CTNF 18/895,899 CTNF 99728 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement 06-52 The information disclosure statement (IDS) were filed on 09/25/2024 and 02/02/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. The claim limitations affected by the interpretation are in claims 10-14, “means for…” . Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (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. 07-15-03-aia AIA Claim s 1-8 and 10-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hibbard, Lyndon (US Pub. No. 20220088410 A1) . As per claim 1, Hibbard teaches “A computer-implemented method comprising: receiving 1) one or more medical images of a patient and 2) a fluence map for each of a plurality of fields for radiation therapy of the patient;” (See paragraphs 8-9 and 49-50 “[0008]… obtaining image data corresponding to a subject of radiotherapy treatment , the image data indicating one or more target dose areas and one or more organs-at-risk areas in the anatomy of the subject ; generating anatomy projection images from the image data, each anatomy projection image providing a view of the subject from a respective beam angle of the radiotherapy treatment ; and using a trained neural network model t o generate estimated fluence maps based on the anatomy projection images, each of the estimated fluence maps indicating a fluence distribution of the radiotherapy treatment at a respective beam angle . In these and other configurations, such a neural network model may be trained with corresponding pairs of anatomy projection images and fluence maps, to produce the estimated fluence map(s) .” See also paragraphs 9 and as it shows that the fluence maps contain the fields for radiation (beam direction, beam angle, gantry angles) “[0009] In some implementations, each of the estimated fluence maps is a two-dimensional array of beamlet weights normal to a respective beam direction, and beam angles of the radiotherapy treatment correspond to gantry angles of a radiotherapy treatment machine . Further, obtaining the three-dimensional set of image data corresponding to a subject may include obtaining and projecting image data for each gantry angle of the radiotherapy treatment machine, such that each generated anatomy projection image represents a view of the anatomy of the subject from a given gantry angle used to provide treatment with a given radiotherapy beam .” See also paragraphs 40 and 50 as it shows the plurality of fields too. See also paragraph 57, as it shows that the received medical images include fluence maps and general medical images. See paragraphs 53-54, it shows the received patient data. See also paragraph 110. Hibbard) “determining fluence-related information for each of the plurality of fields based on the fluence maps;” (See paragraph 50 “[0050] Referring back to FIG. 1, in yet another example, the software programs 144 may generate graphical image representations of fluence map data (variously referred to as fluence map representations, fluence map images, or “fluence maps ”) at various radiotherapy beam and gantry angles , using the machine learning techniques discussed herein. In particular, the software programs 144 may optimize information from these fluence map representations in machine learning-assisted aspects of fluence map optimization . Such fluence map data is ultimately used generate and refine a set of control points that control a radiotherapy device to produce a radiotherapy beam . The control points may represent the beam intensity, gantry angle relative to the patient position, and the leaf positions of the MLC, among other machine parameters, to deliver the dose specified by the fluence map representation .” Hibbard) “aggregating the fluence-related information for the plurality of fields;” (See paragraph 24, the superimposed fluence maps are overlaid (therefore there is an aggregation of fluence information) “[0124] FIG. 13 further depicts, in each respective row , a set of 2D anatomy projections 1310, corresponding 2D fluence maps 1320, and superimposed 2D fluence maps 1330 that show fluence maps overlaid on anatomy projections . FIG. 13 further depicts, in each column, these projections and maps at linac gantry angles 90° (arrangement 1340), 120° (arrangement 1350), 150° (arrangement 1360), and 180° (arrangement 1370), respectively. Through the use of projection transformations, the 3D voxel data can be accurately be represented in a format compatible with the geometry of the treatment. ” See also paragraphs 109-112. Hibbard) “determining a multi-field dose for the patient based on the one or more medical images and the aggregated fluence-related information using a machine learning based dose prediction network; and” (See paragraphs 102, 110, 111 and 120. “[0120] In an example, various data formatting techniques are applied to training and inferencing fluence