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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7-9 and 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites “7. The system according to claim 1, wherein when the processor executes the neural network, the neural network identifies a type of surgical procedure being performed. “ This claim limitation is not clear. The specification discloses a neural network which segments a medical image. There is no disclosure of the imaging being accomplished during a surgical procedure. In fact, the Examiner is unclear how this would even work. In the middle of a surgery is a surgeon supposed to use a user interface to improve the segmentation?? In the middle of a surgery, wouldn’t a surgeon already know what type of surgery they are doing. If this was done pre-operatively, that would mean that no surgical procedure is being performed.
Specification paragraph 44, as filed, recites “The software application stored in the memory 22 may automatically, or may be used to manually, segment the AOI from the 3D model and determine the type or procedure that is required to remove the AOI (e.g., wedge resection, lobectomy, pneumonectomy, segmentectomy, etc.). To this end, it is contemplated that the software application stored on the memory 22 may identify if the AOI or lesion has penetrated a lobe or lobes of the patient's lungs, segments, blood vessels, amongst others. Additionally, the software application determines the size of the lesion, the shape of the lesion, the position of the lesion, and the boundaries of the lesion. Although generally described as being directed to resection of portions of the lung, it is contemplated that the systems and methods described herein may be utilized for many surgical procedures, such as biopsies, etc., and for surgical procedures directed to other anatomical structures, such as the liver, the heart, the spleen, etc.”
Additionally paragraph 44 states that the software application, accomplishes this fact. This claim fails the written description requirement as it fails to disclose HOW a neural network which is programmed to segment an image has the capability to additionally determine the type of treatment.
For purpose of Examination, the Examiner is treating this claim as “determine the type of surgical procedure to perform”.
Claims 8-9 are rejected as dependent upon a rejected claim.
Claim 20 is rejected under similar reasons as claim 7.
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.
Claim(s) 10, 12-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (“Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning”)
Wang discloses 10. A method, comprising:
acquiring image data of an anatomical structure; acquiring information on an area of interest located within image data of the anatomical structure; (Wang, pg. 3, Fig. 1
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, “Image with User-Provided bounding box”)
generating an automatic segmentation of the area of interest from the image data using a neural network; (Wang, Fig. 1 (above), “Initial result” )
receiving information of errors within the automatic segmentation; (Wang, Fig. 1 (above), “Scribbles” )
performing updates to the parameters of the neural network based upon the errors within the automatic segmentation; and (Wang, Fig. 1 (above), “Updated CNN model” )
performing updates to the automatic segmentation based upon the updates to the parameters of the neural network. (Wang, pg. 5 ,Section II.E,
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Wang discloses 12. The method according to claim 10, wherein receiving information of errors within the automatic segmentation includes receiving information of portions of the anatomical structure which should not be included in the automatic segmentation. (Wang, pg. 5, Fig. 3, shows “Foreground” and “Background” scribbles, which indicate that the region should be included with the foreground/background respectively)
Wang discloses 13. The method according to claim 10, wherein receiving information of errors within the automatic segmentation includes receiving information of portions of the anatomical structure which should be included in the automatic segmentation. (Wang, pg. 5, Fig. 3, shows “Foreground” and “Background” scribbles, which indicate that the region should be included with the foreground/background respectively)
Wang discloses 14. The method according to claim 10, wherein performing updates to the parameters of the neural network includes performing updates to only a portion of the parameters of the neural network. (Wang, pg. 5, Section II.D,
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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(s) 1-6, 11, 15-16,18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Top(“Active Learning for Interactive 3D Image Segmentation”).
