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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claim recites a computer-implemented method for identifying a heart condition in a non-human subject, thus is a method.
Step 2A, Prong 1, Judicial Exception: Yes - The claim 1 recites the limitations:
“determining, by a first logic and based on the medical image, a dimensional feature of a heart of the non-human subject, wherein the first logic is trained using medical image training data labeled with anatomical landmarks”
“receiving medical information associated with the non-human subject, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide test and a murmur observation”
“determining, by a second logic and based on the medical information and the dimensional feature of the heart, a likelihood of a heart disease,”
“wherein the second logic is trained using heat disease training data and combinations of which are labeled with different stages of heart disease;”
These limitations, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it recites steps encompassing receiving medical image, patient medical information, determining dimensional feature of the heart by matching and making judgement with image training data, and training logics using image data, and applying models to predict a diagnosis. These actions fall under the Mental Processes category defined in MPEP 2106.04(A)(2) as “concept performed in the human mind (including observation, evaluation, judgement and opinion)”. A human, such as veterinarian, could mentally perform tasks of receiving and reviewing patient data, and patient image of heart, mentally comparing the heart with previous collection of data labeled with heart, and evaluate the dimensional feature of the heart along with medical information, and using this mental framework to diagnose a likelihood of a heart disease. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “a processor executing a machine learning logic,” “ receiving information on a GUI,” and displaying the likelihood of the heart disease on the GUI.”
The use of processors and GUI do not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception. These are generic hardware components invoked merely as a tool to perform the abstract steps more quickly or efficiently, and claims do not specify any particular configuration or improvement in the processor/computer itself, they simply use it to perform the subtract ideas set forth above, which amounts to merely adding “applying it” with a computer (MPEP2106.05(f)).
Using GUI to receive user input and displaying result in GUI: This claim element is a mere data collection and displaying step which amounts to a pre and post-solution insignificant activity. This post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step.
The first and second machine-learning logic: claim require training logic with training data. The machine learning logic, as claimed, represent the set of logic embodying the abstract idea of automated diagnosis of heart disease. Executing specific algorithms on generic processor using data from the generic storage does not integrate the abstract idea into a practical application.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 1 is ineligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 1 rejection.
Claim 2 recites the following elements: “receiving of the medical image comprises receiving one or more of an echocardiogram, an x-ray, a radiograph, and an ultrasound image.” This claim element is a mere data collection step which amounts to a pre-solution insignificant activity. This pre-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step. In light of above, claim 2 is ineligible.
Claims 3-7 and 10-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claims recite a method and therefore, a method.
Step 2A, Prong 1, Judicial Exception: Yes - The claim recites the limitations:
“Determining features in the medical image matching to the anatomical landmarks in the labeled training data and performing a measurement calculation between selected features in the medical image (claim 3)”
“Determining a vertebral heart score, an atrial measurement, a ventricular left atrial size measurement (claims 4-6),
“Determining a ventricular left atrial size measurement, a left atrial to aortic ratio measurement, a left ventricular internal diameter at end-diastole measurement (claim 7)”
“determining a likelihood of a mitral valve disease (Claim 10)”
“determining a stage of the heart disease (Claim 11)”
These limitations, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it is regarding a concept relating to comparing detected features for matching and performing calculation of measurement and analyzing the detected features (dimensions) to draw a conclusion for diagnosing a heart disease. A human, such as veterinarian, could mentally perform tasks of receiving images by selecting one or more images, and mentally matching dimension of the feature on the image to training data and measure dimensions, such as size and ratio as well as diameter, by observation and calculation, and evaluate the dimensional feature of the heart along with medical information, and using this mental framework to diagnose a likelihood of a heart disease, namely, a mitral valve disease and a specific stage of the heart disease. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “receiving medical image comprises receiving a radiograph and an echocardiogram” to determine feature.
These additional elements, taken individually or in combination, merely amount to data collection, insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claims 3-7 and 10-11 are ineligible.
Claims 8-9 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 1 rejection.
