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 .
Drawings
Figures 1A, 1B, 1C, 2A, 2B, and 6 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g). Figures 1A, 1B, 1C, 2A, 2B, and 6 are prior art because, as per their brief descriptions, they are about surgery generally, not the invention (e.g., the statistical data for Fig. 6 was not collected for this invention). Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Blood Presence Detection During Surgery Via Image Analysis.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 (all claims) are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1-20 (all claims) are rejected because the specification does not demonstrate possession of the invention. MPEP 2163(II)(A)(3)(a) explains “An applicant may also show that an invention is complete by disclosure of sufficiently detailed, relevant identifying characteristics which provide evidence that inventor was in possession of the claimed invention, i.e., complete or partial structure, other physical and/or chemical properties, functional characteristics when coupled with a known or disclosed correlation between function and structure, or some combination of such characteristics.”
For this technology, sufficiently detailed, relevant identifying characteristics include specifics of a neural network architecture (or specifics of multiple architectures), how the model was trained, and how it performs. Three articles are attached as representative examples of relevant identifying characteristics for this technology:
Yoon D, Yoo M, Kim BS, Kim YG, Lee JH, Lee E, Min GH, Hwang DY, Baek C, Cho M, Suh YS. Automated deep learning model for estimating intraoperative blood loss using gauze images. Scientific Reports. 2024 Jan 31;14(1):2597.
Sunakawa T, Kitaguchi D, Kobayashi S, Aoki K, Kujiraoka M, Sasaki K, Azuma L, Yamada A, Kudo M, Sugimoto M, Hasegawa H. Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy. Surgical Endoscopy. 2024 Dec;38(12):7656-62.
Pei J, Zhou Z, Guo D, Li Z, Qin J, Du B, Heng PA. Synergistic bleeding region and point detection in surgical videos. arXiv e-prints. 2025 Mar:arXiv-2503.
Each of these articles are about using artificial intelligence to visually detect blood during surgery. Each of them provide details of the architectural neural network (e.g., Pei at Fig. 6, Yoon at Fig. 1, and Sunakawa includes a discussion under “Computing and model training,” at 7657, and provides hyperparameters in supplementary table 1 (not attached)).
Each of these articles provide details about performance of their models (e.g., Pei at Table 1, Yoon at Tables 1 and 2, Sunakawa at Table 2).
Each of these articles provide details about how the models were trained. See, for example, Pei section 3, Yoon’s “Dataset” on page 3, and Sunakawa’s “Materials and Methods” at page 7657.
In contrast, the present application does not provide a diagram of a system architecture. Instead, the present specification makes statements such as “Any architecture or layer structure for machine learning may be used.” This is not a credible statement and fails to demonstrate possession. Specification, [0049]. The specification also states “As another example, a regression model, such as linear, logistic, ridge, Lasso, polynomial, or Bayesian linear regression model is used.” Specification, [0049]. However, one of skill in the art would readily appreciate that these are not computer vision models, so it is unclear how they would be used to detect blood during surgery (e.g., what is the regression model regressing?). The passing reference to a variety of models is insufficient to demonstrate possession.
Specification [0045] states “The model may be a neural network having any number of layers. One example model for computer vision may be a residual neural network (ResNet) such as ResNet 18 with 18 layers.” One of ordinary skill in the art would be curious why this paper used ResNet-18 (as opposed to a different version), but there is no explanation. This lack of explanation does not distinguish between the result of inventive efforts versus an arbitrarily chosen value. For comparison, the examiner ran a search for [resnet bleeding], and has attached the three below papers as examples of discussions of different ResNet versions.
See the three attached papers listed below regarding ResNet versions (also attached):
Liu Z, Zhou S, Chu J, Chai Z, Chang D, Yuan Y, Qin J, Wang X. Research on Arthroscopic Images Bleeding Detection Algorithm Based on ViT-ResNet50 Integrated Model and Transfer Learning. IEEE Access. 2024 Nov 29;12:181436-53.
