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
Acknowledgement is made of Applicant’s claim of this application being a National Stage application of the PCT Application No. PCT/US2023/025603, filed on June 16, 2023. As well as acknowledgement of priority to provisional application 63/353208 with filing date of June 17, 2022.
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 03/04/2025 and 07/09/2026 have been reviewed and the listed references have been considered.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 130, shown in Figures 9A and 9B. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 75a and 75b, as described in paragraph 62. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). 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.
Status of Claims
Claims 1, 8-16, 18-25, and 32-38 are pending.
Claim Objections
Claims 1, 8, 16, and 18 are objected to because of the following informalities:
Claim 1 recites "…the image frame of the video feed…" should be "…an image frame of the video feed…"
Claim 8 recites "…capturing, with the imaging device, the video feed at a second frame rate greater than the first frame rate if the rate greater than a predetermined threshold…" should be "…capturing, with the imaging device, the video feed at a second frame rate greater than the first frame rate if the rate at which the volume of the waste material is increasing within the waste container is greater than a predetermined threshold"
Claim 16 recites "..the least two localization markers…" should be "at least two localization markers"
Appropriate corrections are required.
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, 8-16, 18-19, and 32-38 are rejected under 35 U.S.C. 101, based on abstract idea. The claims recite a method for determining volume of blood loss in a bodily fluid waste container. With respect to independent method claim 1:
STEP 1: Do the claims fall within one of the statutory categories?
YES. Claim 1 is directed to a method.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES, the claims are directed toward a mental process (i.e., abstract idea).
The limitation "analyzing […] the one or more image frames of the video feed to determine a volume of the waste material within the waste container; analyzing […] the image frames to determine a concentration of a blood component within the waste material; estimating […] the blood loss based on the determined volume and the determined concentration of the blood component" as drafted, recite an abstract idea, such as a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind of a person, i.e., concepts performed in the human mind (including observation, evaluation, judgement, opinion).
As such, a person could view images of a canister, determine the volume of the total waste in the canister, identify within the image the amount of blood within the canister, and determine the amount of blood loss with a degree of error or lack thereof either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a processor (e.g., processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
The additional elements “"capturing, with the imaging device, a video feed of the waste container and the waste material disposed therein as a vacuum source of the medical waste collection system is drawing the waste material into the waste container" and "displaying, on a display, the estimated blood loss" are recited as mere data gathering and outputting which are not indicated of integration into a practical application per MPEP 2106.05(g).
The claims recite “one or more processors” at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). See also MPEP 2106.04(a)(2)(III) with respect to Mental Processes: “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer”. See also MPEP 2106.04(a)(2)(III)(C)(3) Using a computer as tool to perform a mental process and MPEP 2106.04(a)(2)(III)(D) as well as the case law cited therein.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO,
The claims herein do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as discussed above with respect to integration of the abstract idea into practical application, the additional step/element/limitation amounts to no more than an abstract idea performed on a computer. The additional elements are simply appending well-understood routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) per MPEP 2106.05(d) and 2106.07(a)(III). Therefore, claim 16 is not patent eligible.
In addition, the elements of claim 32 are analyzed in the same manner as claim 1. The additional element recited in claim 32, i.e., “the step of analyzing the image frames is facilitated by a neural network trained on image data”, which recited at a high level of generality and merely equate to “apply it”. Therefore, independent claims 1 and 32 are not patent eligible, either.
Similar analysis is made for the dependent claims 8-16, 18-19, and 33-38, under their broadest reasonable interpretation are identified as: being either directed towards mere data gathering or an abstract idea, mental process and mathematical calculation, and not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
For all of the above reasons, claims 1, 8-16, 18-19, and 32-38 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claims 1, 8-16, 18-19, and 32-38 are not eligible subject matter under 35 U.S.C 101.
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.
Claims 1, 8-10, 12, 14-15, and 32-35 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Satish et al. (US 2013/0301901 A1), herein referred to as Satish'901.
