CTFR 18/302,107 CTFR 86179 DETAILED ACTION 12-151 AIA 26-51 12-51 Status of Claims This communication is in response to the amendment filed 02/20/2026. Claims 1, 7, 9-10 and 15-16 have been amended. Claims 2, 4-6, 13-14 and 17 have been cancelled. Claims 1, 3, 7-12 and 15-16 are currently pending and have been examined. 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. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1, 3, 7-12 and 15-16 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1, 3, 7-8, 11-12 and 15-17 are directed to a system (i.e., a machine), claim 9 is directed to a method (i.e., a process), and claim 10 is directed to non-transitory computer readable medium (i.e., a manufacture). Accordingly, claims 1, 3, 7-12 and 15-16 are all within at least one of the four statutory categories. Step 2A - Prong One: An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Representative independent claim 1 includes limitations that recite an abstract idea. Note that independent claim 1 is the system claim, while claim 9 covers a method claim and claim 10 covers the matching computer readable medium. Specifically, independent claim 1 recites: A medical information processing system comprising processing circuitry configured to: acquire disease state information regarding a disease state presented by a patient ; perform natural language analysis on text information included in the acquired disease state information, graph each disease state element in the acquired disease state information using a number of each disease state element as an index, and map each graphed disease state clement onto a disease ontology to convert the disease ontology into a modified disease ontology in which the state of the patient has been reflected, wherein as the number of each disease state element in the disease stale information increases. each disease state element is graphed larger, a layer related to disease states, a layer related to diseases, and a layer related to complications or side effects being associated on the modified disease ontology, each of the layer related to diseases and the layer related to complications or side effects being associated with a diagnostic imaging examination ; receive a designation regarding a primary candidate disease from a user via an input interface; graph the primary candidate disease designated by the user in the modified disease ontology ; identify a secondary candidate disease different from the primary candidate disease based on the graphed primary candidate disease and a size of each graphed disease state element in the modified disease ontology ; and read , from the modified disease ontology information on the diagnostic imaging examination associated with the identified secondary candidate disease, automatically generate first order information regarding the examination information on the diagnostic imaging examination , read at least one of the complications or the side effects associated with the primary candidate disease, calculate a degree of recommendation of execution Ei using a following formula that uses an occurrence probability Ri, a recommendation rank Si, and a change coefficient Ci of the read at least one of the complications or the side effects: Ei=RixSixCi, select complications or side effects with a high need for examination for which the calculated degree of recommendation of execution exceeds a predetermined threshold value, read information on the diagnostic imaging examination associated with the selected complications or side effects with the high need for examination, and automatically generate second order information regarding the read information on the diagnostic imaging examination, and transmit the generated first order information and the second order information to a radiology information system (RIS). The Examiner submits that the foregoing underlined limitations constitute: (a) “ certain methods of organizing human activity ” because mapping and graphing disease state information to a disease, converting ontological disease concepts, modifying the disease state information reflective of a patient and designating diseases as primary and secondary are ways of providing healthcare services and prioritizing medical duties and patients within medical workflow, which are managing human behavior/interactions between people. These limitations constitute (b) “ a mental process ” because reflecting on the medical state of a patient, identifying ontological disease concepts, identifying candidate diseases and contemplating a candidate disease a size of a graph disease state element are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper. Furthermore, these limitations constitute (c) “ mathematical concepts ” because graphing a disease state element as an index and calculating the degree of recommendation of execution using a following formula that uses an occurrence probability are mathematical concepts. The foregoing underlined limitations also relate to claim 1 (similarly to claims 9 and 10). Accordingly, the claim describes at least one abstract idea. In relation to claims 3, 7-8, 11 and 15-16, these claims merely recite determining steps such as: claim 3 - storing the disease ontology, claim 7 - the recommendation rank is determined based clinical practice guidelines for the primary candidate disease, claim 8 - the change coefficient is determined on the basis of past examination results of the patient, claim 11 - perform imaging according to imaging conditions determined based on the first order information transmitted to the RIS, claim 12 -identify the