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 .
Claim Objections
Claim 33 is objected to because of the following informalities: the claim recites “Exacerbation of COPD” in the last line of the claim. The limitation recites the abbreviation of COPD without providing the definition. For examination purposes, the limitation is construed as “Exacerbation of Chronic Obstructive Pulmonary Disease (COPD)”. Appropriate correction is required.
Claims 32-35 are objected to because of the following informalities: the claim recites “COVID-19 Disease” in the last line of the claims. The limitations recite the abbreviation of COVID without providing the definition. For examination purposes, the limitations are construed as “Coronavirus (COVID-19) Disease”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 30-31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The dependent claims are also rejected for inheriting the issues of their parent claim.
Claim 30 recites the limitation "a visual input related to the patient's clinical characteristics, wherein the video input comprises." There is insufficient antecedent basis for “the video input” in the claim. For examination purposes this limitation is construed as “a visual input related to the patient's clinical characteristics, wherein the visual input comprises." Appropriate correction is required.
Additionally, Claim 30 recites the limitation "a scripted input related to the patient's clinical characteristics, wherein the transcript input." There is insufficient antecedent basis for “the transcript input” in the claim. For examination purposes this limitation is construed as "a scripted input related to the patient's clinical characteristics, wherein the scripted input." Appropriate correction is required.
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 28-53 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 28-53 are drawn to a method, which is within the four statutory categories (i.e. process).
Step 2A of the Alice/Mayo Test - Prong One
The independent claim 28 recites an abstract idea. For example, claim 28 recites:
An Artificial Intelligence (AI)-based medical screening method for determining a most-specific medical condition from one or more closely mimicking medical conditions, the method is characterized to:
receiving, at an electronic device, a plurality of clinical signs, symptoms, presentations, and manifestations (SSPMs) as inputs (SSPM1, SSPM2, ..., SSPMn) representing a patient's clinical characteristics related to the medical condition which the patient is expected to be suffering from, wherein each of the SSPMs is an individual input;
generating, by the electronic device, a plurality of SSPM-related questionnaires, comprising a plurality of SSPM-specific questions, associated with each of the SSPMs (SSPM1, SSPM2, ..., SSPMn) received, wherein each question in the plurality of SSPM-related questionnaires are predefined;
receiving, at the electronic device, a response to each of the SSPM-specific question related to the plurality of generated SSPM-related questionnaires;
screening, by the electronic device, all the cumulative responses received for the plurality of SSPM-specific questions related to each of the generated SSPM-related questionnaires;
determining, by the electronic device, the most-specific medical condition from the one or more closely mimicking medical conditions based on the received SSPMs (SSPM1, SSPM2, ..., SSPMn) and screened cumulative responses for the plurality of SSPM-specific questions related to each of the generated SSPM-related questionnaires; and
generating, by the electronic device, a notification based on the determined most-specific medical condition from the one or more closely mimicking medical conditions.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to covers to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the electronic device, the limitations in the context of this claim encompass following steps for determining what condition related to the patient’s clinical signs, symptoms, presentations, and manifestations closely matches a known medical condition. If a claim limitation, under its broadest reasonable interpretation, covers personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 29-53 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 28 recites:
An Artificial Intelligence (AI)-based medical screening method for determining a most-specific medical condition from one or more closely mimicking medical conditions, the method is characterized to:
receiving, at an electronic device (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), a plurality of clinical signs, symptoms, presentations, and manifestations (SSPMs) as inputs (SSPM1, SSPM2, ..., SSPMn) representing a patient's clinical characteristics related to the medical condition which the patient is expected to be suffering from, wherein each of the SSPMs is an individual input;
generating, by the electronic device (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), a plurality of SSPM-related questionnaires, comprising a plurality of SSPM-specific questions, associated with each of the SSPMs (SSPM1, SSPM2, ..., SSPMn) received, wherein each question in the plurality of SSPM-related questionnaires are predefined;
receiving, at the electronic device (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), a response to each of the SSPM-specific question related to the plurality of generated SSPM-related questionnaires;
screening, by the electronic device (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), all the cumulative responses received for the plurality of SSPM-specific questions related to each of the generated SSPM-related questionnaires;