data within a machine learning model , to analyze the projection and fluence map image representations provided in FIG. 10. In practice, the anatomy and fluence or dose data exists in 3D rectilinear arrays. The FMO result, however, is the idealized 3D dose distribution corresponding to the optimal fluence maps . With the techniques discussed below, 2D fluence map representations can be produced at geometric planes normal to the beam (in coplanar treatments), in relationship to the linac and patient coordinate systems analogous to a beam's-eye-view projections of patient anatomy . Thus, anatomy and fluence maps may be represented as planar projections in a cylindrical coordinate system and used to train and draw inferences from the machine learning model for FMO .” See also paragraph 124 and 109-112. See paragraphs 37-41 FMO is the resulting multi field dosage plan “[0039] FMO is conventionally performed as a numerical computation, producing a 3D dose distribution covering the target while attempting to minimize the effect of dose on nearby OARs . As will be understood, the optimal fluence maps and the resulting 3D dose distribution produced from use of a fluence map are often referred to as a “plan”, even though the fluence 3D dose distribution must be resampled and transformed to accommodate linac and multileaf collimator (MLC) properties to become a clinical, deliverable treatment pla n… However, for purposes of simplicity, references below to a “plan” used below generally refer to the planned radiation dose derived from the fluence map optimization and the outcome of a trained model adapted to produce a fluence map. ” See also paragraphs 96-101, 10, 69-72 and 76-77, they show more information regarding the multi field dose for radiotherapy treatment. See also paragraph 50, The fluence map representations that is output, specifies the dosage. See also paragraphs 118. Hibbard ) “outputting the multi-field dose.” (See also paragraph 50, The fluence map representations that is output, specifies the dosage. See also paragraphs 137, 139 and 141 “[0139] The model shown in FIG. 15A depicts an arrangement adapted for generating an output data set (output fluence map representation images 1540) based on an input training set 1510 (e.g., paired anatomy images and fluence map representation images).” See also paragraphs 151-160. Hibbard) Claim 10 is rejected under the same analysis as claim 1. Claim 15 is rejected under the same analysis as claim 1. (Paragraph 206 shows non transitory media. Hibbard) As per claim 2, Hibbard teaches “the computer-implemented method of claim 1, further comprising: determining an approximated dose for the patient based on the fluence-related information for the plurality of fields, wherein determining a multi-field dose for the patient based on the one or more medical images and the aggregated fluence-related information using a machine learning based dose prediction network comprises determining the multi-field dose for the patient further based on the approximated dose.” (See paragraphs 119-120, The FMO result shows the approximated dose. “[0120] In an example, various data formatting techniques are applied to training and inferencing fluence data within a machine learning model, to analyze the projection and fluence map image representations provided in FIG. 10. In practice, the anatomy and fluence or dose data exists in 3D rectilinear arrays. The FMO result, however, is the idealized 3D dose distribution corresponding to the optimal fluence maps. ”. See also paragraphs 102, 109-112, 120, 124, 37-41. “[0039] FMO is conventionally performed as a numerical computation, producing a 3D dose distribution covering the target while attempting to minimize the effect of dose on nearby OARs . As will be understood, the optimal fluence maps and the resulting 3D dose distribution produced from use of a fluence map are often referred to as a “plan”, See also paragraphs 96-101, 10, 69-72 and 76-77. Hibbard) Claim 11 is rejected under the same analysis as claim 2. As per claim 3, Hibbard teaches “the computer-implemented method of claim 2, wherein determining a multi-field dose for the patient based on the one or more medical images and the aggregated fluence-related information using a machine learning based dose prediction network comprises: determining an initial multi-field dose based on the one or more medical images and the aggregated fluence-related information using the machine learning based dose prediction network; and combining the initial multi-field dose and the approximated dose to determine the multi-field dose for the patient.” (See paragraphs 37-41. The FMO (fluence map optimization) starts with initial values to then train it with parameters while utilizing its own information to obtain an optimized FMO result, therefore it covers the BRI (broadest reasonable interpretation) of the claim language “0037] The present disclosure includes various techniques to improve and enhance radiotherapy treatment by generating fluence map values , as part of a model-driven fluence map optimization (FMO) process during radiotherapy plan design . This model may comprise a trained machine learning model, such as an artificial neural network model, which is trained to produce (predict) a computer-modeled, image-based representation of fluence map values from a given input .” “[0041] FMO performs an exhaustive optimization of target and OAR constraints dependent on thousands of small beamlets aimed at the target from many directions, and the beamle ts' weights and physics parameters describing the material fluence dispersion for each beamlet. This high-dimensional optimization typically starts from default initial values for the parameters , without regard to the specific patient's anatomy. Among other techniques, the following discusses creation and training of an anatomy-dependent model of the FMO parameters so the calculation can be initialized closer to the ideal end values for the parameters , thus reducing the time needed to produce a satisfactory fluence map. Additionally, such an anatomy-dependent model of the FMO parameters may be adapted for verification or validation of fluence maps, and integrated in a variety of ways for radiotherapy planning ” Therefore the FMO values (which includes the multi field dose and approximated dose) are combined to get the final output. See also paragraphs 119-120. Hibbard) Claim 12 is rejected under the same analysis as claim 3. As per claim 4, Hibbard already teaches “the computer-implemented method of claim 1, wherein determining a multi-field dose for the patient based on the one or more medical images and the aggregated fluence-related information using a machine learning based dose prediction network comprises:”, Hibbard also teaches “determining electron angular fluences using the machine learning based dose prediction network; and determining the multi-field dose for the patient based on the electron angular fluences.” (See paragraphs 2 and 7, it shows the particles used for fluence “[0002] Radiation therapy (or “radiotherapy”) can be used to treat cancers or other ailments in mammalian (e.g., human and animal) tissue… Another such radiotherapy technique is provided using a linear accelerator (linac), whereby a tumor is irradiated by high-energy particles (e.g., electrons, protons, ions, high-energy photons, and the like).” “[0007] As part of the treatment planning process for radiotherapy dosing, fluence is determined and evaluated. Fluence is the density of radiation photons or particles normal to the beam direction, whereas dose is related to the energy released in the material when the photons or particles interact with the material atoms. Dose is therefore dependent on the fluence and the physics of the radiation-matter interactions. Significant planning is conducted as part of determining fluence and dosing for a particular patient and treatment plan.” Paragraph 7 shows that fluence is the density of electrons to a beam direction (which contains the angle). Therefore the FMO process taught by the reference also covers the BRI of the claim limitation as examiner interprets “electron angular fluence” as one of the already covered fluences. It also shows that the created fluence maps use both electron and the angle of the beam that irradiates the electrons “[0009] In some implementations, each of the estimated fluence maps is a two-dimensional array of beamlet weights normal to a respective beam direction, and beam angles of the radiotherapy treatment correspond to gantry angles of a radiotherapy treatment machine. Further, obtaining the three-dimensional set of image data corresponding to a subject may include obtaining and projecting image data for each gantry angle of the radiotherapy treatment machine , such that each generated anatomy projection image represents a view of the anatomy of the subject from a given gantry angle used to provide treatment with a given radiotherapy beam .” See also paragraphs 37-41. Hibbard) Claim 13 is rejected under the same analysis as claim 4. As per claim 5, Hibbard teaches “the computer-implemented method of claim 1, wherein aggregating the fluence-related information for the plurality of fields comprises: transforming a direction of the fluence-related information to coefficient vectors; scaling the coefficient vectors based on the fluence-related information; and combining the scaled coefficient vectors to provide for the aggregated fluence-related information.” (Paragraphs 104-108 and 131 shows a vector of beamlet weights that are aggregated by the presented equations in those paragraphs and as also shown on paragraph 131-132. Paragraphs 118 and 5 shows that the set of parameters are the set of beam directions. Paragraph 72 shows that