DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
This communication is in response to the Request for Continued Examination (RCE) filed 4/27/26. Claims 1-3, 5-8, 10-12, 14, 21-23, 25, and 26 have been amended. Claims 15-20 are cancelled. Claims 1-14 and 21-26 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/23/26 has been entered.
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 1-14 and 21-26 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-9 are directed to a method (i.e., a process), claims 10-14 are directed to a system (i.e., a machine), and claims 21-26 are directed to a non-transitory computer-readable storage medium (i.e., a machine). Accordingly, claims 1-14 and 21-26 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1. A computer-implemented method of compensating for image quality issue in an image study, comprising: measuring a patient positioning parameter indicating a deviation of a patient position in the image study to an optimal patient position for an image exam type of the image study; analyzing, by a trained neural network, the image study and the measured patient positioning parameter to determine a relationship between positioning errors and an observed grading of a diagnostic finding for the image study, wherein the observed grading of the diagnostic finding includes one of a severity of the diagnostic finding or a certainty classification of the diagnostic finding; and generating, by the trained neural network, a reference grading of the diagnostic finding for the optimal patient position, wherein the trained neural network is trained to generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading of the diagnostic finding based on a relationship between positioning errors and diagnostic gradings determined from previous image studies; and outputting the reference grading of the diagnostic finding for diagnosing or treating the patient.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because measuring a patient positioning parameter indicating a deviation of a patient position in the image study to an optimal patient position for an image exam type of the image study; analyzing the image study and the measured patient positioning parameter to determine a relationship between positioning errors and an observed grading of a diagnostic finding for the image study, wherein the observed grading of the diagnostic finding includes one of a severity of the diagnostic finding or a certainty classification of the diagnostic finding; and generating a reference grading of the diagnostic finding for the optimal patient position, generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading of the diagnostic finding based on a relationship between positioning errors and diagnostic gradings determined from previous image studies; and outputting the reference grading of the diagnostic finding for diagnosing or treating the patient amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or with pen and paper.
Accordingly, the claim recites at least one abstract idea.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 10, and 21, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a processor, a non-transitory computer readable storage medium, and an executable program to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the processor, non-transitory computer readable storage medium, and executable program are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of obtaining data, measuring data, analyzing data, generating data, outputting data) such that it amounts no more than mere instructions to apply the exception using generic computer components. The limitations regarding a “trained neural network” do not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2-9, 11-14, and 22-26 are ultimately dependent from Claim(s) 1, 10, and 21 and include all the limitations of Claim(s) 1, 10, and 21. Therefore, claim(s) 2-9, 11-14, and 22-26 recite the same abstract idea. Claims 2-7, 9, 11-14, and 22-26 describe further limitations regarding retrieving the previous image studies; wherein the first and second image studies correspond to the same patient; applying the trained neural network; wherein the trained neural network is trained; and wherein the image study is an X-ray study. These are all just further describing the abstract idea recited in Claim(s) 1, 10, and 21, without adding significantly more. Claim 8 recites wherein the observed grading equals the reference grading plus the offset of the deviation, and the reference grading is estimated via a forward model trained using training data from the previous image studies, the training data mapping the previous diagnostic gradings and previous measured patient positioning parameters to previous reference gradings. This limitation constitutes “mathematical concepts“ because it amounts to mathematical formulas or equations, without adding significantly more.
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 integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, independent claims 1, 10, and 21 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to a processor obtaining an imaging study and measuring a parameter, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of receiving and transmitting data over a network and/or storing and retrieving information in memory, and performing repetitive calculations. See MPEP 2106.05(d)(II).
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-14 and 21-26 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 8 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The newly added recitation of "the training data mapping the previous diagnostic gradings and previous measured patient positioning parameters to previous reference gradings" within claim 8 appears to constitute new matter.
In particular, Applicant does not point to, nor was the Examiner able to find support for this newly added language within the specification as originally filed. As such, Applicant is respectfully requested to clarify the above issues and to specifically point out support for the newly added limitations in the originally filed specification and claims.
Applicant is required to cancel the new matter in the reply to this Office Action.
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.
Claim(s) 1-6, 9-14 and 21-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nye et al. (US 2019/0164285 A1) in view of Amthor et al. (US 2019/0027243 A1).