determining, by the electronic device (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), the most-specific medical condition from the one or more closely mimicking medical conditions based on the received SSPMs (SSPM1, SSPM2, ..., SSPMn) and screened cumulative responses for the plurality of SSPM-specific questions related to each of the generated SSPM-related questionnaires; and
generating, by the electronic device (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), a notification based on the determined most-specific medical condition from the one or more closely mimicking medical conditions.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the electronic device, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification pg. 8, see MPEP 2106.05(f))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim 29 which recites AI training module to train datasets and thus invokes the use of computers; claim 30 recites further defining the SSPMs into audio, visual, or scripted inputs, thus further defining the abstract idea; claim 31 recites defining the imaging input as one of MRI, CT, PET, CT/PET, and Doppler scan which invokes the use of computers; claims 32-47 recite differentiating and detecting different diseases or conditions, thus furthering the abstract idea; claim 48 recites detecting a culpable homicide from a murder thus furthering the abstract idea; claim 49 recites detecting conditions relating to rare diseases thus furthering the abstract idea; claim 50 recites detecting conditions having histopathological features thus furthering the abstract idea; claim 51 recites detecting conditions having radiological features thus furthering the abstract idea; claim 52 recites detecting conditions having hematological features thus furthering the abstract idea; claim 53 recites detecting conditions having biochemical features thus furthering the abstract idea; and claims 29-53 additional limitations which generally link the abstract idea to a particular technological environment or field of use). 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to 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. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the electronic device, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. pg. 8); using an electronic device, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
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 and are generally linking the abstract idea to a particular field of environment. 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, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 28 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kabir (US 11972865).
Regarding claim 28, Kabir discloses an Artificial Intelligence (AI)-based medical screening method for determining a most-specific medical condition from one or more closely mimicking medical conditions, the method is characterized to:
receiving, at an electronic device, a plurality of clinical signs, symptoms, presentations, and manifestations (SSPMs) as inputs (SSPM1, SSPM2, ..., SSPMn) representing a patient's clinical characteristics related to the medical condition which the patient is expected to be suffering from, wherein each of the SSPMs is an individual input; (Kabir col. 9 ln. 7-9 As a user inputs or selects inputs 24 in input list 10, the present invention may operate to isolate all potential diseases or conditions associated with each specific input 24; col. 9 Ln. 29-35 Thus, the DD 16 in symptoms table list 22 is a potential differential diagnosis for the particular input 24. For Example, in FIG. 2, there are two inputs 24A—chest pain and 24B—shortness of breath; thus, there will be at least two SSF-DD sub-tables 14 for each particular input—one for chest pain 24A and one for shortness of breath 24B; col. 5 ln. 40-46 Diagnosis module 110 may also include data input sub-module 111… such as patient signs, symptoms, and clinical data associated with a patient)
generating, by the electronic device, a plurality of SSPM-related questionnaires, comprising a plurality of SSPM-specific questions, associated with each of the SSPMs (SSPM1, SSPM2, ..., SSPMn) received, wherein each question in the plurality of SSPM-related questionnaires are predefined; (Kabir col. 8 ln. 50-62 when a health care provider is interviewing a patient who is experiencing chest pain and shortness of breath, then the health care provider can input or select these symptoms 24 as illustrated in FIG. 2. In inputting or selecting symptoms/inputs 24, a health care provider will preferably input or select the symptoms 24 based upon the severity or importance of the symptoms. Thus, a health care provider in interviewing a patient will preferably input the symptoms 24 based upon his/her analysis of the patient's condition making sure to input the symptoms 24 so that the most severe or important symptom is input first followed by the next important or severe symptom 24; col. 3 ln. 54-67 In addition, one embodiment of the present invention is a smart electronic medical record that can help prevent diagnostic error, prevent unnecessary testing, help increase quality of care and reduce length of stay and readmission. In one embodiment, a nurse or any health care provider starts patient triage by completing an artificial intelligent decision support system popped up by electronic medical record. The diagnostic confirmation algorithm utilizes yes and no questions based on diagnostic criteria of certain diagnosis)