Zhang RY, Qiang PP, Cai LJ, Li T, Qin Y, Zhang Y, Zhao YQ, Wang JP. Automatic detection of small bowel lesions with different bleeding risks based on deep learning models. World Journal of Gastroenterology. 2024 Jan 14;30(2):170.
Singh A, Prakash S, Das A, Kushwaha N, Singh B. ColonNet: A hybrid of DenseNet121 & U-Net models for detection and segmentation of GI bleeding. In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) 2025 Mar 6 (Vol. 3, pp. 1-5). IEEE.
The present application, Figs. 4A and 4B report results of a model, but the specification does not identify which model this is. However, which model is shown is required information to demonstrate possession. There does not appear to be any statistical discussion of effectiveness or correctness.
Therefore, all claims are rejected because the specification does not demonstrate possession.
Claims 1-20 (all claims) are rejected because a person skilled in the art would not understand inventor to have invented, and been in possession of, the invention as broadly claimed. Claims 1-20 read on correctly calculating a blood presence score without significant bleeding. However, as shown in Fig. 4A, Applicant’s model missed each instance of actual blood without significant bleeding, and the only time the model detected blood, it was wrong. Thus, there is not written description support for detecting blood without significant bleeding.
Claims 1-20 are additionally rejected for lack of written description support. MPEP 2163(II)(A)(3)(a) states “Estee Lauder Inc. v. L’Oreal, S.A., 129 F.3d 588, 593, 44 USPQ2d 1610, 1614 (Fed. Cir. 1997) (“[A] reduction to practice does not occur until the inventor has determined that the invention will work for its intended purpose.”).” Here, the specification specifies the problem to be solved at [0003], “Bleeding that occurs during the surgery, referred to as intraoperative bleeding, can be problematic during minimally invasive surgery due to limited vision and mobility. The bleeding is not easily detected by the surgeon when the surgical site is not immediately visible.” However, as discussed above, Fig. 4A shows that the present invention does not work “without significant bleeding,” and thus the present invention does not solve the problem that the specification asserts that it does. Therefore, the invention has not been reduced to practice.
Claims 1, 15, and 20 recite “analyz[ing/e] the at least one image with a machine-learned model,” but this is unlimited functional claiming due to the wide variety of ways that this can be performed. MPEP 2173.05(g). One option to overcome this rejection is to specify a particular neural architecture, such as a convolutional neural network.
Dependent claims are likewise rejected.
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 1-20 (all claims) 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.
Claims 1, 15, and 20 recite “surgery site,” but this is new terminology. MPEP 2173.05(a). Specifically, it is unclear if the intent is that the site is where the incision occurs, the operating theatre, the hospital, or something else.
Claims 1, 15, and 20 recite “blood presence score,” but this is new terminology. MPEP 2173.05(a). One way to overcome this rejection is to define the term in the claim, such as with a wherein clause.
Claim 9 recites “location,” but this is relative terminology. MPEP 2173.05(b).
Claim 11 recites “correlating the one or more instrument timestamps with the one or more blood presence timestamps,” but this does not make sense because timestamps are already correlated by virtue of being timestamps.
Dependent claims are likewise rejected.
Examiner Note
There are a variety of terms such as “instrument threshold,” “instrument performance,” and “user threshold” where the adjective does not constrain the noun. These are being interpreted broadly (e.g., the user threshold can be the same threshold as the instrument threshold).
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 1-20 (all claims) are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Step 1: Claim 1 (and its dependents) recite a method, and processes satisfy Step 1 of the eligibility test.
Claims 15 (and its dependents) and 20 recite systems, and machines satisfy Step 1 of the eligibility test.
Step 2A, prong one: All of the elements of the claims are a mental process because a person can look at an image of a surgery site and decide if they see blood. MPEP 2106.04(a)(2)(III)(C) explains that use of a generic computer or in a computer environment is still a mental process. In particular, this section begins by citing Gottschalk v. Benson, 409 US 63 (1972). “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea.” In Benson the Supreme Court did not separately analyze the computer hardware at issue; the specifics of what hardware was claimed is only included in an appendix to the decision.