Wang discloses 1. A system, comprising:
a processor; and a memory, the memory storing a neural network, which when executed by the processor(Wang, pg. 5, Section II.E. “implementation details”; “In the testing stage, the trained CNN models were deployed to a MacBook Pro (OS X 10.9.5) with 16GB RAM, an Intel Core i7 CPU running at 2.5GHz and an NVIDIA GeForce GT 750M GPU.” ):
generating an automatic segmentation of structures in computed tomography (CT) images of an anatomical structure; (Wang, pg. 3, Fig. 1
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, “Initial Result”)
receives an indication of errors within the automatic segmentation; (Wang, Fig. 1 (above), “Scribbles” )
performs updates to the parameters of the neural network based upon the indication of errors; and (Wang, Fig. 1 (above), “Updated CNN model” )
updates the segmentation based upon the updates to the parameters of the neural network. (Wang, pg. 5 ,Section II.E,
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Wang discloses an algorithm for iterative segmentation of medical images, but fails to disclose specific modalities, therefore does not expressly disclose “CT images”
Top discloses iterative segmentation of CT Images (Top, pg. 608, Fig. 1,
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to apply the algorithm of Wang to CT images such as done by Top.
The suggestion/motivation for doing so would have been to improve the segmentation of CT images.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Wang with Top to obtain the invention as specified in claim 1.
Wang in view of Top discloses 2. The system according to claim 1, wherein the CT images of an anatomical structure are CT images of a lung. (Wang, pg. 11, Section IV, “it performed well on previously unseen fetal lungs and maternal kidneys.”)
Wang in view of Top discloses 3. The system according to claim 1, wherein the indication of errors within the automatic segmentation are portions of the anatomical structure which should be included in the automatic segmentation. (Wang, pg. 5, Fig. 3, shows “Foreground” and “Background” scribbles, which indicate that the region should be included with the foreground/background respectively)
Wang in view of Top discloses 4. The system according to claim 1, wherein the indication of errors within the automatic segmentation are portions of the anatomical structure which should not be included in the automatic segmentation. (Wang, pg. 5, Fig. 3, shows “Foreground” and “Background” scribbles, which indicate that the region should be included with the foreground/background respectively)
Wang in view of Top discloses 5. The system according to claim 1, wherein the updates to the automatic segmentation are non-local. (Wang, Fig. 1)
Wang in view of Top discloses 6. The system according to claim 1, wherein performing updates to the parameters of the neural network includes performing updates to only a portion of the parameters of the neural network. (Wang, pg. 5, Section II.D,
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Wang discloses 11. The method according to claim 10, But fails to disclose “wherein acquiring image data includes acquiring computed tomography (CT) image data of the anatomical structure.”
Wang discloses an algorithm for iterative segmentation of medical images, but fails to disclose specific modalities, therefore does not expressly disclose “CT images”
Top discloses iterative segmentation of CT Images (Top, pg. 608, Fig. 1,
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to apply the algorithm of Wang to CT images such as done by Top.
The suggestion/motivation for doing so would have been to improve the segmentation of CT images.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Wang with Top to obtain the invention as specified in claim 11.
Wang discloses 15. A method, comprising:
acquiring computed tomography (CT) image data of an anatomical structure; generating an automatic segmentation of an area of interest from the CT image data using a neural network; (Wang, pg. 3, Fig. 1
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, “Initial Result”)
receiving information of errors within the automatic segmentation; (Wang, Fig. 1 (above), “Scribbles” )
performing updates to a portion of the parameters of the neural network based upon the errors within the automatic segmentation; (Wang, Fig. 1 (above), “Updated CNN model” ) (Wang, pg. 5, Section II.D,
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performing updates to the segmentation based upon the updates to the parameters of the neural network; (Wang, pg. 5 ,Section II.E,
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receiving information of further errors within the updated segmentation; performing further updates to a portion of the updates to the parameters of the neural network based upon the further errors within the updated segmentation; and performing further updates to the updates to the segmentation based upon the further updates to the parameters of the neural network. (Wang, pg. 5 ,Section II.E,
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, “four iterations”)
Wang discloses an algorithm for iterative segmentation of medical images, but fails to disclose specific modalities, therefore does not expressly disclose “CT images”
Top discloses iterative segmentation of CT Images (Top, pg. 608, Fig. 1,
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to apply the algorithm of Wang to CT images such as done by Top.
The suggestion/motivation for doing so would have been to improve the segmentation of CT images.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Wang with Top to obtain the invention as specified in claim 15.