receiving, at the electronic device, a response to each of the SSPM-specific question related to the plurality of generated SSPM-related questionnaires; (Kabir col. 8 ln. 56-62 Thus, a health care provider in interviewing a patient will preferably input the symptoms 24 based upon his/her analysis of the patient's condition making sure to input the symptoms 24 so that the most severe or important symptom is input first followed by the next important or severe symptom 24)
screening, by the electronic device, all the cumulative responses received for the plurality of SSPM-specific questions related to each of the generated SSPM-related questionnaires; (Kabir Col. 6 ln 37-47 A sign, symptom or clinical finding (SSF) may be present in multiple diseases. For example, a fever (a symptom) can be present in pneumonia, a urinary tract infection, sepsis, cellulitis, a pulmonary embolism, and many other conditions. Diagnosis module 110 may analyze the signs, symptoms, or clinical findings (SSF) related to a patient and then compare this patient data to a database of signs, symptoms or clinical findings that are linked to multiple diseases (called differential diagnoses) to ultimately provide a differential diagnosis for the health care provider)
determining, by the electronic device, the most-specific medical condition from the one or more closely mimicking medical conditions based on the received SSPMs (SSPM1, SSPM2, ..., SSPMn) and screened cumulative responses for the plurality of SSPM-specific questions related to each of the generated SSPM-related questionnaires; and (Kabir Col. 6 ln 37-47 A sign, symptom or clinical finding (SSF) may be present in multiple diseases. For example, a fever (a symptom) can be present in pneumonia, a urinary tract infection, sepsis, cellulitis, a pulmonary embolism, and many other conditions. Diagnosis module 110 may analyze the signs, symptoms, or clinical findings (SSF) related to a patient and then compare this patient data to a database of signs, symptoms or clinical findings that are linked to multiple diseases (called differential diagnoses) to ultimately provide a differential diagnosis for the health care provider; col. 10 Ln. 31-37 In a preferred embodiment, ranked and sorted list 30 would be output to a user/health care provider to notify the health care provider of the listing of diseases that a patient has a high probability of having and also providing a user with ranked and sorted list 32 to notify the health care provider of the listing of diseases that a patient has a low probability of having)
generating, by the electronic device, a notification based on the determined most-specific medical condition from the one or more closely mimicking medical conditions. (Kabir col. 10 Ln. 31-37 In a preferred embodiment, ranked and sorted list 30 would be output to a user/health care provider to notify the health care provider of the listing of diseases that a patient has a high probability of having and also providing a user with ranked and sorted list 32 to notify the health care provider of the listing of diseases that a patient has a low probability of having).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 29-31, 51-53 are rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Kushwah et al. (US 2021/0327562).
Regarding claim 29, Kabir discloses the method as claimed in Claim 28 and wherein the electronic device comprises an AI-based model related to a global standardized list of medical conditions related to a plurality of disease spaces. (Kabir col. 16 ln. 30-26 FIG. 5, the present invention can produce a findings list 36 which lists the signs/symptoms/findings associated with the diseases that a user searched. Thus, findings list 36 illustrates the signs/symptoms/findings from the differential diagnosis data table 12 of FIG. 4 col. 3 ln. 54-67 In addition, one embodiment of the present invention is a smart electronic medical record that can help prevent diagnostic error, prevent unnecessary testing, help increase quality of care and reduce length of stay and readmission. In one embodiment, a nurse or any health care provider starts patient triage by completing an artificial intelligent decision support system popped up by electronic medical record. The diagnostic confirmation algorithm utilizes yes and no questions based on diagnostic criteria of certain diagnosis).
Kabir does not appear to explicitly disclose the following, however, Kushwah teaches it is old and well known in the art of healthcare data processing to have:
an AI-based training model trained using one or more training datasets (Kushwah [0112] The infectious disease detection system 160 can also generate the feature analytical models based on a set of features associated with training image dataset of one or more individuals 110. [0122] The set of training images can include one or more images associated with different individuals, and each image can be associated with an infection level ranging from no infection, low infection, medium infection or high infection. The infectious disease detection system 160 can then generate an analytical model based on the set of training images and the infection level associated with the set of training images. For example, the infectious disease detection system 160 can generate the analytical model by applying a pattern recognition algorithm to the set of training images).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate an AI-based training model trained using one or more training datasets, as taught by Kushwah, in order to recognize patterns and have a better model for detecting infectious diseases. See Kushwah [0112].
