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 .
Notice to Applicant
Claims 1-15 are pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on July 1, 2025 and February 9, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements 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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a system and a method (1-15). Accordingly, claims 1-15 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified system claim 15 as the claim that represents a claimed invention for analysis and is similar to method Claim 1.
Claim 15:
A medical service support system comprising:
at least one processor;
a communication interface; and
a storage where an estimation model is stored, the estimation model being configured to output candidates for a disease which may be affecting a person associated with patient information among a plurality of diseases, in response to input of at least one item of the patient information, wherein
the processor
obtains input of a value of the at least one item of the patient information relating to a patient,
inputs the value of the at least one item to the estimation model to obtain candidates for the disease which may be affecting the patient, and
obtains additional request information to additionally be obtained from the patient, based on the candidates for the disease, and
the communication interface presents the additional request information.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activities. The claim elements are directed towards “obtain[ing] input of a value of the at least one item of the patient information”, “obtain[ing] candidates for the disease which may be affecting the patient”, and “obtain[ing] additional request information to additionally be obtained from the patient”, which encompasses managing personal behaviors of infection specialists as these activities are typically performed by infection specialists, as noted by [0002] and [0003] of Applicant specification, which recites: “In particular in medium- to small-scale hospitals and clinics, an infection non- specialist may have to deal with an infection. It is difficult for the infection non- specialist to be conversant with all infections, and needs for medical service support by an infection specialist are great… Infection specialists conversant in infection medical care are definitely lacking as compared with the number of medical institutions, and demands for infection medical service support tools are great.” These claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass obtaining input of a value of the at least one item of the patient information, inputting the value of the at least one item, obtaining candidates for the disease which may be affecting the patient, obtaining additional request information, and presenting the additional request information.
Accordingly, the claim recites an abstract idea.
Claim 1 is also abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), 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 of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the Claims:
Estimation model (1,7-8,15); storage (10,15); non-transitory computer-readable storage medium (14); medical service support program (14); processor (14,15); computer (14); communication interface (15);
Any computing systems that would be able to perform the method (processor, computer) and associated elements (non-transitory machine readable medium, medical service support program, storage) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0024] of Applicant specification recites: “Medical service support system 100 is configured, for example, based on a personal computer.” [0142] further recites: “A medical service support program according to one aspect, by being executed by at least one processor of a computer, may cause the computer to perform the medical service support method in any one of Clauses 1 to 13.”No specific, technical improvements are being made to the technology of computing devices as any generic personal computer may be applied to perform the abstract idea of patient disease diagnosis.
The estimation model is also taught at a high level of generality. [0089] recites: “Estimation model 230 is a program for performing calculation in accordance with a model. Though an exemplary algorithm of estimation model 230 is logistic regression, the algorithm applied in estimation model 230 is not limited thereto.” [0041] further recites: “ Model generator 262 uses training data 220 to perform machine learning processing on estimation model 230. Estimation model 230 subjected to machine learning processing is herein also referred to as a "trained model.”” No specific, technical improvements are being made to machine learning as a generic logistic regression is applied to perform the abstract idea of disease diagnosis.
The communication interface is also taught at a high level of generality. [0025] recites: “Medical service support system 100 includes a processor 101, a memory 200, and an input and output port 300. A mouse 110, a keyboard 120, and a display device 130 are connected to input and output port 300.” [0026] further recites: “Input and output port 300 may be a communication interface for data communication over the network.” No specific, technical improvements are being made to communication interfaces as any generic display device is applied to perform an insignificant extra-solution activity of outputting data.
Storage mediums are also taught at a high level of generality. [0101] recites: “The type of the information required by the specialist may be stored in advance in the storage such as memory 200, for example, as a request correspondence table, for each type of the infection.” No specific, technical improvements are being made to storage devices as any generic memory device is applied to perform an insignificant extra-solution activity of storing data.
Looking at the additional elements as an ordered combination adds 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 with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the 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 other meaningful way beyond generally linking the use of 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 claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2: This claim recites wherein the obtaining additional request information includes: referring to reference data in which a disease and information to be used for diagnosis or therapy are associated with each other; and identifying the additional request information based on the information to be associated with the candidates for the disease and to be used for diagnosis or therapy; which teaches an abstract idea of mental processes by referring to reference data and identifying information. This claim also only serves to further limit the obtaining of additional request information.