Claims 8-9 and 12 recites the following elements: “displaying the dimensional feature, a digital graphic overlaying the medical image, prediction of a stage of the heart disease, a summary of the dimensional feature of the heart, and interactive menu selectable to modify GUI to display a graph for trend analysis.” This claim elements are a mere displaying step of the results which amounts to a post-solution insignificant activity. This post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step. In light of above, claims 8-9 and 12 are ineligible.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claim recites a server and processor for identifying a heart condition in a non-human subject, thus is an apparatus.
Step 2A, Prong 1, Judicial Exception: Yes - The claim 13 recites the limitations:
“determining, by a first logic and based on the medical image, a dimensional feature of a heart of the non-human subject, wherein the first logic is trained using medical image training data labeled with anatomical landmarks”
“receiving medical information associated with the non-human subject, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide test and a murmur observation”
“determining, by a second logic and based on the medical information and the dimensional feature of the heart, a likelihood of a heart disease,”
“wherein the second logic is trained using heat disease training data and combinations of which are labeled with different stages of heart disease;”
These limitations, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it recites steps encompassing receiving medical image, patient medical information, determining dimensional feature of the heart by matching and making judgement with image training data, and training logics using image data, and applying models to predict a diagnosis. These actions fall under the Mental Processes category defined in MPEP 2106.04(A)(2) as “concept performed in the human mind (including observation, evaluation, judgement and opinion)”. A human, such as veterinarian, could mentally perform tasks of receiving and reviewing patient data, and patient image of heart, mentally comparing the heart with previous collection of data labeled with heart, and evaluate the dimensional feature of the heart along with medical information, and using this mental framework to diagnose a likelihood of a heart disease. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “A server,” “a processor,” “non-transitory computer readable medium having stored therein instructions that when executed by one or more processors, causes the server to perform functions, “ receiving information on a GUI,” and displaying the likelihood of the heart disease on the GUI.”
The use of processors, server, computer readable medium, and GUI do not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception. These are generic hardware components invoked merely as a tool to perform the abstract steps more quickly or efficiently, and claims do not specify any particular configuration or improvement in the processor/computer itself, they simply use it to perform the subtract ideas set forth above, which amounts to merely adding “applying it” with a computer (MPEP2106.05(f)).
Using GUI to receive user input and displaying result in GUI: This claim element is a mere data collection and displaying step which amounts to a pre and post-solution insignificant activity. This post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step.
The first and second machine-learning logic: claim require training logic with training data. The machine learning logic, as claimed, represent the set of logic embodying the abstract idea of automated diagnosis of heart disease. Executing specific algorithms on generic processor using data from the generic storage does not integrate the abstract idea into a practical application.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim 13 is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 13 is ineligible.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 1 rejection.
Claim 14 recites the following elements: “receiving of the medical image comprises receiving one or more of an echocardiogram, an x-ray, a radiograph, and an ultrasound image.” This claim element is a mere data collection step which amounts to a pre-solution insignificant activity. This pre-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step. In light of above, claim 14 is ineligible.
Claims 15-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claims recite a method and therefore, is an apparatus.
Step 2A, Prong 1, Judicial Exception: Yes - The claim recites the limitations:
“Determining features in the medical image matching to the anatomical landmarks in the labeled training data and performing a measurement calculation between selected features in the medical image (claim 15)”
“Determining a ventricular left atrial size measurement, a left atrial to aortic ratio measurement, a left ventricular internal diameter at end-diastole measurement (claim 16)”
These limitations, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it is regarding a concept relating to comparing detected features for matching and performing calculation of measurement and analyzing the detected features (dimensions) to draw a conclusion for diagnosing a heart disease. A human, such as veterinarian, could mentally perform tasks of receiving images by selecting one or more images, and mentally matching dimension of the feature on the image to training data and measure dimensions, such as size and ratio as well as diameter, by observation and calculation, and evaluate the dimensional feature of the heart along with medical information, and using this mental framework to diagnose a likelihood of a heart disease, namely, a mitral valve disease and a specific stage of the heart disease. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “receiving medical image comprises receiving a radiograph and an echocardiogram” to determine feature.
