DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The eight information disclosure statements (IDS) submitted on October 29, 2024 are in compliance with the provisions of 37 CFR 1.97, and have been considered by the examiner.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
Non-statutory Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a non-statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 8, 9, and 16 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 4, 6, and 11 of U.S. Patent No. 11,495,353 in view of McNair et al. (Pub. No. US 2017/0124269). Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Claim 1 in the Present Application (Application Serial No. 18/930,520)
Claim 1 of Patent No. US 11,495,353, issued on November 8, 2022 (with minor differences identified in bold and underlined font)
1. An system providing artificial intelligence (AI)-based diagnostic guidance for a patient, comprising:
1. An apparatus providing at least one non-neurosurgical medical personnel with artificial intelligence (AI)-derived data regarding a patient presenting with neurological indications, comprising:
one or more radiology scanners for scanning a body portion of the patient and to capture radiological scan images of the body portion of the patient;
one or more radiology scanners configured to capture radiological scan images of the patient;
an imaging post-processor connected to the radiology scanners to receive the captured
radiological scan images of the patient; and
an imaging post-processor connected to the radiology scanners to receive the captured radiological scan images of the patient and connected to one or more patient imaging history servers to receive patient imaging history information; and
an AI hub connected to the radiology scanners to receive the captured radiological scan images of the patient, wherein the AI hub is configured to receive input components comprising one or more selected from the group consisting of results of physical examination of the patient and laboratory data of the patient;
a guidance server connected to the radiology scanners to receive the captured radiological scan images of the patient and connected to one or more neurosurgical database and treatment guideline servers which include national treatment guidelines, wherein the guidance server is configured to receive three or more of parameters selected from a group consisting of (i) natural language input from the at least one non-neurosurgical medical personnel, (ii) real-time vital signs telemetry data, (iii) neurosurgical treatment database information, (iv) neurosurgical treatment guideline information, (v) clinical laboratory testing results, (vi) patient historical data, and (vii) patient imaging information;
wherein the imaging post-processor is configured to:
wherein the imaging post-processor is configured to:
recognizing patterns of the received radiological scan images of the patient to detect abnormality;
recognize patterns of the received radiological scan images of the patient to detect abnormality,
reconstructing the received radiological scan images of the patient into three-dimensional reconstructed scans;
reconstruct the received radiological scan images of the patient into three-dimensional reconstructed scans,
providing the AI hub with patient imaging information based on the recognized patterns of the received radiological scan images and the three-dimensional reconstructed scans; and wherein the AI hub is configured to perform:
generating the patient imaging information including the recognized patterns of the received radiological scan images of the patient and the three-dimensional reconstructed scans, and providing to the guidance server the patient imaging information; and wherein the guidance server is configured to:
identifying problems based on predetermined AI criteria and deep-learning based on patient-specific patterns, responses, and the input components;
identify problems by using natural language key words and phrases, logic, comparisons with prior related problems, databases of known problems with solutions, and the received radiological scan images of the patient;
determining risk factors of the patient which cause heath issues;
THIS LIMITATION OR A SIMILAR LIMITATION IS NOT CONTAINED IN THE CLAIM
providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient;
THIS LIMITATION OR A SIMILAR LIMITATION IS NOT CONTAINED IN THE CLAIM
prioritizing the problems to a hot list based on what is important to rule-out and what is most likely based on the probability estimation of diagnoses;
prioritize the problems to a hot list based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel;
providing medical staff with AI-generated interim diagnostic alerts and/or guidance based on the identified and prioritized problems.
provide at least interim medical advice and further diagnostic guidelines based on the national treatment guidelines; and provide one or more AI-generated alerts, suggestions for diagnoses and immediate treatment guidelines to the at least one non-neurosurgical medical personnel until a neurosurgeon is available.