Claim 3: This claim recites wherein the reference data includes essential information and optional information as the information to be used for diagnosis or therapy, and the medical service support method further comprises presenting a result of determination as to whether the at least one item includes all of the essential information; which teaches an abstract idea of mental processes by presenting a determination whether essential information is present. The claim also serves to further limit the reference data.
Claim 4: This claim recites wherein the information to be used for diagnosis or therapy includes diagnosis information and therapy information, the medical service support method further comprises accepting diagnosis or therapy as a purpose of obtainment of the candidates for the disease, and the identifying the additional request information includes: identifying the additional request information based on the diagnosis information when the purpose falls under diagnosis; and identifying the additional request information based on the therapy information when the purpose falls under therapy; which serves to further limit the information and identification of additional request information. The claim also teaches an abstract idea of focusing the processing on either diagnosis or treatment.
Claim 5: This claim recites the method further comprising showing a diagnosis guideline or a therapy guideline for the candidates for the disease; which teaches an abstract idea of providing guidelines/instructions.
Claim 6: This claim recites the method further comprising: accepting a result of diagnosis by a primary doctor together with the patient information; and transmitting to a specialist, the patient information, the candidates for the disease, and the result of diagnosis by the primary doctor; which teaches an abstract idea of certain methods of organizing human activity by requiring a primary doctor to accept diagnostic results and communicating diagnostic information to specialists.
Claim 7: This claim recites the method further comprising showing as being emphasized in the patient information, a basis of determination of the candidates for the disease by the estimation model; which teaches an abstract idea of mental processes as emphasizing important information.
Claim 8: This claim recites wherein the additional request information includes information that allows improvement in accuracy of the candidates for the disease when the information is inputted to the estimation model, and the obtaining additional request information includes obtaining the additional request information by referring to correspondence information in which each of the plurality of diseases is associated with items of the patient information; which only serves to limit the request information.
Claim 9: This claim recites wherein the additional request information includes a lacking item not included in the at least one item among items of the patient information brought in correspondence with the candidates for the disease, and the obtaining additional request information includes identifying the lacking item; which only serves to limit the request information.
Claim 10: This claim recites wherein the obtaining additional request information includes: obtaining the additional request information corresponding to the obtained candidates for the disease from a storage where information describing relation between the plurality of diseases and the additional request information is stored; or obtaining the additional request information by using an answer obtained by presenting the obtained candidates for the disease to a specialist; which teaches an abstract idea of certain methods of organizing human activity by asking a specialist for their opinion and mental processes by obtaining data using known relations between diseases and request data.
Claim 11: This claim recites wherein the obtaining at least one item includes: accepting input of electronic medical chart data of the patient: and generating a value of the at least one item by using the electronic medical chart data; which only serves to limit the obtained data.
Claim 12: This claim recites the method further comprising presenting the candidates for the disease, wherein the presenting the candidates for the disease includes presenting an item having importance equal to or higher than a given value in identification of the candidates for the disease, of the at least one item, or additional information on the candidates for the disease; which only serves to limit the presented information.
Claim 13: This claim recites wherein the patient information includes information on at least one of patient interview information, physical finding, and an examination result; which only serves to limit the patient information.
Claim 14: This claim recites a non-transitory computer-readable storage medium storing a medical service support program that causes, by being executed by at least one processor of a computer, the computer to perform the medical service support method according to claim 1; which teaches a non-transitory computer-readable storage medium storing a medical service support program and a processor of a computer at a high level of generality such that they are only applied to perform the abstract idea of disease diagnosis.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-14, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2-14, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-14, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-15 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7-10, and 13-15 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Lee (US 20190065688).