These additional elements, taken individually or in combination, merely amount to data collection, insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claims 15-16 are ineligible.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claim recites a non-transitory computer readable medium and processors of a computing device for identifying a heart condition in a non-human subject, thus is an apparatus.
Step 2A, Prong 1, Judicial Exception: Yes - The claim 17 recites the limitations:
“determining, by a first logic and based on the medical image, a dimensional feature of a heart of the non-human subject, wherein the first logic is trained using medical image training data labeled with anatomical landmarks”
“receiving medical information associated with the non-human subject, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide test and a murmur observation”
“determining, by a second logic and based on the medical information and the dimensional feature of the heart, a likelihood of a heart disease,”
“wherein the second logic is trained using heat disease training data and combinations of which are labeled with different stages of heart disease;”
These limitations, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it recites steps encompassing receiving medical image, patient medical information, determining dimensional feature of the heart by matching and making judgement with image training data, and training logics using image data, and applying models to predict a diagnosis. These actions fall under the Mental Processes category defined in MPEP 2106.04(A)(2) as “concept performed in the human mind (including observation, evaluation, judgement and opinion)”. A human, such as veterinarian, could mentally perform tasks of receiving and reviewing patient data, and patient image of heart, mentally comparing the heart with previous collection of data labeled with heart, and evaluate the dimensional feature of the heart along with medical information, and using this mental framework to diagnose a likelihood of a heart disease. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “A computing device,” “a processor,” “non-transitory computer readable medium having stored therein instructions that when executed by one or more processors, causes the computing device to perform functions, “ receiving information on a GUI,” and displaying the likelihood of the heart disease on the GUI.”
The use of processors, server, computer readable medium, and GUI do not integrate the judicial exception into a practical application as it is merely used to perform the judicial exception. These are generic hardware components invoked merely as a tool to perform the abstract steps more quickly or efficiently, and claims do not specify any particular configuration or improvement in the processor/computer itself, they simply use it to perform the subtract ideas set forth above, which amounts to merely adding “applying it” with a computer (MPEP2106.05(f)).
Using GUI to receive user input and displaying result in GUI: This claim element is a mere data collection and displaying step which amounts to a pre and post-solution insignificant activity. This post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step.
The first and second machine-learning logic: claim require training logic with training data. The machine learning logic, as claimed, represent the set of logic embodying the abstract idea of automated diagnosis of heart disease. Executing specific algorithms on generic processor using data from the generic storage does not integrate the abstract idea into a practical application.
These additional elements, taken individually or in combination, merely amount to insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim 17 is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claim 17 is ineligible.
Claims 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes - The claims recite a method and therefore, is an apparatus.
Step 2A, Prong 1, Judicial Exception: Yes - The claim recites the limitations:
“Determining features in the medical image matching to the anatomical landmarks in the labeled training data and performing a measurement calculation between selected features in the medical image (claim 18)”
“Determining a ventricular left atrial size measurement, a left atrial to aortic ratio measurement, a left ventricular internal diameter at end-diastole measurement (claim 19)”
These limitations, as drafted, is a process step that, under its broadest reasonable interpretation, covers the performance of the limitation in the mind as it is regarding a concept relating to comparing detected features for matching and performing calculation of measurement and analyzing the detected features (dimensions) to draw a conclusion for diagnosing a heart disease. A human, such as veterinarian, could mentally perform tasks of receiving images by selecting one or more images, and mentally matching dimension of the feature on the image to training data and measure dimensions, such as size and ratio as well as diameter, by observation and calculation, and evaluate the dimensional feature of the heart along with medical information, and using this mental framework to diagnose a likelihood of a heart disease, namely, a mitral valve disease and a specific stage of the heart disease. That is, nothing in the claim element precludes the step from practically being performed in the mind and/or being performed with the aid of a pen and paper. Accordingly, the claim recites a mental process-type abstract idea.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites the following additional elements: “receiving medical image” to determine feature.