Regarding claim 1, Satish'901 teaches “A method of estimating blood loss of waste material within a waste container (Satish'901 paragraph [0016] "a method S100 for estimating a quantity of a blood component in a fluid canister") of a medical waste collection system (Satish'901 paragraph [0020] "The fluid canister can be a suction canister implemented in a surgical or other medical, clinical, or hospital setting to collect blood and other bodily fluids, wherein the fluid canister can be translucent or substantially transparent such that the method S100 can identify and analyze fluid contained within the canister") in which an imaging device is supported in a device cradle (Satish'901 paragraph [0024] "a camera, a camera stand configured to support the camera adjacent the fluid canister"), the method comprising the steps of:
capturing, with the imaging device, a video feed of the waste container and the waste material disposed therein (Satish'901 paragraph [0025] "Block S102 can capture the image that is a static, single-frame image including at least a portion of the fluid canister. Alternatively, Block S102 can capture the image that is a multi-frame video feed including multiple static images of the fluid canister") as a vacuum source of the medical waste collection system is drawing the waste material into the waste container (Satish'901 paragraph [0026] "Block S102 captures the image of the canister according to a time schedule, such as every thirty seconds or every two minutes during a surgery");
analyzing, with one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), the one or more image frames of the video feed (Satish'901 paragraph [0025] "Block S102 can capture the image that is a static, single-frame image including at least a portion of the fluid canister. Alternatively, Block S102 can capture the image that is a multi-frame video feed including multiple static images of the fluid canister") to determine a volume of the waste material within the waste container (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140");
analyzing, with one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), the image frames to determine a concentration of a blood component within the waste material (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140 and a concentration of the blood component in Block S160");
estimating, with the one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), the blood loss based on the determined volume and the determined concentration of the blood component (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140 and a concentration of the blood component in Block S160, data that can be combined to derive the content of the blood component within the canister in Block S170"); and
displaying, on a display, the estimated blood loss (Satish'901 paragraph [0023] "The computing device can also include or be coupled to a digital display such that the method S100 can display information to a user ( e.g., a nurse or anesthesiologist) through the display").
Claim 32 recites a method with steps corresponding to the method with steps
recited in claim 1. Therefore, the recited steps of this claim are mapped in the same manner as the corresponding steps of method claim 1. Additionally Satish’901 teaches “analyzing, with one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), the image frames of the video feed to determine a concentration of a blood component within the waste material (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140 and a concentration of the blood component in Block S160"), wherein the step of analyzing the image frames is facilitated by a neural network trained on image datasets (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140") of at least one of relative positioning of a camera of the imaging device relative to the waste container, position of a flash reflected on a surface of the waste container, color component values associated with at least two reference markers affixed to the surface of the waste container, and color component values associated with an imaging surface of an insert disposed within the waste container (Satish'901 paragraph [0029] "Block S100 can implement any suitable machine vision technique and/or machine learning technique to identify the reference marker. For example, Block S120 can implement object localization, segmentation ( e.g. edge detection, background subtraction, grab cut-based algorithms, etc.), gauging, clustering, pattern recognition, template matching, feature extraction, descriptor extraction (e.g. extraction oftexton maps, color histograms, HOG, SIFT, MSER (maximally stable extremal regions for removing blob-features from the selected area) etc.), feature dimensionality reduction (e.g. PCA, K-Means, linear discriminant analysis, etc.), feature selection, thresholding, positioning, color analysis, parametric regression, non-parametric regression, unsupervised or semi-supervised parametric or non-parametric regression, or any other type of machine learning or machine vision to estimate a physical dimension of the canister")”.
Regarding claim 8, Satish’901 teaches “The method of claim 1(Satish'901 paragraph [0026] "Block S102 captures the image of the canister according to a time schedule, such as every thirty seconds or every two minutes during a surgery");
determining, with the one or more processors, a rate at which the volume of the waste material is increasing within the waste container (Satish'901 paragraph [0026] "Therefore, Block S112 can capture images of the canister automatically, such as based on a timer, changes in canister fluid volume, or availability of the canister for imaging, which can enable the method Sl00 to track fluid collection in the canister over time, as shown in FIGS. 7 A and 7B"); and
capturing, with the imaging device, the video feed at a second frame rate greater than the first frame rate (Satish'901 paragraph [0026] "Block S102 captures the image of the canister according to a time schedule, such as every thirty seconds or every two minutes during a surgery") if the rate greater than a predetermined threshold (Satish'901 paragraph [0026] "Block S102 can cooperate with Block S140 to capture the image of the canister once a threshold increase is canister fluid volume is detected. Therefore, Block S112 can capture images of the canister automatically, such as based on a timer, changes in canister fluid volume, or availability of the canister for imaging, which can enable the method S100 to track fluid collection in the canister over time, as shown in FIGS. 7 A and 7B").”