Regarding claim 1, Lee teaches a medical service support method comprising:
obtaining at least one item of patient information relating to a patient ([0039], “The system 10 also includes a medical knowledge-based inference engine 24 (processor and associated programming), which operates on the patient's medical history and findings (either provided directly by the device or from a medical records database 26) and the validated probabilistic health model 16.”);
inputting the at least one item to an estimation model and obtaining candidates for a disease which may be affecting the patient from among a plurality of diseases ([0020], “The system includes a validated probabilistic model that is informed from aggregated electronic medical records and other sources of medical knowledge, such as medical journal articles, or expert user input. The system further includes a medical knowledge-based inference engine (processing unit and associated programming) that uses the patient's known findings (i.e., medical history and new findings, if any) and the model to determine a set of the most probable diseases.”);
obtaining additional request information to additionally be obtained from the patient, based on the candidates for the disease ([0043], “in order for the inference engine to perform its tasks, including determine a set of the most probable diseases of the patient, suggest a set of one or more tests or additional findings that differentiate the set of most probable diseases, and generate indicia, such as statistical data, text, or otherwise, indicating the effectiveness or relevancy of the one or more tests or additional findings, it uses the knowledge base 18 and the validated probabilistic health model 16. The knowledge base 18 includes validated (i.e., proven or established) medical knowledge in the form of data indicating relationships between medical findings (i.e., facts as to a patient condition, such as test results, symptoms, etc.) and diagnoses.”). Examiner notes that the suggestions for additional tests (additional request information) is obtained from the knowledge base.
and presenting the additional request information ([0054], “FIG. 5 shows another arrangement of the display of the interface 40 of the electronic device in which the device includes in one screen a display of known findings 500, a display of most probable diagnosis 502, likelihood data for these diagnoses 504, and a display of suggested new findings or tests in one column 506 and associated indicia 508 for each of proposed additional tests or findings, such as for example relevancy scores, costs, or other attributes for the findings. In FIG. 5, the provider can click on the “suggested new findings or tests” icon 510 and the display of FIG. 6 appears. In this case there are two new tests which are suggested—a CT scan and an ultrasound.”).
Regarding claim 2, Lee teaches the medical service support method of claim 1. Lee further teaches wherein the obtaining additional request information includes:
referring to reference data in which a disease and information to be used for diagnosis or therapy are associated with each other ([0043], “As noted above, in order for the inference engine to perform its tasks, including determine a set of the most probable diseases of the patient, suggest a set of one or more tests or additional findings that differentiate the set of most probable diseases, and generate indicia, such as statistical data, text, or otherwise, indicating the effectiveness or relevancy of the one or more tests or additional findings, it uses the knowledge base 18 and the validated probabilistic health model 16. The knowledge base 18 includes validated (i.e., proven or established) medical knowledge in the form of data indicating relationships between medical findings (i.e., facts as to a patient condition, such as test results, symptoms, etc.) and diagnoses. The medical knowledge may be stored in a data structure format which preserves these relationships as indicated in the box 100.”). Examiner interprets the data in the knowledge base to be reference data.
and identifying the additional request information based on the information to be associated with the candidates for the disease and to be used for diagnosis or therapy ([0048], “In the new findings section 202, the system suggests findings that are unknown and can be confirmed or denied by the physician (through questioning, examining, or placing an order and getting a result).” [0049], “Suggestions may change depending on the probabilities of diagnoses in the differential.”).
Regarding claim 3, Lee teaches the medical service support method of claims 1 and 2. Lee further teaches wherein the reference data includes essential information and optional information as the information to be used for diagnosis or therapy ([0070], “FIG. 10 illustrates that the engine 24 will prioritize findings that differentiate between diseases… A CT scan that shows appendicolith strongly suggests appendicitis (LR 9.0) and strongly suggests against diverticulitis (LR −8.0). An abscess on ultrasound has an LR of 3.0 for appendicitis and 3.5 for diverticulitis, which does not differentiate between the diseases.” [0059], “inference engine 24 makes suggestions of diagnosis and additional findings making use of a stored knowledge base 18 and validated probabilistic model 16.”). Examiner interprets appendicolith to encompass essential information and an abscess o encompass optional information as it does not meaningfully differentiate between two diseases.
and the medical service support method further comprises presenting a result of determination as to whether the at least one item includes all of the essential information ([0050], “Consider the following example: a patient may have a set of medical findings associated with a chief complaint, i.e., abdominal pain. From the medical findings, the inference engine 24, with the aid of the probabilistic model 16 and knowledge base, determine a set of the most probable diseases (appendicitis and gall bladder disease) and infers a set of two tests that differentiate the set of most probable diseases: 1) an abdominal CT scan and 2) an abdominal ultrasound. Indicia are also generated by the engine: based on the prevalences of the two diseases and the known medical findings the patient's probability of having gallbladder disease is 60% and appendicitis is 40%. This information is displayed in regions 502 and 504 of FIG. 5. A CT scan that shows an inflamed gall bladder would increase the probability of gallbladder disease being the correct diagnosis to 90%, and would decrease the probability of appendicitis to 10%, increasing the delta probability by 80%.” [0040], “The interface 40 has a region for display of three types of information that a physician can collect about a patient: 1) known findings, shown in region 200, 2) proposed new findings in region 202, including proposed new tests 204,…”). Examiner interprets displaying proposed new tests to encompass a result of a determination that the item does not include all the essential information.