Claim 1 is rejected on the ground of non-statutory double patenting as being unpatentable over claim 1 of Patent No. US 11,495,353 in view of: McNair. The major difference between independent claim 1 in Applicant's claimed invention and claim 1 in Patent No. US 11,495,353 is that claim 1 in Applicant's claimed invention requires: (1) "determining risk factors of the patient which cause heath issues", in addition to (2) "providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient". However, it would have been obvious to one of ordinary skill in the art of clinical decision support systems and methods that the apparatus described in claim 1 of Patent No. US 11,495,353, which identifies problems and prioritizes the problems to a hot list based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel, would have been an obvious variation of the system defined in claim 1 of the Applicant’s claimed invention, which (i) determines risk factors of the patient which cause heath issues, in addition to (ii) providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient. For example, McNair teaches that some embodiments of a clinical support system present a set of risk factors corresponding to a subset of clinical concepts (i.e., determines risk factors of the patient which cause heath issues). McNair, paragraph [0172].
Further, McNair teaches that the clinical condition includes one or more of a disease, diagnoses, medical issue, or medical event; the probability for the first clinical condition is a calculated probability that the patient has or will develop the first clinical condition based on at least a portion of the set of clinical concepts for the patient (i.e., providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient). McNair, paragraph [0172]. McNair teaches that these features are beneficial for assisting clinical decision making at point of care by enabling caregivers and other users to leverage this intelligent agent system to detect a change in personal health or to leverage up to date knowledge about medical conditions, preventive care, and other relevant interests. As such, it would have been obvious one of ordinary skill in the art of clinical decision support systems and methods to also (i) determine risk factors of the patient which cause heath issues, in addition to (ii) provide probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient, as taught by McNair, with the motivation of assist clinical decision making at point of care.
Claim 8 in the Present Application (Application Serial No. 18/930,520)
Claim 4 of Patent No. US 11,495,353, issued on November 8, 2022 (in view of McNair et al. (Pub. No. US 2017/0124269))
8. The system of claim 1 wherein imaging post-processor is integrated into the AI hub.
4. The apparatus according to claim 1, wherein the imaging post-processor is integrated into the guidance server.
Claim 9 in the Present Application (Application Serial No. 18/930,520)
Claim 6 of Patent No. US 11,495,353, issued on November 8, 2022 (with minor differences identified in bold and underlined font)
6. A method for providing artificial intelligence (AI)-based diagnostic guidance for a patient, comprising:
6. A method for providing at least one non-neurosurgical medical personnel with artificial intelligence (AI)-derived data regarding a patient presenting with neurological indications, comprising:
scanning a body portion of the patient and capturing, by using one or more radiology scanners, radiological scan images of the body portion of the patient;
capturing, with one or more radiology scanners, radiological scan images of the patient;
receiving the captured radiological scan images of the patient;
receiving, with an imaging post-processor, the captured radiological scan images of the patient from the radiology scanners and patient imaging history information from one or more patient imaging history servers;
receiving input components comprising one or more selected from the group consisting of results of physical examination of the patient and laboratory data of the patient;
receiving, with a guidance server, three or more of parameters selected from a group consisting of (i) natural language input from the at least one non-neurosurgical medical personnel, (ii) real-time vital signs telemetry data, (iii) neurosurgical treatment database information, (iv) neurosurgical treatment guideline information, (v) clinical laboratory testing results, (vi) patient historical data, and (vii) patient imaging information, wherein guidance server is connected to the radiology scanners to receive the captured radiological scan images of the patient and is connected to one or more neurosurgical database and treatment guideline servers which include national treatment guidelines;
recognizing patterns of the received radiological scan images of the patient to detect abnormality;
recognizing, via the imaging post-processor, patterns of the received radiological scan images of the patient to detect abnormality;
reconstructing the received radiological scan images of the patient into three-dimensional reconstructed scans;
reconstructing, via the imaging post-processor, the received radiological scan images of the patient into three-dimensional reconstructed scans;
providing patient imaging information based on the recognized patterns of the received radiological scan images and the three-dimensional reconstructed scans;
generating, via the imaging post-processor, the patient imaging information including the recognized patterns of the received radiological scan images of the patient and the three-dimensional reconstructed scans, and providing to the guidance server the patient imaging information;
identifying problems based on predetermined AI criteria and deep-learning based on patient-specific patterns, responses, and the input components;
identifying problems, via the guidance server, by using natural language key words and phrases, logic, comparisons with prior related problems, databases of known problems with solutions, and the received radiological scan images of the patient;
determining risk factors of the patient which cause heath issues;
THIS LIMITATION OR A SIMILAR LIMITATION IS NOT CONTAINED IN THE CLAIM
providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient;
THIS LIMITATION OR A SIMILAR LIMITATION IS NOT CONTAINED IN THE CLAIM
prioritizing the problems to a hot list based on what is important to rule-out and what is most likely based on the probability estimation of diagnoses;
prioritizing the problems to a hot list, via the guidance server, based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel;
providing medical staff with AI-generated interim diagnostic alerts and/or guidance based on the identified and prioritized problems.