Regarding claim 9, Satish’901 teaches “The method of claim 8, further comprising activating or increasing a level of a flash (Satish'901 paragraph [0027] "Block S102 can also interface with a light source or flash system to control lighting of the canister during image capture of the canister") of the imaging device if the rate is greater than the predetermined threshold (Satish'901 paragraph [0026] "Block S102 can cooperate with Block S140 to capture the image of the canister once a threshold increase is canister fluid volume is detected. Therefore, Block S112 can capture images of the canister automatically, such as based on a timer, changes in canister fluid volume, or availability of the canister for imaging, which can enable the method S100 to track fluid collection in the canister over time, as shown in FIGS. 7A and 7B).”
Regarding claim 10, Satish’901 teaches “The method of claim 9, further comprising: detecting, with the one or more processors, a pixel-based location of a fluid meniscus in the image frames (Satish'901 paragraph [0036] "Block S130 can identify a fluid meniscus within the set of pixels and compare the fluid meniscus to the volume marker in order to estimate the fluid level in the canister"); and
determining, with the one or more processors, whether the pixel-based location of the fluid meniscus (Satish'901 paragraph [0036] "Block S110 identifies the reference marker that is a volume marker on the canister, and Block S120 selects the area of the image that is a set of pixels adjacent a portion of the image corresponding to the volume marker. In this example implementation, Block S130 can identify a fluid meniscus within the set of pixels and compare the fluid meniscus to the volume marker in order to estimate the fluid level in the canister") changes by an amount greater than a predetermined threshold in a predetermined period of time (Satish'901 paragraph [0026] "Block S102 can cooperate with Block S140 to capture the image of the canister once a threshold increase is canister fluid volume is detected").”
Regarding claim 12, Satish’901 teaches “The method of claim 1 (Satish'901 paragraph [0037] "Block S110 identifies horizontal volume indicator markings on the fluid canister") affixed to the waste container (Satish'901 paragraph [0035] "Block S100 implements machine vision to identify the reference marker arranged at a standardized position on the canister"); and
aligning, with one or more processors, the image frames based on relative positioning of the at least two reference markers (Satish'901 paragraph [0027] "Block S102 can prompt the user to align an edge of the canister in the field of view of the camera with the alignment graphic rendered on the display, as shown in FIGURE 4A. The alignment graphic can include points, lines, and/or shapes ( e.g., an outline of a canister) to be aligned with a side, feature, decal, and/or the reference marker of or on the fluid canister").”
Regarding claim 14, Satish’901 teaches “The method of claim 1 (Satish'901 paragraph [0019] "The blood component can be any of whole blood, red blood cells, hemoglobin, platelets, plasma, or white blood cells. However, the method SlO0 can also implement Block S180, which recites estimating a quantity of a non-blood component within the canister based on the estimated volume and the concentration of the non-blood component within the canister. The non-blood component can be saline, ascites, bile, irrigant saliva, gastric fluid, mucus, pleural fluid, urine, fecal matter, or any other bodily fluid of a patient"), and optionally, froth or a fluid surf ace; and
assigning class labels based on the pixel values (Satish'901 paragraph [0039] "Alternatively, Block S120 can characterize the color of various pixels within a portion of the image correlated with the canister and select the segment that contains pixels characterized as substantially red ( e.g., containing blood)").”
Regarding claim 15, Satish’901 teaches “The method of claim 14, further comprising determining a location of the fluid meniscus based on the class label of meniscus (Satish'901 paragraph [0026] "Block S120 selects the area of the image that is a set of pixels adjacent a portion of the image corresponding to the volume marker. In this example implementation, Block S130 can identify a fluid meniscus within the set of pixels and compare the fluid meniscus to the volume marker in order to estimate the fluid level in the canister").”
