DETAILED ACTION
Status of Claims
This is the first office action on the merits in response to the application filed on 20 March 2025.
Claim(s) 1-20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application claims priority of JP Application No. 2024-045396 filed on 21 March 2024 and JP Application No. 2025-044573 filed on 19 March 2025. Applicant’s claim for the benefit of this prior filed application is acknowledged.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 20 March 2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 19, which is representative of claims 1 and 20, recites: a medical information processing method comprising:
acquiring examination purpose information regarding an examination purpose including a description regarding a first disease candidate of a patient, and an image interpretation report regarding an image examination performed according to the examination purpose;
acquiring image interpretation information including the description regarding the first disease candidate from the image interpretation report;
specifying an incidental finding described for a second disease candidate different from the first disease candidate in the information interpretation information; and
displaying a specified result .
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of reviewing a report to determine additional diagnoses. This concept describes a mental process that a healthcare provider should follow to identify alternative diagnoses similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such, these limitation set forth a method of organizing human activity. Therefore the claims are determined to recite an abstract idea. Alternatively, this concept is analogous to the examples of “observation”, “evaluation”, and “judgement” given in MPEP 2106.04(a)(2)(III). Further, this concept as claimed does not require a scale of data beyond the mental faculties of a human being and the operations of the abstract idea can be practically performed in the human mind. As such, these limitations are determined to recite a mental process. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 1 recites the additional application of an apparatus comprising processing circuitry. Claim 20 recites the additional element of a non-transitory computer readable storage medium. These additional elements are recited at an extremely high level of generality, and may be interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite the additional element of displaying information on a display. This additional element reflects no improvement to technology, no particular machine, and no transformation of an article. This additional element does not meaningfully limit the abstract idea, and instead only generally links the abstract idea to a technological environment of a computing device. Alternatively, this additional element amounts to insignificant extra-solution activity as it is simply data output. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not amount to significantly more.
As previously notes, the claims recite an additional element of displaying information on a display. Dezonno et al. (US 2002/0021797 A1) demonstrates (See at least [0024]) that displaying information on a display was conventional long before the priority date of the claimed invention. As such, this additional element does not amount to significantly more.
There are no further additional elements. When considered as a combination, the additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not amount to significantly more than the abstract idea. Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 2-18 further narrow the abstract idea, but the claims continue to recite an abstract idea. Dependent claims 2-8 and 12-16 recite no further additional elements. The previously identified additional elements, when considered individually or as a combination, only generally link the abstract idea to a technological environment of a computing device for the same reasons as given above. Therefore claims 2-8 and 12-16 remain directed to an abstract idea. At Step 2B, the previously identified additional elements, when considered individually or as a combination, only generally link the abstract idea to a technological environment of a computing device for the same reasons as given above. Therefore the additional elements of claims 2-8 and 12-16 do not amount to significantly more than the abstract idea.
Claims 9-11, 17, and 18 recite the additional element of generative AI. This additional element is interpreted as instructions to implement the abstract idea with a computing device. As such, this additional element does not integrate the abstract idea into a practical application. When considered as a combination with the previously identified additional element, the additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore claims 9-11, 17, and 18 claims are determined to be directed to an abstract idea. At Step 2B, the generative AI additional element continues to amount to instructions to implement the abstract idea with a computing device. As such, this additional element does not amount to significantly more. When considered as a combination with the previously identified additional element, the additional elements only generally link the abstract idea to a technological environment of a computing device. Therefore the additional elements of claims 9-11, 17, and 18 do not amount to significantly more. Because the dependent claims remain directed to an abstract idea without reciting significantly more, the dependent claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8, 12-14, 19, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kawagishi et al. (US 2015/0006447 A1).