These additional elements, taken individually or in combination, merely amount to data collection, insignificant pre/post-solution activities and do not integrate the judicial exception into a practical application. This claim is therefore directed to an abstract idea.
Step 2B, Inventive Concept: No - Similarly to Step 2A Prong 2, the additional claim elements merely recite insignificant extra-solution activities, which do not amount to significantly more than the judicial exception. For these reasons, there is no inventive concept in the claim. In light of the above, claims 18-19 are ineligible.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 and Step 2A, Prong 1, Judicial Exception are discussed above in the claim 17 rejection.
Claim 20 recites the following elements: “displaying the dimensional feature, a digital graphic overlaying the medical image on the GUI.” This claim elements are a mere displaying step of the results which amounts to a post-solution insignificant activity. This post-solution insignificant activity does not integrate the judicial exception into a practical application nor does it contain an inventive step. In light of above, claim 20 is ineligible.
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 (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 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 1-6, 8-11, 13-15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over “LI et al.,” US 2022/0351854 (hereinafter Li), and “Weissman et al.,” US 2023/0108955 (hereinafter Weissman), and further in view of “Choi et al.,” US 2020/0202527 (hereinafter Choi).
Regarding to claim 1, Li teaches a computer-implemented method for identifying a heart condition in a non-human subject, the method comprising:
receiving a medical image of the non-human subject (canine radiographic image [0020]);
determining, by a processor executing a first machine-learning logic and based on the medical image (machine learning framework supports an inference processing unit receives raw image data and deliver raw image data to the trained neural network [0023]), a dimensional feature of a heart of the non-human subject (left atrial size [0015]; left atrium characteristics features belong to classes of left atrial enlargement characteristics [0023]), wherein the first machine-learning logic is trained using medical image training data labeled with anatomical landmarks (training system starts with creating a definition of left atrial enlargement characteristics of canine patients using multiple labeled thoracic radiographic images identifying canine patient specimen with or without left atrial enlargement [0019]);
determining, by the processor executing a second machine-learning logic and based on the dimensional feature of the heart, a likelihood of a heart disease (algorithm for a percent likelihood [0040])
Li does not further explicitly disclose following limitations:
Receiving medical information associated with the non-human subject, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide test, and a murmur observation;
Determining, by the processor executing a second machine-learning logic and based on the medical information , a likelihood of a heart disease
However, in the analogous field of endeavor in health condition of an animal, Weissman teaches following limitations:
Receiving medical information associated with the non-human subject, the medical information including one or more of a species (species [0051]), an age, a weight, an output of a brain natriuretic peptide test, and a murmur observation (age and breed of animals are input on a meta-model for calculating probability of the condition with medical image is prepared into the training set [0053]);
Determining, by the processor executing a second machine-learning logic (machine learning [0052]; [0064]) and based on the medical information and the dimensional feature of the heart ([0027]-[0028]), a likelihood of a heart disease (machine learning for assessing a likelihood that a patient has a disease [0019]; cardiac/enlarged heart[0050]; medical image, age, breed of animals input to the model [0053]; cardiac diagnosis [0065])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning logic as taught by Li to incorporate teaching of Weissman, since additional medical data was well known in the art as taught by Weisman. One of ordinary skill in the art could have combined the elements as claimed by Li with no change in their respective functions, its machine learning logic to use medical information and the dimension of the heart to predict a probability of heart disease, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to automatic analysis of medical images and other data to generate diagnostic predictions of medical purposes ([0027]-[0028]), and there was reasonable expectation of success.
Li and Weissman do not further disclose GUI and details of training data for second machine learning logic as claimed.