Regarding claim 4, Lee teaches the medical service support method of claims 1 and 2. Lee further teaches wherein the information to be used for diagnosis or therapy includes diagnosis information and therapy information ([0057], “The patient logs into the medical records system for the hospital or provider they are using and navigate to a page having the interface 50, which includes a display of the pertinent aspects of their medical records, including findings, a diagnosis and proposed additional tests or treatments. Each of the proposed additional tests or treatments is associated with indicia, such as cost, delta probability, survivability data, relevancy, side effects, risks, etc.,”),
the medical service support method further comprises accepting diagnosis or therapy as a purpose of obtainment of the candidates for the disease ([0018], “In the illustrated embodiment the system is part of or embedded in a larger clinical decision making system including device and software components aiding healthcare providers in arriving at a diagnosis and treatment options for a patient.”). Examiner notes that since the system of Lee performs both diagnosis and therapy and displays on the same screen, as shown in Fig. 8 below, consenting to use of the system encompasses an acceptance of diagnosis and therapy as the purpose of obtainment.
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and the identifying the additional request information includes:
identifying the additional request information based on the diagnosis information when the purpose falls under diagnosis ([0057], “The patient logs into the medical records system for the hospital or provider they are using and navigate to a page having the interface 50, which includes a display of the pertinent aspects of their medical records, including findings, a diagnosis and proposed additional tests or treatments.” [0070], “FIG. 10 illustrates that the engine 24 will prioritize findings that differentiate between diseases. For example, assume here that a CT scan and ultrasound (US) are equally costly. A CT scan that shows appendicolith strongly suggests appendicitis (LR 9.0) and strongly suggests against diverticulitis (LR −8.0).”); and
identifying the additional request information based on the therapy information when the purpose falls under therapy ([0057], “The patient logs into the medical records system for the hospital or provider they are using and navigate to a page having the interface 50, which includes a display of the pertinent aspects of their medical records, including findings, a diagnosis and proposed additional tests or treatments.” [0087], “providing a medical knowledge-based inference engine 24 operating on the patient's medical history and findings and the validated probabilistic health model to (1) determine a set of potential treatments for the patient and (2) generate indicia indicating effectiveness or relevancy of the potential treatments in the set”).
Regarding claim 5, Lee teaches the medical service support method of claim 1. Lee further teaches the method further comprising showing a diagnosis guideline or a therapy guideline for the candidates for the disease ([0057], “Clinical pathways: A guideline for asthma management appears in the chart of a patient who has a history of asthma.” [0065], “With this knowledge representation, we can guide the user towards a differential diagnosis.”).
Regarding claim 7, Lee teaches the medical service support method of claim 1. Lee further teaches the method further comprising showing as being emphasized in the patient information, a basis of determination of the candidates for the disease by the estimation model ([0048], “In the known findings section 200, the system suggests relevant facts from the patient's history that the physician confirms or denies with one tap.” [0053], “As indicated previously, when new findings are made, as in this case a new finding of “dysuria,” the Diagnostic Model Explorer is updated. See FIG. 4. “Dysuria” is now listed in the first column 302 as a known new finding. The differential diagnosis is refreshed, putting urinary tract infection at the top of the column 308 of suggested diagnoses. The decision tree is also recalculated, placing “lower abdominal pain” as the best next finding to obtain as indicated at the top of column 304, since it has the highest relevancy score as indicated by the third column 306. Likewise, the diagnostic tree 312 is updated.” [0054], “FIG. 5 shows another arrangement of the display of the interface 40 of the electronic device in which the device includes in one screen a display of known findings 500, a display of most probable diagnosis 502, likelihood data for these diagnoses 504, and a display of suggested new findings or tests in one column 506 and associated indicia 508 for each of proposed additional tests or findings, such as for example relevancy scores, costs, or other attributes for the findings.”). Under the broadest reasonable interpretation, Examiner interprets displaying relevant facts/findings/symptoms with relevancy scores and listing the most relevant facts at the top of a list for diagnostic relevancy to encompass an emphasis of the patient information over other types of information.