providing at least interim medical advice and further diagnostic guidelines, via the guidance server, based on the national treatment guidelines; and providing, via the guidance server, one or more AI-generated alerts, suggestions for diagnoses and immediate treatment guidelines to the at least one non-neurosurgical medical personnel until a neurosurgeon is available.
Claim 9 is rejected on the ground of non-statutory double patenting as being unpatentable over claim 6 of Patent No. US 11,495,353 in view of: McNair. The major difference between independent claim 9 in Applicant's claimed invention and claim 6 in Patent No. US 11,495,353 is that claim 9 in Applicant's claimed invention requires: (1) "determining risk factors of the patient which cause heath issues", in addition to (2) "providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient". However, it would have been obvious to one of ordinary skill in the art of clinical decision support systems and methods that the method described in claim 6 of Patent No. US 11,495,353, which identifies problems and prioritizes the problems to a hot list based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel, would have been an obvious variation of the method defined in claim 9 of the Applicant’s claimed invention, which (i) determines risk factors of the patient which cause heath issues, in addition to (ii) providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient. For example, McNair teaches that some embodiments of a clinical support system present a set of risk factors corresponding to a subset of clinical concepts (i.e., determines risk factors of the patient which cause heath issues). McNair, paragraph [0172].
Further, McNair teaches that the clinical condition includes one or more of a disease, diagnoses, medical issue, or medical event; the probability for the first clinical condition is a calculated probability that the patient has or will develop the first clinical condition based on at least a portion of the set of clinical concepts for the patient (i.e., providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient). McNair, paragraph [0172]. McNair teaches that these features are beneficial for assisting clinical decision making at point of care by enabling caregivers and other users to leverage this intelligent agent system to detect a change in personal health or to leverage up to date knowledge about medical conditions, preventive care, and other relevant interests. As such, it would have been obvious one of ordinary skill in the art of clinical decision support systems and methods to also (i) determine risk factors of the patient which cause heath issues, in addition to (ii) provide probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient, as taught by McNair, with the motivation of assist clinical decision making at point of care.
Claim 16 in the Present Application (Application Serial No. 18/930,520)
Claim 11 of Patent No. US 11,495,353, issued on November 8, 2022 (with minor differences identified in bold and underlined font)
16. At least one non-transitory computer readable medium that includes program codes for providing artificial intelligence (AI)-based diagnostic guidance for a patient, the program codes comprising instructions causing one or more processors to perform operations comprising:
11. At least one non-transitory computer readable media that includes program codes for providing at least one non-neurosurgical medical personnel with artificial intelligence (AI)-derived data regarding a patient presenting with neurological indications, said program codes comprising instructions causing at least one processor to:
scanning a body portion of the patient and capturing, by using one or more radiology scanners, radiological scan images of the body portion of the patient;
capture, with one or more radiology scanners, radiological scan images of the patient;
receiving the captured radiological scan images of the patient;
receive, with an imaging post-processor, the captured radiological scan images of the patient from the radiology scanners and patient imaging history information from one or more patient imaging history servers;
receiving input components comprising one or more selected from the group consisting of results of physical examination of the patient and laboratory data of the patient;
receive, with a guidance server, three or more of parameters selected from a group consisting of (i) natural language input from the at least one non-neurosurgical medical personnel, (ii) real-time vital signs telemetry data, (iii) neurosurgical treatment database information, (iv) neurosurgical treatment guideline information, (v) clinical laboratory testing results, (vi) patient historical data, and (vii) patient imaging information, wherein guidance server is connected to the radiology scanners to receive the captured radiological scan images of the patient and is connected to one or more neurosurgical database and treatment guideline servers which include national treatment guidelines;