However, in the analogous field of endeavor in heart disease diagnosis using machine learning model, Choi teaches following limitations:
wherein the second machine-learning logic is trained using heart disease training data and combinations of which are labeled with different stages of heart disease (training data include obtaining fundus image training data labeled with heart disease diagnostic information [0541]; training data includes a plurality of images to which grade labels assigned [0642]; label in training data is a grade label [0717]); and
receiving a user input on a graphical user interface (graphical user interface [0038]-[0039] Figures 29-30 show GUI interactive with user input, user input unit obtain diagnostic assistance information) and displaying the likelihood of the heart disease on the graphical user interface (Graphical interface includes a score display, extent of risk of target heart disease [0854], [0858]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning algorithm/logic as taught by Li to incorporate teaching of Choi, since (1) training data for heart disease labeled with a grade and (2) using graphical user interface for user input and displaying result were well known in the art as taught by Choi. One of ordinary skill in the art could have combined the elements as claimed by Li with no change in their respective functions, implementing training data of heart disease data and label them with various grades, and implementing graphical user interface, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to (1) indicate an extent of risk of heart disease ([0567]) and (2) provide user interactive display and input interface ([0854]), and there was reasonable expectation of success.
Regarding to claims 2-6 and 10, Li, Weisman, and Choi together teach all limitations of claim 1 as discussed above.
Li further teaches following limitations:
Of claim 2, wherein said receiving of the medical image comprises receiving one or more of an echocardiogram, an x-ray, a radiograph, and an ultrasound image (thoracic radiographs, radiographic images [0002] and [0005]).
Of claim 3, wherein determining features in the medical image matching to the anatomical landmarks in the labeled training data (images showing left atrial size are labeled, designating each with a left atrial enlargement positive or negative classification [0015], performing left atrial enlargement recognition and classify left atrial enlargement characteristics [0021]-[0022]) and performing a measurement calculation between selected features in the medical image (two linear left atrial measurements indexed to the aorta, left atrium to aortic root ratio, maximum left atrial dimension [0015]).
Of claim 4, wherein said determining of the dimensional feature of the heart comprises determining a vertebral heart score (vertebral heart score estimations[0004])
Of claim 5, wherein said determining of the dimensional feature of the heart comprises determining an atrial measurement (images showing left atrial size [0015])
Of claim 6, wherein said determining of the dimensional feature of the heart comprises determining a ventricular left atrial size (VLAS) measurement (left atrial enlargement [0003])
Of claim 10, wherein said determining of the likelihood of the heart disease comprises determining a likelihood of a mitral valve disease (mitral valve disease [0006])
Regarding to claims 8-9, Li, Weissman, and Choi together teach all limitations of claim 1 as discussed above.
Choi further teaches displaying the dimensional feature of the heart on the graphical user interface (figures 74-75 show heart with colored region indicating an extent of risk of target heart disease [0852]) and wherein said displaying of the dimensional feature of the heart on the graphical user interface comprises displaying the dimensional feature of the heart on the graphical user interface as a digital graphic overlaying the medical image on the graphical user interface (a color overlaid on the heart shape to indicate extent of risk of the target heart disease [0852]-[0856]).
Regarding to claim 11, Li, Weisman, and Choi together teach all limitations of claim 1 as discussed above.
Choi further teaches wherein said determining of the likelihood of the heart disease comprises determining a stage of the heart disease (grade [0498]).