parameters are considered weights. Paragraphs 40-41, 97, 101, 189, 9, show that beamlet weights contain a direction. Therefore a “direction” is transformed into a vector used in paragraphs 104-108 and 131-132. “[0009] In some implementations, each of the estimated fluence maps is a two-dimensional array of beamlet weights normal to a respective beam direction, ” “[0105] Where d i (b) is the dose deposited in voxel i from beamlet j with intensity b j , and the vector of n beamlet weights is b=(b 1 , . . . , b n ) T . D ij is the dose deposition matrix.” Paragraph 138 shows that the machine learning model creates scaled versions of the data arrays (arrays of vector coefficient beamlet weights that contain beam direction), it also shows that the scaled data (which contains vector coefficients) are recombined (therefore aggregated). See also equations 1-11. “0138] A U-Net CNN creates scaled versions of the input data arrays on the encoding side by max pooling and re-combines the scaled data with learned features at increasing scales by transposed convolution on the encoding side to achieve high performance inference. The black rectangular blocks represent combinations of convolution/batch normalization/rectified linear unit (ReLU) layers; two or more are used at each scale level . The blocks' vertical dimension corresponds to the image scale (S) and the horizontal dimension is proportional to the number of convolution filters (F) at that scale. Equation 7 above is a typical U-Net loss function.” Hibbard) Claim 16 is rejected under the same analysis as claim 5. As per claim 6, Hibbard already teaches “the computer-implemented method of claim 5, wherein transforming a direction of the fluence-related information to coefficient vectors comprises: transforming the direction of the fluence-related information to coefficient vectors for each voxel of the fluence maps.” (First see the processes shown in the rejection of claim 5, they are for each voxel as already expressed in paragraphs 104-108 “[0105] Where di(b) is the dose deposited in voxel i from beamlet j with intensity bj, and the vector of n beamlet weights is b=(b1, . . . , bn)T. Dij is the dose deposition matrix.” “[0108] where the sum is over all voxels…”. See also paragraphs 114, 115, 121 and 44. Hibbard ) Claim 17 is rejected under the same analysis as claim 6. As per claim 7, Hibbard teaches “the computer-implemented method of claim 5, wherein combining the scaled coefficient vectors to provide for the aggregated fluence-related information comprises: combining the scaled coefficient vectors into accumulated coefficients for each of the plurality of fields; and combining the accumulated coefficients for each of the plurality of fields into combined coefficients.” (See paragraphs 104-108 which show the coefficient vectors, “b” these are accumulated and combined as coefficients of the field (parameters) as seen in paragraphs 124-125 “[0124] FIG. 13 further depicts, in each respective row, a set of 2D anatomy projections 1310, corresponding 2D fluence maps 1320, and superimposed 2D fluence maps 1330 that show fluence maps overlaid on anatomy projections . FIG. 13 further depicts, in each column, these projections and maps at linac gantry angles 90° (arrangement 1340), 120° (arrangement 1350), 150° (arrangement 1360), and 180° (arrangement 1370), respectively. Through the use of projection transformations, the 3D voxel data can be accurately be represented in a format compatible with the geometry of the treatment…[0125] In various examples, these projections may be used for training machine learning models to produce a prediction of fluence map or plan parameters … ” and Paragraph 138 shows that the machine learning model creates scaled versions of the data arrays (arrays of vector coefficient beamlet weights that contain beam direction), it also shows that the scaled data (which contains vectors and its coefficients) are recombined (therefore aggregated).. See also equations 1-11. “0138] A U-Net CNN creates scaled versions of the input data arrays on the encoding side by max pooling and re-combines the scaled data with learned features at increasing scales by transposed convolution on the encoding side to achieve high performance inference. The black rectangular blocks represent combinations of convolution/batch normalization/rectified linear unit (ReLU) layers; two or more are used at each scale level . The blocks' vertical dimension corresponds to the image scale (S) and the horizontal dimension is proportional to the number of convolution filters (F) at that scale. Equation 7 above is a typical U-Net loss function.” Hibbard) Claim 18 is rejected under the same analysis as claim 7. As per claim 8, Hibbard already teaches “the computer-implemented method of claim 1, wherein determining a multi-field dose for the patient based on the one or more medical images and the