(A) Referring to claim 1, Nye discloses A computer-implemented method of compensating for image quality issue in an image study, comprising (abstract of Nye):
measuring a patient positioning parameter indicating a deviation of a patient position in the image study to an optimal patient position for an image exam type of the image study (para. 55, 93-95, and 127 of Nye; High quality medical image data can be acquired using one or more imaging modalities, such as x-ray, computed tomography (CT), molecular imaging and computed tomography (MICT), magnetic resonance imaging (MRI), etc. Medical image quality is often not affected by the machines producing the image but the patient. A patient moving during an MRI can create a blurry or distorted image that can prevent accurate diagnosis, for example. If the image data appears to match its prescribed position and region, then, at 1208, the image data is analyzed to determine whether the image passes image quality control check(s). For example, the image data is analyzed to determine whether the associated image has good patient positioning (e.g., the patient is positioned such that an anatomy or region of interested is centered in the image, etc.). Other quality control checks can include an evaluation of sufficient contrast, an analysis of a level of noise or artifact in the image, an examination of appropriate/sufficient dosage for image clarity, etc. The quality checker 1022 can leverage AI and/or other processing to analyze image anatomy, orientation/position, sufficient contrast, appropriate dose, too much noise/artifacts, etc., to evaluate image quality and sufficiency to enable further automated analysis.); and
analyzing, by a trained neural network, the image study and the measured patient positioning parameter to determine a relationship between positioning errors and an observed grading of a diagnostic finding for the image study, wherein the observed grading of the diagnostic finding includes one of a severity of the diagnostic finding or a certainty classification of the diagnostic finding (para. 72, 95-100, 116, 117, and 127 of Nye; Machine learning can be applied to a variety of processes including image acquisition, image reconstruction, image analysis/diagnosis, etc. As shown in the example configuration 600 of FIG. 6A, raw data 610 (e.g., raw data 610 such as sonogram raw data, etc., obtained from an imaging scanner such as an x-ray, computed tomography, ultrasound, magnetic resonance, etc., scanner) is fed into a learning network 620. The learning network 620 processes the data 610 to correlate and/or otherwise combine the raw data 620 into processed data 630 (e.g., a resulting image, etc.) (e.g., a “good quality” image and/or other image providing sufficient quality for diagnosis, etc.). The learning network 620 includes nodes and connections (e.g., pathways) to associate raw data 610 with the processed data 630. The learning network 620 can be a training network that learns the connections and processes feedback to establish connections and identify patterns, for example. In certain examples, a probability and/or confidence indicator or score can be associated with the indication of critical and/or other clinical finding(s), a confidence associated with the finding, a location of the finding, a severity of the finding, a size of the finding, and/or an appearance of the finding in conjunction with another finding or in the absence of another finding, etc. For example, a strength of correlation or connection in the learning network 1026 can translate into a percentage or numerical score indicating a probability of correct detection/diagnosis of the finding in the image data, a confidence in the identification of the finding, etc.).
Nye does not expressly disclose generating, by the trained neural network, a reference grading of the diagnostic finding for the optimal patient position, wherein the trained neural network is trained to generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading of the diagnostic finding based on a relationship between positioning errors and diagnostic gradings determined from previous image studies; and outputting the reference grading of the diagnostic finding for diagnosing or treating the patient.
Amthor generating, by the trained neural network, a reference grading of the diagnostic finding for the optimal patient position, wherein the trained neural network is trained to generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading of the diagnostic finding based on a relationship between positioning errors and diagnostic gradings determined from previous image studies (para. 3, 13, 14, 78, 80, 81, 86-88, and 90 of Amthor; Machine learning techniques which may be used to generate the predictive model include, but are not limited to, Support Vector Machines (SVM), decision trees/forests, neural networks/deep learning, k-Nearest Neighbors (kNN), etc. The predictive model may be generated to be indicative of these regions as well as the diagnostic value within each region. Having generated the predictive model, the predictive model may be used to predict the most probable diagnostic value of a particular clinical workflow, namely by determining in which region the feature vector of the particular workflow metadata falls. The predictive model may also be used to identify an adjustment of the particular clinical workflow which improves the diagnostic value of the acquired medical images. Different intensities and patterns denote different events. Examples of events indicated by such log file information may be the occurrence of patient table motion, a diagnostic scan, a survey scan, and/or an automatic reference scan taking place. In the example of FIG. 4, the hatching indicates that a scan has been aborted (here, the aborted scan was repeated after a 30-second break). The region of lightest intensity represents idle time. For example, the fact that a particular scan was aborted may relate to the patient being nervous and moving a lot. Such an event may therefore be indicative of image qualities below