secondary candidate disease using a trained machine learning model using each graphed disease state element as an input to the model, claim 15 - to generate the second order information regarding examination of a predetermined number of elements with higher degrees of recommendation of execution among a plurality of elements of the complications or the side effects, and claim 16 - to calculate the degree of recommendation of execution based on text information included in medical inquiry information of the patient, basic examination information, clinical practice guidelines for the primary candidate disease, and past examination results of the patient. Step 2A - Prong Two: Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” The limitations of claims 1, 9 and 10, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a medical information processing system comprising processing circuitry, an input interface, a storage, a computer, and computer-readable non-transitory storage medium storing a program , nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. The judicial exception is not integrated into a practical application. In particular, the medical information processing system comprising processing circuitry, input interface, storage, computer, and computer-readable non-transitory storage medium storing a program are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components. Regarding the additional limitation “performing natural language analysis on text information included in the acquired disease state information” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible. Step 2B: Regarding Step 2B, in representative independent claim 1, regarding the additional limitations of the medical information processing system comprising processing circuitry, input interface, storage, computer, and computer-readable non-transitory storage medium storing a program, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Thus, representative independent claim 1 and analogous independent claims 9 and 10 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application. Therefore, claims 1, 3, 7-12 and 15-16 are ineligible under 35 USC §101. 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 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1, 3 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Sasidharan (US 2023/0051982 A1) in view of Kano (US 2022/0301716 A1) further in view of Tominaga (US 2023/0238148 A1) . Claim 1: Sasidharan discloses A medical information processing system, comprising: processing circuitry (See P0044 system includes hardware as electronic circuits and logic circuits to perform operations.) configured to: acquire disease state information regarding a disease state presented by a patient (See P0035 symptoms from text, patient events and encounters also mentioned in P0067.); perform natural language analysis on text information included in the acquired disease state information, graph each disease state element in the acquired disease state information using a number of each disease state element as an index (See natural language processing (NLP) (Fig. 1, Fig. 7, P0031-P0032) on patient data utilizing medical knowledge graphs to generate an oncology knowledge overlay shown in Fig. 8 mentioned in P0072 . With an index to graph elements as magnitude or degree of emphasis besides a number such as color or shape, see shapes and color-coded representation in Fig. 2-3, P0050-P0051, P0054 indicating graphed timeline and events.); receive a designation regarding a primary candidate disease from a user via an input interface (See Fig. 5, P0024-P0026 where graphical representations of relevant patient medical events are arranged chronologically, clinically and according to treatment of exemplary patient condition of cancer serve as a designation regarding a primary candidate disease.); and transmit the generated first order information and the second order information to a radiology information system (RIS) (See Fig. 1, P0026-P0027 presentation system 102 communicating with RIS 112 medical event such as timelines, findings from diagnostic imaging, pathology reports, lab test results, biomarker testing results, and any other clinically relevant information.). Although Sasidharan discloses a system, method and software for designating a candidate disease from natural language analysis of text information and transmitting order information to RIS as mentioned above, Sasidharan does not explicitly teach mapping graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology, reflective of the patient, disease state information increases graphically, documenting necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease to generate order information. Kano teaches: map each graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology in which the state of the patient has been reflected (See Fig. 5, Fig. 7 exemplary symptom graph with element (node) in P0082-P0084 where other graph categories reflect on body, patient examination, treatment and reaction mentioned in P0058-P0060 .) , wherein as the number of each disease state element in the disease state information increases, each disease state element is graphed larger (See Fig. 7, P0083-P0084 taught as patient feature increases and display changes from a small size to a large size as the disease influence level increases.); graph the primary candidate disease designated by the user in the modified disease ontology (See Fig. 2 medical knowledge graph 20 based on the medical knowledge, findings and medical ontology mentioned in P0039.) : identify a secondary candidate