Regarding claim 33, Satish’901 teaches “The method of claim 32, wherein the step of analyzing the image frames is facilitated by the neural network (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140") being further trained on the image datasets of regions of interest associated with at least two imaging surfaces of the insert with each of the at least two imaging surfaces providing a different color component value to the waste material disposed between the insert and the waste container (Satish'901 paragraph [0026] "The selected area can further bisect a surface of the fluid within the canister such that Block S130 can subsequently identify the level of fluid within the canister based on analysis of the selection area. The selected area can therefore be one or more contiguous and/or discontiguous pixels within the image and containing information substantially characteristic of the contents of the canister. Furthermore, the selected are can correspond to a surface of the canister ( e.g. a vertical white stripe) that is opaque enough to eliminate background noise but exhibits a substantially abrupt color transition proximal a fluid surface, thus enabling Block S130 to estimate the fluid height in the canister").”
Regarding claim 34, Satish’901 teaches “The method of claim 32, wherein the step of analyzing the image frames is facilitated by the neural network (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140") being further trained on the image datasets of a region of interest associated with the imaging surface of the insert providing a color gradient to the waste material disposed between the insert and the waste container (Satish'901 paragraph [0041] "Block S130 characterizes the color of each pixel ( e.g., a redness value of each pixel) along a vertical line of pixels within the selected area. By scanning the line of pixels from the bottom of the line of pixels (i.e. from proximal the base of the canister) upward, Block S130 can identify a first abrupt shift in pixel color, which can be correlated with a lower bound of the surface of the fluid. By further scanning the line of pixels from the top of the line of pixels (i.e. from proximal the top of the canister) downward, Block S130 can identify a second abrupt shift in pixel color, which can be correlated with an upper bound of the surface of the fluid").”
Regarding claim 35, Satish’901 teaches “The method of claim 32, wherein the step of analyzing the image frames is facilitated by the neural network (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140") further trained on the image datasets of at least one of various volumes of the waste material and differing phase characteristics of the waste material (Satish'901 paragraph [0036] "Block S130 can identify a fluid meniscus within the set of pixels and compare the fluid meniscus to the volume marker in order to estimate the fluid level in the canister. For example, Block S120 can select a rectangular area of the image that is twenty pixels wide and one hundred pixels tall with upper right corner of the area offset from a left edge of the volume marker by ten pixels along the -x axis and twenty pixels along the +y axis of the image").”
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 13, 16, 18-19, 20-25, 36-38 are rejected under 35 U.S.C. 103 as being unpatentable over Satish'901 in view of Satish et al. (US 9,824,441 B2), herein referred to as Satish'441.
Regarding claim 20, Satish'901 teaches “A method of estimating blood loss of waste material within a waste container (Satish'901 paragraph [0016] "a method S100 for estimating a quantity of a blood component in a fluid canister") of a medical waste collection system (Satish'901 paragraph [0020] "The fluid canister can be a suction canister implemented in a surgical or other medical, clinical, or hospital setting to collect blood and other bodily fluids, wherein the fluid canister can be translucent or substantially transparent such that the method S100 can identify and analyze fluid contained within the canister") in which an imaging device is supported in a device cradle (Satish'901 paragraph [0024] "a camera, a camera stand configured to support the camera adjacent the fluid canister"), the method comprising the steps of:
capturing, with the imaging device, a video feed (Satish'901 paragraph [0025] "Block S102 can capture the image that is a static, single-frame image including at least a portion of the fluid canister. Alternatively, Block S102 can capture the image that is a multi-frame video feed including multiple static images of the fluid canister"); and
processing, with one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), frames of the video feed, wherein the step of processing the image frames (Satish'901 paragraph [0064] "system 100 for estimating a quantity of a blood component in a fluid canister includes: an optical sensor 110; a processor 120 coupled to the optical sensor 11 0; a software module 122 executing on the processor 120 and instructing the optical sensor 110 to capture an image of a canister, the software module 122 further instructing the processor 120 to select an area of the image correlated with a portion of the canister containing fluid") further comprises:
determining a location of a fluid meniscus (Satish'901 paragraph [0036] "Block S130 can identify a fluid meniscus within the set of pixels and compare the fluid meniscus to the volume marker in order to estimate the fluid level in the canister");