average also for some other scans of this exam. As another example, a patient table motion following an aborted scan may indicate that the patient had not been positioned correctly at the beginning of the exam and that, consequently, all images taken before the repositioning may be of limited diagnostic value. The system 102 may use all, or a selection of the obtained (meta)data as input in the machine learning technique to correlate the estimated diagnostic value of the one or more medical images 204 with the information known about the clinical workflow, as well as other information such as patient information. This may enable clinical workflow parameters, including those of the patient exam, which correlate with a good/bad diagnostic value, to be identified and then visualized or otherwise used to improve the clinical workflow.); and outputting the reference grading of the diagnostic finding for diagnosing or treating the patient (para. 14, 79, 90, 83, and 21 & Fig. 5; system 100 may display the predictive model, or a visual representation of the predictive model, on the display 060. Additionally or alternatively, the system 100 may visualize one or more adjustments to the clinical workflow which have been identified using the predictive model. The user interface subsystem 180 may also be used to query a radiologist on the diagnostic value, as will be further explained with reference to FIG. 5. The radiology report may be indicative of the diagnostic value of the one or more medical images as the radiology report typically reports on the clinical relevance of said medical images. For example, if the radiology report does not include a diagnosis and/or clinical findings, it may indicate a lesser or no diagnostic value of the medical images. By taking into account the radiology report in addition to the viewing actions, the diagnostic value can be more reliably estimated.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Amthor within Nye. The motivation for doing so would have been to increase the diagnostic value of the acquired images (abstract of Amthor).
(B) Referring to claim 2, Nye discloses wherein analyzing the image study includes retrieving, via a search engine, the previous image studies from a database, the previous image studies including patient positioning parameters corresponding to the patient positioning parameter determined for the image study (para. 36, 45, and 92-97 of Nye).
(C) Referring to claim 3, Nye discloses wherein analyzing the image study includes retrieving, via a search engine, the previous image studies including a first image study including an optimal patient position for diagnostic grading and a second image study including a patient positioning parameter corresponding to the patient positioning parameter of the image study (para. 36, 45, 72, and 92-97 of Nye).
(D) Referring to claim 4, Nye discloses wherein the first and second image studies correspond to the same patient (para. 9 and 105-108 of Nye).
(E) Referring to claim 5, Nye discloses wherein analyzing the image study includes applying the trained neural network to the image study, and wherein the trained neural network is trained using training data including the previous image studies including confirmed diagnostic gradings (para. 43, 44, 58, 59, 72, 73, 98, and 99 of Nye).
(F) Referring to claim 6, Nye discloses wherein the trained neural network is trained to predict the grading of the diagnostic finding for the image study (para. 44, 58, 59, 72, and 73 of Nye).
(G) Referring to claim 9, Nye discloses wherein the image study is an X-ray study (para. 45 and 72 of Nye).
(H) Referring to claim 10, Nye discloses A system compensating for image quality issue in an image study, comprising (abstract of Nye):
a non-transitory computer readable storage medium storing an executable program (para. 111 of Nye; The program may be embodied in machine readable instructions stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor 3012); and
a processor executing the executable program to cause the processor to (para. 111 of Nye; the machine readable instructions include a program for execution by a processor such as the processor 3012 shown in the example processor platform 3000):
measure a patient positioning parameter indicating a deviation of a patient position in the image study to an optimal patient position for an image exam type of the image study (para. 55, 93-95, and 127 of Nye; High quality medical image data can be acquired using one or more imaging modalities, such as x-ray, computed tomography (CT), molecular imaging and computed tomography (MICT), magnetic resonance imaging (MRI), etc. Medical image quality is often not affected by the machines producing the image but the patient. A patient moving during an MRI can create a blurry or distorted image that can prevent accurate diagnosis, for example. If the image data appears to match its prescribed position and region, then, at 1208, the image data is analyzed to determine whether the image passes image quality control check(s). For example, the image data is analyzed to determine whether the associated image has good patient positioning (e.g., the patient is positioned such that an anatomy or region of interested is centered in the image, etc.). Other quality control checks can include an evaluation of sufficient contrast, an analysis of a level of noise or artifact in the image, an examination of appropriate/sufficient dosage for image clarity, etc. The quality checker 1022 can leverage AI and/or other processing to analyze image anatomy, orientation/position, sufficient contrast, appropriate dose, too much noise/artifacts, etc., to evaluate image quality and sufficiency to enable further automated analysis.); and