Regarding Claim 1, 19, and 20: Kawagishi discloses a medical information processing apparatus comprising processing circuitry (See at least [0066]) configured to:
acquire examination purpose information regarding an examination purpose including a description regarding a first disease candidate of a patient (In step S3010, the medical information acquisition unit 102 acquires, as an expected diagnosis name, the diagnosis name expected by the doctor concerning the abnormal shadow of the lung which is input to the medical diagnosis support apparatus 100. See at least [0043]);
acquire image interpretation information including the description regarding the first disease candidate from an image interpretation report regarding an image examination performed according to the examination purpose (The following is an example in which the medical diagnosis support apparatus acquires a plurality of interpretation findings concerning an abnormal shadow of the lung on a chest X-ray CT image as already input information. See at least [0022]. Also: In step S3000, the medical diagnosis support apparatus 100 acquires already input information concerning an abnormal shadow of the lung input to the medical diagnosis support apparatus 100 and accompanying data. This operation is performed as processing by the medical information acquisition unit 102. Assume that the interpretation finding information which the medical diagnosis support apparatus 100 has acquired in step S3000 is I1 "shape": S11 "sphere", I3 "radial": S33 "weak", . . . , In "Engulfment (Blood Vessel)": Sn3 "none". In this case, the set Nf of interpretation findings of the already input information is given as Nf={I1, I3, . . . , In}, and the set Ef of the states of Nf is given as Ef={S11, S33, . . . , Sn3}. See at least [0042]. Also: In the following description, each interpretation finding item is represented by Ij (j=1 to n), and n types of interpretation findings I1 to In are handled. See at least [0037]. Also: In this case, a set of interpretation findings of already input information is written as NF, and a set of states of NF is written as Ef. See at least [0040]);
specify an incidental finding described for a second disease candidate different from the first disease candidate from the image interpretation information (In step S3030, the inference unit 108 infers the probability (already input information inference result) of the abnormal shadow of the lung being each diagnosis name based on the already input information (that is, Ef) of the abnormal shadow of the lung acquired in step S3000. In addition, the inference unit 108 infers the probability (support information candidate inference result) of the abnormal shadow being each diagnosis name based on only each support information candidate (Efl in this embodiment) acquired in step S3020. See at least [0045]. Also: In step S3040, the comparative diagnosis name acquisition unit 110 determines a comparative diagnosis name by using the already input information inference result acquired in step S3030. See at least [0047]); and
display a specified result on a display (In step S3070, the presentation unit 116 displays the following information on the monitor 1005: … the comparative diagnosis name obtained in step S3040. See at least [0051]-[0056]).
Regarding Claim 2: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to highlight and display the specified result on the display (The presentation information 400 also includes already input information inference result 4030 acquired in step S3030 and a comparative diagnosis name (estimated diagnosis name) 4040 acquired in step S3040. See at least [0057] and Fig. 4).
Regarding Claim 3: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to further specify the description regarding the first disease candidate from the image interpretation information (The following is an example in which the medical diagnosis support apparatus acquires a plurality of interpretation findings concerning an abnormal shadow of the lung on a chest X-ray CT image as already input information. See at least [0022]. Also: In step S3000, the medical diagnosis support apparatus 100 acquires already input information concerning an abnormal shadow of the lung input to the medical diagnosis support apparatus 100 and accompanying data. This operation is performed as processing by the medical information acquisition unit 102. Assume that the interpretation finding information which the medical diagnosis support apparatus 100 has acquired in step S3000 is I1 "shape": S11 "sphere", I3 "radial": S33 "weak", . . . , In "Engulfment (Blood Vessel)": Sn3 "none". In this case, the set Nf of interpretation findings of the already input information is given as Nf={I1, I3, . . . , In}, and the set Ef of the states of Nf is given as Ef={S11, S33, . . . , Sn3}. See at least [0042]. Also: In the following description, each interpretation finding item is represented by Ij (j=1 to n), and n types of interpretation findings I1 to In are handled. See at least [0037]. Also: In this case, a set of interpretation findings of already input information is written as NF, and a set of states of NF is written as Ef. See at least [0040]).