Regarding to claim 13, Li teaches a server (server [0016]) comprising:
one or more processors (processor [0017]); and non-transitory computer readable medium having stored therein instructions (algorithm implemented [0017]) that when executed by the one or more processors (computer-executed method Abstract, processor [0017]), causes the server to perform functions comprising:
receiving a medical image of a non-human subject canine radiographic image [0020]);
determining, by the one or more processors executing a first machine-learning logic (machine learning [0052]; [0064]) and based on the medical image ([0027]-[0028]), a dimensional feature of a heart of the non-human subject (left atrial size [0015]; left atrium characteristics features belong to classes of left atrial enlargement characteristics [0023]), wherein the first machine-learning logic is trained using medical image training data labeled with anatomical landmarks (training system starts with creating a definition of left atrial enlargement characteristics of canine patients using multiple labeled thoracic radiographic images identifying canine patient specimen with or without left atrial enlargement [0019]);
determining, by the one or more processors executing a second machine-learning logic and based on the dimensional feature of the heart, a likelihood of a heart disease (algorithm for a percent likelihood [0040])
Li does not further explicitly disclose following limitations:
Receiving medical information associated with the non-human subject, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide test, and a murmur observation;
Determining, by the processor executing a second machine-learning logic and based on the medical information , a likelihood of a heart disease
However, in the analogous field of endeavor in health condition of an animal, Weissman teaches following limitations:
A non-transitory computer readable medium having stored therein instructions that when executed by the one or more processors, cause the server to perform functions comprising (a computer readable storage medium, storing instructions, which when executed by one or more processors cause performance of the methods [0112]):
Receiving medical information associated with the non-human subject, the medical information including one or more of a species (species [0051]), an age, a weight, an output of a brain natriuretic peptide test, and a murmur observation (age and breed of animals are input on a meta-model for calculating probability of the condition with medical image is prepared into the training set [0053]);
Determining, by the one or more processors executing a second machine-learning logic (machine learning [0052]; [0064]) and based on the medical information and the dimensional feature of the heart ([0027]-[0028]), a likelihood of a heart disease (machine learning for assessing a likelihood that a patient has a disease [0019]; cardiac/enlarged heart[0050]; medical image, age, breed of animals input to the model [0053]; cardiac diagnosis [0065])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning logic as taught by Li to incorporate teaching of Weisman, since additional medical data was well known in the art as taught by Weissman. One of ordinary skill in the art could have combined the elements as claimed by Li with no change in their respective functions, its machine learning logic to use medical information and the dimension of the heart to predict a probability of heart disease, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to automatic analysis of medical images and other data to generate diagnostic predictions of medical purposes ([0027]-[0028]), and there was reasonable expectation of success.
Li and Weisman do not further disclose GUI and details of training data for second machine learning logic as claimed.
However, in the analogous field of endeavor in heart disease diagnosis using machine learning model, Choi teaches following limitations:
wherein the second machine-learning logic is trained using heart disease training data and combinations of which are labeled with different stages of heart disease (training data include obtaining fundus image training data labeled with heart disease diagnostic information [0541]; training data includes a plurality of images to which grade labels assigned [0642]; label in training data is a grade label [0717]); and
receiving a user input on a graphical user interface (graphical user interface [0038]-[0039] Figures 29-30 show GUI interactive with user input, user input unit obtain diagnostic assistance information) and displaying the likelihood of the heart disease on the graphical user interface (Graphical interface includes a score display, extent of risk of target heart disease [0854], [0858]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning algorithm/logic as taught by Li to incorporate teaching of Choi, since (1) training data for heart disease labeled with a grade and (2) using graphical user interface for user input and displaying result were well known in the art as taught by Choi. One of ordinary skill in the art could have combined the elements as claimed by Li with no change in their respective functions, implementing training data of heart disease data and label them with various grades, and implementing graphical user interface, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to (1) indicate an extent of risk of heart disease ([0567]) and (2) provide user interactive display and input interface ([0854]), and there was reasonable expectation of success.
Regarding to claims 14-15, Li, Weissman, and Choi together teach all limitations of claim 13 as discussed above.
Li further teaches following limitations:
Of claim 14, wherein said receiving of the medical image comprises receiving one or more of an echocardiogram, an x-ray, a radiograph, and an ultrasound image (thoracic radiographs, radiographic images [0002] and [0005]).
Of claim 15, determining features in the medical image matching to the anatomical landmarks in the labeled training data (images showing left atrial size are labeled, designating each with a left atrial enlargement positive or negative classification [0015], performing left atrial enlargement recognition and classify left atrial enlargement characteristics [0021]-[0022]) and performing a measurement calculation between selected features in the medical image (two linear left atrial measurements indexed to the aorta, left atrium to aortic root ratio, maximum left atrial dimension [0015]).