aligning the image frames (Satish'901 paragraph [0027] "Block S102 can prompt the user to align an edge of the canister in the field of view of the camera with the alignment graphic rendered on the display, as shown in FIGURE 4A. The alignment graphic can include points, lines, and/or shapes ( e.g., an outline of a canister) to be aligned with a side, feature, decal, and/or the reference marker of or on the fluid canister");
converting a y-axis value of the mapped image in the canonical coordinate to a determined volume (Satish'901 paragraph [0045] "Block S130 can subsequently convert the pixel-based distance measurement to a physical distance measurement ( e.g., inches, millimeters), such as by translating the pixel value according to the type of canister and/or an actual or estimated angle between the camera and the canister, distance between the camera and the canister, geometry of the canister (e.g., diameters at the canister base and at the fluid surface), and/or any other relevant metric of or between the canister and the camera. Alternately, Block S140 can directly convert the pixel-based fluid level measurement into an estimate fluid volume within the canister");
detecting, with one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), at least two reference markers (Satish'901 paragraph [0037] "Block S110 identifies horizontal volume indicator markings on the fluid canister") affixed to the waste container (Satish'901 paragraph [0035] "Block S100 implements machine vision to identify the reference marker arranged at a standardized position on the canister"), wherein the reference marker includes location data (Satish'901 paragraph [0030] "Block S110 identifies the reference marker that is a symbol arranged on the canister, as shown in FIGURE 5B. For example, the symbol can be a manufacturer's label printed on the canister, a fluid volume scale printed on the canister"); and
extracting, with one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), a region of interest in a raw image based on the location data (Satish'901 paragraph [0034] "Block S120 functions to select a particular area of the image corresponding to a particular region of interest of the surface of the canister");
analyzing, with one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), the image to determine a concentration of a blood component within the waste material (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method S100 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140 and a concentration of the blood component in Block S160");
estimating, with the one or more processors (Satish'901 paragraph [0023] "a camera integral with the computing device can capture the image of the fluid canister, and a processor integral with the computing device implement Blocks S110,S120, S130, etc"), the blood loss based on the determined volume and the determined concentration of the blood component (Satish'901 paragraph [0018] "The method S100 functions to implement machine vision to estimate the content of a blood component within a fluid canister. Generally, the method Sl00 can analyze an image of a fluid canister to determine a fluid volume within the canister in Block S140 and a concentration of the blood component in Block S160, data that can be combined to derive the content of the blood component within the canister in Block S170"); and
displaying, on a display, the estimated blood loss (Satish'901 paragraph [0023] "The computing device can also include or be coupled to a digital display such that the method S100 can display information to a user ( e.g., a nurse or anesthesiologist) through the display").
However, Satish’901 is not relied on to teach “mapping the image frames from an image coordinate space to a canonical coordinate space”.
Satish’441 teaches “mapping the image frames from an image coordinate space to a canonical coordinate space (Satish’441 column 7 lines 43-50 "the processing module can comprise a palette extraction module configured to use the identifier (e.g., alphanumeric identifier) of the canister to define masks of a set of regions of the array of color elements 310 of the color grid 300 in canonical space, and to use the one or more positional features (e.g., extracted QR-code corners) to fit a transformation model ( e.g., homography) between canonical space and image space associated with the image data")”.
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine method for determining the volume of blood loss in a waste container as by Satish’901 to include the method of changing coordinate spaces of image frames as taught by Satish’441.
The suggestion/motivation for doing so would have been that there is a need in
the field of medical imaging to quantify information about patient bodily waste during medical procedures, " The method 100 can therefore be useful in quantifying an
amount and/or a concentration of a blood component (e.g., hemoglobin) and/or other fluids ( e.g., saline) contained within a fluid canister through non-contact means and in
real-time, such as during a surgery or other medical event. A patient's blood loss and euvolemia status can then be tracked according to these data" as noted by the Satish’441 disclosure in column 3, lines 16-22.
Therefore, it would have been obvious to combine the disclosure of Satish’901 with the Satish’441 disclosure to obtain the invention as specified in claim 20 as there is a reasonable expectation of success and/or because doing so merely combines prior art
elements according to known methods to yield predictable results.