analyze, by a trained neural network, the image study and the measured patient positioning parameter to determine a relationship between positioning errors and an observed grading of a diagnostic finding for the image study, wherein the grading of the diagnostic finding includes one of a severity of the diagnostic finding and a certainty classification of the diagnostic finding (para. 72, 95-100, 116, 117, and 127 of Nye; Machine learning can be applied to a variety of processes including image acquisition, image reconstruction, image analysis/diagnosis, etc. As shown in the example configuration 600 of FIG. 6A, raw data 610 (e.g., raw data 610 such as sonogram raw data, etc., obtained from an imaging scanner such as an x-ray, computed tomography, ultrasound, magnetic resonance, etc., scanner) is fed into a learning network 620. The learning network 620 processes the data 610 to correlate and/or otherwise combine the raw data 620 into processed data 630 (e.g., a resulting image, etc.) (e.g., a “good quality” image and/or other image providing sufficient quality for diagnosis, etc.). The learning network 620 includes nodes and connections (e.g., pathways) to associate raw data 610 with the processed data 630. The learning network 620 can be a training network that learns the connections and processes feedback to establish connections and identify patterns, for example. In certain examples, a probability and/or confidence indicator or score can be associated with the indication of critical and/or other clinical finding(s), a confidence associated with the finding, a location of the finding, a severity of the finding, a size of the finding, and/or an appearance of the finding in conjunction with another finding or in the absence of another finding, etc. For example, a strength of correlation or connection in the learning network 1026 can translate into a percentage or numerical score indicating a probability of correct detection/diagnosis of the finding in the image data, a confidence in the identification of the finding, etc.).
Nye does not expressly disclose generate, by the trained neural network, a reference grading of the diagnostic finding for the optimal patient position, wherein the trained neural network is trained to generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading based on a relationship between positioning errors and diagnostic gradings determined from previous image studies; and output the reference grading of the diagnostic finding for diagnosing or treating the patient.
Amthor discloses generate, by the trained neural network, a reference grading of the diagnostic finding for the optimal patient position, wherein the trained neural network is trained to generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading based on a relationship between positioning errors and diagnostic gradings determined from previous image studies (para. 3, 13, 14, 78, 80, 81, 86-88, and 90 of Amthor; Machine learning techniques which may be used to generate the predictive model include, but are not limited to, Support Vector Machines (SVM), decision trees/forests, neural networks/deep learning, k-Nearest Neighbors (kNN), etc. The predictive model may be generated to be indicative of these regions as well as the diagnostic value within each region. Having generated the predictive model, the predictive model may be used to predict the most probable diagnostic value of a particular clinical workflow, namely by determining in which region the feature vector of the particular workflow metadata falls. The predictive model may also be used to identify an adjustment of the particular clinical workflow which improves the diagnostic value of the acquired medical images. Different intensities and patterns denote different events. Examples of events indicated by such log file information may be the occurrence of patient table motion, a diagnostic scan, a survey scan, and/or an automatic reference scan taking place. In the example of FIG. 4, the hatching indicates that a scan has been aborted (here, the aborted scan was repeated after a 30-second break). The region of lightest intensity represents idle time. For example, the fact that a particular scan was aborted may relate to the patient being nervous and moving a lot. Such an event may therefore be indicative of image qualities below average also for some other scans of this exam. As another example, a patient table motion following an aborted scan may indicate that the patient had not been positioned correctly at the beginning of the exam and that, consequently, all images taken before the repositioning may be of limited diagnostic value. The system 102 may use all, or a selection of the obtained (meta)data as input in the machine learning technique to correlate the estimated diagnostic value of the one or more medical images 204 with the information known about the clinical workflow, as well as other information such as patient information. This may enable clinical workflow parameters, including those of the patient exam, which correlate with a good/bad diagnostic value, to be identified and then visualized or otherwise used to improve the clinical workflow.); and output the reference grading of the diagnostic finding for diagnosing or treating the patient (para. 14, 79, 90, 83, and 21 & Fig. 5; system 100 may display the predictive model, or a visual representation of the predictive model, on the display 060. Additionally or alternatively, the system 100 may visualize one or more adjustments to the clinical workflow which have been identified using the predictive model. The user interface subsystem 180 may also be used to query a radiologist on the diagnostic value, as will be further explained with reference to FIG. 5. The radiology report may be indicative of the diagnostic value of the one or more medical images as the radiology report typically reports on the clinical relevance of said medical images. For example, if the radiology report does not include a diagnosis and/or clinical findings, it may indicate a lesser or no diagnostic value of the medical images. By taking into account the radiology report in addition to the viewing actions, the diagnostic value can be more reliably estimated.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Amthor within Nye. The motivation for doing so would have been to increase the diagnostic value of the acquired images (abstract of Amthor).
