CTNF 18/892,888 CTNF 101461 DETAILED ACTION 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 The information disclosure statements (IDS) submitted on 09/23/2024 and 02/13/2026 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. 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. Claims 8-10 and 12 recite limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f): Claim 8; recites the limitation, “means for receiving one or more medical images of a patient…..” [Line 2]. Claim 8; recites the limitation, “means for detecting one or more candidate nodules in the one or more medical images…..” [Line 3]. Claim 8; recites the limitation, “means for determining a malignancy score for each of the one or more candidate nodules…...” [Line 6]. Claim 8; recites the limitation, “means for weighting……,” [Line 8]. Claim 8; recites the limitation, “means for outputting……,” [Line 10]. Claim 9; recites the limitation, “means for determining a malignancy score for each of the one or more candidate nodules……,” [Line 1]. Claim 9; recites the limitation, “means for determining a nodule type score…...” [Line 4]. Claim 9; recites the limitation, “means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule……,” [Line 6]. Claim 10; recites the limitation, “means for detecting one or more candidate nodules in the one or more medical images…..” [Line 2]. Claim 10; recites the limitation, “means for determining a size of each of the one or more candidate nodules……,” [Line 3]. Claim 10; recites the limitation, “means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule ……,” [Line 5]. Claim 10; recites the limitation, “means for determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule……,” [Line 7-8]. Claim 12; recites the limitation, “means for receiving user input…..” [Line 2]. Claim 12; recites the limitation, “means for filtering the one or more candidate nodules……,” [Line 4]. Claim 12; recites the limitation, “means for displaying the one or more filtered candidate nodules……,” [Line 6]. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 8-10 and 12; (i) “means for receiving one or more medical images of a patient” (Fig. 7, #702. Paragraph [0020]-The one or more medical images may be received, for example, by directly receiving the one or more medical images from the image acquisition device (e.g., image acquisition device 714 of Figure 7) as the one or more medical images are acquired, by loading the one or more medical images from a storage or memory of a computer system (e.g., storage 712 or memory 710 of computer 702 of Figure 7), or by receiving the one or more medical images from a remote computer system (e.g., computer 702 of Figure 7). Such a computer system or remote computer system may comprise one or more patient databases, such as, e.g. , an EHR (electronic health record), EMR (electronic medical record), PHR (personal health record), HIS (health information system), RIS (radiology information system), PACS (picture archiving and communication system), LIMS (laboratory information management system), or any other suitable database or system. The means for receiving one or more medical images of a patient is illustrated in Fig. 7, as a black box #702 thus has sufficient structure or material wherein is a computer with storage and memory. ). (ii) “means for detecting one or more candidate nodules in the one or more medical images” (Paragraph [0022]- The one or more candidate nodules are detected using a machine learning based detection model. For example, as shown in workflow 200 of Figure 2, candidate nodules 206 are detected in 3D CT scan 202 by detection model 204. The one or more candidate nodules may be detected from the one or more medical images using the machine learning detection model according to any suitable (e.g., well-known) approach. The means for detecting one or more candidate nodules in the one or more medical images does not have sufficient structure or material. ). (iii) “means for determining a malignancy score for each of the one or more candidate nodules” (Paragraph [0028]- The malignancy score of the respective candidate nodule is then determined from the extracted patch using a machine learning based malignancy classification model based on the classification of the type of the respective candidate nodule and the size of the respective candidate nodule (determined by the machine learning based detection model at step 104 of Figure 1). For example, as shown in workflow 200 of Figure 2, a malignancy 216 of the respective candidate nodule 206 is determined from the extracted 3D nodule patch 208 by malignancy classification model 214 based on type 212 of the respective candidate nodule 206 and a size of the respective candidate nodule 206 (determined by detection model 204). The malignancy of the respective candidate nodule may be determined from the extracted patch using the machine learning based malignancy classification model according to any suitable (e.g., well-known) approach. The means for determining a malignancy score for each of the one or more candidate nodules does not have sufficient structure or material wherein. ). (iv) “means for weighting” (Paragraph [0029-0030]- At step 108 of Figure 1, the nodule detection scores are weighted based on their malignancy scores. In one example, as shown in workflow 200 of Figure 2, the probability associated with candidate nodules 206 are weighted by malignancy-weighting module 218 based on malignancy 216 to provide an updated candidate nodules 220 defining an updated probability (i.e., weighted nodule detection scores) as well as the locations and sizes (as determined by detection model 204). In one embodiment, the nodule detection scores may be weighted according to the weighting equation of Equation (1): p’=[(m + c) γ /(1 + c) γ ]p, where p' represents the weighted nodule detection score, p represents the nodule detection score, m represents the malignancy score, and c and γ are parameters for regulating the weight adjustment. The means for weighting has sufficient structure or material wherein is Equation (1): p’=[(m + c) γ /(1 + c) γ ]p , where p' represents the weighted nodule detection score, p represents the nodule detection score, m represents the malignancy score, and c and γ are parameters for regulating the weight adjustment. ). (v) “means for outputting” (Paragraph [0032]- At step 110 of Figure 1, the weighted nodule detection scores are output. In some embodiments, the locations and/or sizes of the one or more candidate nodules are also output. For example, the locations, sizes, and/or weighted nodule detection scores for the one or more candidate nodules can be output by displaying the locations, sizes, and/or weighted nodule detection scores on a display device of a computer system (e.g., I/O 708 of computer 702 of Figure 7), storing the locations, sizes, and/or weighted nodule detection scores on a memory or storage of a computer system (e.g., memory 710 or storage 712 of computer 702 of Figure 7), or by transmitting the locations, sizes, and/or weighted nodule detection scores to a remote computer system (e.g., computer 702 of Figure 7). The means for outputting has sufficient structure or material wherein is the memory of a computer. ). (vi) “means for determining a nodule type score” (Fig. 5. Paragraph [0062-0063 and 0088]- [0026]- The type of the respective candidate nodule may be determined from the extracted patch using the machine learning based type classification model according to any suitable (e.g., well- known) approach. [0027] The machine learning based type classification model receives as input the extracted patch and generates as output a classification of a type of the respective candidate nodule. The type of the respective candidate nodule may comprise, for example, solid nodule, part-solid nodule, non-solid nodule, calcified nodule, perifissural nodule, and non-nodule. The classification of the type of the respective candidate nodule may comprise a nodule type score for each of a plurality of types of nodules. The nodule type score represents a probability that the respective candidate nodule is of a particular type. The nodule type score may be represented in any suitable format (e.g., a value between 0 and 1). The means for determining a nodule type score does not have sufficient structure or material. ). (vii) “means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule” (Paragraph [0028]- The malignancy of the respective candidate nodule may be determined from the extracted patch using the machine learning based malignancy classification model according to any suitable (e.g., well-known) approach. In some embodiments, the malignancy score of the respective candidate nodule may be determined according to U.S. Patent Application No. 18/629,023, entitled "Computer- Aided Diagnosis System for Pulmonary Nodule Analysis using PCCT Images," the disclosure of which is incorporated herein by reference in its entirety. The machine learning based malignancy classification model receives as input the extracted patch, the classification of the type of the respective candidate nodules (i.e., the nodule type scores), and the size of the respective candidate nodule and generates as output a classification of a malignancy of the respective candidate nodule defining the malignancy score of the respective candidate nodule. The means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule does not have sufficient structure or material. ). (viii) “means for determining a size of each of the one or more candidate nodules” (Paragraph [0022 and 0028]- The one or more candidate nodules may be detected from the one or more medical images using the machine learning detection model according to any suitable (e.g., well-known) approach. The malignancy score of the respective candidate nodule is then determined from the extracted patch using a machine learning based malignancy classification model based on the classification of the type of the respective candidate nodule and the size of the respective candidate nodule (determined by the