aggregated fluence-related information using a machine learning based dose prediction network comprises:”, Hibbard also teaches “determining the multi-field dose for the patient further based on a material volume of tissue to be irradiated.” (See paragraphs 192, 112, 10, 54, 72, 133 and 123 “[0072] After the target tumor and the OAR(s) have been located and delineated, a dosimetrist, physician, or healthcare worker may determine a dose of radiation to be applied to the target tumor , as well as any maximum amounts of dose that may be received by the OAR proximate to the tumor ( e.g., left and right parotid, optic nerves, eyes, lens, inner ears, spinal cord, brain stem, and the like). After the radiation dose is determined for each anatomical structure (e.g., target tumor, OAR), a process known as inverse planning may be performed to determine one or more treatment plan parameters that would achieve the desired radiation dose distribution . Examples of treatment plan parameters include volume delineation parameters (e.g., which define target volumes, contour sensitive structures, etc.), margins around the target tumor and OARs, beam angle selection, collimator settings, and beam-on times.” See also paragraphs 123 and 137 “0123] FIG. 12 next depicts a set of equivalent-geometry fluence maps, corresponding to the views depicted in FIG. 11. As depicted, 2D fluence maps are produced from converting an FMO dose distribution volume 1200 into a thresholded volume 1202, as a dose distribution from radiotherapy beams (e.g., as depicted in a 2D dose distribution projection 1201)…” Hibbard) Claim 14 is rejected under the same analysis as claim 8. Claim 19 is rejected under the same analysis as claim 8 . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hibbard in view of Gifford et. al. (Gifford, Kent A., et al. "Comparison of a finite-element multigroup discrete-ordinates code with Monte Carlo for radiotherapy calculations." Physics in Medicine & Biology 51.9 (2006): 2253-2265.) . As per claim 9, Hibbard already teaches “the computer-implemented method of claim 1, wherein the fluence-related information comprises”, however Hibbard does not teach “uncollided fluence.”, Gifford teaches “uncollided fluence.” (See pages 8-9 section 2.1.5 Mitigation of ray effects “Let us separate the angular fluence into collided and uncollided parts so that… is the uncollided angular fluence and… is the collided angular fluence… The uncollided solution for each point source is then obtained using equation (32) and the final uncollided solution is obtained from… ” Gifford.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Hibbard with the teachings of Gifford to include uncollided fluence data. The modification would have been motivated by the desire to mitigate ray effects, in addition it also improves performance, therefore it is an improvement, as suggested by Gifford (See pages 8-9 section 2.1.5 Mitigation of ray effects “ Ray-effects can be mitigated by increasing the quadrature order, but this is often computationally expensive . Many discrete-ordinates codes employ the first collision source method to mitigate ray-effects with a lower quadrature order . This method can use either stochastic (Monte Carlo methods) or semi-analytic methods for the solution of the uncollided fluence, hence, the first collision source .” Gifford) Claim 20 is rejected under the same analysis as claim 9. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN J MENDEZ MUNIZ whose telephone number is (703)756-5672. The examiner can normally be reached M-F, 8AM - 5PM ET. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DYLAN JOHN MENDEZ MUNIZ/Examiner, Art Unit 2675 /ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675 Application/Control Number: 18/895,899 Page 2 Art Unit: 2675 Application/Control Number: 18/895,899 Page 3 Art Unit: 2675 Application/Control Number: 18/895,899 Page 4 Art Unit: 2675 Application/Control Number: 18/895,899 Page 5 Art Unit: 2675 Application/Control Number: 18/895,899 Page 6 Art Unit: 2675 Application/Control Number: 18/895,899 Page 7 Art Unit: 2675 Application/Control Number: 18/895,899 Page 8 Art Unit: 2675 Application/Control Number: 18/895,899 Page 9 Art Unit: 2675 Application/Control Number: 18/895,899 Page 10 Art Unit: 2675 Application/Control Number: 18/895,899 Page 11 Art Unit: 2675 Application/Control Number: 18/895,899 Page 12 Art Unit: 2675 Application/Control Number: 18/895,899 Page 13 Art Unit: 2675 Application/Control Number: 18/895,899 Page 14 Art Unit: 2675 Application/Control Number: 18/895,899 Page 15 Art Unit: 2675 Application/Control Number: 18/895,899 Page 16 Art Unit: 2675 Application/Control Number: 18/895,899 Page 17 Art Unit: 2675 Application/Control Number: 18/895,899 Page 18 Art Unit: 2675 Application/Control Number: 18/895,899 Page 19 Art Unit: 2675
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