Wang in view of Top discloses 16. The method according to claim 15, wherein acquiring CT image data of an anatomical structure includes acquiring CT image data of the lungs. (Wang, pg. 11, Section IV, “it performed well on previously unseen fetal lungs and maternal kidneys.”)
17. The method according to claim 15, wherein receiving information of errors within the automatic segmentation includes receiving information of portions of the anatomical structure which should not be included in the automatic segmentation. (Wang, pg. 5, Fig. 3, shows “Foreground” and “Background” scribbles, which indicate that the region should be included with the foreground/background respectively)
Wang in view of Top discloses 18. The method according to claim 15, wherein receiving information of errors within the automatic segmentation includes receiving information of portions of the anatomical structure which should be included in the automatic segmentation. (Wang, pg. 5, Fig. 3, shows “Foreground” and “Background” scribbles, which indicate that the region should be included with the foreground/background respectively)
Wang in view of Top discloses 19. The method according to claim 15, further comprising displaying the automatic segmentation on a user interface of a display. (Wang ,Fig. 1)
Claim(s) 7, 9 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Top in further view of Buch (PGPub 20210307841)
Wang in view of Top discloses 7. The system according to claim 1, but does not expressly disclose “wherein when the processor executes the neural network, the neural network identifies a type of surgical procedure being performed.”
Buch discloses “wherein when the processor executes the neural network, the neural network identifies a type of surgical procedure being performed.”(Buch, Abstract,” A method for generating and providing artificial intelligence assisted surgical guidance includes analyzing video images from surgical procedure and training a neural network to identify at least one of anatomical objects, surgical objects, and tissue manipulation in video images. The method includes receiving, by the neural network, a live feed of video images from the surgery. The method further includes classifying, by the neural network, at least one of anatomical objects, surgical objects, and tissue manipulation in the live feed of video images. The method further includes outputting, in real time, surgical guidance based on algorithms created using the classified at least on of anatomical objects, surgical objects, and tissue manipulations in the live feed of video images.”, where the surgical guidance reads on the type)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use the segmented image of Wang in order to identify the type of surgery such as disclosed by Buch.
The suggestion/motivation for doing so would have been to suggest to the surgeon, the best possible surgical guidance.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Wang in view of top with Buch to obtain the invention as specified in claim 7.
Wang in view of Top in view of Buch discloses 9. The system according to claim 7, further including a display associated with the processor and the memory, wherein the neural network, when executed by the processor, displays the automatic segmentation in a user interface. (Wang, pg. 5, Section II.E, “Macbook Pro”; where an interactive segmentation model requires a user interface, additionally see Fig.3)
Wang in view of Top discloses 20. The method according to claim 15, further including receiving information of a type of surgical procedure being performed and performing update to the parameters of the neural network based upon the type of surgical procedure being performed. (See claim 7 in combination with claim 1..)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Top in view of Buch in further view of Koster (PGPub 2021/0142485)
Wang in view of Top in view of Buch discloses 8. The system according to claim 7, Wang in view of Top in view of Buch discloses guidance for any surgery. But doesn’t disclose “wherein the type of surgical procedure being performed is selected from the group consisting of a biopsy of a lesion, a wedge resection of the lungs, a lobectomy of the lungs, a segmentectomy of the lungs, and a pneumonectomy of the lungs. “
Koster discloses “wherein the type of surgical procedure being performed is selected from the group consisting of a biopsy of a lesion, a wedge resection of the lungs, a lobectomy of the lungs, a segmentectomy of the lungs, and a pneumonectomy of the lungs. “ (Koster, paragraphs 26-34, discloses using medical imaging in surgical planning for determining a type of surgery including a wedge resection of the lungs, a lobectomy of the lungs, a segmentectomy of the lungs, and a pneumonectomy of the lungs )
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use any surgery type including those of Koster for the surgery types of Wang in view of Top in view of Buch.
The suggestion/motivation for doing so would have been for the system to work in a wide variety of surgeries.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Wang in view of Top in view of Buch with Koster to obtain the invention as specified in claim 8.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GANDHI THIRUGNANAM/Primary Examiner, Art Unit 2672