Regarding Claim 4: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to display the specified result on the display so as to distinguish the incidental finding and the description regarding the first disease candidate (FIG. 4 shows an example of presentation information to be displayed on the monitor 1005 in this embodiment. Presentation information 400 includes a representative image 4000 of the abnormal shadow of the lung, already input information 4010 of the abnormal shadow of the lung acquired in step S3000, and an expected diagnosis name 4020 acquired in step S3010. The presentation information 400 also includes already input information inference result 4030 acquired in step S3030 and a comparative diagnosis name (estimated diagnosis name) 4040 acquired in step S3040. In the example shown in FIG. 4, the apparatus displays in a pie chart, as the already input information inference result 4030, an inference probability 4031 of a primary lung cancer in the already input information inference result, an inference probability 4032 of a lung cancer metastasis, and an inference probability 4033 of others. In addition, the presentation information 400 includes support information 4050 acquired in step S3060. See at least [0057] and Fig. 4).
Regarding Claim 5: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to display that the incidental finding is inconsistent with the examination purpose and the description regarding the first disease candidate is consistent with the examination purpose on the display (See at least Fig. 5).
Regarding Claim 6: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to highlight and display the incidental finding and the description regarding the first disease candidate on the display (FIG. 4 shows an example of presentation information to be displayed on the monitor 1005 in this embodiment. Presentation information 400 includes a representative image 4000 of the abnormal shadow of the lung, already input information 4010 of the abnormal shadow of the lung acquired in step S3000, and an expected diagnosis name 4020 acquired in step S3010. The presentation information 400 also includes already input information inference result 4030 acquired in step S3030 and a comparative diagnosis name (estimated diagnosis name) 4040 acquired in step S3040. In the example shown in FIG. 4, the apparatus displays in a pie chart, as the already input information inference result 4030, an inference probability 4031 of a primary lung cancer in the already input information inference result, an inference probability 4032 of a lung cancer metastasis, and an inference probability 4033 of others. In addition, the presentation information 400 includes support information 4050 acquired in step S3060. See at least [0057] and Fig. 4).
Regarding Claim 7: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to: extract the first disease candidate from the examination purpose information; extract, from the image interpretation information, an image interpretation disease candidate including at least the first disease candidate out of the first disease candidate and the second disease candidate; and specify the incidental finding based on the extracted first disease candidate and the image interpretation disease candidate (In this case, a set of interpretation findings of already input information is written as NF, and a set of states of NF is written as Ef. In addition, a subset of Ef is written as Efl (l=1, 2, . . . ). Efl corresponds to a support information candidate. In the following description, a diagnosis name will be written as a symbol D. According to this embodiment, the diagnosis name has three values respectively representing primary lung cancer, lung cancer metastasis, and others, which are respectively written as D1, D2, and D3. In this case, the inference probability of a diagnosis name Dr (r=1, 2, 3) with no information being given will be written as P(Dr) (to be also referred to as the prior probability). In addition, the inference probability of the diagnosis name Dr with input information being given as E is written as P(Dr|E) (to be also referred as the posterior probability). In addition, this embodiment uses a diagnosis name (estimated diagnosis name) exhibiting a highest probability as a comparative diagnosis name, and an expected diagnosis name and an estimated diagnosis name are respectively written as Ddct and Dinf. Ddct and Dinf each take any one of D1, D2, and D3. See at least [0040]. Also: In step S3040, the comparative diagnosis name acquisition unit 110 determines a comparative diagnosis name by using the already input information inference result acquired in step S3030. This embodiment uses a diagnosis name (estimated diagnosis name), of the types D1, D2, and D3 of abnormal shadows, which exhibits a highest probability as a comparative diagnosis name. If, for example, D1=25.2%, D2=42.5%, and D3=32.3%, the comparative diagnosis name (estimated diagnosis name) Dinf is expressed as Dinf=D2. See at least [0047]).
Regarding Claim 8: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to: extract the first disease candidate by analyzing the examination purpose information; and extract the image interpretation disease candidate by analyzing the image interpretation information (A medical information acquisition unit 102 acquires the already input information associated with the abnormal shadow of the lung and the accompanying data which are input from the case information input terminal 200 to the medical diagnosis support apparatus 100, and outputs the acquired data to a support information candidate acquisition unit 106, an inference unit 108, and a presentation unit 116. See at least [0026]).