disease different from the primary candidate disease based on the graphed primary candidate disease and a size of each graphed disease state element in the modified disease ontology (See Fig. 5, P0058- P0060 where the medical care events and relevance 11 serve as identifying a secondary candidate disease different from the primary candidate disease .): and read, from a memory storing necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease, automatically generate first order information regarding the read examinat ion information (see [P0039] the medical care events used in the medical knowledge graph 20 are selected from the medical care information that is stored in the medical care information storage apparatus 1, based on the medical knowledge, medical ontology, and the like. The relationship between one medical care event and another medical care event is analyzed based on the medical knowledge, medical ontology, and the like, and, when the relationship is recognized, two nodes 21 corresponding to these two medical care events are connected by the edge 22 . Also, see Fig. 4 SA4 mentioned in P0087.). Therefore, it would have been obvious to one of ordinary skill in the art of medical ontology before the effective filing date of the claimed invention to modify the method of Sasidharan to include mapping graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology, reflective of the patient, disease state information increases graphically, documenting necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease to generate order information as taught by Kano to reduce fluctuation of different judgments from occurring in doctors who diagnose the disease as mentioned in Kano ’s P0004. Although Sasidharan and Kano teach medical information processing system, method and software that reads, from the modified disease ontology i nformation on the diagnostic imaging examination associated with the identified secondary candidate disease, automatically generate first order information read information on the diagnostic imaging examination , as mentioned above, Sasidharan and Kano do not explicitly teach a layer related to disease states, the layer related to complications and calculating the complications. Tominaga teaches: a layer related to disease states, a laver related to diseases, and a layer related to complications or side effects being associated on the modified disease ontology, each of the layer related to diseases and the layer related to complications or side effects being associated with a diagnostic imaging examination (See neural network layers include prognosis mentioned in P0043- P0044. Also, see Fig. 6, convolution layer, a pooling layer, hidden layer and a fully connected layer in [P0059-P0062] a plurality of complications that may occur according to catheter treatment. In addition, a learning model may be prepared for each medical image such as an ultrasonic tomographic image, an optical coherence tomographic image, an angiographic image, a CT image, or an MRI image, and the learning model to be used may be switched according to the acquired medical image .); and read at least one of the complications or the side effects associated with the primary candidate disease, calculate a degree of recommendation of execution Ei using a following formula that uses an occurrence probability Ri, a recommendation rank Si, and a change coefficient Ci of the read at least one of the complications or the side effects: Ei=RixSixCi (Taught in P0076-P0079, P0088-P0089 calculating attribute score as complications at risk of poor prognosis.) , select complications or side effects with a high need for examination for which the calculated degree of recommendation of execution exceeds a predetermined threshold value, read information on the diagnostic imaging examination associated with the selected complications or side effects with the high need for examination, and automatically generate second order information regarding the read information on the diagnostic imaging examination (Taught in P0063-P0064 where level of catheter treatment complications determined from medical images are scored and include preset threshold values.). Therefore, it would have been obvious to one of ordinary skill in the art of complications of catheter treatment before the effective filing date of the claimed invention to modify the system, method and software of Sasidharan and Kano to include a layer related to disease states, the layer related to complications and calculating the complications as taught by Tominaga to provide the medical worker with information on similar cases having closer conditions as mentioned in Kano’s P0094. Regarding claim 3, Sasidharan discloses t he medical information processing system according to claim 1, further comprising a storage storing the disease ontology (See P0036 and [P0113] identifying and extracting the relevant patient condition-specific medical events, records, and/or reports in the patient data comprises applying natural language processing to the patient data to generate processed patient data and performing medical ontology inferencing on the processed patient data .). Claim 9: Sasidharan discloses A medical information processing method, using a computer (See P0044 system includes hardware as electronic circuits and logic circuits to perform operations.) comprising: acquiring disease state information regarding a disease state presented by a patient (See P0035 symptoms from text, patient events and encounters also mentioned in P0067.); performing natural language analysis on text Information included in