machine learning based detection model at step 104 of Figure 1). The means for determining a size of each of the one or more candidate nodules does not have sufficient structure or material. ). (ix) “means for determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule” (Paragraph [0028]- The malignancy of the respective candidate nodule may be determined from the extracted patch using the machine learning based malignancy classification model according to any suitable (e.g., well-known) approach. In some embodiments, the malignancy score of the respective candidate nodule may be determined according to U.S. Patent Application No. 18/629,023, entitled "Computer- Aided Diagnosis System for Pulmonary Nodule Analysis using PCCT Images," the disclosure of which is incorporated herein by reference in its entirety. The machine learning based malignancy classification model receives as input the extracted patch, the classification of the type of the respective candidate nodules (i.e., the nodule type scores), and the size of the respective candidate nodule and generates as output a classification of a malignancy of the respective candidate nodule defining the malignancy score of the respective candidate nodule. The means for determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule does not have sufficient structure or material. ). (x) “means for receiving user input” (Paragraph [0034 and 0085]- Figure 3 shows a user interface 300 for defining visualization settings for viewing the one or more candidate nodules, in accordance with one or more embodiments. A user may interact with user interface 300 via, e.g., a touchscreen, a mouse, a keyboard, or any other suitable device (e.g., I/O 708 of Figure 7) for defining the criteria to filter the one or more candidate nodules. Input/output devices 708 may include a display device such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor for displaying information to the user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to computer 702. The means for receiving user input has sufficient structure or material wherein is a mouse or trackball. ). (xi) “means for filtering the one or more candidate nodules” (Paragraph [0034]-Figure 3 shows a user interface 300 for defining visualization settings for viewing the one or more candidate nodules, in accordance with one or more embodiments. A user may interact with user interface 300 via, e.g., a touchscreen, a mouse, a keyboard, or any other suitable device (e.g., I/O 708 of Figure 7) for defining the criteria to filter the one or more candidate nodules. The one or more candidate nodules are filtered according to the visualization settings. Detected nodule settings 302 enable a user to filter the one or more candidate nodules to view high-confident detected candidate nodules, high & median-confident detected candidate nodules, or high & median & low-confident detected candidate nodules. The high, median, and low- confidence settings are each associated with a threshold of the weighted nodule detection scores of the one or more candidate modules. Nodule size settings 304 enable the user to define one or more thresholds to filter the one or more candidate nodules to view candidate nodules according to nodule size. Nodule types settings 306 enable a user to filter the one or more candidate nodules according to one or more nodule types. Malignant nodule settings 308 enable a user to filter the one or more candidate nodules to view high-confident malignant candidate nodules, high & median-confident malignant candidate nodules, or high & median & low-confident malignant candidate nodules. The means for filtering the one or more candidate nodules does not have sufficient structure or material. ). (xii) “means for displaying the one or more filtered candidate nodules” (Paragraph [0034 and 0085]- Figure 3 shows a user interface 300 for defining visualization settings for viewing the one or more candidate nodules, in accordance with one or more embodiments. A user may interact with user interface 300 via, e.g., a touchscreen, a mouse, a keyboard, or any other suitable device (e.g., I/O 708 of Figure 7) for defining the criteria to filter the one or more candidate nodules. The one or more candidate nodules are filtered according to the visualization settings. Detected nodule settings 302 enable a user to filter the one or more candidate nodules to view high-confident detected candidate nodules, high & median-confident detected candidate nodules, or high & median & low-confident detected candidate nodules. The high, median, and low- confidence settings are each associated with a threshold of the weighted nodule detection scores of the one or more candidate modules. Nodule size settings 304 enable the user to define one or more thresholds to filter the one or more candidate nodules to view candidate nodules according to nodule size. Input/output devices 708 may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices 708 may include a display device such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor for displaying information to the user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to computer 702. The means for displaying the one or more filtered candidate nodules has sufficient structure or material wherein is a cathode ray tube or liquid crystal display. ). If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 AIA Claim s 8-10 and 12 along with their dependent claims, claims 11 and 13-14, 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 pre-AIA the applicant regards as the invention. Claims 8-10 and 12 limitations: Claim 8; recites the limitation, “means for detecting one or more candidate nodules in the one or more medical images…..” [Line 3]. Claim 8; recites the limitation, “means for determining a malignancy score for each of the one or more candidate nodules…...” [Line 6]. Claim 9; recites the limitation, “means for determining a malignancy score for each of the one or more candidate nodules……,” [Line 1]. Claim 9; recites the limitation, “means for determining a nodule type score…...” [Line 4]. Claim 9; recites the limitation, “means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule……,” [Line 6]. Claim 10; recites the limitation, “means for detecting one or more candidate nodules in the one or more medical images…..” [Line 2]. Claim 10; recites the limitation, “means for determining a size of each of the one or more candidate nodules……,” [Line 3]. Claim 10; recites the limitation, “means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule ……,” [Line 5]. Claim 10; recites the limitation, “means for determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule……,” [Line 7-8]. Claim 12; recites the limitation, “means for filtering the one or more candidate nodules……,” [Line 4]. 07-34-23 Claims 8-10 and 12 respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. 07-30-01 AIA 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. 07-31-01 AIA Claim 8-10 and 12 along with their dependent claims, claims 11 and 13-14, 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation . Claim 8; recites the limitation, “means for detecting one or more candidate nodules in the one or more medical images…..” [Line 3]. Claim 8; recites the limitation, “means for determining a malignancy score for each of the one or more candidate nodules…...” [Line 6]. Claim 9; recites the limitation, “means for determining a malignancy score for each of the one or more candidate nodules……,” [Line 1]. Claim 9; recites the limitation, “means for determining a nodule type score…...” [Line 4]. Claim 9; recites the limitation, “means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule……,” [Line 6]. Claim 10; recites the limitation, “means for detecting one or more candidate nodules in the one or more medical images…..” [Line 2]. Claim 10; recites the limitation, “means for determining a size of each of the one or more candidate nodules……,” [Line 3]. Claim 10; recites the limitation, “means for determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule ……,” [Line 5]. Claim 10; recites the limitation, “means for determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule……,” [Line 7-8]. Claim 12; recites the limitation, “means for filtering the one or more candidate nodules……,” [Line 4]. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 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. 07-21-aia AIA Claim s 1, 7, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over SONG et al. (US 20190050982 A1), hereinafter referenced as SONG, in view of HEAMES et al. (US 20240212136 A1), hereinafter referenced as HEAMES . Regarding claim 1, SONG explicitly teaches a computer-implemented method comprising (Fig. 1. Paragraph [0048]-SONG discloses in a first embodiment of the invention a computer implemented method for analysing medical scan images from a single patient is provided) : receiving one or more medical images (Fig. 1. Paragraph [0023]-SONG discloses lung volumetric CT images may be obtained by a chest CT scan and then input into a nodule detection system 100.) of a patient (Fig. 1. Paragraph [0023]-SONG discloses FIG. 1 illustrates an exemplary prediction system 101 for automatically predicting physiological condition from a medical image of a patient according to an embodiment of present disclosure.) ; detecting one or more candidate nodules in the one or more medical images (Fig. 2. Paragraph [0026]-SONG discloses the nodule detection system 100 acquires 3D medical images from 3D medical image database 206, detects the nodules therefrom, and outputting the 3D lung nodule patches.) , each of the one or more candidate nodules associated with a nodule detection score (Fig. 7. Paragraph [0041]-SONG discloses the n nodule patches may be the top n nodules determined based on nodule detection confidence by using a threshold. Further in paragraph [0046]-SONG discloses top n nodules may be determined based on the confidence level of nodule detection. (wherein the confidence level is a confidence score and wherein the nodules n are candidate