Because there are no additional elements, no further analysis is required for Step 2A, prong two or Step 2B.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 (all claims) are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated by US20210307864A1 (“Wolf”). References are listed in the Notice of Cited References when they were first cited. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text.
1. A method for bleeding assessment at a surgery site, the method comprising:
receiving at least one image collected at a surgery site; (Wolf, abstract “A method may include receiving a plurality of video frames associated with at least one surgical procedure and accessing stored data based on prior surgical procedures.”)
analyzing the at least one image with a machine-learned model; (Wolf, “FIG. 8A is a graphical illustration of an exemplary machine-learning model, consistent with disclosed embodiments.”)
calculating a blood presence score for the surgery site based on the analysis of the at least one image; and (Wolf, [0063] “By way of another example, bleeding may be detected by one camera, and one or more other cameras may be used to identify the source of the bleeding.”)
outputting the blood presence score in association with the surgery site. (Wolf, [0090] “For example, analyzing the received plurality of video frames may include identifying an incision, a fluid leak, excessive bleeding, or any other surgical event.”)
2. The method of claim 1, further comprising:
identifying a time period for the blood presence score, (Wolf, [0091] “For instance, frames associated with multiple surgeons can be analyzed to identify properties or events, … excessive bleeding amount,”)
wherein the at least one image includes a plurality of images collected during the time period, wherein the blood presence score is based on an average of output values of the machine-learned model for the plurality of images. (Wolf, [0097] “When performed, the evaluating may derive any number of statistics or statistical data from a subgroup of frames. Statistical data may include average values … .”)
3. The method of claim 1, further comprising:
identifying an instrument for the surgery site; (Wolf, [0061] “For example, the camera control application may identify an anatomical structure, identify a surgical tool,”)
associating the at least one image with the instrument; and assigning the blood presence score with the instrument. (Wolf, [0061] “For example, the camera control application may identify an anatomical structure, identify a surgical tool, hand of a surgeon, bleeding, motion, and the like at a particular location within the anatomical structure”)
4. The method of claim 3, further comprising:
comparing the blood presence score to an instrument threshold; and (Wolf, [0091] “excessive bleeding”)
reporting instrument performance in response to the comparison. (Wolf, [0091] “analyzing the plurality of video frames may include analyzing the plurality of video frames to determine an average skill of a category of physicians.”)
5. The method of claim 1, further comprising:
identifying a medical professional for the surgery site; (Wolf, abstract “Systems and methods for analyzing surgical procedures and assessing surgical competency of subjects are disclosed.”)
associating the at least one image with the medical professional; and (Wolf, abstract “Systems and methods for analyzing surgical procedures and assessing surgical competency of subjects are disclosed.”)
assigning the blood presence score with the medical professional. (Wolf, [0091] “For instance, frames associated with multiple surgeons can be analyzed to identify properties or events, such as … excessive bleeding amount”)
6. The method of claim 5, further comprising:
comparing the blood presence score to a user threshold; and (Wolf, [0091] “excessive bleeding”)
reporting medical professional performance in response to the comparison. (Wolf, [0091] “analyzing the plurality of video frames may include analyzing the plurality of video frames to determine an average skill of a category of physicians.”)
7. The method of claim 5, further comprising:
accessing instructional information in response to the blood presence score; and (Wolf, [0160] “Non-limiting examples of indications that the surgical instrument and its corresponding interface area is outside of the surgical plane include … bleeding”)
providing the instructional information to the medical professional. (Wolf, [0165] “This continuous monitoring may enable a surgeon wielding a surgical instrument to experience real-time notification via the out-of-surgical plane signal that a deviation from the surgical plane by the surgical instrument has occurred.”)