the acquired disease state information, graphing each disease state element in the disease state information using a number of each disease state element as an index (See natural language processing (NLP) (Fig. 1, Fig. 7, P0031-P0032) on patient data utilizing medical knowledge graphs to generate an oncology knowledge overlay shown in Fig. 8 mentioned in P0072 . With an index to graph elements as magnitude or degree of emphasis besides a number such as color or shape, see shapes and color-coded representation in Fig. 2-3, P0050-P0051, P0054 indicating graphed timeline and events.); receiving a designation regarding a primary candidate disease from a user via an input interface (See Fig. 5, P0024-P0026 where graphical representations of relevant patient medical events are arranged chronologically, clinically and according to treatment of exemplary patient condition of cancer serve as a designation regarding a primary candidate disease.); and transmit the generated first order information and the second order information to a radiology information system (RIS) (See Fig. 1, P0026-P0027 presentation system 102 communicating with RIS 112 medical event such as timelines, findings from diagnostic imaging, pathology reports, lab test results, biomarker testing results, and any other clinically relevant information.). Although Sasidharan discloses a system, method and software for designating a candidate disease from natural language analysis of text information and transmitting order information to RIS as mentioned above, Sasidharan does not explicitly teach mapping graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology, reflective of the patient, disease state information increases graphically, documenting necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease to generate order information. Kano teaches: mapping each graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology in which the state of the patient has been reflected (See Fig. 5, Fig. 7 exemplary symptom graph with element (node) in P0082-P0084 where other graph categories reflect on body, patient examination, treatment and reaction mentioned in P0058-P0060 .) , wherein as the number of each disease state element in the disease state information increases, each disease state element is graphed larger (See Fig. 7, P0083-P0084 taught as patient feature increases and display changes from a small size to a large size as the disease influence level increases.); graphing the primary candidate disease designated by the user in the modified disease ontology (See Fig. 2 medical knowledge graph 20 based on the medical knowledge, findings and medical ontology mentioned in P0039.) : identifying a secondary candidate disease different from the primary candidate disease based on the graphed primary candidate disease and a size of each graphed disease state element in the modified disease ontology (See Fig. 5, P0058- P0060 where the medical care events and relevance 11 serve as identifying a secondary candidate disease different from the primary candidate disease .): and reading, from the modified disease ontology information on the diagnostic imaging examination associated with the identified secondary candidate disease, automatically generating first order information regarding the read information (see [P0039] the medical care events used in the medical knowledge graph 20 are selected from the medical care information that is stored in the medical care information storage apparatus 1, based on the medical knowledge, medical ontology, and the like. The relationship between one medical care event and another medical care event is analyzed based on the medical knowledge, medical ontology, and the like, and, when the relationship is recognized, two nodes 21 corresponding to these two medical care events are connected by the edge 22 . Also, see Fig. 4 SA4 mentioned in P0087.). Therefore, it would have been obvious to one of ordinary skill in the art of medical ontology before the effective filing date of the claimed invention to modify the method of Sasidharan to include mapping graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology, reflective of the patient, disease state information increases graphically, documenting necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease to generate order information as taught by Kano to reduce fluctuation of different judgments from occurring in doctors who diagnose the disease as mentioned in Kano ’s P0004. Although Sasidharan and Kano teach medical information processing system, method and software that reads, from the modified disease ontology i nformation on the diagnostic imaging examination associated with the identified secondary candidate disease, automatically generate first order information read information on the diagnostic imaging examination , as mentioned above, Sasidharan and Kano do not explicitly teach a layer related to disease states, the layer related to complications and calculating the complications. Tominaga teaches: a layer related to disease states, a laver related to diseases, and a layer related to complications or side effects being associated on the modified disease ontology, each of the layer related to diseases and the layer related to complications or side effects being associated with a diagnostic imaging examination (See neural network layers include prognosis mentioned in P0043- P0044. Also, see Fig. 6, convolution layer, a pooling layer, hidden layer and a fully connected layer in [P0059-P0062] a plurality of complications that may occur according to catheter treatment. In addition, a