Regarding claim 13, the combination of Satish’901 and Satish’441 teaches “The method of claim 12, wherein the at least two reference markers are quick response (QR) codes arranged in a generally vertical configuration (Satish’441column 6 lines 44-57 "the color grid 300 contains colored blocks of various discrete colors ( e.g., red 45 hues, saturation of red hues, intensity of red hues, etc.) arranged in a matrix barcode 5 (e.g., a Quick Response or "QR" code, another optical machine-readable barcode, etc.), as shown in FIG. 2B. In this implementation, the set of colored blocks-along with a set of white blocks (e.g., interspersed white blocks )-can be arranged in a square grid pattern printed on or otherwise applied to a sticker ( or decal, etc.), wherein information including one or more of: alignment information, position information, version information, identification information, and any other suitable information is encoded within the square grid pattern based on the (relative) positions of colored and white blocks therein"), wherein the step of aligning the image frames further comprises aligning the QR codes to be exactly vertical (Satish’901 paragraph [0027] "Block S102 can prompt the user to align an edge of the canister in the field of view of the camera with the alignment graphic rendered on the display, as shown in FIGURE 4A. The alignment graphic can include points, lines, and/or shapes ( e.g., an outline of a canister) to be aligned with a side, feature, decal, and/or the reference marker of or on the fluid canister […] The alignment graphics can additionally or alternatively include curves suggestive of a perspective view of the fluid canister, which can guide the user in positioning the canister in a preferred orientation, (e.g., vertical and/or horizontal pitch) relative to and/or distance from the camera").“
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Satish’441 Figure 1A
The proposed combination as well as the motivation for combining Satish’901 and Satish’441 references presented in the rejection of claim 20, applies to claim 13. Finally, the method recited in claim 13 is met by Satish’901 and Satish’441.
Regarding claim 21, the combination of Satish’901 and Satish’441 teaches “The method of claim 20, wherein the step of determining the location of a fluid meniscus further comprises: segmenting, with the one or more processors, the image frames by characterizing whether pixel values of each of the image frames are blood, meniscus, or non-blood (Satish’901 paragraph [0019] " The blood component can be any of whole blood, red blood cells, hemoglobin, platelets, plasma, or white blood cells. However, the method SlO0 can also implement Block S180, which recites estimating a quantity of a non-blood component within the canister based on the estimated volume and the concentration of the non-blood component within the canister. The non-blood component can be saline, ascites, bile, irrigant saliva, gastric fluid, mucus, pleural fluid, urine, fecal matter, or any other bodily fluid of a patient") , and optionally, froth or a fluid surface; and
assigning, with one or more processors, class labels based on the pixel values, wherein the location of the fluid meniscus based on the class label of meniscus (Satish’901 paragraph [0039] "Alternatively, Block S120 can characterize the color of various pixels within a portion of the image correlated with the canister and select the segment that contains pixels characterized as substantially red ( e.g., containing blood)").”
Regarding claim 16 (similarly claim 23 and claim 36), the combination of Satish’901 and Satish’441 teaches “The method of claim 1 or 2, further comprising_mapping the image frames from an image coordinate space to a canonical coordinate space by generating homography of the least two localization markers, and transforming the image coordinates to two-dimensional canonical coordinates (Satish’441 Figure 3a and column 7 lines 42-50 "the processing module can comprise a palette extraction module configured to use the identifier (e.g., alphanumeric identifier) of the canister to define masks of a set of regions of the array of color elements 310 of the color grid 300 in canonical space, and to use the one or more positional features (e.g., extracted QR-code corners) to fit a transformation model ( e.g., homography) between canonical space and image space associated with the image data").”
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Satish’441 Figure 3A
The proposed combination as well as the motivation for combining Satish’901 and Satish’441 references presented in the rejection of claim 20, applies to claim 16. Finally, the method recited in claim 16 is met by Satish’901 and Satish’441.
Regarding claim 18 (similarly claim 24 and claim 37), the combination of Satish’901 and Satish’441 teaches “The method of claim 16, further comprising cropping the mapped image frames in the canonical coordinate space to include the at least two reference markers, and a portion of the waste container disposed therebetween (Satish’441 Figure 3a and column 11 lines 25-31 " Block S110 can comprise defining masks of a set of regions, each region associated with a color element of the array of color elements in canonical space, and using one or more positional features (e.g., extracted QR-code corners) to fit a transformation model (e.g., homography) between canonical space and image space associated with the image").”
