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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 4, 9-10, 14, and 19-20 are objected to because of the following informalities:
Claim 4 should be amended to recite, “…classifies[[, manages, and uses]] the plurality of arthroscopic images into a first group for a patient having no re-rupture [[and]] or a second group for a patient having re-rupture.”
Claim 9 recites acronym “AUC” without first providing the full phrase/term for AUC.
Claim 10, line 2 should be amended to recite, “…uses a J statistic[[s]] to calculate…”.
Claim 14 should be amended to recite, “…classifying[[, managing, and using]] the plurality of arthroscopic images into a first group for a patient having no re-rupture [[and]] or a second group for a patient having re-rupture.”
Claim 19 recites acronym “AUC” without first providing the full phrase/term for AUC.
Claim 20, line 4 should be amended to recite, “…using a J statistic[[s]] to calculate…”.
Appropriate correction is respectfully requested.
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.
Claims 5-7, 10, 15-17, and 20 are 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.
Regarding claims 5 and 15, the limitation of “labels at least one of whether the at least one region specified is re-ruptured or a time point at which the at least one region specified is re-ruptured” renders the claim unclear. Specifically, it is unclear whether the time point is referring to a predicted time of when the tendon was/will rupture, or if it is a time stamp designating when it was determined that the tendon had ruptured based on the image analysis. In order to further advance prosecution, Examiner is interpreting the time point to be a prediction of when the tendon was previously ruptured or when the tendon will likely rupture. Dependent claims inherit the same deficiencies.
Regarding claims 10 and 20, it is unclear if the F1 score is being calculated using the negative predictive value and the positive predictive value or if the F1 score is being calculated using a different sensitivity value and the positive predictive value. If the F1 score is being calculated using a different value than the negative predictive value, it is unclear how that sensitivity value is being calculated. In order to further advance prosecution, Examiner is interpreting the claim as requiring a different sensitivity value other than the negative predictive rate to calculate the F1 score as claimed.
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, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of predicting a probability of a re-rupture of a tendon) without significantly more.
Step 1
The claimed invention in claims 1, 3-11, and 13-20 are directed to statutory subject matter as the claims recite a method/system for predicting a probability of a re-rupture of a tendon.
Step 2A, Prong One
Regarding claims 1, 3-11, and 13-20, the recited steps are directed to mental processes of performing concepts in a human mind or by a human using a pen and paper (See MPEP 2106.05(a)(2) subsection (III)).
Regarding claims 1 and 11, the limitations of “performing a pre-processing operation…”, “predicting a probability…”, “generating prediction information…”, and “training an original model…” are a process, as drafted, that can be performed by a human mind (including an observation, evaluation, and judgment) under the broadest reasonable interpretation but for the recitation of generic computer components. For example, these limitations recited in claims 1 and 11 are nothing more than a medical professional gathering data, analyzing it, generating a prediction based on the analysis, and training another medical professional using the gathered data.
Step 2A, Prong Two
For claims 1, 3-11, and 13-20, the judicial exception is not integrated into a practical application. For claims 1 and 11, the additional limitation of “a communication module”, “a storage module”, “a control module”, and “a training module” are recited at a high level of generality and amount to nothing more than parts of a generic computer. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into a practical application.
Further, the limitations of “acquiring at least one arthroscopic image” and “collecting a plurality of arthroscopic images” amount to nothing more than the pre-solution activity of data gathering (MPEP 2106.05(g)).
Step 2B
The claims do not include additional elements that are sufficient enough to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “acquiring at least one arthroscopic image” and “collecting a plurality of arthroscopic images” are directed towards insignificant extra-solution activities which do not amount to an inventive concept. In addition, an “acquiring module” is recited at a high level of generality and considered to be well known, routine, and conventional in the art. For examples see Kumar (WO 2021/11516) [0078] and Park (US 2021/0100530) [0036].
Dependent claims 4-10 and 14-20 are further directed towards the abstract idea. The above mentioned claims do not introduce any additional elements which amount to significantly more under the Step 2A prong 2 and Step 2B analyses.
Dependent claims 3 and 13 are further directed towards insignificant extra-solution activities (MPEP 2106.05(g)). The above mentioned claims do not introduce any additional elements which amount to significantly more under the Step 2A prong 2 and Step 2B analyses.
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, 3-7, 11, and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (WO 2021/211516) in view of Park (US 2021/0100530) and further in view of Watanabe et al (US 2023/0111852) hereinafter Watanabe.