recognizing patterns of the received radiological scan images of the patient to detect abnormality;
recognize, via the imaging post-processor, patterns of the received radiological scan images of the patient to detect abnormality;
reconstructing the received radiological scan images of the patient into three-dimensional reconstructed scans;
reconstruct, via the imaging post-processor, the received radiological scan images of the patient into three-dimensional reconstructed scans;
providing patient imaging information based on the recognized patterns of the received radiological scan images and the three-dimensional reconstructed scans;
generate, via the imaging post-processor, the patient imaging information including the recognized patterns of the received radiological scan images of the patient and the three-dimensional reconstructed scans, and providing to the guidance server the patient imaging information;
identifying problems based on predetermined AI criteria and deep-learning based on patient-specific patterns, responses, and the input components;
identify problems, via the guidance server, by using natural language key words and phrases, logic, comparisons with prior related problems, databases of known problems with solutions, and the received radiological scan images of the patient;
determining risk factors of the patient which cause heath issues;
THIS LIMITATION OR A SIMILAR LIMITATION IS NOT CONTAINED IN THE CLAIM
providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient;
THIS LIMITATION OR A SIMILAR LIMITATION IS NOT CONTAINED IN THE CLAIM
prioritizing the problems to a hot list based on what is important to rule-out and what is most likely based on the probability estimation of diagnoses;
prioritize the problems to a hot list, via the guidance server, based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel;
providing medical staff with AI-generated interim diagnostic alerts and/or guidance based on the identified and prioritized problems.
provide at least interim medical advice and further diagnostic guidelines, via the guidance server, based on the national treatment guidelines; and provide, from the guidance server, one or more AI-generated alerts, suggestions for diagnoses and immediate treatment guidelines to the at least one non-neurosurgical medical personnel until a neurosurgeon is available.
Claim 16 is rejected on the ground of non-statutory double patenting as being unpatentable over claim 11 of Patent No. US 11,495,353 in view of: McNair. The major difference between independent claim 16 in Applicant's claimed invention and claim 11 in Patent No. US 11,495,353 is that claim 16 in Applicant's claimed invention requires: (1) "determining risk factors of the patient which cause heath issues", in addition to (2) "providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient". However, it would have been obvious to one of ordinary skill in the art of clinical decision support systems and methods that the method described in claim 11 of Patent No. US 11,495,353, which identifies problems and prioritizes the problems to a hot list based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel, would have been an obvious variation of the non-transitory computer readable medium defined in claim 16 of the Applicant’s claimed invention, which (i) determines risk factors of the patient which cause heath issues, in addition to (ii) providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient. For example, McNair teaches that some embodiments of a clinical support system present a set of risk factors corresponding to a subset of clinical concepts (i.e., determines risk factors of the patient which cause heath issues). McNair, paragraph [0172].
Further, McNair teaches that the clinical condition includes one or more of a disease, diagnoses, medical issue, or medical event; the probability for the first clinical condition is a calculated probability that the patient has or will develop the first clinical condition based on at least a portion of the set of clinical concepts for the patient (i.e., providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient). McNair, paragraph [0172]. McNair teaches that these features are beneficial for assisting clinical decision making at point of care by enabling caregivers and other users to leverage this intelligent agent system to detect a change in personal health or to leverage up to date knowledge about medical conditions, preventive care, and other relevant interests. As such, it would have been obvious one of ordinary skill in the art of clinical decision support systems and methods to also (i) determine risk factors of the patient which cause heath issues, in addition to (ii) provide probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient, as taught by McNair, with the motivation of assist clinical decision making at point of care.