8. The method of claim 1, further comprising:
aggregating blood presence data including the blood presence score for a facility including the surgery site; and (Wolf, [0101] “… Other medical professionals may be associated with a particular location, hospital, department, specialty, or residency class.”)
providing an assessment for the facility including a plurality of surgery sites. (Wolf, [0101] “Comparisons can be made for any surgical-event related category. … .”)
9. The method of claim 1, further comprising:
identifying a bleeding location in the at least one image; and (Wolf, [0223] “the surgeon may be shown video clips of the incomplete suture in the current surgery in order to help the surgeon understand the source of the bleed.”)
modifying the at least one image to highlight the bleeding location. (Wolf, [0072] “For example, display screen 113 may show a zoomed-in image of a tip of a surgical instrument and a surrounding tissue of an anatomical structure in proximity to the surgical instrument.” Wolf’s “zoomed-in” teaches the claimed modifying to highlight. Additionally, [0072] teaches displaying “other information.” Further, the claimed modification is nonfunctional descriptive material and not owed weight. MPEP 2111.05.)
10. The method of claim 1, further comprising:
determining one or more timestamps for the at least one image, wherein the one or more timestamps corresponds to a predetermined step of a procedure at the surgical site; and (Wolf, Fig. 6, event location 623. The event locations are timestamps)
assigning the one or more timestamps to the blood presence score. (Wolf, [0096] “Tags may include a timestamp, time range, frame number, or other means for associating the surgical event-related category to the subgroup of frames.”)
11. The method of claim 10, further comprising:
receiving instrument data associated with one or more instrument timestamps; and (Wolf, [0061] “For example, the camera control application may identify an anatomical structure, identify a surgical tool,”)
correlating the one or more instrument timestamps with the one or more blood presence timestamps. (Wolf, Fig. 6, event location 623. The event locations are timestamps)
12. The method of claim 1, further comprising:
collecting ground truth data for a plurality of training images; and (Wolf, [0054] “Such algorithms be trained using training examples, such as described below.”)
training the machine-learned model based on the ground truth data. (Wolf, [0053] “the computer image analysis may include using a neural network model trained using example video frames including previously identified surgical events to thereby identify a similar surgical event in a set of frames.”)
13. The method of claim 1, further comprising:
identifying a surgery type for the surgery site; (Wolf, Fig. 6)
associating the at least one image with the surgery type; and (Wolf, Fig. 6)
assigning the blood presence score with the surgery type. (Wolf, [0090] “For example, analyzing the received plurality of video frames may include identifying … excessive bleeding)
14. The method of claim 1, wherein the at least one image is included in a video of the surgery site. (Wolf, Fig. 1)
Claim 15 is rejected as per claim 1. Additionally, the recited hardware is shown in Wolf, Figs. 1 (display 113) and 4.
16. The surgery assessment system of claim 15, wherein the display provides a video including the plurality of images in association with the blood presence score. (Wolf, abstract “presenting in association with the at least one score, a link to the at least one video clip.”)
17. The surgery assessment system of claim 15, wherein output values of the machine-learned model for the plurality of images are averaged to calculate the blood presence score. (Wolf, [0097] “When performed, the evaluating may derive any number of statistics or statistical data from a subgroup of frames. Statistical data may include average values … .”)
18. The surgery assessment system of claim 15, further comprising:
an instrument interface configured to receive instrument data for an instrument associated with the surgery site. (Wolf, [0299] “In some embodiments, the piece of medical equipment may be configured to capture medical information during a medical procedure.”)
19. The surgery assessment system of claim 15, wherein the display is configured to provide a plurality of blood presence scores for a plurality of surgeries. (Wolf, abstract, “Systems and methods for analyzing surgical procedures and assessing surgical competency of subjects are disclosed.”)
Claim 20 is rejected as per claim 1. Additionally, the recited hardware is shown in Wolf, Fig. 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US11790637B2 – “Method for estimating blood component quantities in surgical textiles”
US11666226B2 – “Method for projecting blood loss of a patient during a surgery”
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/DAVID ORANGE/ Primary Examiner, Art Unit 2663
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