Regarding claims 1 and 11, Kumar discloses an apparatus and method for predicting re-rupture of a tendon based on artificial intelligence (AI) [0061], the apparatus comprising:
a communication module (media connector) configured to make communication with an external device ([0061] CPU or GPU may be connected to the plurality of interfaces via a media connector (e.g., an HDMI cable, a DVI connector));
an acquiring module (CCU interface 111) configured to acquire at least one arthroscopic image including a surgical portion of a patient experiencing a surgery [0069];
a storage module (AI inferencing pipeline (VAIP) 105) configured to store at least one process based on the AI ([0069] VAIP 105 supports the AI and CV modules);
a control module (GPU 107) configured to perform an operation for predicting the re-rupture of the tendon based on the AI, through the at least one process ([0069] execution of the AI algorithms on a GPU 107).
Kumar fails to disclose wherein the control module is configured to: perform a pre-processing operation for the at least one arthroscopic image, predict a probability of the re-rupture of the tendon by inputting the at least one arthroscopic image, which is pre-processed, into a pre-trained model based on the AI, and generate prediction information for the patient based on a prediction result; and
a training module configured to: collect and pre-process a plurality of arthroscopic images for each of different patients for a preset period, and train an original model by inputting the plurality of arthroscopic images, which are preprocessed, as learning data to implement the pre-trained model.
Park discloses a control module ([0020] system controller 116) configured to: perform a pre-processing operation for at least one image ([0037] At 215, method 200 may include generating an ultrasound image depicting the anatomical feature (e.g., tendon) from the ultrasound imaging data),
predict a probability of the re-rupture of the tendon by inputting the at least one image, which is pre-processed, into a pre-trained model based on the AI ([0042] Returning to 220, if the most similar sample image to the generated ultrasound image is determined via the trained neural network, method 200 may proceed to 230 to determine a degree of damage), and
generate prediction information for the patient based on a prediction result ([0044] an empty bar may indicate no tendon damage, a partially filled bar may indicate a partial rupture, and a completely filled bar may indicate a complete rupture).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kumar with a control module configured to: perform a pre-processing operation for the at least one arthroscopic image, predict a probability of the re-rupture of the tendon by inputting the at least one arthroscopic image, which is pre-processed, into a pre-trained model based on the AI, and generate prediction information for the patient based on a prediction result as taught by Park. Such a modification would provide the predictable results of determining a diagnosis recommendation for the anatomical feature based on the determined degree of damage (Park, [0046]).
Watanabe discloses a training module ([0040] controller 2) configured to: collect and pre-process a plurality of images for each of different patients for a preset period ([0072] first learning data 32 may be time series data in which the patient images acquired from the plurality of individual patients at a plurality of points in time in the past are associated with information regarding the joint-related symptoms at the points in time when the patient images were taken), and
train an original model by inputting the plurality of images, which are preprocessed, as learning data to implement the pre-trained model ([0070] first learning data 32 is data used for machine learning for generating the first prediction model).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kumar with a training module configured to: collect and pre-process a plurality of arthroscopic images for each of different patients for a preset period, and train an original model by inputting the plurality of arthroscopic images, which are preprocessed, as learning data to implement the pre-trained model as taught by Watanabe. Such a modification would provide the predictable results of presenting reliable information regarding the onset of the disease or the progression stage of the joint-related symptoms of the subject (Watanabe, [0017]).
Regarding claims 3 and 13, the modified Kumar discloses the system/method of claims 1 and 11 as discussed above, but fails to disclose wherein the plurality of arthroscopic images are taken and collected with respect to a relevant patient at mutually different time points, for the preset period, and include a time-series change in a surgical site of the relevant patient.
However, Watanabe discloses wherein the plurality of arthroscopic images are taken and collected with respect to a relevant patient at mutually different time points, for the preset period, and include a time-series change in a surgical site of the relevant patient ([0072] first learning data 32 may contain parameters representing features extracted from information of periarticular alignment, articular cartilage thickness, osteophyte formation, presence or absence of synovitis, KL classification, articular range of motion, degree of pain, degree of joint stiffness, presence or absence of claudication, and the like of the patient at a certain point in time and one year after the certain point in time). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kumar with the plurality of arthroscopic images are taken and collected with respect to a relevant patient at mutually different time points, for the preset period, and include a time-series change in a surgical site of the relevant patient as taught by Watanabe. Such a modification would provide the predictable results of training the neural network to provide reliable information regarding the onset of the disease or the progression stage of the joint-related symptoms of the subject (Watanabe, [0017]).
