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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because under the broadest reasonable interpretation, the term “computer readable medium” is not limited to a non-transitory computer-readable storage medium and therefore encompasses transitory propagating signals. Transitory signals are not a statutory process, machine, manufacture, or composition of matter under 35 U.S.C. § 101. See In re Nuijten, 500 F.3d 1346, 1357, 84 USPQ2d 1495 (Fed. Cir. 2007); see also MPEP § 2106.03. Accordingly, claim 1 is rejected because the claimed “computer readable medium” is not limited to statutory subject matter. Claims 2-7 are rejected for not curing the deficiencies of claim 1.
Further, even if claim 1 were amended or interpreted as being limited to a non-transitory computer-readable storage medium, claim 1 would still be directed to patent-ineligible subject matter under the 2019 Revised Patent Subject Matter Eligibility Guidance.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process of detecting/evaluating a state of resection without significantly more. The claim(s) recite(s) high-level data acquisition and analysis steps (“acquiring,” “detecting,” “providing notification”), generic application of a pre-trained machine learning model without disclosing the model architecture, training methodology, or technical innovation, standard computer implementation (“computer readable medium,” “causes a computer to execute”) without meaningful technological constraints. While the claims involve a machine learning model and medical imaging, the core inventive concept is the abstract idea of analyzing surgical images to determine resection status. The claim language does not tie this analysis to a specific technological improvement in computer functionality, image processing, or surgical technology. The dependent claims 2-7 add only routine notifications, displays, and procedural logic—all conventional extra-solution activity that does not integrate the exception into a practical application.
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.
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(s) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musha et al. herein referred to as [D1] in view of Kitamura et al. herein referred to as [D2], as provided in the IDS.
Claim 1: A computer readable medium storing a program that causes a computer to execute a process for surgery assistance, the process comprising: D1 teaches a computer-aided polyp removal detection system using deep learning algorithms (Abstract; Page 2, first paragraph and col. 2, Section C).
acquiring a surgery image in which a surgical site is imaged during surgery for resecting a resection target; D1 teaches acquiring endoscopic images during polyp resection procedures using dyed resection margins images and dyed and lifted polyps images; polyps are removed via Endoscopic Mucosal Resection (Abstract; Page 1, col. 2, Section II; FIG. 1).
detecting a state of resection of the resection target from the acquired surgery image by using a machine learning model generated by training with training data in which a surgery image is an input and information related to completion of resection is an output; and D1 teaches: “we developed an algorithm that uses endoscopy images to detect polyp removal status” and “classify whether a polyp is completely removed or not” using convolutional neural networks including DenseNet, ResNet, VGG, and MobileNet (Abstract; Page 2, col. 2, Section C; Table I). D1 teaches training with dyed resection margins images (complete resection) and dyed and lifted polyps images (incomplete resection) as training data, with binary classification output using binary cross-entropy loss function (Abstract; Page 1, col. 2, Section II; Page 3, col. 1, Section D).
providing notification about a state of resection of the resection target, wherein D1 teaches the system “assists physicians in diagnosing polyp status” by outputting classification results with 85% sensitivity and 88% precision (Abstract; Table I).
D1 does not explicitly teach:
the information related to completion of resection includes region information of an organ that appears in the surgery image when the resection target is resected. D2 teaches recognizing living tissues such as organs and connective tissue by analyzing operation field images: “A tissue ORG that constitutes the preservation organ 2, a tissue NG that constitutes the resection organ 3, and a loose connective tissue LCT that binds the tissues are included in the operation field” (D2, [0077], [0097]; FIG. 13). D2 teaches generating a recognition image representing the recognized organ regions and displaying this recognition image superimposed on the operation field image (D2, [0111]-[0112], [0165]; FIG. 15, FIG. 18 showing recognition image 53 of loose connective tissue superimposed on operation field with specific color coding). It would have been obvious before the effective filing date to one of ordinary skill in the art to combine D2’s teaching of recognizing and outputting region information of organs in surgical images with D1’s system for detecting resection completion status. Both references operate in the field of computer-aided surgical assistance using machine learning to analyze endoscopic surgery images (D1: Abstract; D2: [0065]-[0066]). D1 acknowledges that “Endoscopic images are tough to examine, making it difficult for medical experts to determine whether polyps have been entirely eliminated” (D1, Page 2, col. 1), and D2 provides a solution of displaying recognized tissue regions to assist medical experts during endoscopic procedures (D2, [0128]-[0129], FIG. 18). The combination would yield the predictable result of providing comprehensive surgical assistance by not only detecting whether resection is complete but also showing the surgeon, which organ regions are now visible after resection, thereby confirming complete removal of the target tissue.