Regarding claim 30, Kabir discloses the method as claimed in Claim 28, and the SSPMs (SSPM1, ..., SSPMn) (see above), but does not appear to teach the following, however, Kushwah teaches it is old and well known in the art of healthcare data processing wherein each of the SSPMs (SSPM1, ..., SSPMn) comprises at least one of: an audio input related to the patient's clinical characteristics, wherein the audio input comprises at least one of: an audio call record, and/or verbal transcript; a visual input related to the patient's clinical characteristics, wherein the video input comprises at least one of a video call record, and/or real time interaction with a medical practitioner; and a scripted input related to the patient's clinical characteristics, wherein the transcript input comprises laboratory assessment reports comprising imaging inputs. (Kushwah [0107] The RGB image of the individual 110 may include RGB image of the hand, the face, the forehead and the eyes. In some embodiment, the infectious disease detection system 160 may evaluate input received as parameter from one or more input devices 120 in an ordered sequence, for example, RGB image (highest priority), CT scan image (second priority) and audio and/or video image (third priority)). The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 31, Kabir-Kushwah teaches the method as claimed in Claim 30, and Kushwah further teaches wherein the imaging input comprises at least one of: Magnetic Resonance Imaging (MRI) scan, Computed Tomography (CT) scan, Positron Emission Tomography (PET) scan, CT/PET, and Doppler scan. (Kushwah [0107] The RGB image of the individual 110 may include RGB image of the hand, the face, the forehead and the eyes. In some embodiment, the infectious disease detection system 160 may evaluate input received as parameter from one or more input devices 120 in an ordered sequence, for example, RGB image (highest priority), CT scan image (second priority) and audio and/or video image (third priority)).
Regarding claim 51, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Kushwah teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting the one medical condition from other closely mimicking medical conditions having identical radiological features. (Kushwah [0138] In some other embodiments, the infectious disease detection system 160 can determine different levels of infection by accessing data from the clinical analyzer 134, the hematology analyzer 136, and the X-ray 126. The clinical data, CT scan, and X-ray provide additional parameters for further investigation of the infectious diseases specifically COVID-19). The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 52, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Kushwah teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting the one medical condition from other closely mimicking medical conditions having identical hematological features. (Kushwah [0138] In some other embodiments, the infectious disease detection system 160 can determine different levels of infection by accessing data from the clinical analyzer 134, the hematology analyzer 136, and the X-ray 126. The clinical data, CT scan, and X-ray provide additional parameters for further investigation of the infectious diseases specifically COVID-19)). The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 53, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Kushwah teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting the one medical condition from other closely mimicking medical conditions having identical biochemical features. (Kushwah [0161] For example, the dataset can include physiological and/or vital sign measurements of the individual 110, such as, but not limited to, a heart rate recording, hydration levels, and/or an electrocardiogram (ECG) recording, CT scan, x-ray, clinical test reports and other type of medical test reports). The motivation to combine the references is discussed above and incorporated herein.
Claims 32, 35, 49 are rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Narayanan et al. (US 2021/0327585).
Regarding claim 32, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Narayanan teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the respiratory disease space, a 'Pulmonary Tuberculosis' from the COVID-19 Disease. (Narayanan [0043] For example, in the case mentioned in S212, where COVID-19 may be identified as the minority classification, sample images without the COVID-19 classification obtained from the same area of the human corpus may be collected and labeled as the majority class. In such cases, other medical conditions such as, pneumonia, tuberculosis, lung cancer, healthy, and the like, may also be collected as a majority class sample images. In one or more embodiments, including such other medical conditions within the majority class dataset may have technical benefits including, but not limited to, improving performance of a machine learning model to differentiate between images representing a target disease (e.g., COVID-19) other diseases (e.g., flu, pneumonia, and/or the like), and image samples without disease).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate wherein the screening comprises: differentiating and detecting, in the respiratory disease space, a 'Pulmonary Tuberculosis' from the COVID-19 Disease, as taught by Narayanan, in order to improve performance and differentiating different diseases. See Narayanan [0043].