Regarding claim 8, Lee teaches the medical service support method of claim 1. Lee further teaches wherein the additional request information includes information that allows improvement in accuracy of the candidates for the disease when the information is inputted to the estimation model ([0045], “The system of FIG. 1 is preferably configured to augment or increase the knowledge base 18 over time as more and more patients are treated by the system of FIG. 1. In this regard, there is a computer 120 functioning as an interpreter which looks at raw information and turns it into a format useful to the knowledge base... In essence, the flow of data through the system indicated by the arrows 20 improves the knowledge base 18 and the performance of the engine 24 over time in recommending additional findings, additional tests, suggested diagnoses, and generation of useful and accurate indicia accompanying the proposed additional tests or findings.”), and
the obtaining additional request information includes obtaining the additional request information by referring to correspondence information in which each of the plurality of diseases is associated with items of the patient information ([0048], “in order for the inference engine to perform its tasks, including determine a set of the most probable diseases of the patient, suggest a set of one or more tests or additional findings that differentiate the set of most probable diseases, and generate indicia, such as statistical data, text, or otherwise, indicating the effectiveness or relevancy of the one or more tests or additional findings, it uses the knowledge base 18 and the validated probabilistic health model 16. The knowledge base 18 includes validated (i.e., proven or established) medical knowledge in the form of data indicating relationships between medical findings (i.e., facts as to a patient condition, such as test results, symptoms, etc.) and diagnoses.”). Examiner interprets the knowledge base to encompass correspondence information, as both connect symptoms and diagnoses together, supported by [0030] of Applicant specification, which recites: “For example, correspondence database 240 brings an infection "pyelonephritis" in correspondence with three types of items (fever, frequent urination, and flank pain).”
Regarding claim 9, Lee teaches the medical service support method of claim 1. Lee further teaches wherein the additional request information includes a lacking item not included in the at least one item among items of the patient information brought in correspondence with the candidates for the disease, and the obtaining additional request information includes identifying the lacking item ([0055], “Consider the following example: a patient may have a set of medical findings associated with a chief complaint, i.e., abdominal pain. From the medical findings, the inference engine 24, with the aid of the probabilistic model 16 and knowledge base, determine a set of the most probable diseases (appendicitis and gall bladder disease) and infers a set of two tests that differentiate the set of most probable diseases: 1) an abdominal CT scan and 2) an abdominal ultrasound. Indicia are also generated by the engine: based on the prevalences of the two diseases and the known medical findings the patient's probability of having gallbladder disease is 60% and appendicitis is 40%. This information is displayed in regions 502 and 504 of FIG. 5. A CT scan that shows an inflamed gall bladder would increase the probability of gallbladder disease being the correct diagnosis to 90%, and would decrease the probability of appendicitis to 10%, increasing the delta probability by 80%.”).
Regarding claim 10, Lee teaches the medical service support method of claim 1. Lee further teaches wherein the obtaining additional request information includes:
obtaining the additional request information corresponding to the obtained candidates for the disease from a storage where information describing relation between the plurality of diseases and the additional request information is stored ([0037], “A system 14, which may be configured as a computer or complex of computers with ancillary memory, stores a validated probabilistic health model 16 and a database 18 of medical knowledge informed from aggregated electronic medical records or other sources of medical knowledge.” [0043], “in order for the inference engine to perform its tasks, including determine a set of the most probable diseases of the patient, suggest a set of one or more tests or additional findings that differentiate the set of most probable diseases, and generate indicia, such as statistical data, text, or otherwise, indicating the effectiveness or relevancy of the one or more tests or additional findings, it uses the knowledge base 18 and the validated probabilistic health model 16. The knowledge base 18 includes validated (i.e., proven or established) medical knowledge in the form of data indicating relationships between medical findings (i.e., facts as to a patient condition, such as test results, symptoms, etc.) and diagnoses.”); or
obtaining the additional request information by using an answer obtained by presenting the obtained candidates for the disease to a specialist ([0055], “The system of FIG. 1 is preferably configured to augment or increase the knowledge base 18 over time as more and more patients are treated by the system of FIG. 1. In this regard, there is a computer 120 functioning as an interpreter which looks at raw information and turns it into a format useful to the knowledge base. The interpreter generates useful data and updates which can be applied to the knowledge base 18... In particular, the interpreter 120 constructs relationships between medical findings and diagnoses, effectiveness scores for the relationships, and attributes of the findings and the diagnoses. This can be done manually (by experts reviewing de-identified patient health records, medical journals, etc.) or using machine-learning models..”).