Similarly, claims 1, 8, 9, and 16 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 4, 6, and 11 of U.S. Patent No. 11,996,200 in view of McNair et al. (Pub. No. US 2017/0124269). Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Claims 1, 9, and 16 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 6, and 11 of Patent No. US 11,996,200 in view of: McNair. The major difference between independent claims 1, 9, and 16 in Applicant's claimed invention and claims 1, 6, and 11 in Patent No. US 11,996,200 is that claims 1, 9, and 16 in Applicant's claimed invention require: (1) "determining risk factors of the patient which cause heath issues", in addition to (2) "providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient". However, it would have been obvious to one of ordinary skill in the art of clinical decision support systems and methods that the apparatus, method, and non-transitory computer readable medium described in claims 1, 6, and 11 of Patent No. US 11,996,200, which identifies problems and prioritizes the problems to a hot list based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel, would have been an obvious variation of the system, method, and non-transitory computer readable medium defined in claims 1, 9, and 16 of the Applicant’s claimed invention, which (i) determines risk factors of the patient which cause heath issues, in addition to (ii) providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient. For example, McNair teaches that some embodiments of a clinical support system present a set of risk factors corresponding to a subset of clinical concepts (i.e., determines risk factors of the patient which cause heath issues). McNair, paragraph [0172].
Further, McNair teaches that the clinical condition includes one or more of a disease, diagnoses, medical issue, or medical event; the probability for the first clinical condition is a calculated probability that the patient has or will develop the first clinical condition based on at least a portion of the set of clinical concepts for the patient (i.e., providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient). McNair, paragraph [0172]. McNair teaches that these features are beneficial for assisting clinical decision making at point of care by enabling caregivers and other users to leverage this intelligent agent system to detect a change in personal health or to leverage up to date knowledge about medical conditions, preventive care, and other relevant interests. As such, it would have been obvious one of ordinary skill in the art of clinical decision support systems and methods to also (i) determine risk factors of the patient which cause heath issues, in addition to (ii) provide probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient, as taught by McNair, with the motivation of assist clinical decision making at point of care.
Claim 8 in the Present Application (Application Serial No. 18/930,520)
Claim 4 of Patent No. US 11,996,200, issued on May 28, 2024 (in view of McNair et al. (Pub. No. US 2017/0124269))
8. The system of claim 1 wherein imaging post-processor is integrated into the AI hub.
4. The apparatus according to claim 1, wherein the imaging post-processor is integrated into the guidance server.
Similarly, claims 1, 9, and 16 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 9, and 17 of U.S. Patent No. 12,154,689 in view of McNair et al. (Pub. No. US 2017/0124269). Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Claims 1, 9, and 16 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 9, and 17 of Patent No. US 12,154,689 in view of: McNair. The major difference between independent claims 1, 9, and 16 in Applicant's claimed invention and claims 1, 9, and 17 in Patent No. US 12,154,689 is that claims 1, 9, and 16 in Applicant's claimed invention require: (1) "determining risk factors of the patient which cause heath issues", in addition to (2) "providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient". However, it would have been obvious to one of ordinary skill in the art of clinical decision support systems and methods that the system, method, and non-transitory computer readable medium described in claims 1, 9, and 17 of Patent No. US 12,154,689, which identifies problems and prioritizes the problems to a hot list based on what is important to rule-out and what is most likely based on the clinical laboratory testing results, the patient imaging information, and a problem list of the non-neurosurgical medical personnel, would have been an obvious variation of the system, method, and non-transitory computer readable medium defined in claims 1, 9, and 16 of the Applicant’s claimed invention, which (i) determines risk factors of the patient which cause heath issues, in addition to (ii) providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient. For example, McNair teaches that some embodiments of a clinical support system present a set of risk factors corresponding to a subset of clinical concepts (i.e., determines risk factors of the patient which cause heath issues). McNair, paragraph [0172].