Regarding claim 35, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Narayanan teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the respiratory disease space, a 'Pneumonia' from the COVID-19 Disease. (Narayanan [0043] For example, in the case mentioned in S212, where COVID-19 may be identified as the minority classification, sample images without the COVID-19 classification obtained from the same area of the human corpus may be collected and labeled as the majority class. In such cases, other medical conditions such as, pneumonia, tuberculosis, lung cancer, healthy, and the like, may also be collected as a majority class sample images. In one or more embodiments, including such other medical conditions within the majority class dataset may have technical benefits including, but not limited to, improving performance of a machine learning model to differentiate between images representing a target disease (e.g., COVID-19) other diseases (e.g., flu, pneumonia, and/or the like), and image samples without disease). The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 49, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Narayanan teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, a condition of a syndrome from the one or more closely mimicking medical conditions relating to rare diseases. (Narayanan [0043] For example, in the case mentioned in S212, where COVID-19 may be identified as the minority classification, sample images without the COVID-19 classification obtained from the same area of the human corpus may be collected and labeled as the majority class. In such cases, other medical conditions such as, pneumonia, tuberculosis, lung cancer, healthy, and the like, may also be collected as a majority class sample images. In one or more embodiments, including such other medical conditions within the majority class dataset may have technical benefits including, but not limited to, improving performance of a machine learning model to differentiate between images representing a target disease (e.g., COVID-19) other diseases (e.g., flu, pneumonia, and/or the like), and image samples without disease). The motivation to combine the references is discussed above and incorporated herein.
Claims 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Patel et al. US 2023/0329630.
Regarding claim 33, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Patel teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the respiratory disease space, an 'Exacerbation of COPD' from the COVID-19 Disease. (Patel [0176] a prediction may be provided in the form of a trend or outlook for the user (e.g., the user is recovering or worsening) or may be provided as a probability/likelihood that the user will get sick or recover…. Alternatively, user patterns from other users, either a reference population representing the population at large, a population of individuals having a particular respiratory condition (e.g., a cohort having influenza, asthma, rhinovirus, chronic obstructive pulmonary disease (COPD), COVID-19, etc.) or a population of individuals similar to the user, may be utilized for forecasting a future respiratory condition of the particular user).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the respiratory disease space, an 'Exacerbation of COPD' from the COVID-19 Disease, as taught by Patel, in order to look at trends and outlooks for patients with particular worsening diseases to forecast their future conditions. See Patel [0176].
Regarding claim 34, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Patel teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the respiratory disease space, an 'Exacerbation of Asthma' from the COVID-19 Disease. (Patel [0176] a prediction may be provided in the form of a trend or outlook for the user (e.g., the user is recovering or worsening) or may be provided as a probability/likelihood that the user will get sick or recover…. Alternatively, user patterns from other users, either a reference population representing the population at large, a population of individuals having a particular respiratory condition (e.g., a cohort having influenza, asthma, rhinovirus, chronic obstructive pulmonary disease (COPD), COVID-19, etc.) or a population of individuals similar to the user, may be utilized for forecasting a future respiratory condition of the particular user). The motivation to combine the references is discussed above and incorporated herein.
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Sahadevan (US 2002/0094119).
Regarding claim 36, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Sahadevan teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the respiratory disease space, a condition of an 'Asbestosis' from that of a 'Pleural Mesothelioma'. (Sahadevan [0256] In this patient, if the suspicion of an evolving tumor through the digitized analysis of the chest x-ray was available, the diagnosis and treatment of the lung cancer could have initiated much earlier. Furthermore, the distinct visualization of visceral pleura 190, and mediastinal pleura 192 aids the deferential diagnosis of diseases affecting such structures like malignant tumors, mesothelioma and asbestosis).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the respiratory disease space, a condition of an 'Asbestosis' from that of a 'Pleural Mesothelioma', as taught by Sahadevan, in order to properly treat the cancer after proper visual detection of the correct disease. See Sahadevan [0256].
Claims 37, 50 are rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Morrison (US 2017/0183723).