Regarding claim 13, Lee teaches the medical service support method of claim 1. Lee further teaches wherein the patient information includes information on at least one of patient interview information, physical finding, and an examination result ([0039], “The system 10 also includes a medical knowledge-based inference engine 24 (processor and associated programming), which operates on the patient's medical history and findings (either provided directly by the device or from a medical records database 26) and the validated probabilistic health model 16.” [0002], “a finding (ask a question, do an examination procedure, order a diagnostic test, etc.)” [0043], “a finding, or a treatment, has one or more attributes 104. For example, a finding attribute such as “abdominal pain” may have an attribute of “cost” of zero, since the finding can be made by interview of the patient”). Examiner notes that as findings include questions to a patient (interview), abdominal pain (physical finding), and examination procedures and tests (examination result), a patient’s records and medical history would include these types of information.
Regarding claim 14, Lee teaches the medical service support method of claim 1. Lee further teaches wherein a non-transitory computer-readable storage medium storing a medical service support program that causes, by being executed by at least one processor of a computer, the computer to perform the medical service support method according to claim 1 ([0037], “A system 14, which may be configured as a computer or complex of computers with ancillary memory, stores a validated probabilistic health model 16 and a database 18 of medical knowledge informed from aggregated electronic medical records or other sources of medical knowledge.”).
Regarding claim 15, this claim is rejected for the same reasons as claim 1. Lee further teaches at least one processor; a communication interface; and a storage where an estimation model is stored in response to input of at least one item of the patient information ([0037], “A system 14, which may be configured as a computer or complex of computers with ancillary memory, stores a validated probabilistic health model 16 and a database 18 of medical knowledge informed from aggregated electronic medical records or other sources of medical knowledge.” [0078], “c) using a computer processor 24 configured as a medical knowledge-based inference engine operating on the patient's medical history and findings and the validated probabilistic health model” [0042], “Additionally, the system further supports a web-based patient interface 50 providing patients with diagnostic effectiveness information which they can access from their own electronic device, such as smartphone or PC.”). Examiner notes that a web-based interface that allows third parties to access a system using their own device includes a web-based communication interface.
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 6 is rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190065688) in view of Barish (US 20080243539).
Regarding claim 6, Lee teaches the medical service support method of claim 1. Lee further teaches the method further comprising: accepting a result of diagnosis by a primary doctor together with the patient information ([0019], “In one embodiment the system generates data for a computing device containing a software application which is used by a healthcare provider to assist in reviewing the patient's medical history and entering new findings as to the patient's condition or symptoms.”). Under the broadest reasonable interpretation, Examiner interprets usage of the system of Lee to encompass an acceptance of diagnosis by a primary doctor, as a primary doctor using the system would receive a diagnosis that they would then consider in the diagnosis of a patient. Such usage of the system encompasses an acceptance of the result of the system.
Lee does not teach the method further comprising: transmitting to a specialist, the patient information, the candidates for the disease, and the result of diagnosis by the primary doctor.
However, Barish does teach the method further comprising:
transmitting to a specialist, the patient information, the candidates for the disease, and the result of diagnosis by the primary doctor ([0076], “An example embodiment may provide second reads or quality assurance. For example, a method of providing quality assurance may involve receiving a first health information from a health information provider; providing the first health information to a first interpreter; receiving a first interpretations associated with the first health information from the first interpreter; providing the first health information to a second interpreter; …; providing the first interpretation to the second interpreter; receiving a rating of the first interpretation from the second interpreter;… The second interpreter may be a specialist interpreter.” [0122], “A final interpretation may be a full and complete interpretation provided by the interpreter. For example, a final radiology interpretation, may include a full clinical history, comparison with relevant prior examinations, a full description of pertinent normal findings, a full description of all abnormal findings, a differential diagnosis for each abnormal finding individually or in combination and an overall impression of all of the findings that may include recommendations for additional testing.” [0239], “an interpreter suspected that a health information would result in a particular diagnosis”).