learning model may be prepared for each medical image such as an ultrasonic tomographic image, an optical coherence tomographic image, an angiographic image, a CT image, or an MRI image, and the learning model to be used may be switched according to the acquired medical image .); and read at least one of the complications or the side effects associated with the primary candidate disease, calculate a degree of recommendation of execution Ei using a following formula that uses an occurrence probability Ri, a recommendation rank Si, and a change coefficient Ci of the read at least one of the complications or the side effects: Ei=RixSixCi (Taught in P0076-P0079, P0088-P0089 calculating attribute score as complications at risk of poor prognosis .) , select complications or side effects with a high need for examination for which the calculated degree of recommendation of execution exceeds a predetermined threshold value, read information on the diagnostic imaging examination associated with the selected complications or side effects with the high need for examination, and automatically generate second order information regarding the read information on the diagnostic imaging examination (Taught in P0063-P0064 where level of catheter treatment complications determined from medical images are scored and include preset threshold values.). Therefore, it would have been obvious to one of ordinary skill in the art of complications of catheter treatment before the effective filing date of the claimed invention to modify the system, method and software of Sasidharan and Kano to include a layer related to disease states, the layer related to complications and calculating the complications as taught by Tominaga to provide the medical worker with information on similar cases having closer conditions as mentioned in Kano’s P0094. Claim 10: Sasidharan discloses A non-transitory computer-readable storage medium storing a program (See non-transitory computer readable storage medium in P0043-P0044 system includes hardware as electronic circuits and logic circuits to perform operations.) causing a computer to: acquire disease state information regarding a disease state presented by a patient (See P0035 symptoms from text, patient events and encounters also mentioned in P0067.); perform natural language analysis on text information included in the acquired disease state information, graph each disease state element in the acquired disease state information using a number of each disease state element as an index (See natural language processing (NLP) (Fig. 1, Fig. 7, P0031-P0032) on patient data utilizing medical knowledge graphs to generate an oncology knowledge overlay shown in Fig. 8 mentioned in P0072 . With an index to graph elements as magnitude or degree of emphasis besides a number such as color or shape, see shapes and color-coded representation in Fig. 2-3, P0050-P0051, P0054 indicating graphed timeline and events.); receive a designation regarding a primary candidate disease from a user via an input interface (See Fig. 5, P0024-P0026 where graphical representations of relevant patient medical events are arranged chronologically, clinically and according to treatment of exemplary patient condition of cancer serve as a designation regarding a primary candidate disease.); and transmit the generated first order information and the second order information to a radiology information system (RIS) (See Fig. 1, P0026-P0027 presentation system 102 communicating with RIS 112 medical event such as timelines, findings from diagnostic imaging, pathology reports, lab test results, biomarker testing results, and any other clinically relevant information.). Although Sasidharan discloses a system, method and software for designating a candidate disease from natural language analysis of text information and transmitting order information to RIS as mentioned above, Sasidharan does not explicitly teach mapping graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology, reflective of the patient, disease state information increases graphically, documenting necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease to generate order information. Kano teaches: map each graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology in which the state of the patient has been reflected (See Fig. 5, Fig. 7 exemplary symptom graph with element (node) in P0082-P0084 where other graph categories reflect on body, patient examination, treatment and reaction mentioned in P0058-P0060 .) , wherein as the number of each disease state element in the disease state information increases, each disease state element is graphed larger (See Fig. 7, P0083-P0084 taught as patient feature increases and display changes from a small size to a large size as the disease influence level increases.); graph the primary candidate disease designated by the user in the modified disease ontology (See Fig. 2 medical knowledge graph 20 based on the medical knowledge, findings and medical ontology mentioned in P0039.) : identify a secondary candidate disease different from the primary candidate disease based on the graphed primary candidate disease and a size of each graphed disease state element in the modified disease ontology (See Fig. 5, P0058- P0060 where the medical care events and relevance 11 serve as identifying a secondary candidate disease different from the primary candidate disease .): and read, from a memory storing necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease, automatically generate first order information regarding the read examinat ion information (see [P0039] the