Further, McNair teaches that the clinical condition includes one or more of a disease, diagnoses, medical issue, or medical event; the probability for the first clinical condition is a calculated probability that the patient has or will develop the first clinical condition based on at least a portion of the set of clinical concepts for the patient (i.e., providing probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient). McNair, paragraph [0172]. McNair teaches that these features are beneficial for assisting clinical decision making at point of care by enabling caregivers and other users to leverage this intelligent agent system to detect a change in personal health or to leverage up to date knowledge about medical conditions, preventive care, and other relevant interests. As such, it would have been obvious one of ordinary skill in the art of clinical decision support systems and methods to also (i) determine risk factors of the patient which cause heath issues, in addition to (ii) provide probability estimations of diagnoses that include probabilities of occurrences of the health issues to the patient based on the risk factors of the patient, as taught by McNair, with the motivation of assist clinical decision making at point of care.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
Claims 1-20 are deemed to be eligible under 35 U.S.C. § 101 for the following reasons.
While it can be argued that the claims include limitations that are directed to an abstract idea within the Certain Methods of Organizing Human Activities grouping of abstract ideas by guiding medical personnel in the treatment of a patient presenting with neurological indications by outputting interim diagnostic alerts (e.g., the steps directed to: “recognizing patterns of the received radiological scan images”; “identifying problems”; “determining risk factors”; and “prioritizing the problems to a hot list”, described in independent claims 1, 9, and 16), the claims are patient eligible because they are deemed to recite a combination of additional elements that are indicative of integrating an abstract concept into a practical application under Prong Two of Step 2A of the Alice/Mayo Test as described in the 2019 Revised Patent Subject Matter Eligibility Guidance (the “2019 Revised PEG”). See MPEP § 2106. Specifically, the additional elements of: (1) utilizing radiology scanners to capture radiological scan images that are received by an imaging post-processor; (2) reconstructing the received radiological scan images into three-dimensional reconstructed scans; (3) providing patient imaging information and the three-dimensional reconstructed scans to an artificial intelligence (AI) hub; and (4) providing medical staff with AI-generated interim diagnostic alerts and/or guidance based on the identified and prioritized problems, is interpreted to as applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole are more than a drafting effort designed to monopolize the exception. See MPEP § 2106.05(e) (citing Classen Immunotherapies Inc. v Biogen IDEC).
For example, in Classen Immunotherapies Inc. v Biogen IDEC, the Supreme Court determined that an immunization step in a method of analyzing immunization schedules on the later development of chronic immune-mediated disorders in mammals in order to identify a lower risk immunization schedule was meaningful, because it integrated the results of the analysis into a specific and tangible method that resulted in the method “moving from abstract scientific principle to specific application.” Similar to how identifying a lower immunization schedule was considered a meaningful limitation in Classen, the steps and features directed to: (1) utilizing radiology scanners to capture radiological scan images that are received by an imaging post-processor; (2) reconstructing the received radiological scan images into three-dimensional reconstructed scans; (3) providing patient imaging information and the three-dimensional reconstructed scans to an artificial intelligence (AI) hub; and (4) providing medical staff with AI-generated interim diagnostic alerts and/or guidance based on the identified and prioritized problems (as described in independent claims 1, 9, and 16), are also deemed to provide meaningful limitations, because they provide a specific manner for generating patient imaging information and diagnoses and guidance based on identified and prioritized problems.
Further, Examiner notes lines 1-8 on page 18, lines 24-31 on page 19, and lines 1-10 on page 20 in Applicant’s specification, as providing additional support for utilizing the artificial intelligence system to provide the diagnostic alerts and/or guidance, such as recognizing and discriminating problems based on history, imaging, data sources, comparisons, and pattern recognition; prioritizing problems and treatment next steps; and recognizing patient-specific patterns based on deep-learning, in order to improve diagnostic accuracy and ensure that important data elements are presented to medical personnel. See Applicant’s specification, as filed on October 29, 2024, lines 1-8 on p. 18, lines 24-31 on p. 19, and lines 1-10 on p. 20. Therefore, the additional elements of claims 1, 9, and 16 are at least deemed to integrate the aforementioned abstract method of organizing human activity into a practical application under Prong Two of Step 2A of the Alice/Mayo Test revised in the 2019 Revised PEG, and thus are patent eligible.
Claims 2-7, 10-15, and 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 1-20 are deemed to be allowable over the prior art for the following reasons.