nodules).) ; determining a malignancy score for each of the one or more candidate nodules (Fig. 1. Paragraph [0024]-SONG discloses the prediction system 101 obtains the nodule patches from the nodule detection system 100, predicts the malignancy level (such as malignancy probability or malignancy score) of each lung nodule and/or a probability that the patient will develop or has developed a lung cancer as prediction results and outputs the same (wherein a nodule is a candidate nodule).) ; SONG fails to explicitly teach weighting the nodule detection score for each of the one or more candidate nodules based on its malignancy score; and outputting the weighted nodule detection scores. However, HEAMES explicitly teaches weighting the nodule detection score (Fig. 7. Paragraph [0084-0086]-HEAMES discloses the workflow optimisation model (180) then computes an ordering of the findings by considering all feature characterisations simultaneously. For example, lung nodules are detected by the feature detection circuit (125), and risk of malignancy and predicted invasiveness (e.g., according to their histological type) of a potential malignant nodule are given as characterisation of each lung nodule by the feature characterisation circuit (150). In an example of this embodiment, where the nodule invasiveness potential is codified as a probability of invasiveness within a pre-specified time period t, the clinical relevance of each of the lung nodule findings can be computed as: lung_nodule_relevance = p (malignancy | im )* p (invasiveness | im, t ) (1) That is, the relevance of each lung nodule finding is scored as the probability of malignancy, as estimated form the medical image, multiplied by the probability of the lung nodule invading surrounding tissue within as time period t (wherein in equation (1) the lung nodule relevance is weighted according to the risk of malignancy, wherein lung nodule relevance is the nodule detection score and wherein the risk of malignancy is the malignancy score).) for each of the one or more candidate nodules based on its malignancy score (Fig. 7. Paragraph [0086]-HEAMES discloses the relevance of each lung nodule finding is scored as the probability of malignancy, as estimated form the medical image, multiplied by the probability of the lung nodule invading surrounding tissue within as time period t (wherein a lung nodule is a candidate nodule).) ; and outputting the weighted nodule detection scores (Fig. 7. Paragraph [0085]-HEAMES discloses in an example of this embodiment, where the nodule invasiveness potential is codified as a probability of invasiveness within a pre-specified time period t, the clinical relevance of each of the lung nodule findings can be computed as: lung_nodule_relevance = p (malignancy | im )* p (invasiveness | im, t ) (1) That is, the relevance of each lung nodule finding is scored as the probability of malignancy, as estimated form the medical image, multiplied by the probability of the lung nodule invading surrounding tissue within as time period t (wherein calculating the lung nodule relevance through equation (1) is outputting the weighted nodule detection score as the results of equations are outputs).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of HEAMES of weighting the nodule detection score for each of the one or more candidate nodules based on its malignancy score; and outputting the weighted nodule detection scores. Wherein having SONG’s method of detecting and evaluating nodules having weighting the nodule detection score for each of the one or more candidate nodules based on its malignancy score; and outputting the weighted nodule detection scores. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and HEAMES are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while HEAMES radiologists can reduce the time invested in clinically trivial cases and prioritise those patients who require closer attention. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and HEAMES et al. (US 20240212136 A1), Paragraph [0050]. Regarding claim 7,SONG in view of HEAMES explicitly teach computer-implemented method of claim 1, SONG further explicitly teaches wherein the one or more candidate nodules comprises one or more candidate pulmonary nodules (Fig. 1 and 8. Paragraph [0023]-SONG discloses FIG. 1 illustrates an exemplary prediction system 101 for automatically predicting physiological condition from a medical image of a patient according to an embodiment of present disclosure. In this embodiment, the target object is a lung nodule. A lung nodule may become a target site for a treatment such as radiotherapy treatment (wherein a lung nodule is a pulmonary nodule).) . Regarding claim 15, SONG explicitly teaches a non-transitory computer-readable storage medium comprising instructions which (Fig. 9, #922 called MEMORY. Paragraph [0052]-SONG discloses the image processor 921 can execute sequences of computer program instructions, stored in memory 922, to perform various operations, processes, methods disclosed herein.) , when executed by a computer, cause the computer to carry out operations comprising (Fig. 9. Paragraph [0060]-SONG discloses the software code or instructions may be stored in computer readable storage medium, and when executed, may cause a machine to perform the described functions or operations and include any mechanism for storing information in the form accessible by a machine (e.g., computing device, electronic system, etc.), such as recordable or non-recordable media (e.g., read-only memory (ROM), random access memory (RAM), disk storage media, optical storage media, flash memory devices, etc.).) : receiving one or more medical images (Fig. 1. Paragraph [0023]-SONG discloses lung volumetric CT images may be obtained by a chest CT scan and then input into a nodule detection system 100.) of a patient (Fig. 1. Paragraph [0023]-SONG discloses FIG. 1 illustrates an exemplary prediction system 101 for automatically predicting physiological condition from a medical image of a patient according to an embodiment of present disclosure.) ; detecting one or more candidate nodules in the one or more medical images (Fig. 2. Paragraph [0026]-SONG discloses the nodule detection system 100 acquires 3D medical images from 3D medical image database 206, detects the nodules therefrom, and outputting the 3D lung nodule patches.) , each of the one or more candidate nodules associated with a nodule detection score (Fig. 7. Paragraph [0041]-SONG discloses the n nodule patches may be the top n nodules determined based on nodule detection confidence by using a threshold. Further in paragraph [0046]-SONG discloses top n nodules may be determined based on the confidence level of nodule detection. (wherein the confidence level is a confidence score and wherein the nodules n are candidate nodules).) ; determining a malignancy score for each of the one or more candidate nodules (Fig. 1. Paragraph [0024]-SONG discloses the prediction system 101 obtains the nodule patches from the nodule detection system 100, predicts the malignancy level (such as malignancy probability or malignancy score) of each lung nodule and/or a probability that the patient will develop or has developed a lung cancer as prediction results and outputs the same (wherein a nodule is a candidate nodule).) ; SONG fails to explicitly teach weighting the nodule detection score for each of the one or more candidate nodules based on its malignancy score; and outputting the weighted nodule detection scores. However, HEAMES explicitly teaches weighting the nodule detection score (Fig. 7. Paragraph [0084-0086]-HEAMES discloses the workflow optimisation model (180) then computes an ordering of the findings by considering all feature characterisations simultaneously. For example, lung nodules are detected by the feature detection circuit (125), and risk of malignancy and predicted invasiveness (e.g., according to their histological type) of a potential malignant nodule are given as characterisation of each lung nodule by the feature characterisation circuit (150). In an example of this embodiment, where the nodule invasiveness potential is codified as a probability of invasiveness within a pre-specified time period t, the clinical relevance of each of the lung nodule findings can be computed as: lung_nodule_relevance = p (malignancy | im )* p (invasiveness | im, t ) (1) That is, the relevance of each lung nodule finding is scored as the probability of malignancy, as estimated form the medical image, multiplied by the probability of the lung nodule invading surrounding tissue within as time period t (wherein in equation (1) the lung nodule relevance is weighted according to the risk of malignancy, wherein lung nodule relevance is the nodule detection score and wherein the risk of malignancy is the malignancy score).) for each of the one or more candidate nodules based on its malignancy score (Fig. 7. Paragraph [0086]-HEAMES discloses the relevance of each lung nodule finding is scored as the probability of malignancy, as estimated form the medical image, multiplied by the probability of the lung nodule invading surrounding tissue within as time period t (wherein a lung nodule is a candidate nodule).) ; and outputting the weighted nodule detection scores (Fig. 7. Paragraph [0085]-HEAMES discloses in an example of this embodiment, where the nodule invasiveness potential is codified as a probability of invasiveness within a pre-specified time period t, the clinical relevance of each of the lung nodule findings can be computed as: lung_nodule_relevance = p (malignancy | im )* p (invasiveness | im, t ) (1) That is, the relevance of each lung nodule finding is scored as the probability of malignancy, as estimated form the medical image, multiplied by the probability of the lung nodule invading surrounding tissue within as time period t (wherein calculating the lung nodule relevance through equation (1) is outputting the weighted nodule detection score as the results of equations are outputs).