Regarding claim 37, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Morrison teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the respiratory disease space, a condition of a 'Pulmonary Sarcoidosis' from the one or more closely mimicking medical conditions relating to a 'Lung Cancer'. (Morrison [0075] Recent reports have described the successful use of such a method to measure the gene expression of several promising biomarkers in samples of blood (Rots et al., Leukemia 2000 December; 14(12):2166-75; Peters et al., Clin Chem 2007 June; 53(6):1030-7) or other tissues. StaRT-PCR has been used successfully to identify patterns of gene expression associated with diagnosis of lung cancer (Warner et al., J Mol Diagn 2003 August; 5(3):176-83), risk of lung cancer (Crawford et al., Carcinogenesis 2007 December; 28(12):2552-9), pulmonary sarcoidosis).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the respiratory disease space, a condition of a 'Pulmonary Sarcoidosis' from the one or more closely mimicking medical conditions relating to a 'Lung Cancer', as taught by Morrison, in order to identify gene expression patterns in the biomarkers and requiring much less starting material than conventional diagnostic methods. See Morrison [0108].
Regarding claim 50, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Morrison teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting the one medical condition from other closely mimicking medical conditions having identical histopathological features. (Morrison [0075] Recent reports have described the successful use of such a method to measure the gene expression of several promising biomarkers in samples of blood (Rots et al., Leukemia 2000 December; 14(12):2166-75; Peters et al., Clin Chem 2007 June; 53(6):1030-7) or other tissues. StaRT-PCR has been used successfully to identify patterns of gene expression associated with diagnosis of lung cancer (Warner et al., J Mol Diagn 2003 August; 5(3):176-83), risk of lung cancer (Crawford et al., Carcinogenesis 2007 December; 28(12):2552-9), pulmonary sarcoidosis). The motivation to combine the references is discussed above and incorporated herein.
Claims 38, 40, 43-44 are rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Pino et al. (US 2021/0132070).
Regarding claim 38, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Pino teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the gastrointestinal disease space, a condition of an 'Ulcerative Colitis' from that of a 'Crohn's Disease'. (Pino [0180] Detection a level of one or more tumor-associated proteins or detection of interactions between autoantibodies and tumor-associated proteins can lead to a medical diagnosis. The sample can be a sample from a subject with a condition or disease…. The disease or condition can also be a premalignant condition, such as Barrett's Esophagus. The disease or condition can also be an inflammatory disease, immune disease, or autoimmune disease. For example, the disease may be inflammatory bowel disease (IBD), Crohn's disease (CD), ulcerative colitis (UC), pelvic inflammation, vasculitis, psoriasis, diabetes, autoimmune hepatitis, Multiple Sclerosis, Myasthenia Gravis, Type I diabetes, Rheumatoid Arthritis, Psoriasis, Systemic Lupus Erythematosis (SLE), Hashimoto's Thyroiditis, Grave's disease, Ankylosing Spondylitis Sjogrens Disease, CREST syndrome, Scleroderma, Rheumatic Disease, organ rejection, Primary Sclerosing Cholangitis, or sepsis).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the gastrointestinal disease space, a condition of an 'Ulcerative Colitis' from that of a 'Crohn's Disease', as taught by Pino, in order to determine the correct disease in the early stages to have better benefits of intervention and success for treatment. See Pino [0002]-[0003].
Regarding claim 40, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Pino teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the cerebrovascular disease space, a condition of a 'Stroke' (Cerebrovascular accident) from that of a 'Multiple Sclerosis (MS)'. (Pino [0180] Detection a level of one or more tumor-associated proteins or detection of interactions between autoantibodies and tumor-associated proteins can lead to a medical diagnosis. The sample can be a sample from a subject with a condition or disease…. The disease or condition can also be a cardiovascular disease, such as atherosclerosis, congestive heart failure, vulnerable plaque, stroke, or ischemia. The cardiovascular disease or condition can be high blood pressure, stenosis, vessel occlusion or a thrombotic event. The disease or condition can also be a neurological disease, such as Multiple Sclerosis (MS)). The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 43, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Pino teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the cancer biology space, a condition of a 'Pancreatic Cancer' from that of a 'Primary Gastric Lymphoma'. (Pino [0180] Detection a level of one or more tumor-associated proteins or detection of interactions between autoantibodies and tumor-associated proteins can lead to a medical diagnosis. The sample can be a sample from a subject with a condition or disease…. The