(I) Referring to claim 11, Nye discloses wherein the processor executes the executable program to cause the processor to retrieve the previous image studies from a database, the previous image studies including patient positioning parameters corresponding to the patient positioning parameter determined for the image study (para. 36, 45, and 92-97 of Nye).
(J) Referring to claim 12, Nye discloses wherein the processor executes the executable program to cause the processor to retrieve the previous image studies including a first image study including an optimal patient position for diagnostic grading and a second image study including a patient positioning parameter corresponding to the patient positioning parameter of the image study (para. 36, 45, 72, and 92-97 of Nye).
(K) Referring to claim 13, Nye discloses wherein the processor executes the executable program to cause the processor to retrieve the first image study and the second image study that correspond to the same patient (para. 9 and 105-108 of Nye).
(L) Referring to claim 14, Nye discloses wherein the processor executes the executable program to cause the processor to apply the trained neural network to the image study, and wherein the trained neural network is trained using training data including the previous image studies including confirmed diagnostic gradings (para. 43, 44, 58, 59, 72, and 99 of Nye).
(M) Referring to claim 21, Nye discloses A non-transitory computer-readable storage medium including a set of instructions executable by a processor, the set of instructions, when executed by the processor, cause the processor to (para. 111 & 113 of Nye; the machine readable instructions include a program for execution by a processor such as the processor 3012 shown in the example processor platform 3000. The program may be embodied in machine readable instructions stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor 3012):
obtain an image study (para. 45 of Nye; the configuration can receive the images (A) from the x-ray and/or other imaging system directly (e.g., set up as secondary push destination such as a Digital Imaging and Communications in Medicine (DICOM) node, etc.), (B) by tapping into a Picture Archiving and Communication System (PACS) destination for redundant image access, (C) by retrieving image data via a sniffer methodology (e.g., to pull a DICOM image off the system once it is generated), etc.);
measure a patient positioning parameter indicating a deviation of a patient position in the image study to an optimal patient position for an image exam type of the image study (para. 55, 93-95, and 127 of Nye; High quality medical image data can be acquired using one or more imaging modalities, such as x-ray, computed tomography (CT), molecular imaging and computed tomography (MICT), magnetic resonance imaging (MRI), etc. Medical image quality is often not affected by the machines producing the image but the patient. A patient moving during an MRI can create a blurry or distorted image that can prevent accurate diagnosis, for example. If the image data appears to match its prescribed position and region, then, at 1208, the image data is analyzed to determine whether the image passes image quality control check(s). For example, the image data is analyzed to determine whether the associated image has good patient positioning (e.g., the patient is positioned such that an anatomy or region of interested is centered in the image, etc.). Other quality control checks can include an evaluation of sufficient contrast, an analysis of a level of noise or artifact in the image, an examination of appropriate/sufficient dosage for image clarity, etc. The quality checker 1022 can leverage AI and/or other processing to analyze image anatomy, orientation/position, sufficient contrast, appropriate dose, too much noise/artifacts, etc., to evaluate image quality and sufficiency to enable further automated analysis.); and
analyze, by a trained neural network, the image study and the measured patient positioning parameter to determine a relationship between positioning errors and an observed grading of a diagnostic finding for the image study (para. 72, 95-100, 116, 117, and 127 of Nye; Machine learning can be applied to a variety of processes including image acquisition, image reconstruction, image analysis/diagnosis, etc. As shown in the example configuration 600 of FIG. 6A, raw data 610 (e.g., raw data 610 such as sonogram raw data, etc., obtained from an imaging scanner such as an x-ray, computed tomography, ultrasound, magnetic resonance, etc., scanner) is fed into a learning network 620. The learning network 620 processes the data 610 to correlate and/or otherwise combine the raw data 620 into processed data 630 (e.g., a resulting image, etc.) (e.g., a “good quality” image and/or other image providing sufficient quality for diagnosis, etc.). The learning network 620 includes nodes and connections (e.g., pathways) to associate raw data 610 with the processed data 630. The learning network 620 can be a training network that learns the connections and processes feedback to establish connections and identify patterns, for example. In certain examples, a probability and/or confidence indicator or score can be associated with the indication of critical and/or other clinical finding(s), a confidence associated with the finding, a location of the finding, a severity of the finding, a size of the finding, and/or an appearance of the finding in conjunction with another finding or in the absence of another finding, etc. For example, a strength of correlation or connection in the learning network 1026 can translate into a percentage or numerical score indicating a probability of correct detection/diagnosis of the finding in the image data, a confidence in the identification of the finding, etc.).