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of HEAMES of weighting the nodule detection score for each of the one or more candidate nodules based on its malignancy score; and outputting the weighted nodule detection scores. Wherein having SONG’s method of detecting and evaluating nodules having weighting the nodule detection score for each of the one or more candidate nodules based on its malignancy score; and outputting the weighted nodule detection scores. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and HEAMES are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while HEAMES radiologists can reduce the time invested in clinically trivial cases and prioritise those patients who require closer attention. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and HEAMES et al. (US 20240212136 A1), Paragraph [0050] . 07-21-aia AIA Claim s 2-3 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over SONG et al. (US 20190050982 A1), hereinafter referenced as SONG, in view of HEAMES et al. (US 20240212136 A1), hereinafter referenced as HEAMES, and further in view of FUJISAWA (US 20130208970 A1), hereinafter referenced as FUJISAWA . Regarding claim 2, SONG in view of HEAMES explicitly teach the computer-implemented method of claim 1, SONG further explicitly teaches wherein determining a malignancy score for each of the one or more candidate nodules comprises (Fig. 1. Paragraph [0024]-SONG discloses the prediction system 101 obtains the nodule patches from the nodule detection system 100, predicts the malignancy level (such as malignancy probability or malignancy score) of each lung nodule and/or a probability that the patient will develop or has developed a lung cancer as prediction results and outputs the same (wherein a nodule is a candidate nodule).) : for each respective candidate nodule of the one or more candidate nodules (Fig. 1. Paragraph [0024]-SONG discloses the prediction system 101 obtains the nodule patches from the nodule detection system 100, predicts the malignancy level (such as malignancy probability or malignancy score) of each lung nodule and/or a probability that the patient will develop or has developed a lung cancer as prediction results and outputs the same (wherein a nodule is a candidate nodule).) : SONG in view of HEAMES fails to explicitly teach determining a nodule type score for each of a plurality of nodule types for the respective candidate nodule; and determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule. However, FUJISAWA explicitly teaches determining a nodule type score (Fig. 1. Paragraph [0036]-FUJISAWA discloses if the shape of the probable pulmonary nodule is determined and the shapes are categorized, the morphological tumor degree calculator 42 scores the shape of the probable pulmonary nodule. For example, it stores in advance a score table in which the shape of the probable pulmonary nodule and the score are associated with each other in storage (not shown in the figures) (wherein the score of the nodule shape is a nodule type score).) for each of a plurality of nodule types (Fig. 1. Paragraph [0036]-FUJISAWA discloses a score table is stored in advance in which each of spherical, triangular, linear, oval, and irregular shapes and the scores are associated with each other in storage (not shown in the figures) (wherein spherical, triangular, linear, oval, and irregular shapes are nodule types).) for the respective candidate nodule (Fig. 1. Paragraph [0036]-FUJISAWA discloses if the shape of the probable pulmonary nodule is determined and the shapes are categorized, the morphological tumor degree calculator 42 scores the shape of the probable pulmonary nodule (wherein the probable pulmonary nodule is the respective candidate nodule).) ; and determining the malignancy score (Fig. 1. Paragraph [0066]-FUJISAWA discloses the feature amount calculator 61 calculates the nodule characteristics score (the amount of malignant characteristics) based on the morphological tumor degree and the functional tumor degree (wherein the amount of malignant characteristics is the malignancy score and wherein the morphological tumor degree is calculated based on the nodule type score and the amount of malignant characteristics is calculated based on the morphological tumor degree. Thus, the malignant characteristics is calculated based on the nodule type score).) for the respective candidate nodule based on the nodule type scores for the respective candidate nodule (Fig. 1. Paragraph [0040]-FUJISAWA discloses the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of the morphological information. For example, the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of at least one piece of information among the size of the probable pulmonary nodule, the shape of the probable pulmonary nodule, the shape of unevenness of the surface of the probable pulmonary nodule, the state of the hollow of the probable pulmonary nodule, and the uniformity of the pixel value (CT value) (wherein the score of the shape of the probably pulmonary nodule is the nodule type score).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of FUJISAWA of determining a nodule type score for each of a plurality of nodule types for the respective candidate nodule; and determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule. Wherein having SONG’s method of detecting and evaluating nodules having determining a nodule type score for each of a plurality of nodule types for the respective candidate nodule; and determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and FUJISAWA are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while FUJISAWA is to provide a medical image processing apparatus, an X-ray CT scanner, and a medical image processing program that can improve the accuracy of identifying the amount of characteristics of progression indicating the extent of probable disease. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and FUJISAWA (US 20130208970 A1), Paragraph [0008]. Regarding claim 3, SONG in view of HEAMES and further in view of FUJISAWA explicitly teach the computer-implemented method of claim 2, wherein: SONG further explicitly teaches detecting one or more candidate nodules in the one or more medical images (Fig. 2. Paragraph [0026]-SONG discloses the nodule detection system 100 acquires 3D medical images from 3D medical image database 206, detects the nodules therefrom, and outputting the 3D lung nodule patches.) comprises determining a size of each of the one or more candidate nodules (Fig. 8. Paragraph [0045]-SONG discloses in FIG. 8, several features are obtained for each nodule patch, including but not limited to malignancy, texture, nodule size, lobulation, speculation, solidarity, etc. (wherein a nodule patch is a candidate nodule).) , and SONG in view of HEAMES fail to explicitly teach determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule comprises determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule. However, FUJISAWA explicitly teaches determining the malignancy score (Fig. 1. Paragraph [0066]-FUJISAWA discloses the feature amount calculator 61 calculates the nodule characteristics score (the amount of malignant characteristics) based on the morphological tumor degree and the functional tumor degree (wherein the amount of malignant characteristics is the malignancy score and wherein the morphological tumor degree is calculated based on the nodule type score and the amount of malignant characteristics is calculated based on the morphological tumor degree. Thus, the malignant characteristics is calculated based on the nodule type score).) for the respective candidate nodule based on the nodule type scores for the respective candidate nodule (Fig. 1. Paragraph [0040]-FUJISAWA discloses the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of the morphological information. For example, the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of at least one piece of information among the size of the probable pulmonary nodule, the shape of the probable pulmonary nodule, the shape of unevenness of the surface of the probable pulmonary nodule, the state of the hollow of the probable pulmonary nodule, and the uniformity of the pixel value (CT value) (wherein the score of the shape of the probably pulmonary nodule is the nodule type score).) comprises determining the malignancy score (Fig. 1. Paragraph [0066]-FUJISAWA discloses the feature amount calculator 61 calculates the nodule characteristics score (the amount of malignant characteristics) based on the morphological tumor degree and the functional tumor degree (wherein the amount of malignant characteristics is the malignancy score and wherein the morphological tumor degree is calculated based on the size of the probable pulmonary nodule and the amount of malignant characteristics is calculated based on the morphological tumor degree. Thus, the malignant characteristics is calculated based on the size of the candidate nodule).) for the respective candidate nodule further based on the size of the respective candidate nodule (Fig. 1. Paragraph [0040]-FUJISAWA discloses the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of the morphological information. For example, the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of at least one piece of information among the size of the probable pulmonary nodule, the shape of the probable pulmonary nodule, the shape of unevenness of the surface of the probable pulmonary nodule, the state of the hollow of the probable pulmonary nodule, and the uniformity of the pixel value (CT value) (wherein the size of the probable pulmonary nodule is the size of the respective candidate nodule).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of FUJISAWA of determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule comprises determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule. Wherein having SONG’s method of detecting and evaluating nodules having determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule comprises determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and FUJISAWA are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while FUJISAWA is to provide a medical image processing apparatus, an X-ray CT scanner, and a medical image processing program that can improve the accuracy of identifying the amount of characteristics of progression indicating the extent of probable disease. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and FUJISAWA (US 20130208970 A1), Paragraph [0008]. Regarding claim 16, SONG in view of HEAMES explicitly teach the non-transitory computer-readable storage medium of claim 15, SONG further explicitly teaches wherein determining a malignancy score for each of the one or more candidate nodules comprises (Fig. 1. Paragraph [0024]-SONG discloses the prediction system 101 obtains the nodule patches from the nodule detection system 100, predicts the malignancy level (such as malignancy probability or malignancy score) of each lung nodule and/or a probability that the patient will develop or has developed a lung cancer as prediction results and outputs the same (wherein a nodule is a candidate nodule).) : for each respective candidate nodule of the one or more candidate nodules (Fig. 1. Paragraph [0024]-SONG discloses the prediction system 101 obtains the nodule patches from the nodule detection system 100, predicts the malignancy level (such as malignancy probability or malignancy score) of each lung nodule and/or a probability that the patient will develop or has developed a lung cancer as prediction results and outputs the same (wherein a nodule is a candidate nodule).) : SONG in view of HEAMES fails to explicitly teach determining a nodule type score for each of a plurality of nodule types for the respective candidate nodule; and determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule. However, FUJISAWA explicitly teaches determining a nodule type score (Fig. 1. Paragraph [0036]-FUJISAWA discloses if the shape of the probable pulmonary nodule is determined and the shapes are categorized, the morphological tumor degree calculator 42 scores the shape of the probable pulmonary nodule. For example, it stores in advance a score table in which the shape of the probable pulmonary nodule and the score are associated with each other in storage (not shown in the figures) (wherein the score of the nodule shape is a nodule type score).) for each of a plurality of nodule types (Fig. 1. Paragraph [0036]-FUJISAWA discloses a score table is stored in advance in which each of spherical, triangular, linear, oval, and irregular shapes and the scores are associated with each other in storage (not shown in the figures) (wherein spherical, triangular, linear, oval, and irregular shapes are nodule types).) for the respective candidate nodule (Fig. 1. Paragraph [0036]-FUJISAWA discloses if the shape of the probable pulmonary nodule is determined and the shapes are categorized, the morphological tumor degree calculator 42 scores the shape of the probable pulmonary nodule (wherein the probable pulmonary nodule is the respective candidate nodule).) ; and determining the malignancy score (Fig. 1. Paragraph [0066]-FUJISAWA discloses the feature amount calculator 61 calculates the nodule characteristics score (the amount of malignant characteristics) based on the morphological tumor degree and the functional tumor degree (wherein the amount of malignant characteristics is the malignancy score and wherein the morphological tumor degree is calculated based on the nodule type score and the amount of malignant characteristics is calculated based on the morphological tumor degree. Thus, the malignant characteristics is calculated based on the nodule type score).) for the respective candidate nodule based on the nodule type scores for the respective candidate nodule (Fig. 1. Paragraph [0040]-FUJISAWA discloses the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of the morphological information. For example, the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of at least one piece of information among the size of the probable pulmonary nodule, the shape of the probable pulmonary nodule, the shape of unevenness of the surface of the probable pulmonary nodule, the state of the hollow of the probable pulmonary nodule, and the uniformity of the pixel value (CT value) (wherein the score of the shape of the probably pulmonary nodule is the nodule type score).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of FUJISAWA of determining a nodule type score for each of a plurality of nodule types for the respective candidate nodule; and determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule. Wherein having SONG’s method of detecting and evaluating nodules having determining a nodule type score for each of a plurality of nodule types for the respective candidate nodule; and determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and FUJISAWA are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while FUJISAWA is to provide a medical image processing apparatus, an X-ray CT scanner, and a medical image processing program that can improve the accuracy of identifying the amount of characteristics of progression indicating the extent of probable disease. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and FUJISAWA (US 20130208970 A1), Paragraph [0008]. Regarding claim 17, SONG in view of HEAMES and further in view of FUJISAWA explicitly teach the non-transitory computer-readable storage medium of claim 16, SONG further explicitly teaches wherein: detecting one or more candidate nodules in the one or more medical images (Fig. 2. Paragraph [0026]-SONG discloses the nodule detection system 100 acquires 3D medical images from 3D medical image database 206, detects the nodules therefrom, and outputting the 3D lung nodule patches.) comprises determining a size of each of the one or more candidate nodules (Fig. 8. Paragraph [0045]-SONG discloses in FIG. 8, several features are obtained for each nodule patch, including but not limited to malignancy, texture, nodule size, lobulation, speculation, solidarity, etc. (wherein a nodule patch is a candidate nodule).) , and SONG in view of HEAMES fail to explicitly teach determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule comprises determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule. However, FUJISAWA explicitly teaches determining the malignancy score (Fig. 1. Paragraph [0066]-FUJISAWA discloses the feature amount calculator 61 calculates the nodule characteristics score (the amount of malignant characteristics) based on the morphological tumor degree and the functional tumor degree (wherein the amount of malignant characteristics is the malignancy score and wherein the morphological tumor degree is calculated based on the nodule type score and the amount of malignant characteristics is calculated based on the morphological tumor degree. Thus, the malignant characteristics is calculated based on the nodule type score).) for the respective candidate nodule based on the nodule type scores for the respective candidate nodule (Fig. 1. Paragraph [0040]-FUJISAWA discloses the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of the morphological information. For example, the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of at least one piece of information among the size of the probable pulmonary nodule, the shape of the probable pulmonary nodule, the shape of unevenness of the surface of the probable pulmonary nodule, the state of the hollow of the probable pulmonary nodule, and the uniformity of the pixel value (CT value) (wherein the score of the shape of the probably pulmonary nodule is the nodule type score).) comprises determining the malignancy score (Fig. 1. Paragraph [0066]-FUJISAWA discloses the feature amount calculator 61 calculates the nodule characteristics score (the amount of malignant characteristics) based on the morphological tumor degree and the functional tumor degree (wherein the amount of malignant characteristics is the malignancy score and wherein the morphological tumor degree is calculated based on the size of the probable pulmonary nodule and the amount of malignant characteristics is calculated based on the morphological tumor degree. Thus, the malignant characteristics is calculated based on the size of the candidate nodule).) for the respective candidate nodule further based on the size of the respective candidate nodule (Fig. 1. Paragraph [0040]-FUJISAWA discloses the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of the morphological information. For example, the morphological tumor degree calculator 42 calculates the morphological tumor degree based on the scores of at least one piece of information among the size of the probable pulmonary nodule, the shape of the probable pulmonary nodule, the shape of unevenness of the surface of the probable pulmonary nodule, the state of the hollow of the probable pulmonary nodule, and the uniformity of the pixel value (CT value) (wherein the size of the probable pulmonary nodule is the size of the respective candidate nodule).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of FUJISAWA of determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule comprises determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule. Wherein having SONG’s method of detecting and evaluating nodules having determining the malignancy score for the respective candidate nodule based on the nodule type scores for the respective candidate nodule comprises determining the malignancy score for the respective candidate nodule further based on the size of the respective candidate nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and FUJISAWA are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while FUJISAWA is to provide a medical image processing apparatus, an X-ray CT scanner, and a medical image processing program that can improve the accuracy of identifying the amount of characteristics of progression indicating the extent of probable disease. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and FUJISAWA (US 20130208970 A1), Paragraph [0008] . 