medical care events used in the medical knowledge graph 20 are selected from the medical care information that is stored in the medical care information storage apparatus 1, based on the medical knowledge, medical ontology, and the like. The relationship between one medical care event and another medical care event is analyzed based on the medical knowledge, medical ontology, and the like, and, when the relationship is recognized, two nodes 21 corresponding to these two medical care events are connected by the edge 22 . Also, see Fig. 4 SA4 mentioned in P0087.). Therefore, it would have been obvious to one of ordinary skill in the art of medical ontology before the effective filing date of the claimed invention to modify the method of Sasidharan to include mapping graphed disease state element onto a disease ontology to convert the disease ontology into a modified disease ontology, reflective of the patient, disease state information increases graphically, documenting necessary examination information in which a disease is associated with examination information, particular examination information associated with the identified secondary candidate disease to generate order information as taught by Kano to reduce fluctuation of different judgments from occurring in doctors who diagnose the disease as mentioned in Kano ’s P0004. Although Sasidharan and Kano teach medical information processing system, method and software that reads, from the modified disease ontology i nformation on the diagnostic imaging examination associated with the identified secondary candidate disease, automatically generate first order information read information on the diagnostic imaging examination , as mentioned above, Sasidharan and Kano do not explicitly teach a layer related to disease states, the layer related to complications and calculating the complications. Tominaga teaches: larger, a layer related to disease states, a layer related to diseases, and a layer related to complications or side effects being associated on the modified disease ontology, each of the layer related to diseases and the layer related to complications or side effects being associated with a diagnostic imaging examination (See neural network layers include prognosis mentioned in P0043- P0044. Also, see Fig. 6, convolution layer, a pooling layer, hidden layer and a fully connected layer in [P0059-P0062] a plurality of complications that may occur according to catheter treatment. In addition, a learning model may be prepared for each medical image such as an ultrasonic tomographic image, an optical coherence tomographic image, an angiographic image, a CT image, or an MRI image, and the learning model to be used may be switched according to the acquired medical image .); and read at least one of the complications or the side effects associated with the primary candidate disease, calculate a degree of recommendation of execution Ei using a following formula that uses an occurrence probability Ri, a recommendation rank Si, and a change coefficient Ci of the read at least one of the complications or the side effects: Ei=RixSixCi (Taught in P0076-P0079, P0088-P0089 calculating attribute score as complications at risk of poor prognosis .) , select complications or side effects with a high need for examination for which the calculated degree of recommendation of execution exceeds a predetermined threshold value, read information on the diagnostic imaging examination associated with the selected complications or side effects with the high need for examination, and automatically generate second order information regarding the read information on the diagnostic imaging examination (Taught in P0063-P0064 where level of catheter treatment complications determined from medical images are scored and include preset threshold values.). Therefore, it would have been obvious to one of ordinary skill in the art of complications of catheter treatment before the effective filing date of the claimed invention to modify the system, method and software of Sasidharan and Kano to include a layer related to disease states, the layer related to complications and calculating the complications as taught by Tominaga to provide the medical worker with information on similar cases having closer conditions as mentioned in Kano’s P0094 . 07-21-aia AIA Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Sasidharan (US 2023/0051982 A1) in view of Kano (US 2022/0301716 A1) further in view of Tominaga (US 2023/0238148 A1) and Richens (US 11,379,747 B1) . Regarding claim 2, although Sasidharan, Kano and Tominaga teach the medical information processing system according to claim 1 mentioned above, Sasidharan, Kano and Tominaga do not explicitly teach a layer related to disease states and a layer related to diseases. Richens teaches wherein the disease ontology includes at least a layer related to disease states and a layer related to diseases (See Fig. 2, Fig. 3 and [column 11, lines 16-50] The diseases are found in the middle layer with the bottom layer corresponding to symptoms . ). Therefore, it would have been obvious to one of ordinary skill in the art of medical diagnosing before the effective filing date of the claimed invention to modify the method of Sasidharan, Kano and Tominaga to include a layer related to disease states and a layer related to diseases as taught by Richens in order to determine the disease or diseases that are the most likely underlying cause of symptoms presented mentioned in Richens’ column 1, lines 24-35 . 