Regarding Claim 12: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to acquire the examination purpose information based on at least one of an electronic medical record of the patient, an examination order of the image examination, or the image interpretation report (The case information input terminal 200 acquires information (medical images, electronic chart information, and the like) associated with the abnormal shadow of the lung concerning a case as a diagnosis target from a server (not shown). The apparatus then displays a medical image on a monitor in a form that allows the user (doctor) to perform radiogram interpretation, and acquires the interpretation findings input by the user as already input information. The apparatus also acquires the diagnosis name input by the user as a result of radiogram interpretation as the diagnosis name expected by the user. See at least [0024]).
Regarding Claim 13: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry acquires the examination purpose information based on at least items of patient information, the examination purpose, and suspicion of diagnosis among the items of the patient information, main complaint, previous disease, concurrent disease, internal medicine, family history, physical finding, the examination purpose, and the suspicion of diagnosis regarding the patient (The case information input terminal 200 acquires information (medical images, electronic chart information, and the like) associated with the abnormal shadow of the lung concerning a case as a diagnosis target from a server (not shown). The apparatus then displays a medical image on a monitor in a form that allows the user (doctor) to perform radiogram interpretation, and acquires the interpretation findings input by the user as already input information. The apparatus also acquires the diagnosis name input by the user as a result of radiogram interpretation as the diagnosis name expected by the user. See at least [0024]).
Regarding Claim 14: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry acquires the image interpretation information based on at least an item of finding among items of the finding, evaluation and interpretation, and image interpretation result included in the image interpretation report (The case information input terminal 200 acquires information (medical images, electronic chart information, and the like) associated with the abnormal shadow of the lung concerning a case as a diagnosis target from a server (not shown). The apparatus then displays a medical image on a monitor in a form that allows the user (doctor) to perform radiogram interpretation, and acquires the interpretation findings input by the user as already input information. The apparatus also acquires the diagnosis name input by the user as a result of radiogram interpretation as the diagnosis name expected by the user. See at least [0024]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 9-11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawagishi et al. (US 2015/0006447 A1) in view of Charlap (US 2024/0355471 A1).
Regarding Claim 9: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry includes AI, and in a case where an instruction for specifying the incidental finding and the description regarding the first disease candidate from the examination purpose information and the image interpretation information, the examination purpose information, and the image interpretation information are input, the AI outputs the specified result and displays the specified result on the display (As an inference technique at this time, it is possible to use one of various existing inference techniques such as a Bayesian network, neural network, and support vector machine. See at least [0046]. The inference unit 108 executes inference based on the already input information acquired by the medical information acquisition unit 102 which is associated with the abnormal shadow of the lung as a diagnosis target, and calculates the probability (already input information inference result) of the abnormal shadow being each diagnosis name. See at least [0029]. Also: In step S3070, the presentation unit 116 displays the following information on the monitor 1005: [0052] the information (the already input information and representative image) concerning the abnormal shadow of the lung which is obtained in step S3000, [0053] the expected diagnosis name obtained in step S3010, [0054] the already input information inference result acquired in step S3030, [0055] the comparative diagnosis name obtained in step S3040, and [0056] the support information obtained in step S3060. See at least [0051]). However,
Kawagishi does not appear to disclose generative AI. Charlap teaches generative AI (leveraging generative artificial intelligence to generate diagnoses. See at least [0044]).
Kawagishi provides a system which uses AI to identify and evaluate alternative diagnoses, which differs from the claimed invention by the substitution of Kawagishi’s generic AI to a generative AI. Charlap demonstrates that the prior art already knew of using generative AI to identify and evaluate diagnoses. One of ordinary skill in the art could have trivially substituted Charlap’s AI into the system of Kawagishi. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would use generative AI to produce diagnosis inferences. As such, the identified substitution and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Kawagishi and the teachings of Charlap.