cancer can be, but is not limited to, breast cancer, ovarian cancer, lung cancer, colon cancer, hyperplastic polyp, adenoma, colorectal cancer, high grade dysplasia, low grade dysplasia, prostatic hyperplasia, prostate cancer, melanoma, pancreatic cancer, brain cancer (such as a glioblastoma), hematological malignancy, hepatocellular carcinoma, cervical cancer, endometrial cancer, head and neck cancer, esophageal cancer, gastrointestinal stromal tumor (GIST), renal cell carcinoma (RCC) or gastric cancer). The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 44, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Pino teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in a musculoskeletal disease space, a condition of 'Lupus' from that of a 'Fibromyalgia'. (Pino [0180] Detection a level of one or more tumor-associated proteins or detection of interactions between autoantibodies and tumor-associated proteins can lead to a medical diagnosis. The sample can be a sample from a subject with a condition or disease…. Systemic Lupus Erythematosis (SLE)…. The disease or condition can also be a neurological disease, such as Multiple Sclerosis (MS), Parkinson's Disease (PD), Alzheimer's Disease (AD), schizophrenia, bipolar disorder, depression, autism, Prion Disease, Pick's disease, dementia, Huntington disease (HD), Down's syndrome, cerebrovascular disease, Rasmussen's encephalitis, viral meningitis, neuropsychiatric systemic lupus erythematosus (NPSLE), amyotrophic lateral sclerosis, Creutzfeldt-Jacob disease, Gerstmann-Straussler-Scheinker disease, transmissible spongiform encephalopathy, ischemic reperfusion damage (e.g. stroke), brain trauma, microbial infection, or chronic fatigue syndrome. The condition may also be fibromyalgia). The motivation to combine the references is discussed above and incorporated herein.
Claim 39 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Anderberg et al. (US 2020/0400686).
Regarding claim 39, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Anderberg teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the gastrointestinal disease space, a condition of an 'Appendicitis' from that of an 'Intestinal Obstruction'. (Anderberg [0031] Conditions within the differential diagnosis include gallbladder attack, kidney infection, pneumonia, rheumatic fever, diabetic ketoacidosis, ectopic pregnancy, twisted ovarian cyst, hemorrhaging ovarian follicle, urinary tract infection, ulcerative colitis, pancreatitis, intestinal obstruction, pelvic inflammatory disease, diverticulitis, carcinoma of the colon, and aortic aneurysm. In preferred embodiments, the biomarkers of the present invention distinguish appendicitis from one or more of these mimicking conditions).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the gastrointestinal disease space, a condition of an 'Appendicitis' from that of an 'Intestinal Obstruction', as taught by Anderberg, in order to treat and prevent such conditions. See Anderberg [0007].
Claim 41 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Taha et al. (US 2002/0120206).
Regarding claim 41, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Taha teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the cardiovascular disease space, a condition of an 'Atrial Fibrillation' from that of an 'Atrial Flutter'. (Taha [0003] A method and apparatus are provided for differentiating among atrial-flutter, atrial- fibrillation and other cardiac rhythms. The method includes the steps of estimating a spectral entropy of atrial cardiac activity from an electrocardiogram of a patient and determining that the patient has atrial fibrillation when the spectral entropy is greater than a predetermined value).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the cardiovascular disease space, a condition of an 'Atrial Fibrillation' from that of an 'Atrial Flutter', as taught by Taha, in order to properly treat the identified condition since they require different management. See Taha [0002].
Claim 42 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Yu et al. (US 2022/0020450).
Regarding claim 42, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Yu teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the cardiovascular disease space, a condition of a 'Pericarditis' from that of a 'Costochondritis'. (Yu [0006] can be associated with a diagnosis of pericarditis, which can be associated with a pathology of external heart and diaphragm muscle pain that can be tested using one or more of EKG, CXR, CT, or MRI…. a diagnosis of costochondritis, which can be associated with a pathology of cartilage inflammation that can be suitably tested…. approximately twenty tests potentially can be used to evaluate a spectrum of potential causes for chest pain).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the cardiovascular disease space, a condition of a 'Pericarditis' from that of a 'Costochondritis', as taught by Yu, in order to evaluate the patient’s initial complaint of chest pain and further determine the cause of the first symptom and hopefully exclude most life-threatening conditions. See Yu [0006].
Claim 45 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Langston et al. (US 2019/0085394).