07-21-aia AIA Claim s 7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Sasidharan (US 2023/0051982 A1) in view of Kano (US 2022/0301716 A1) further in view of Balicer (WO-2022200967-A1) . Claim 7, Sasidharan, Kano, Tominaga and Balicer teach the medical information processing system according to claim 6 mentioned above. However, Balicer further teaches wherein the recommendation rank is determined based on clinical practice guidelines for the primary candidate disease ( See calculating a degree of recommendation of execution mentioned in P0070 .). Therefore, it would have been obvious to one of ordinary skill in the art of medical machine learning models designated diagnosing before the effective filing date of the claimed invention to modify the method of Sasidharan, Kano and Tominaga to include recommendation rank is determined on the basis of clinical practice guidelines for the primary candidate disease as taught by Balicer to reduce guessing and treating reactively during emergencies to assist with prioritizing patients. Regarding claim 15, Sasidharan discloses the medical information processing system of claim 1, wherein the processing circuitry is further configured to generate the second order information regarding examination of a predetermined number of elements with higher degrees of recommendation of execution among a plurality of elements of the complications or the side effects ( Besides implied ordering biopsy in P0055, see chemotherapy agent ordered in Fig. 15A, and ordering in [P0103-P0104] relationship for the segment “metastasis” and the clinical marker “secondary tumor” for a patient .). Regarding claim 16, Sasidharan discloses the medical information processing system according to claim 1, wherein the processing circuitry is farther configured io calculate the degree of recommendation of execution based on text information included in medical inquiry information of the patient, basic examination information, clinical practice guidelines for the primary candidate disease, and past examination results of the patient (Established in P0003 examinations, medical procedures and lab test results, . Also, see Fig. 1, P0026-P0027 presentation system 102 communicating with RIS 112 medical event such as timelines, findings from diagnostic imaging, biomarker testing results, and any other clinically relevant information.) . 07-21-aia AIA Claim s 8 are rejected under 35 U.S.C. 103 as being unpatentable over Sasidharan (US 2023/0051982 A1) in view of Kano (US 2022/0301716 A1) further in view of Tominaga (US 2023/0238148 A1), Balicer (WO-2022/200967-A1) and Miyasa (US 2011/0099032 A1) . Regarding claim 8, although Sasidharan, Kano and Balicer teach the medical information processing system according to claim 1 mentioned above, Sasidharan, Kano and Balicer do not explicitly teach a change coefficient is determined on the basis of past examination results of the patient. Miyasa teaches wherein the processing circuitry is further configured to calculate the degree of recommendation of execution on the basis of at least one of a probability of occurrence of at least one of the complications or the side effects, a recommendation rank, or a change coefficient (See Fig. 9, where number of similar cases serve as past patient examination results (P0149-P0151), and Fig. 10, Fig. 11 correction coefficient of display recommendation levels (P0155-P0160). Also, see P0140 and P0147.). Therefore, it would have been obvious to one of ordinary skill in the art of medical image diagnosing before the effective filing date of the claimed invention to modify the method of Sasidharan , Kano and Balicer to include a change coefficient is determined on the basis of past examination results of the patient as taught by Miyasa to reduce the burden of a physician who reads a shadow image and improve the accuracy of a read shadow result when diagnosing for cancer mentioned in Miyasa’s P0005-P0006 . 07-21-aia AIA Claim s 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Sasidharan (US 2023/0051982 A1) in view of Kano (US 2022/0301716 A1) further in view of Tominaga (US 2023/0238148 A1) and El Sayed (US 20222/0148166 A1) . Regarding claim 11, although Sasidharan, Kano and Tominaga teach the medical information processing system according to claim 1 mentioned above, Sasidharan, Kano and Tominaga do not explicitly teach an imaging apparatus configured to perform imaging according to imaging conditions determined based on the first order information transmitted to the RIS . El Sayed teaches further comprising an imaging apparatus configured to perform imaging according to imaging conditions determined based on the first order information transmitted to the RIS (See Fig. 1, P0082-P0084 obtaining from a medical imaging modality 4 and depict typical lines of communication between information sources 8 including RIS (P0088) when diagnosing.). Therefore, it would have been obvious to one of ordinary skill in the art of medical image studies before the effective filing date of the claimed invention to modify the system of Sasidharan, Kano and Tominaga to an imaging apparatus configured to perform imaging according to imaging conditions determined based on the first order information transmitted to the RIS as taught by El Sayed to assess a likelihood that a subject patient is suffering from chronic thromboembolic pulmonary hypertension CTEPH mentioned in El Sayed’s P0002 and P0004. Regarding claim 12, although Sasidharan, Kano and Tominaga teach the medical information processing system according to claim 1 mentioned above, Sasidharan, Kano and Tominaga do not explicitly teach identifying a candidate disease using a trained machine