Regarding to claim 17, Li teaches one or more processors ([0017]):
receiving a medical image of a non-human subject canine radiographic image [0020]);
determining, by the one or more processors executing a first machine-learning logic (machine learning [0052]; [0064]) and based on the medical image ([0027]-[0028]), a dimensional feature of a heart of the non-human subject (left atrial size [0015]; left atrium characteristics features belong to classes of left atrial enlargement characteristics [0023]), wherein the first machine-learning logic is trained using medical image training data labeled with anatomical landmarks (training system starts with creating a definition of left atrial enlargement characteristics of canine patients using multiple labeled thoracic radiographic images identifying canine patient specimen with or without left atrial enlargement [0019]);
determining, by the one or more processors executing a second machine-learning logic and based on the dimensional feature of the heart, a likelihood of a heart disease (algorithm for a percent likelihood [0040])
Li does not further explicitly disclose following limitations:
A non-transitory computer readable medium having stored therein instructions that when executed by the one or more processors, cause the server to perform functions comprising:
Receiving medical information associated with the non-human subject, the medical information including one or more of a species, an age, a weight, an output of a brain natriuretic peptide test, and a murmur observation;
Determining, by the processor executing a second machine-learning logic and based on the medical information , a likelihood of a heart disease
However, in the analogous field of endeavor in health condition of an animal, Weisman teaches following limitations:
A non-transitory computer readable medium having stored therein instructions that when executed by the one or more processors, cause the server to perform functions comprising (a computer readable storage medium, storing instructions, which when executed by one or more processors cause performance of the methods [0112]):
Receiving medical information associated with the non-human subject, the medical information including one or more of a species (species [0051]), an age, a weight, an output of a brain natriuretic peptide test, and a murmur observation (age and breed of animals are input on a meta-model for calculating probability of the condition with medical image is prepared into the training set [0053]);
Determining, by the one or more processors executing a second machine-learning logic (machine learning [0052]; [0064]) and based on the medical information and the dimensional feature of the heart ([0027]-[0028]), a likelihood of a heart disease (machine learning for assessing a likelihood that a patient has a disease [0019]; cardiac/enlarged heart[0050]; medical image, age, breed of animals input to the model [0053]; cardiac diagnosis [0065])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning logic as taught by Li to incorporate teaching of Weissman, since additional medical data was well known in the art as taught by Weisman. One of ordinary skill in the art could have combined the elements as claimed by Li with no change in their respective functions, its machine learning logic to use medical information and the dimension of the heart to predict a probability of heart disease, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to automatic analysis of medical images and other data to generate diagnostic predictions of medical purposes ([0027]-[0028]), and there was reasonable expectation of success.
Li and Weissman do not further disclose GUI and details of training data for second machine learning logic as claimed.
However, in the analogous field of endeavor in heart disease diagnosis using machine learning model, Choi teaches following limitations:
wherein the second machine-learning logic is trained using heart disease training data and combinations of which are labeled with different stages of heart disease (training data include obtaining fundus image training data labeled with heart disease diagnostic information [0541]; training data includes a plurality of images to which grade labels assigned [0642]; label in training data is a grade label [0717]); and
receiving a user input on a graphical user interface (graphical user interface [0038]-[0039] Figures 29-30 show GUI interactive with user input, user input unit obtain diagnostic assistance information) and displaying the likelihood of the heart disease on the graphical user interface (Graphical interface includes a score display, extent of risk of target heart disease [0854], [0858]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning algorithm/logic as taught by Li to incorporate teaching of Choi, since (1) training data for heart disease labeled with a grade and (2) using graphical user interface for user input and displaying result were well known in the art as taught by Choi. One of ordinary skill in the art could have combined the elements as claimed by Li with no change in their respective functions, implementing training data of heart disease data and label them with various grades, and implementing graphical user interface, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to (1) indicate an extent of risk of heart disease ([0567]) and (2) provide user interactive display and input interface ([0854]), and there was reasonable expectation of success.
Regarding to claim 18, Li, Weisman, and Choi together teach all limitations of claim 17 as discussed above.
