of the BMD, and an axis representing the measurement date/time, as shown in FIG. 8.” [0073-0075])
21. Claim 14 is similar in scope to claim 1, and thus is rejected under similar rationale. Noda additionally teaches A medical image display system (“The medical image processing apparatus 30 executes various kinds of processing to be described later using the medical image collected by the medical image diagnosis apparatus 10. For example, the medical image processing apparatus 30 includes an input interface 31, a display 32, a memory 33, and a processing circuit 34, as shown in FIG. 1.” [0026] “Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s).” [0115])
22. Claim 15 is similar in scope to claim 1, and thus is rejected under similar rationale. Noda additionally teaches A non-transitory computer-readable storage medium storing a program causing a computer to perform (“The medical image processing apparatus 30 executes various kinds of processing to be described later using the medical image collected by the medical image diagnosis apparatus 10. For example, the medical image processing apparatus 30 includes an input interface 31, a display 32, a memory 33, and a processing circuit 34, as shown in FIG. 1.” [0026] “Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s).” [0115])
23. Claim 16 is similar in scope to claim 1, and thus is rejected under similar rationale.
Conclusion
24. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Chin whose telephone number is (571)270-3697. The examiner can normally be reached on Monday-Friday 8:00 AM-4:30 PM.
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/MICHELLE CHIN/
Primary Examiner, Art Unit 2614
Prosecution Insights