The proposed combination as well as the motivation for combining Satish’901 and Satish’441 references presented in the rejection of claim 20, applies to claim 18. Finally, the method recited in claim 18 is met by Satish’901 and Satish’441.
Regarding claim 19 (similarly claim 38), the combination of Satish’901 and Satish’441 teaches “The method of claim 16, wherein the step of analyzing the image frames to determine the volume of the waste material further comprises: receiving container-specific calibration data (Satish’901 paragraph [0027] "Block S102 can prompt the user to align an edge of the canister in the field of view of the camera with the alignment graphic rendered on the display, as shown in FIGURE 4A. The alignment graphic can include points, lines, and/or shapes ( e.g., an outline of a canister) to be aligned with a side, feature, decal, and/or the reference marker of or on the fluid canister"); and
converting a y-axis value of the mapped image frames in the canonical coordinate space to a volumetric value (Satish’901 paragraph [0045] "Block S130 can subsequently convert the pixel-based distance measurement to a physical distance measurement ( e.g., inches, millimeters), such as by translating the pixel value according to the type of canister and/or an actual or estimated angle between the camera and the canister, distance between the camera and the canister, geometry of the canister (e.g., diameters at the canister base and at the fluid surface), and/or any other relevant metric of or between the canister and the camera. Alternately, Block S140 can directly convert the pixel-based fluid level measurement into an estimate fluid volume within the canister").
Regarding claim 22, the combination of Satish’901 and Satish’441 teaches “The method of claim 20, wherein the step of aligning the image further comprises detecting and aligning at least two reference markers to be exactly vertical (Satish’901 paragraph [0027] "Block S102 can prompt the user to align an edge of the canister in the field of view of the camera with the alignment graphic rendered on the display, as shown in FIGURE 4A. The alignment graphic can include points, lines, and/or shapes ( e.g., an outline of a canister) to be aligned with a side, feature, decal, and/or the reference marker of or on the fluid canister").”
Regarding claim 25, the combination of Satish’901 and Satish’441 teaches “The method of claim 20, wherein the step of analyzing the image frames to determine the volume of the waste material further comprises applying container specific calibration data (Satish’901 paragraph [0027] "Block S102 can prompt the user to align an edge of the canister in the field of view of the camera with the alignment graphic rendered on the display, as shown in FIGURE 4A. The alignment graphic can include points, lines, and/or shapes ( e.g., an outline of a canister) to be aligned with a side, feature, decal, and/or the reference marker of or on the fluid canister").”
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Satish'901 in view of Cui et al. (US 2026/0253270 A1).
Regarding claim 11, Satish'901 teaches “The method of claim 10, further comprising (Satish’901 paragraph [0036] "Block S130 can identify a fluid meniscus within the set of pixels and compare the fluid meniscus to the volume marker in order to estimate the fluid level in the canister").”
However, Satish’901 is not relied on to teach “down sampling the image frames prior to the step of detecting”.
Cui teaches “The method of claim 10, further comprising down sampling the image frames prior to the step of detecting (Cui paragraph [0059] "This downsampling or downscaling may be performed prior to detecting salient objects in an image, such that a low resolution image may be utilized for
the color tuning process") the pixel-based location of the fluid meniscus (Satish’901 paragraph [0036] "Block S130 can identify a fluid meniscus within the set of pixels and compare the fluid meniscus to the volume marker in order to estimate the fluid level in the canister").”
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine method for determining the volume of blood loss in a waste container as by Satish’901 to include the method of image downsampling as taught by Cui.
The suggestion/motivation for doing so would have been “Aspects presented herein may also utilize an improved color tuning process using a downsampled image for deep learning. This may also improve the color tuning process' resiliency to saliency map edge accuracy, which may further improve the processing speed" as noted by the Cui disclosure in paragraph 17.
Therefore, it would have been obvious to combine the disclosure of Satish’901 with the Cui disclosure to obtain the invention as specified in claim 11 as there is a reasonable expectation of success and/or because doing so merely combines prior art
elements according to known methods to yield predictable results.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Publication 20180296102 A1 to Satish et al. discloses estimating amount of blood in a canister with analysis of features using neural network.
US Publication 20160335779 A1 to Satish et al. discloses estimating volume of blood as it passes through a tube within a time period.
Conclusion
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/JASPREET KAUR/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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