Regarding Claim 10: Kawagishi in view of Charlap makes obvious the above limitations. Kawagishi further discloses wherein the processing circuitry is further configured to further output an importance level for the specified result based on the image interpretation information (the apparatus acquires a diagnosis name (estimated diagnosis name) exhibiting a highest inference probability as a comparative diagnosis name. However, the apparatus may acquire a comparative diagnosis name by other methods. For example, the apparatus may acquire a diagnosis name having a higher importance than an expected diagnosis name as a comparative diagnosis name. In this case, assume that a predetermined importance Qr is defined in advance concerning each diagnosis name Dr. In step S3040, the apparatus acquires, as a comparative diagnosis name, a diagnosis name having an importance higher than an importance Qdoc of the expected diagnosis name (that is, satisfying Qdoc<Qr). For example, in the above case, if Q1=0.9, Q2=0.5, and Q3=0.1 and D2 "lung cancer metastasis" is acquired as an expected diagnosis name, the apparatus acquires D1 "primary lung cancer" as a comparative diagnosis name. If there are a plurality of diagnosis names having higher importances than the expected diagnosis name, one of the diagnosis names which exhibits a highest inference probability can be acquired as a comparative diagnosis name. Alternatively, the apparatus may acquire all diagnosis names, of the inference results, which exhibit high possibilities (inference probabilities higher than a predetermined threshold). Alternatively, the apparatus may acquire a diagnosis name having the highest importance as a comparative diagnosis name. See at least [0060]). Kawagishi does not expressly disclose display the importance level on the display. However, Kawagishi separately teaches display information on the display (In step S3070, the presentation unit 116 displays the following information on the monitor 1005: … the comparative diagnosis name obtained in step S3040. See at least [0051]-[0056]).
Kawagishi and Charlap suggest a system which identifies an important value of a diagnosis, upon which the claimed invention’s display of an importance value can be seen as an improvement. However, Kawagishi separately teaches displaying various information regarding determined diagnoses. One of ordinary skill in the art could have trivially applied these techniques to the importance value determined. Further, one of ordinary skill in the art would have recognized that such an application of Kawagishi would have resulted in an improved system which would provide a doctor with additional information regarding the determined diagnosis. As such, the identified application of Kawagishi the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Kawagishi and the teachings of Charlap.
Regarding Claim 11: Kawagishi in view of Charlap makes obvious the above limitations. Kawagishi further discloses wherein the processing circuitry is configured to determine the importance level for the second disease candidate described in the incidental finding of the specified result based on a relationship between a predetermined disease candidate and the importance level (the apparatus acquires a diagnosis name (estimated diagnosis name) exhibiting a highest inference probability as a comparative diagnosis name. However, the apparatus may acquire a comparative diagnosis name by other methods. For example, the apparatus may acquire a diagnosis name having a higher importance than an expected diagnosis name as a comparative diagnosis name. In this case, assume that a predetermined importance Qr is defined in advance concerning each diagnosis name Dr. In step S3040, the apparatus acquires, as a comparative diagnosis name, a diagnosis name having an importance higher than an importance Qdoc of the expected diagnosis name (that is, satisfying Qdoc<Qr). For example, in the above case, if Q1=0.9, Q2=0.5, and Q3=0.1 and D2 "lung cancer metastasis" is acquired as an expected diagnosis name, the apparatus acquires D1 "primary lung cancer" as a comparative diagnosis name. If there are a plurality of diagnosis names having higher importances than the expected diagnosis name, one of the diagnosis names which exhibits a highest inference probability can be acquired as a comparative diagnosis name. Alternatively, the apparatus may acquire all diagnosis names, of the inference results, which exhibit high possibilities (inference probabilities higher than a predetermined threshold). Alternatively, the apparatus may acquire a diagnosis name having the highest importance as a comparative diagnosis name. See at least [0060]). As previously noted in combination with Kawagishi and Charlap, Kawagishi teaches displaying various information associated with a determined diagnosis. The motivation to combine Kawagishi and Charlap is the same as explained under claim 10 above, and is incorporated herein.