Li further teaches determining features in the medical image matching to the anatomical landmarks in the labeled training data (images showing left atrial size are labeled, designating each with a left atrial enlargement positive or negative classification [0015]) and performing a measurement calculation between selected features in the medical image (two linear left atrial measurements indexed to the aorta, left atrium to aortic root ratio, maximum left atrial dimension [0015]).
Regarding to claim 20, Li, Weissman, and Choi together teach all limitations of claim 17 as discussed above.
Choi further teaches wherein said displaying of the dimensional feature of the heart on the graphical user interface comprises displaying the dimensional feature of the heart on the graphical user interface as a digital graphic overlaying the medical image on the graphical user interface (a color overlaid on the heart shape to indicate extent of risk of the target heart disease [0852]-[0856]).
Claims 7, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li, Weisman, and Choi as applied to claims 1, 13, and 17 above, and further in view of “Wilshaw et al.,” US 2023/0314449 (hereinafter Wilshaw).
Regarding to claims 7, 16, and 19, Li, Weissman, and Choi together teach all limitations of claims 1, 13, and 17 as discussed above.
Li further teaches wherein said receiving of the medical image comprises receiving a radiograph and an echocardiogram (radiographs and echocardiograms [0025]), and wherein said determining the dimensional feature of the heart comprises: determining a ventricular left atrial size (VLAS) measurement based on the radiograph (left atrial size in the images, radiographs [0015]); determining a left atrial to aortic ratio (LA/Ao) measurement based on the echocardiogram (left atrium to aortic root ratio in early diastole, echocardiograph [0015]).
Li does not explicitly disclose determining a left ventricular internal diameter at end-diastole (LVIDdN) measurement based on the echocardiogram.
However, in the analogous field of endeavor in diagnosing animal’s heart condition, Wilshaw teaches determining a left ventricular internal diameter at end-diastole (LVIDdN) measurement based on the echocardiogram (echocardiography used to determine the left atrial to aortic root ratio and ventricular internal diameter at end diastole normalized to bodyweight [0082]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify echocardiogram as taught by Li to incorporate teaching of Wilshaw, as both are directed to diagnose heart condition of a dog(animal) using images, since ventricular internal diameter at end diastole measurement was well known in the art as taught by Wilshaw. One of ordinary skill in the art could have combined the elements as claimed by Li with no change in their respective functions, using its echocardiograms to determine ventricular internal diameter at end diastole, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide accurate diagnose of stage B2 degenerative mitral valve disease ([0082] and [0097]), and there was reasonable expectation of success.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Li, Weissman, and Choi as applied to claim 1 above, and further in view of “Nye et al.,” US 2021/0232967(hereinafter Nye).
Regarding to claim 12, Li, Weissman, and Choi together teach all limitations of claim 1 as discussed above.
Choi further teaches wherein said displaying of the likelihood of the heart disease on the graphical user interface (Figures 74-75) comprises:
displaying a prediction of a stage of the heart disease (output the grade information on the target heart disease [0799]);
displaying a summary of the dimensional feature of the heart (Figures 74-75); and
Choi does not explicitly disclose displaying interactive menus selectable to modify the graphical user interface to display a graph for trend analysis.
However, in the analogous field of endeavor in diagnosing condition of a subject, including a heart, utilizes a graphical user interface to display summary, images, and user can use result output menu 506 to select that result output be a graphical representation (Figures 4-5), such that result can be displayed as summary table and graphs ([0053]-[0054]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify echocardiogram as taught by Li, Weissman, and Choi to incorporate teaching of Choi, as all of the prior arts are directed to diagnose health condition of a subject using medical image data, and since GUI displaying and user interface was already disclosed by Choi, and user interactive menu to select to display a graph was well known in the art as taught by Nye. One of ordinary skill in the art could have combined the elements as claimed by Choi with no change in their respective functions, modifying its GUI to have selectable menu to change display options, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide result in desired output ([0053]), and there was reasonable expectation of success.
Conclusion
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/PATRICIA J PARK/Primary Examiner, Art Unit 3798