Regarding claim 45, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Langston teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting a 'Wilson's Disease' from 'Parkinson's Disease'. (Langston [0070] After a thorough neurological exam and medical history, the neurologist may order computerized tomography (CT scan) or magnetic resonance imaging (MRI scan) to meet the other criterion for a diagnosis of Parkinson's disease: ruling out disorders (e.g., brain tumor, stroke) that produce Parkinson's-like symptoms. Some examples follow: medications—antipsychotics (e.g., Haldol) and anti-emetics (e.g., Compazine); multiple strokes; hydrocephalus; progressive supranuclear palsy—degeneration of midbrain structures; Shy-Drager syndrome—atrophy of central and sympathetic nervous systems; Wilson's disease—copper excretion causes degeneration of the liver and basal ganglia; Blood and/or cerebrospinal fluid (CSF) analysis may be ordered to look for specific abnormalities associated with other disorders).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting a 'Wilson's Disease' from 'Parkinson's Disease', as taught by Langston, in order to rule out the disorders that do not apply to the patient’s symptoms. See Langston [0070].
Claim 46 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of CHOU et al. (CA 3064744).
Regarding claim 46, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Chou teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the toxicology space, a condition of an 'Arsenic Poisoning' from that of a 'Cholera'. (Chou pg. 127 and 132 teaches testing for markers for poisons/toxins, heavy metals, and pathogens/microbes. It looks for arsenic as well as cholera. See tables 4.8 and 4.9).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in the toxicology space, a condition of an 'Arsenic Poisoning' from that of a 'Cholera', as taught by Chou, in order to test the patient and correctly determine which condition the patient is suffering. See Chou pg. 127-132.
Claim 47 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Shekarriz et al. (US 2021/0068719).
Regarding claim 47, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Shekarriz teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in the toxicology space, a condition of a 'Cyanide Poisoning' from that of a 'Carbon Monoxide Poisoning'. (Shekarriz [0006] Body fluids such as blood, urine and even breath are routinely analyzed for medical, diagnostic and legal reasons. Among the many analytes that are examined there are a number of small molecules like carbon dioxide (CO.sub.2), oxygen (O.sub.2), nitric oxide (NO), nitric dioxide (NO.sub.2), hydrogen peroxide (H.sub.2O.sub.2), acetaldehyde (C.sub.2H.sub.4O), carbon monoxide (CO), ammonia (NH.sub.3), hydrogen sulfide (H.sub.2S), acetone (C.sub.3H.sub.6O), hydrogen cyanide (HCN), and formaldehyde (CH.sub.2O) that have been associated with various diseases or conditions).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify , as modified above, to incorporate the screening comprises: differentiating and detecting, in the toxicology space, a condition of a 'Cyanide Poisoning' from that of a 'Carbon Monoxide Poisoning', as taught by Shekarriz, in order to test the patient and correctly determine which condition the molecules are associated based on analyzing the patient’s analytes. See Shekarriz [0006].
Claim 48 is rejected under 35 U.S.C. 103 as being unpatentable over Kabir in view of Montisci (US 2021/0241924).
Regarding claim 48, Kabir discloses the method as claimed in Claim 28, but does not appear to teach the following, however, Montisci teaches it is old and well known in the art of healthcare data processing wherein the screening comprises: differentiating and detecting, in a forensic and criminology space, a 'Culpable Homicide' from a 'Murder'. (Montisci [0074] In many cases of violent death the body injury pattern is critically important for the differential diagnosis between suicide, murder and accidental death. The proposed score identifies typical characteristics of a suicidal dynamic, differentiating them based on the methods adopted by the victim. [0081] differential diagnosis is based on the vitality characteristic of the injuries, particularly on the presence of hemorrhages, bruising in proximity of the ligature furrow. Suicide by self-strangulation, although not frequent, can cause important difficulties in the distinction from homicide. It presupposes a constriction of the neck that lasts beyond the loss of consciousness implying the use of method by the victim to prevent the release of the tourniquet (i.e. multiple revolutions or knotting). The ligature furrow in these cases is continuous, horizontal and equally deep around the perimeter of the neck, and in most cases, it is the only finding detectable, while in cases of murder the victim often shows signs of a struggle).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Kabir to incorporate the screening comprises: differentiating and detecting, in a forensic and criminology space, a 'Culpable Homicide' from a 'Murder', as taught by Montisci, in order to distinguish death to the body that is violent and intentional murder or an accidental death. Montisci [0074]
Conclusion
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686