Lee in view of Barish are considered analogous to the claimed invention because they are in the field of patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee with Barish for the advantage of “providing quality assurance” (Barish; [0076]).
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190065688) in view of Van Assel (US 20210233658).
Regarding claim 11, Lee teaches the medical service support method of claim 1. Lee further teaches wherein the obtaining at least one item includes:
accepting input of electronic medical chart data of the patient ([0039], “The system 10 also includes a medical knowledge-based inference engine 24 (processor and associated programming), which operates on the patient's medical history and findings (either provided directly by the device or from a medical records database 26) and the validated probabilistic health model 16.”).
Lee does not teach generating a value of the at least one item by using the electronic medical chart data.
However, Van Assel does teach generating a value of the at least one item by using the electronic medical chart data ([0156], “It takes as input a set of one or more presenting concepts and the medical history of a given user. In an embodiment, it outputs a score∈[0, 1] for each item in the medical history, showing the relevance of that item to the set of input concepts, where 0 indicates the lowest likelihood of relevancy and 1 the highest likelihood of relevancy. A concept may be considered as ‘relevant’ if the score assigned is over a threshold for example.” [0368], “The relevance feature is configured to extract relevant data from the patient health record stored in the User Graph 115.”).
Lee in view of Van Assel are considered analogous to the claimed invention because they are in the field of patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee with Van Assel for the advantage of “enables the diseases to be computed with greater certainty (higher probability)” (Van Assel; [0219]).
Regarding claim 12, Lee teaches the medical service support method of claim 1. Lee further teaches the method further comprising presenting the candidates for the disease ([0042], “the interface provides a display of at least one of (i) the set of the most probable diseases of the patient”).
Lee does not teach wherein the presenting the candidates for the disease includes presenting an item having importance equal to or higher than a given value in identification of the candidates for the disease, of the at least one item, or additional information on the candidates for the disease.
However, Van Assel does teach wherein the presenting the candidates for the disease includes presenting an item having importance equal to or higher than a given value in identification of the candidates for the disease, of the at least one item, or additional information on the candidates for the disease ([0060], “Determining whether to include the information corresponding to an item of medical data in a first set of information based on the measure of relevance for the item of medical data may comprise determining whether the measure of relevance meets a pre-determined threshold.” [0368], “The relevance module 201 may output the set of as representing the relevant symptoms, risk factors and/or diseases, together with the information (for example, the information may indicate whether the concept is present or absent)... The output displayed on the user device 202 comprises the information corresponding to the relevant concepts from the clinical history 115.”).
Lee in view of Van Assel are considered analogous to the claimed invention because they are in the field of patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lee with Van Assel for the advantage of “identif[ying] the most impactful subset of concepts” (Van Assel; [0242]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Decision-Support Application and System for Problem Solving Using a Question-Answering System (US 20120078837) teaches a decision-support system for problem solving comprises software modules embodied on a computer readable medium, and the software modules comprise an input/output module and a question-answering module. The method receives problem case information using the input/output module, generates a query based on the problem case information, and generates a plurality of answers for the query using the question-answering module. The method also calculates numerical values for multiple evidence dimensions from evidence sources for each of the answers using the question-answering module and calculates a corresponding confidence value for each of the answers based on the numerical value of each evidence dimension using the question-answering module. Further, the method outputs the answers, the corresponding confidence values, and the numerical values of each evidence dimension for one or more selected answers using the input/output module.
Patient Data Mining Improvements (US 20060265253) teaches improvements in mining information from patient records and/or use of such mined information. A scheduled appointment is identified. In response, the patient record is mined to identify any possible lack of adherence. A form requiring authorization and including a clinical action or prescription is generated to address, at least in part, the lack of adherence. Contact with a patient is initiated by a processor, at least in part, in response to a lack of adherence. A request for documentation is generated in response to mining indicating an inadequate probability of adherence to a guideline. During real-time, additional information to be obtained which may alter a probability is suggested.
Diagnostic Information Systems (US 20050033121) teaches a system wherein relevant clinician determined diagnostic and marker information forming a patients profile are fed into a computer system containing comparative profiles. The comparative results are reported to the clinician as are recommendations for treatment and further investigation if desired. A final diagnosis is reported and treatment, if utilized, is fed back to enhance the computerized profiles.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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