07-21-aia AIA Claim s 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over SONG et al. (US 20190050982 A1), hereinafter referenced as SONG, in view of HEAMES et al. (US 20240212136 A1), hereinafter referenced as HEAMES, and further in view of FUJISAWA (US 20130208970 A1), hereinafter referenced as FUJISAWA, and further in view of LI et al. (US 20240240260 A1), hereinafter referenced as LI, and further in view of FAN et al. (US 20020028008 A1), hereinafter referenced as FAN . Regarding claim 4, SONG in view of HEAMES and further in view of FUJISAWA explicitly teach the computer-implemented method of claim 2, SONG in view of HEAMES and further in view of FUJISAWA fail to explicitly teach wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule. However, LI explicitly teaches wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule (Paragraph [0053]-LI discloses nodule radiographic characteristics comprised the maximum transverse size; location; and nodule type (nonsolid or ground-glass opacity, perifissural, part-solid, solid, and spiculation).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of FUJISAWA of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of LI of wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule. Wherein having SONG’s method of detecting and evaluating nodules having wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and LI are related to detecting pulmonary nodules in patients, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while LI to improve the diagnostic accuracy for malignant PNs, a rigorous machine-learning approach was applied to assess the combined use of methylation biomarkers and all clinically-relevant variables in classifying PNs. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and LI et al. (US 20240240260 A1), Paragraph [0065]. SONG in view of HEAMES and further in view of FUJISAWA and further in view of LI fail to explicitly teach calcified nodule, or non-nodule. However, FAN explicitly teaches calcified nodule (Fig. 3. Paragraph [0061]-FAN discloses a lung nodule is considered benign if it is highly calcified or has certain patterns of distribution of calcified spots (wherein a highly calcified nodule is a calcified nodule).) , or non-nodule (Fig. 3. Paragraph [0062]-FAN discloses multiple criteria, including geometric and intensity criteria, are set up for categorizing the suspicious volume of interest (VOI) as a lung nodule or non-nodule structure (wherein a non-nodule structure is a non-nodule nodule type).) , Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of FUJISAWA and further in view of LI of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of FAN of calcified nodule, or non-nodule. Wherein having SONG’s method of detecting and evaluating nodules having calcified nodule, or non-nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and FAN are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while FAN it is highly desirable to detect lung nodules at an early stage via non-invasive methods. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and FAN et al. (US 20020028008 A1), Paragraph [0006]. Regarding claim 18, SONG in view of HEAMES and further in view of FUJISAWA explicitly teach the non-transitory computer-readable storage medium of claim 16, SONG in view of HEAMES and further in view of FUJISAWA fail to explicitly teach wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule. However, LI explicitly teaches wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule (Paragraph [0053]-LI discloses nodule radiographic characteristics comprised the maximum transverse size; location; and nodule type (nonsolid or ground-glass opacity, perifissural, part-solid, solid, and spiculation).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of FUJISAWA of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of LI of wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule. Wherein having SONG’s method of detecting and evaluating nodules having wherein the plurality of nodule types comprises one or more of solid nodule, part-solid nodule, non-solid nodule, perifissural nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and LI are related to detecting pulmonary nodules in patients, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while LI to improve the diagnostic accuracy for malignant PNs, a rigorous machine-learning approach was applied to assess the combined use of methylation biomarkers and all clinically-relevant variables in classifying PNs. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and LI et al. (US 20240240260 A1), Paragraph [0065]. SONG in view of HEAMES and further in view of FUJISAWA and further in view of LI fail to explicitly teach calcified nodule, or non-nodule. However, FAN explicitly teaches calcified nodule (Fig. 3. Paragraph [0061]-FAN discloses a lung nodule is considered benign if it is highly calcified or has certain patterns of distribution of calcified spots (wherein a highly calcified nodule is a calcified nodule).) , or non-nodule (Fig. 3. Paragraph [0062]-FAN discloses multiple criteria, including geometric and intensity criteria, are set up for categorizing the suspicious volume of interest (VOI) as a lung nodule or non-nodule structure (wherein a non-nodule structure is a non-nodule nodule type).) , Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of FUJISAWA and further in view of LI of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of FAN of calcified nodule, or non-nodule. Wherein having SONG’s method of detecting and evaluating nodules having calcified nodule, or non-nodule. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and FAN are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while FAN it is highly desirable to detect lung nodules at an early stage via non-invasive methods. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and FAN et al. (US 20020028008 A1), Paragraph [0006] . 07-21-aia AIA Claim s 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over SONG et al. (US 20190050982 A1), hereinafter referenced as SONG, in view of HEAMES et al. (US 20240212136 A1), hereinafter referenced as HEAMES, and further in view of WIEMKER et al. (US 20120081386 A1), hereinafter referenced as WIEMKER . Regarding claim 5, SONG in view of HEAMES explicitly teach the computer-implemented method of claim 1, SONG in view of HEAMES fail to explicitly teach further comprising: receiving user input defining visualization settings for presenting the one or more candidate nodules; filtering the one or more candidate nodules according to the visualization settings; displaying the one or more filtered candidate nodules. However, WIEMKER explicitly teaches receiving user input defining visualization settings for presenting the one or more candidate nodules (Fig. 2, illustrates the various visualization settings that the user can set via slide controls #15, 19, 20, 21, 22, 23. Paragraph [0091]-WIEMKER discloses the smoothing setting unit 10 comprises a graphical user interface allowing a user to set and modify the degree of smoothing. In this embodiment, the graphical user interface is a slide control 15 shown on the display 12 of the display unit 7. By sliding the slide control 15, for example, by using a computer mouse, the degree of smoothing can be modified (wherein the display receives using input via a computer mouse and wherein a visualization setting is the degree of smoothing).) ; filtering the one or more candidate nodules according to the visualization settings (Fig. 2. Paragraph [0093]-WIEMKER discloses a user can slide the slide control 15 such that the user can detect a desired object or structure. If the image data set is a medical image data set and the user wants to detect regions being suspicious of indicating cancer, the user can slide the slide control 15 such that a region becomes visible, which is suspicious for indicating cancer (wherein adjusting slide control 15 to detect suspicious regions is filtering the candidate nodules). Further in paragraph [0094]-WIEMKER discloses the smoothing unit 3 is adapted to apply a Gaussian filter to the image data set, which corresponds to the degree of smoothing set by the user by using the smoothing setting unit 10 comprising the slide control 15, for smoothing the image data set (wherein filtering the image data set with the Gaussian filter is filtering the candidate nodules as the nodules are in the data set).) ; and displaying the one or more filtered candidate nodules (Fig. 2-6, illustrate displaying filtered candidate nodules. Paragraph [0105]-WIEMKER discloses the first display region 13 is preferentially used as a navigation window for detecting a suspicious region which could show, for example, a nodule, and in the second display region 14 the suspicious region can be investigated in more detail in the originally provided image data set (wherein a nodule in the display region is a filtered candidate nodule).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of WIEMKER of receiving user input defining visualization settings for presenting the one or more candidate nodules; filtering the one or more candidate nodules according to the visualization settings; displaying the one or more filtered candidate nodules. Wherein having SONG’s method of detecting and evaluating nodules having receiving user input defining visualization settings for presenting the one or more candidate nodules; filtering the one or more candidate nodules according to the visualization settings; displaying the one or more filtered candidate nodules. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and WIEMKER are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while a visualization method and a computer program for visualizing an image data set such that different objects are visually separated from each other with reduced computational costs. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and WIEMKER et al. (US 20120081386 A1), Paragraph [0004]. Regarding claim 19, SONG in view of HEAMES explicitly teach the non-transitory computer-readable storage medium of claim 15, further comprising: SONG in view of HEAMES fail to explicitly teach receiving user input defining visualization settings for presenting the one or more candidate nodules; filtering the one or more candidate nodules according to the visualization settings; displaying the one or more filtered candidate nodules. However, WIEMKER explicitly teaches receiving user input defining visualization settings for presenting the one or more candidate nodules (Fig. 2, illustrates the various visualization settings that the user can set via slide controls #15, 19, 20, 21, 22, 23. Paragraph [0091]-WIEMKER discloses the smoothing setting unit 10 comprises a graphical user interface allowing a user to set and modify the degree of smoothing. In this embodiment, the graphical user interface is a slide control 15 shown on the display 12 of the display unit 7. By sliding the slide control 15, for example, by using a computer mouse, the degree of smoothing can be modified (wherein the display receives using input via a computer mouse and wherein a visualization setting is the degree of smoothing).) ; filtering the one or more candidate nodules according to the visualization settings (Fig. 2. Paragraph [0093]-WIEMKER discloses a user can slide the slide control 15 such that the user can detect a desired object or structure. If the image data set is a medical image data set and the user wants to detect regions being suspicious of indicating cancer, the user can slide the slide control 15 such that a region becomes visible, which is suspicious for indicating cancer (wherein adjusting slide control 15 to detect suspicious regions is filtering the candidate nodules). Further in paragraph [0094]-WIEMKER discloses the smoothing unit 3 is adapted to apply a Gaussian filter to the image data set, which corresponds to the degree of smoothing set by the user by using the smoothing setting unit 10 comprising the slide control 15, for smoothing the image data set (wherein filtering the image data set with the Gaussian filter is filtering the candidate nodules as the nodules are in the data set).) ; and displaying the one or more filtered candidate nodules (Fig. 2-6, illustrate displaying filtered candidate nodules. Paragraph [0105]-WIEMKER discloses the first display region 13 is preferentially used as a navigation window for detecting a suspicious region which could show, for example, a nodule, and in the second display region 14 the suspicious region can be investigated in more detail in the originally provided image data set (wherein a nodule in the display region is a filtered candidate nodule).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of WIEMKER of receiving user input defining visualization settings for presenting the one or more candidate nodules; filtering the one or more candidate nodules according to the visualization settings; displaying the one or more filtered candidate nodules. Wherein having SONG’s method of detecting and evaluating nodules having receiving user input defining visualization settings for presenting the one or more candidate nodules; filtering the one or more candidate nodules according to the visualization settings; displaying the one or more filtered candidate nodules. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and WIEMKER are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while a visualization method and a computer program for visualizing an image data set such that different objects are visually separated from each other with reduced computational costs. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and WIEMKER et al. (US 20120081386 A1), Paragraph [0004] . 07-21-aia AIA Claim s 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over SONG et al. (US 20190050982 A1), hereinafter referenced as SONG, in view of HEAMES et al. (US 20240212136 A1), hereinafter referenced as HEAMES, and further in view of WIEMKER et al. (US 20120081386 A1), hereinafter referenced as WIEMKER, and further in view of ZHANG et al. (US 20210118130 A1), and further in view of CASTEELE et al. (US 20070236490 A1), hereinafter referenced as CASTEELE . Regarding claim 6, SONG in view of HEAMES and further in view of WIEMKER explicitly teach the computer-implemented method of claim 5, SONG in view of HEAMES fail to explicitly teach wherein the visualization settings comprise at least one of: or one or more thresholds of a malignancy scores. However, WIEMKER explicitly teaches wherein the visualization settings comprise at least one of (Fig. 2, illustrates the various visualization settings that the user can set via slide controls #15, 19, 20, 21, 22, 23. Paragraph [0091]-WIEMKER discloses the smoothing setting unit 10 comprises a graphical user interface allowing a user to set and modify the degree of smoothing. In this embodiment, the graphical user interface is a slide control 15 shown on the display 12 of the display unit 7. By sliding the slide control 15, for example, by using a computer mouse, the degree of smoothing can be modified (wherein the display receives using input via a computer mouse and wherein a visualization setting is the degree of smoothing).): or one or more thresholds of a malignancy scores (Fig. 2. Paragraph [0094]-WIEMKER discloses the smoothing unit 3 is adapted to apply a Gaussian filter to the image data set, which corresponds to the degree of smoothing set by the user by using the smoothing setting unit 10 comprising the slide control 15, for smoothing the image data set. Different degrees of smoothing, i.e. different resolution scales, are preferentially achieved by applying different Gaussian filters having different filter widths to the image data set. Further in paragraph [0158]-WIEMKER discloses an indication of malignancy of a tumor can be given by the mean local shape index, i.e. e.g. the local shape index averaged over a tumor volume. In an embodiment, this can be realized without an explicit segmentation of the tumor by applying a Gaussian filter which is equivalent to a weighted averaging over an area of the size of the width of the Gaussian filter. Appropriate widths of the Gaussian filter can be predefined, for example, by calibration (wherein the indication of malignancy is a malignancy score and wherein applying a Gaussian filter involves using slide controls).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of WIEMKER of wherein the visualization settings comprise at least one of: or one or more thresholds of a malignancy scores. Wherein having SONG’s method of detecting and evaluating nodules having wherein the visualization settings comprise at least one of: or one or more thresholds of a malignancy scores. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and WIEMKER are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while WIEMKER a visualization method and a computer program for visualizing an image data set such that different objects are visually separated from each other with reduced computational costs. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and WIEMKER et al. (US 20120081386 A1), Paragraph [0004]. SONG in view of HEAMES and further in view of WIEMKER fail to explicitly teach one or more thresholds of the weighted nodule detection scores, However, ZHANG explicitly teaches one or more thresholds of the weighted nodule detection scores (Fig. 4A. Paragraph [0255]-ZHANG discloses the processing device 140A may obtain a first confidence coefficient corresponding to the first malignancy degree and the second confidence coefficient corresponding to the second malignancy degree. The first confidence coefficient and the second confidence coefficient may be set by a user, or according to a default setting of the imaging system 100, or determined by the processing device 140A according to an actual need (wherein the confidence coefficient is the weighted nodule detection score).) , Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of WIEMKER of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of ZHANG of one or more thresholds of the weighted nodule detection scores. Wherein having SONG’s method of detecting and evaluating nodules having one or more thresholds of the weighted nodule detection scores. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and ZHANG are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while ZHANG it may be desirable to develop systems and methods for automated evaluation of a lung nodule, thereby improving the evaluation efficiency and/or accuracy. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and ZHANG et al. (US 20210118130 A1), Paragraph [0004]. SONG in view of HEAMES and further in view of WIEMKER and further in view of ZHANG one or more thresholds of a nodule size, a selection of one or more nodule types However, CASTEELE explicitly teaches one or more thresholds of a nodule size, a selection of one or more nodule types (Fig. 1. Paragraph [0047-0050]-CASTEELE discloses a toolbar can be provided which may contain: a) a list of detected nodules characterized by name, classification, size and viewed status (seen/not seen) b) a tab for choosing window-level presets of the top two) views: mediastinum, lung and bone setting c) algorithm search criteria settings e.g. in semi-automatic mode, i.e. the user can choose size and density characteristics of the suspect areas to be found, prior to the running of the algorithm (wherein suspect areas are nodules, wherein a size characteristic set by a user is a nodule size threshold and wherein a density characteristic indicates different nodule types as nodules with differing densities are different types).) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of WIEMKER and further in view of ZHANG of a computer-implemented method comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of CASTEELE of one or more thresholds of a nodule size, a selection of one or more nodule types Wherein having SONG’s method of detecting and evaluating nodules having one or more thresholds of a nodule size, a selection of one or more nodule types The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and CASTEELE are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while CASTEELE a user interface is provided which enables the user to toggle in a viewport from one representation of the image to another, for example from an axial MIP (maximum intensity projection) to a coronal or sagittal view. Suitable means enabling said user interaction are for example mouse clicks or cursor movements. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and CASTEELE et al. (US 20070236490 A1), Paragraph [0046]. Regarding claim 20, SONG in view of HEAMES and further in view of WIEMKER explicitly teach the non-transitory computer-readable storage medium of claim 19, SONG in view of HEAMES fail to explicitly teach wherein the visualization settings comprise at least one of: or one or more thresholds of a malignancy scores. However, WIEMKER explicitly teaches wherein the visualization settings comprise at least one of (Fig. 2, illustrates the various visualization settings that the user can set via slide controls #15, 19, 20, 21, 22, 23. Paragraph [0091]-WIEMKER discloses the smoothing setting unit 10 comprises a graphical user interface allowing a user to set and modify the degree of smoothing. In this embodiment, the graphical user interface is a slide control 15 shown on the display 12 of the display unit 7. By sliding the slide control 15, for example, by using a computer mouse, the degree of smoothing can be modified (wherein the display receives using input via a computer mouse and wherein a visualization setting is the degree of smoothing).): or one or more thresholds of a malignancy scores (Fig. 2. Paragraph [0094]-WIEMKER discloses the smoothing unit 3 is adapted to apply a Gaussian filter to the image data set, which corresponds to the degree of smoothing set by the user by using the smoothing setting unit 10 comprising the slide control 15, for smoothing the image data set. Different degrees of smoothing, i.e. different resolution scales, are preferentially achieved by applying different Gaussian filters having different filter widths to the image data set. Further in paragraph [0158]-WIEMKER discloses an indication of malignancy of a tumor can be given by the mean local shape index, i.e. e.g. the local shape index averaged over a tumor volume. In an embodiment, this can be realized without an explicit segmentation of the tumor by applying a Gaussian filter which is equivalent to a weighted averaging over an area of the size of the width of the Gaussian filter. Appropriate widths of the Gaussian filter can be predefined, for example, by calibration (wherein the indication of malignancy is a malignancy score and wherein applying a Gaussian filter involves using slide controls).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of WIEMKER of wherein the visualization settings comprise at least one of: or one or more thresholds of a malignancy scores. Wherein having SONG’s method of detecting and evaluating nodules having wherein the visualization settings comprise at least one of: or one or more thresholds of a malignancy scores. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and WIEMKER are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while WIEMKER a visualization method and a computer program for visualizing an image data set such that different objects are visually separated from each other with reduced computational costs. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and WIEMKER et al. (US 20120081386 A1), Paragraph [0004]. SONG in view of HEAMES and further in view of WIEMKER fail to explicitly teach one or more thresholds of the weighted nodule detection scores. However, ZHANG explicitly teaches one or more thresholds of the weighted nodule detection scores (Fig. 4A. Paragraph [0255]-ZHANG discloses the processing device 140A may obtain a first confidence coefficient corresponding to the first malignancy degree and the second confidence coefficient corresponding to the second malignancy degree. The first confidence coefficient and the second confidence coefficient may be set by a user, or according to a default setting of the imaging system 100, or determined by the processing device 140A according to an actual need (wherein the confidence coefficient is the weighted nodule detection score).) , Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of WIEMKER of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of ZHANG of one or more thresholds of the weighted nodule detection scores. Wherein having SONG’s method of detecting and evaluating nodules having one or more thresholds of the weighted nodule detection scores. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and ZHANG are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while ZHANG it may be desirable to develop systems and methods for automated evaluation of a lung nodule, thereby improving the evaluation efficiency and/or accuracy. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and ZHANG et al. (US 20210118130 A1), Paragraph [0004]. SONG in view of HEAMES and further in view of WIEMKER and further in view of ZHANG one or more thresholds of a nodule size, a selection of one or more nodule types. However, CASTEELE explicitly teaches one or more thresholds of a nodule size, a selection of one or more nodule types (Fig. 1. Paragraph [0047-0050]-CASTEELE discloses a toolbar can be provided which may contain: a) a list of detected nodules characterized by name, classification, size and viewed status (seen/not seen) b) a tab for choosing window-level presets of the top two) views: mediastinum, lung and bone setting c) algorithm search criteria settings e.g. in semi-automatic mode, i.e. the user can choose size and density characteristics of the suspect areas to be found, prior to the running of the algorithm (wherein suspect areas are nodules, wherein a size characteristic set by a user is a nodule size threshold and wherein a density characteristic indicates different nodule types as nodules with differing densities are different types).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of SONG in view of HEAMES and further in view of WIEMKER and further in view of ZHANG of a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations comprising: receiving one or more medical images of a patient; detecting one or more candidate nodules in the one or more medical images, each of the one or more candidate nodules associated with a nodule detection score; determining a malignancy score for each of the one or more candidate nodules with the teachings of CASTEELE of one or more thresholds of a nodule size, a selection of one or more nodule types. Wherein having SONG’s method of detecting and evaluating nodules having one or more thresholds of a nodule size, a selection of one or more nodule types. The motivation behind the modification would have been to obtain a method of detecting and evaluating nodules having that enhances the accuracy and efficiency in analyzing nodules in medical images. Since both SONG and CASTEELE are related to detecting and evaluating medical images and nodules in images, wherein SONG provides a system that can quickly, accurately, and automatically predicting target object level and/or image (patient) level physiological condition from a medical image of a patient by means of learning network, such as 3D learning network, while CASTEELE a user interface is provided which enables the user to toggle in a viewport from one representation of the image to another, for example from an axial MIP (maximum intensity projection) to a coronal or sagittal view. Suitable means enabling said user interaction are for example mouse clicks or cursor movements. Please see SONG et al. (US 20190050982 A1), Paragraph [0005], and CASTEELE et al. (US 20070236490 A1), Paragraph [0046]. Conclusion Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure. GRBIC et al. (US 20200143540 A1) – Systems and method are described for determining a malignancy of a nodule. A medical image of a nodule of a patient is received. A patch surrounding the nodule is identified in the medical image. A malignancy of the nodule in the patch is predicted using a trained deep image-to-image network…Abstract, Fig. 2. PARK et al. (US 20220198668 A1) – Disclosed is a method for analyzing a lesion based on a medical image, which is performed by a computing device. The method may include: obtaining positional information of a suspicious nodule which exists in the medical image; generating a mask for the suspicious nodule based on a patch of the medical image corresponding to the positional information; and determining a class for a state of the suspicious nodule based on the patch of the medical image and the mask for the suspicious nodule…Abstract, Fig. 5. BAGCI et al. (US 20200160997 A1) – A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented. To detect nodules in an imaging scan, a 3D CNN using a single feed forward pass of a single network is used. After detection, risk stratification is performed using a supervised or an unsupervised deep learning method to assist in characterizing the detected nodule/neoplasm as benign or malignant. The supervised learning method relies on a 3D CNN used with transfer learning and a graph regularized sparse MTL to determine malignancy. The unsupervised learning method uses clustering to generate labels after which label proportions are used with a novel algorithm to classify malignancy. The method assists radiologists in improving detection rates of lung nodules to facilitate early detection and minimizing errors in diagnosis…Abstract, Fig. 1. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN N WOLFSON whose telephone number is (571)272-1898. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ETHAN N WOLFSON/Examiner, Art Unit 2673 /CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673 Application/Control Number: 18/892,888 Page 2 Art Unit: 2673 Application/Control Number: 18/892,888 Page 3 Art Unit: 2673 Application/Control Number: 18/892,888 Page 4 Art Unit: 2673
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