Regarding claims 4 and 14, the modified Kumar discloses the system of claim 3 as discussed above, but fails to disclose wherein the training module classifies, manages, and uses the plurality of arthroscopic images into a first group for a patient having no re-rupture and a second group having re-rupture. However, Park discloses a training module classifying a plurality of images into a first group of patients having a re-rupture or a second group of patients not having a re-rupture ([0039-0040] the trained NN may pair a corresponding one of one or more predetermined image aspects of the sample anatomical feature depicted by the most similar sample image to each of the one or more image aspects of the anatomical feature depicted by the generated ultrasound image. The one or more aspects may include one or more tendon features and the sample images may depict a non-damaged shoulder and a partial or complete rupture. Each of the one or more identified image aspects may be labelled on the generated ultrasound image; Fig. 2 shows this process repeating with each image; Examiner notes that the system/method disclosed by park of determining a rupture would also determine a re-rupture).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kumar with a training module classifying a plurality of images into a first group of patients having a re-rupture or a second group of patients not having a re-rupture as taught by Park. Such a modification would provide the predictable results of determining a degree of damage of the anatomical feature depicted by the ultrasound image based on the most similar image (Park, [0042]).
Regarding claims 5 and 15, the modified Kumar discloses the system/method of claims 4 and 14 as discussed above, but fail to disclose wherein the training module specifies at least one region in each of the plurality of arthroscopic images when pre-processing the plurality of arthroscopic images, and labels at least one of whether the at least one region specified is re-ruptured or a time point at which the at least one region specified is re-ruptured. However, Park discloses a training module specifies at least one region in each of the plurality of images when pre-processing the plurality of images ([0037] At 215, method 200 may include generating an ultrasound image depicting the anatomical feature (e.g., tendon) from the ultrasound imaging data), and labels at least one of whether the at least one region specified is re-ruptured or a time point at which the at least one region specified is re-ruptured ([0042-0044] a degree of damage of 100% may indicate a tendon having a complete rupture; the indication of the determined degree of damage may be a visual indicator).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kumar with a training module specifies at least one region in each of the plurality of arthroscopic images when pre-processing the plurality of arthroscopic images, and labels at least one of whether the at least one region specified is re-ruptured or a time point at which the at least one region specified is re-ruptured as taught by Park. Such a modification would provide the predictable results of determining a degree of damage of the anatomical feature depicted by the ultrasound image based on the most similar image (Park, [0042]).
Regarding claims 6 and 16, the modified Kumar discloses the system/method of claims 5 and 15 as discussed above, but fails to disclose wherein the prediction information includes: a region to be predicted to be re-ruptured with respect to the patient, a tendon state for each region, a probability of the re-rupture, and timing predicted to be re-ruptured.
However, Park discloses wherein the prediction information includes: a region to be predicted to be re-ruptured with respect to the patient ([0042] determine a degree of damage of the anatomical feature depicted by the generated ultrasound image based on the most similar sample image),
a tendon state for each region ([0042] the anatomical feature may be a tendon in a shoulder with a degree of damage from 0%-100%),
a probability of the re-rupture ([0042] a degree of damage of 50% (or any value greater than 0% and less than 100%) may indicate a tendon having a partial rupture, and a degree of damage of 100% may indicate a tendon having a complete rupture), and
timing predicted to be re-ruptured ([0046] diagnosis recommendation may include diagnosing a tendon in a shoulder of a patient based on the determined degree of damage; Examiner notes that a tendon with 75% degree of damage would be predicted to rupture prior to a tendon with 0% damage).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kumar with the prediction information includes: a region to be predicted to be re-ruptured with respect to the patient, a tendon state for each region, a probability of the re-rupture, and timing predicted to be re-ruptured as taught by Park. Such a modification would provide the predictable results of providing a recommendation based off of the determined degree of damage in order to prevent a complete rupture (Park, [0046]).
Regarding claims 7 and 17, the modified Kumar discloses the system/method of claims 6 and 16 as discussed above, but fails to disclose wherein the control module performs a categorizing operation depending on the probability of the re-rupture of the patient. However, Park discloses wherein the control module ([0020] system controller 116) performs a categorizing operation (diagnosis recommendation) depending on the probability of the re-rupture of the patient ([0046] diagnosis recommendation may include diagnosing a tendon in a shoulder of a patient based on the determined degree of damage; Examiner notes that there is a direct relationship between a degree of damage and a probability of re-rupture). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Kumar with the control module performs a categorizing operation depending on the probability of the re-rupture of the patient as taught by Park. Such a modification would provide the predictable results of providing a recommendation based off of the determined degree of damage in order to prevent a complete rupture (Park, [0046]).