learning model . El Sayed teaches wherein the processing circuitry is further configured to identify the secondary candidate disease using a trained machine learning model using each graphed disease state element as an input to the model (See using machine learning when diagnosing in P0012, P0016, P0082, P0107, P0138 and Fig. 6.). Therefore, it would have been obvious to one of ordinary skill in the art of medical image studies before the effective filing date of the claimed invention to modify the system of Sasidharan, Kano and Tominaga to an identifying a candidate disease using a trained machine learning model as taught by El Sayed to assess a likelihood that a subject patient is suffering from chronic thromboembolic pulmonary hypertension CTEPH mentioned in El Sayed’s P0002 and P0004 . Response to Amendment Applicant argues on the basis that the Sasidharan reference does not teach “ processing circuitry ”. With processing circuitry as receiving electrical signals according to Applicant’s specification, Sasidharan’s electronic circuits with associated software instructions, module and microprocessor in P0044 construe using processing circuitry. Regarding the prior art rejection, Applicant’s argued amendments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103(a) and applied new art and art already of record. Applicant alleges that claim 1 does not recite “providing health care services” or a “medical workflow”, see pg. 15-16 of Remarks – Examiner disagrees. Having knowledge about disease states, disease ontology, disease complications, side effects, ordering imaging examinations when diagnosing medical patients and reviewing a ranking of recommendations are duties that healthcare providers would be able to do within the healthcare industry. Also, the healthcare providers are expected to listen for key words, terms and concepts acquired as symptoms/complaints during exams and while reviewing the patient’s medical history and reports to ensure that a mis-diagnosis is not performed. Analyzing medical text of arranged words, terms and concepts acquired about the disease state of a patient to identify a second candidate disease different from a primary candidate disease is what healthcare providers would be able to do within the healthcare industry. For example, the healthcare providers are expected to listen for key words, terms and concepts acquired as symptoms/complaints during exams and while reviewing the patient’s medical history and reports to ensure that a mis-diagnosis is not performed. Furthermore, calculating degrees, using equations and predetermined thresholds are mathematical concepts. Applicant alleges that claim 1 integrates any purported abstract idea into a practical application, by automatically generating first order information and second order information regarding a diagnostic imaging examination as a technical improvement, see pg. 15-16 of Remarks – Examiner disagrees. Limitations like “performing natural language analysis on text information” and “identify a secondary candidate disease different from the primary candidate disease based on the graphed primary candidate disease and a size of each graphed disease state element” does not indicate a significant improvement to the functioning of a computer or any other technology, and merely provide a conventional computer implementation of an abstract idea. Furthermore, neither technology is claimed in the instant case, nor is natural language processing, nor graphical display used in a technical way to perform, acquire, graph or identify. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Grubbs (US 2018/0250086 A1) . THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 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, Mamon Obeid can be reached at (571) 270-1813. 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. /T.S.W./Examiner, Art Unit 3687 05/29/2026 /ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687 Application/Control Number: 18/302,107 Page 2 Art Unit: 3687 Application/Control Number: 18/302,107 Page 3 Art Unit: 3687 Application/Control Number: 18/302,107 Page 4 Art Unit: 3687 Application/Control Number: 18/302,107 Page 5 Art Unit: 3687 Application/Control Number: 18/302,107 Page 6 Art Unit: 3687 Application/Control Number: 18/302,107 Page 7 Art Unit: 3687 Application/Control Number: 18/302,107 Page 8 Art Unit: 3687 Application/Control Number: 18/302,107 Page 9 Art Unit: 3687 Application/Control Number: 18/302,107 Page 10 Art Unit: 3687 Application/Control Number: 18/302,107 Page 11 Art Unit: 3687 Application/Control Number: 18/302,107 Page 12 Art Unit: 3687 Application/Control Number: 18/302,107 Page 13 Art Unit: 3687 Application/Control Number: 18/302,107 Page 14 Art Unit: 3687 Application/Control Number: 18/302,107 Page 15 Art Unit: 3687 Application/Control Number: 18/302,107 Page 16 Art Unit: 3687 Application/Control Number: 18/302,107 Page 17 Art Unit: 3687 Application/Control Number: 18/302,107 Page 18 Art Unit: 3687 Application/Control Number: 18/302,107 Page 19 Art Unit: 3687 Application/Control Number: 18/302,107 Page 20 Art Unit: 3687 Application/Control Number: 18/302,107 Page 21 Art Unit: 3687 Application/Control Number: 18/302,107 Page 22 Art Unit: 3687 Application/Control Number: 18/302,107 Page 23 Art Unit: 3687 Application/Control Number: 18/302,107 Page 24 Art Unit: 3687 Application/Control Number: 18/302,107 Page 25 Art Unit: 3687 Application/Control Number: 18/302,107 Page 26 Art Unit: 3687 Application/Control Number: 18/302,107 Page 27 Art Unit: 3687
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