Regarding Claim 18: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry includes AI (As an inference technique at this time, it is possible to use one of various existing inference techniques such as a Bayesian network, neural network, and support vector machine. See at least [0046]) and in a case where an instruction for outputting a finding assumed from the examination purpose information or the image interpretation information, the examination purpose information or the image interpretation information corresponding to the instruction are input, the AI outputs the assumed finding and displays the output on the display (In step S3030, the inference unit 108 infers the probability (already input information inference result) of the abnormal shadow of the lung being each diagnosis name based on the already input information (that is, Ef) of the abnormal shadow of the lung acquired in step S3000. In addition, the inference unit 108 infers the probability (support information candidate inference result) of the abnormal shadow being each diagnosis name based on only each support information candidate (Efl in this embodiment) acquired in step S3020. See at least [0045]. Also: In step S3040, the comparative diagnosis name acquisition unit 110 determines a comparative diagnosis name by using the already input information inference result acquired in step S3030. See at least [0047]. Also: In step S3070, the presentation unit 116 displays the following information on the monitor 1005. See at least [0051] and Fig. 4).
Kawagishi does not expressly disclose generative AI or a treatment candidate specified from a main complaint of the patient and a physical finding for the assumed finding. Charlap teaches generative AI (leveraging generative artificial intelligence to generate diagnoses. See at least [0044]) and determining and outputting a treatment candidate specified from a main complaint of the patient and a physical finding for the assumed finding (The diagnosis 112 can also include one or more treatment recommendations or other recommended interventions, such as prescribing a medication or deprescribing a medication. For instance, if the diagnosis indicates that the patient's condition is a result of medication side effect, the diagnosis 112 generated by the diagnosis model 110 can recommend, via the output devices 214, to discontinue (and deprescribe) a medication the patient is currently taking and/or prescribe a different medication in its place. See at least [0140]).
Kawagishi provides a system which uses AI to evaluate medical information, which differs from the claimed invention by the substitution of Kawagishi’s generic AI to a generative AI that identifies a treatment based on input information. Charlap demonstrates that the prior art already knew of using generative AI to determine a diagnosis and identify a treatment. One of ordinary skill in the art could have easily applied the techniques of Charlap to the system of Kawagishi. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would use generative AI to evaluate medical information and recommend treatments. As such, the application of Charlap and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Kawagishi and the teachings of Charlap.
Claim(s) 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawagishi et al. (US 2015/0006447 A1) in view of Halsted (US 2008/0046286 A1)
Regarding Claim 15: Kawagishi discloses the above limitations. Kawagishi does not appear to disclose notify a clinical department regarding the specified result of the specified result and patient information regarding the patient. However, Halsted teaches notify a clinical department regarding the specified result of the specified result and patient information regarding the patient (the system would notify the emergency department physician of the diagnosis once it is made. See at least [0043]. Also: For example, FIG. 13 shows a tracer notification sent to one physician in the form of an electronic report that includes a link 218. See at least [0084] and Fig. 13).
Kawagishi provides a system which makes a diagnosis determination from provider input, upon which the claimed invention’s distribution of a result to a clinical department can be seen as an improvement. However, Halsted demonstrates that the prior art already knew of distributing a determined result to a clinical department. One of ordinary skill in the art could have trivially applied the techniques of Halsted to the system of Kawagishi. Further, one of ordinary skill in the art would have recognized that such an application of Halsted would have resulted in an improved system which would inform relevant parties of important results. As such, the identified application of Halsted and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Kawagishi and the teachings of Halsted.
Regarding Claim 16: Kawagishi discloses the above limitations. Kawagishi does not appear to disclose transmit examination reservation information of the patient to an examination department regarding the specified result. However, Halsted teaches transmit examination reservation information of the patient to an examination department regarding the specified result (an electronic reporting method that permits the reporting physician to send an electronic report to the ordering physician that includes a recommendation for further testing, examinations and/or procedures in the form of links. The ordering physician need only click on the link or icon associated with a particular test, exam or procedure to order that test, exam or procedure or begin the process of ordering that same test, exam or procedure. See at least [0052]).