Claim(s) 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (WO 2021/211516) in view of Park (US 2021/0100530) and Watanabe (US 2023/0111852) and further in view of Thubagere Jagadeesh et al (US 2021/0174965) hereinafter Thubagere Jagadeesh.
Regarding claims 8 and 18 , the modified Kumar discloses the system/method of claims 1 and 11 as discussed above, but fails to disclose wherein the training module removes at least one layer from the original model, finely adjusts a parameter using an additional layer, and performs the training in a preset number of times using average square root propagation (RMSProp) at a preset speed. However, Thubagere Jagadeesh discloses the training module (machine learning model 204) removes at least one layer from the original model, finely adjusts a parameter using an additional layer, and performs the training in a preset number of times using average square root propagation (RMSProp) at a preset speed [0030].
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the method/system as taught by Kumar with the training module removes at least one layer from the original model, finely adjusts a parameter using an additional layer, and performs the training in a preset number of times using average square root propagation (RMSProp) at a preset speed as taught by Thubagere Jagadeesh. Such a modification would provide the predictable results of optimizing the training of the network (Thubagere Jagadeesh, [0030]).
Claim(s) 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (WO 2021/211516) in view of Park (US 2021/0100530) and Watanabe (US 2023/0111852) and Thubagere Jagadeesh (US 2021/0174965) and further in view of Shino (US 2023/0162351).
Regarding claims 9 and 19, the modified Kumar discloses the system/method of claims 1 and 11 as discussed above, but fails to disclose wherein the training module evaluates and verifies performance by using predictive accuracy, F1 score, AUC, sensitivity, and specificity with respect to the pre-trained model. However, Shino discloses the training module (Fig. 2: machine learning device 1) evaluates and verifies performance by using predictive accuracy, F1 score, AUC, sensitivity, and specificity with respect to the pre-trained model ([0213-0215]; Table 1).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the method/system as taught by Kumar with the training module evaluates and verifies performance by using predictive accuracy, F1 score, AUC, sensitivity, and specificity with respect to the pre-trained model as taught by Shino. Such a modification would provide the predictable results of determining whether or not the prediction algorithm his a high accuracy rate (Shino, [0216]).
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (WO 2021/211516) in view of Park (US 2021/0100530) and Watanabe (US 2023/0111852) and Thubagere Jagadeesh (US 2021/0174965) and Shino (US 2023/0162351) and Rinehart et al (US 2021/0244882) hereinafter Rinehart and further in view of Rezvani and Wang (Salim Rezvani, Xihao Wang, A broad review on class imbalance learning techniques, Elsevier, pg. 4, published May 26, 2023, (accessed on 7/30/2026)) hereinafter Rezvani.
Regarding claims 10 and 20, the modified Kumar discloses the system/method of claims 9 and 19 as discussed above, but fails to disclose wherein the training module uses Equation 1 to calculate the predictive accuracy and the F1 score and uses a J statistic to calculate a threshold value for the sensitivity and a threshold value for the specificity.
However, Rezvani discloses using Equation 1 to calculate the predictive accuracy (Pg. 4, Eqn. 1) and the F1 score (Pg. 4, Eqn. 2). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the system/method as taught by Kumar with module uses Equation 1 to calculate the predictive accuracy and the F1 score as taught by Rezvani. Such a modification would provide the predictable results of assessing the performance of imbalanced data sets (Rezvani, Pg. 4, 2. Performance evaluation in imbalanced areas).
Rinehart discloses using a J statistic to calculate a threshold value for the sensitivity and a threshold value for the specificity ([0128] Youden's J statistic was calculated for each ROC curve; Table 8). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to further modify the system/method as taught by Kumar with using a J statistic to calculate a threshold value for the sensitivity and a threshold value for the specificity as taught by Rinehart. Such a modification would provide the predictable results of providing a single example point from which sensitivity and specificity could be demonstrated for each curve (Rinehart, [0128]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLOW GRACE WELCH whose telephone number is (703)756-1596. The examiner can normally be reached Usually M-F 8:00am - 4:00pm.
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/WILLOW GRACE WELCH/Examiner, Art Unit 3792
/William J Levicky/Primary Examiner, Art Unit 3796
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