Claims 8 and 9 are rejected for similar reasons as those described in claim 1.
Claim 2: The computer readable medium according to claim 1, wherein the providing of notification about the state of resection of the resection target is providing notification that resection of the resection target is completed, or providing notification of a ratio of a resected part to the resection target or a ratio of an unresected part to the resection target. D1 teaches providing notification that resection is completed by classifying whether a polyp is “completely removed or not” with binary output (D1, Abstract; Table I). D2 teaches calculating and displaying ratios of tissue portions: “the CPU 301 may calculate… a ratio of the pixels recognized as the loose connective tissue among the entirety of pixels in the operation field image as the exposed area” and displaying “indicator 55 indicating the magnitude of an exposed area” (D2, [0125], [0128]-[0129]; FIG. 18).
Claim 3: The computer readable medium according to claim 1, wherein the program further causes the computer to cause the state of resection of the resection target to be displayed on the acquired surgery image. D2 teaches displaying recognition results superimposed on the operation field image during surgery: “the treatment possible region display unit 313 can display a figure indicating the connective tissue 4 in a manner of being superimposed on an image displayed on the sub-monitor 31” and “The CPU 301 displays the generated recognition image in a manner of being superimposed on the operation field image” (D2, [0128]-[0129], [0165]; FIG. 7-9, FIG. 18 showing recognition image 53 and character information 54 superimposed on operation field image).
Claim 4: The computer readable medium according to claim 1, wherein the program further causes the computer to, if resection of the resection target is not completed, provide notification of an unresected site in the resection target. D1 teaches detecting whether resection is incomplete by classifying whether a polyp is “completely removed or not” (D1, Abstract; Table I). D2 teaches recognizing and displaying specific cutting sites: “The CPU 301 inputs the operation field image that is acquired to the learning model 430… to recognize a cutting site in the loose connective tissue” and generates “a recognition image representing a recognition result of the recognized cutting site” displayed on the operation field image (D2, [0164]-[0167]; FIG. 26-28 showing cutting site 53a).
Claim 5: The computer readable medium according to claim 1, wherein the program further causes the computer to, when an operator proceeds from a resection step to a next step of the surgery while resection of the resection target is not completed, notify the operator to return to the resection step. D1 teaches detecting incomplete resection status (D1, Abstract; Table I).
D2 teaches providing step-specific instructions based on detected states: “the image processing device 30 performs operating assistance for an assistant” by giving instructions, with examples showing progression through surgical steps with notifications such as “develop target region and remove looseness” and “no more looseness in target region” (D2, [0188]-[0192]; FIG. 31-33 showing progression of surgical steps with instructions).
Claim 6: The computer readable medium according to claim 1, wherein the program further causes the computer to, when a predetermined time or more has elapsed after the resection step of the surgery is started while resection of the resection target is not completed, provide notification that the resection step of the surgery has been performed for a predetermined time or more. D1 teaches detecting incomplete resection status over time during surgery (D1, Abstract; Table I). D2 teaches continuous monitoring of surgical procedures: “The CPU 301 of the image processing device 30 acquires the operation field image… The CPU 301 executes the following process whenever the operation field image is acquired” (D2, [0123]; FIG. 17).
Claim 7: The computer readable medium according to claim 1, wherein the program further causes the computer to evaluate a procedure of the surgery performed by the operator based on information related to completion of resection that is output in response to an input of the acquired surgery image to the machine learning model. D1 teaches a machine learning model that outputs resection completion information with quantitative performance metrics including 85% sensitivity and 88% precision (D1, Abstract; Table I). D2 teaches evaluating surgical procedures by scoring: “The doctor may confirm the anatomical state from the operation field image, and may determine the score from the viewpoints of an exposed area of the connective tissue, a tense state of the connective tissue, the number of structures such as blood vessels and adipose tissues existing at the periphery of the connective tissue, the degree of damage of a preservation organ, and the like” and “The operation field image stored in association with the score can be used for evaluation of the surgical operation, educational assistance such as training” (D2, [0180], [0183]; FIG. 29-33).
Conclusion
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/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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