Kawagishi provides a system which makes a diagnosis determination from provider input, upon which the claimed invention’s distribution of a result can be seen as an improvement. However, Halsted demonstrates that the prior art already knew of distributing a determined result for reservation purposes. One of ordinary skill in the art could have trivially applied the techniques of Halsted to the system of Kawagishi. Further, one of ordinary skill in the art would have recognized that such an application of Halsted would have resulted in an improved system which would inform relevant parties of important results. As such, the identified application of Halsted and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Kawagishi and the teachings of Halsted.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawagishi et al. (US 2015/0006447 A1) in view of Charlap (US 2024/0355471 A1) and Marchosky (US 2003/0050803 A1).
Regarding Claim 17: Kawagishi discloses the above limitations. Kawagishi further discloses wherein the processing circuitry includes AI (As an inference technique at this time, it is possible to use one of various existing inference techniques such as a Bayesian network, neural network, and support vector machine. See at least [0046]) and in a case where an instruction for outputting a finding assumed from the examination purpose information or the image interpretation information, the examination purpose information or the image interpretation information corresponding to the instruction are input, the AI outputs the assumed finding and displays the output on the display (In step S3030, the inference unit 108 infers the probability (already input information inference result) of the abnormal shadow of the lung being each diagnosis name based on the already input information (that is, Ef) of the abnormal shadow of the lung acquired in step S3000. In addition, the inference unit 108 infers the probability (support information candidate inference result) of the abnormal shadow being each diagnosis name based on only each support information candidate (Efl in this embodiment) acquired in step S3020. See at least [0045]. Also: In step S3040, the comparative diagnosis name acquisition unit 110 determines a comparative diagnosis name by using the already input information inference result acquired in step S3030. See at least [0047]. Also: In step S3070, the presentation unit 116 displays the following information on the monitor 1005. See at least [0051] and Fig. 4).
Kawagishi does not expressly disclose generative AI. Charlap teaches generative AI (leveraging generative artificial intelligence to generate diagnoses. See at least [0044]).
Kawagishi provides a system which uses AI to evaluate medical information, which differs from the claimed invention by the substitution of Kawagishi’s generic AI to a generative AI. Charlap demonstrates that the prior art already knew of using generative AI to identify and evaluate diagnoses. One of ordinary skill in the art could have trivially substituted Charlap’s AI into the system of Kawagishi. Further, one of ordinary skill in the art would have recognized that such a substitution would have predictably resulted in a system which would use generative AI to evaluate medical information. As such, the identified substitution would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Kawagishi and the teachings of Charlap.
Further, Kawagishi does not appear to disclose determining whether or not treatment for the assumed finding is described in the electronic medical record. However, Marchosky teaches determining and outputting whether or not treatment for the assumed finding is described in the electronic medical record (This enables the system to compare patient diagnosis and prescribed services or treatment records present in the medical and biographical records database with the approved therapeutic treatment for the identified diagnosis. See at least [0129]).
Kawagishi and Charlap suggest a system which supplements patient evaluation, upon which the claimed invention’s review of a medical record to determine if a treatment is included can be seen as an improvement. However, Marchosky demonstrates that the prior art already knew of reviewing a medical record for the inclusion of a treatment. One of ordinary skill in the art could have easily applied the techniques of Marchosky to the system of Kawagishi and Charlap. Further, one of ordinary skill in the art would have recognized that such an application of Marchosky would have resulted in an improved system which would indicate to a provider if the inferred diagnosis was also being treated. As such, the application of Marchosky and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Kawagishi and the teachings of Charlap and Marchosky.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Sugiyama et al. (US 2022/0280124 A1) discusses identifying incidental findings while processing medical data.
Conclusion
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-04-17
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