Nye et al. (Pub. No. US 2019/0150857) teaches an apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings in image data, and generation of notification at or near a point of care for a patient (i.e., a system and method). See Nye, paragraph [0005]. Paragraph [0038] teaches that certain examples use neural networks and/or other machine learning to implement a new workflow for image and associated patient analysis including generating alerts based on radiological findings may be generated and delivered at the point of care of a radiology exam (i.e., providing medical staff with AI-generated diagnostic alerts). Nye, paragraph [0038]. Certain examples use Artificial Intelligence (AI) algorithms to immediately (e.g., with a data processing, transmission, and/or storage/retrieval latency) process a radiological exam (e.g., an image or set of images), and provide an alert based on the automated exam analysis at the point of care. Id. The alerts may be intended for the technologist acquiring the exam, clinical team providers (e.g., nurse, doctor, etc.) (i.e., the alerts are provided to medical staff), radiologist (i.e., the alerts are provided to medical staff), administration (i.e., the alerts are provided to medical staff), operations, and/or even the patient. Id.
Sadeghi (Pub. No. WO 2014/197669) teaches a system, method, and at least one computer-readable storage medium (i.e., a system, method, and at least one non-transitory computer readable media) which perform the method, comprising: using at least one natural language understanding engine to analyze at least one narrative provided by a radiologist in connection with a study of one or more medical images; applying one or more decision rules to one or more facts extracted by the at least one natural language understanding engine from the at least one narrative; and providing guidance to the radiologist based at least in part on a result of applying the one or more decision rules to the one or more facts extracted from the at least one narrative exam. See Sadeghi, p. 2, lines 25-33, p. 3, lines 21-28, and p. 4, lines 14-22. Paragraph [0075] generally teaches that one or more decision rules may be applied to one or more extract facts in the CLU [Clinical Language Understanding] engine output and/or other information related to the medical report (e.g., information available from the medical record of a patient to whom the medical report pertains) to provide guidance in connection with diagnosis, treatment, reporting, etc. (i.e., providing medical staff with AI-generated diagnostic alerts and/or guidance based on the identified problems). Sadeghi, p. 22, lines 26-32.
Murthy V. Devarakonda et al., Automated problem list generation and physicians perspective from a pilot study, 105 International Journal of Medical Informatics , 121–129 (2017), https://www.sciencedirect.com/science/article/pii/S1386505617301648 (last visited Jan 9, 2026), hereinafter referred to as Devarakonda. The Devarakonda article generally teaches the use of machine learning and natural language processing technologies for automatically generating a problem list from data in an HER and keeping it current. Abstract. Specifically, Devarakonda teaches that machine learning is framed to generate the problem list as a supervised binary classification task, which incorporates: (1) identifying candidate problems from textual narratives of clinical notes using natural language process, (2) determining if candidate problems are true problems or not based on if a confidence score is above a learned threshold. See Devarakonda, 2. Method: Automated Problem List Generation Section, at p. 122. Further, the machine learning model was engineered to think the way a human reader, such as a physician or nurse would think, including: modeling the context of problem; modeling disease prevalence in a population; modeling medical treatments; modeling formal diagnoses by physicians; modeling chronic conditions; and modeling confidence in clinical terms recognition. See Devarakonda, 3. Modeling Clinical Thinking Section, at p. 123.
However, Nye; Sadeghi; and Devarakonda, do not teach an apparatus, method, and at least one non-transitory computer readable media, comprising: (1) “reconstructing the received radiological scan images of the patient into three-dimensional reconstructed scans”; and (2) “prioritizing the problems to a hot list based on what is important to rule-out and what most likely based on the probability estimation of diagnoses”, in combination with the other limitations described in independent claims 1, 9, and 16. As such, claims 1, 9, and 16 are deemed to be novel and non-obvious over the prior art. Similarly, dependent claims 2-8, 10-15, and 17-20 are also deemed to be novel and non-obvious over the prior art due to their individual chains of dependency on claims 1, 9, and 16. As such, claims 1-20 are deemed to be allowable over the prior art under 35 U.S.C. §§ 102 and 103.
Conclusion
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/N.A.A./Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686