Nye does not expressly disclose generate, by the trained neural network, a reference grading of the diagnostic finding for the optimal patient position, wherein the trained neural network is trained to generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading based on a relationship between positioning errors and diagnostic gradings determined from previous image studies; and output the reference grading of the diagnostic finding for diagnosing or treating the patient.
Amthor discloses generate, by the trained neural network, a reference grading of the diagnostic finding for the optimal patient position, wherein the trained neural network is trained to generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading based on a relationship between positioning errors and diagnostic gradings determined from previous image studies (para. 3, 13, 14, 78, 80, 81, 86-88, and 90 of Amthor; Machine learning techniques which may be used to generate the predictive model include, but are not limited to, Support Vector Machines (SVM), decision trees/forests, neural networks/deep learning, k-Nearest Neighbors (kNN), etc. The predictive model may be generated to be indicative of these regions as well as the diagnostic value within each region. Having generated the predictive model, the predictive model may be used to predict the most probable diagnostic value of a particular clinical workflow, namely by determining in which region the feature vector of the particular workflow metadata falls. The predictive model may also be used to identify an adjustment of the particular clinical workflow which improves the diagnostic value of the acquired medical images. Different intensities and patterns denote different events. Examples of events indicated by such log file information may be the occurrence of patient table motion, a diagnostic scan, a survey scan, and/or an automatic reference scan taking place. In the example of FIG. 4, the hatching indicates that a scan has been aborted (here, the aborted scan was repeated after a 30-second break). The region of lightest intensity represents idle time. For example, the fact that a particular scan was aborted may relate to the patient being nervous and moving a lot. Such an event may therefore be indicative of image qualities below average also for some other scans of this exam. As another example, a patient table motion following an aborted scan may indicate that the patient had not been positioned correctly at the beginning of the exam and that, consequently, all images taken before the repositioning may be of limited diagnostic value. The system 102 may use all, or a selection of the obtained (meta)data as input in the machine learning technique to correlate the estimated diagnostic value of the one or more medical images 204 with the information known about the clinical workflow, as well as other information such as patient information. This may enable clinical workflow parameters, including those of the patient exam, which correlate with a good/bad diagnostic value, to be identified and then visualized or otherwise used to improve the clinical workflow.); and output the reference grading of the diagnostic finding for diagnosing or treating the patient (para. 14, 79, 90, 83, and 21 & Fig. 5; system 100 may display the predictive model, or a visual representation of the predictive model, on the display 060. Additionally or alternatively, the system 100 may visualize one or more adjustments to the clinical workflow which have been identified using the predictive model. The user interface subsystem 180 may also be used to query a radiologist on the diagnostic value, as will be further explained with reference to FIG. 5. The radiology report may be indicative of the diagnostic value of the one or more medical images as the radiology report typically reports on the clinical relevance of said medical images. For example, if the radiology report does not include a diagnosis and/or clinical findings, it may indicate a lesser or no diagnostic value of the medical images. By taking into account the radiology report in addition to the viewing actions, the diagnostic value can be more reliably estimated.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Amthor within Nye. The motivation for doing so would have been to increase the diagnostic value of the acquired images (abstract of Amthor).
(N) Referring to claim 22, Nye discloses wherein analyzing the image study includes retrieving, via a search engine, the previous image studies from a database, the previous image studies including patient positioning parameters corresponding to the patient positioning parameter determined for the image study (para. 36, 45, and 92-97 of Nye).
(O) Referring to claim 23, Nye discloses wherein analyzing the image study includes retrieving, via a search engine, the previous image studies including a first image study including an optimal patient position for diagnostic grading and a second image study including a patient positioning parameter corresponding to the patient positioning parameter of the image study (para. 36, 45, 72, and 92-97 of Nye).
(P) Referring to claim 24, Nye discloses wherein the first and second image studies correspond to the same patient (para. 9 and 105-108 of Nye).
(Q) Referring to claim 25, Nye discloses wherein analyzing the image study includes applying the trained neural network to the image study, and wherein the trained neural network is trained using training data including the previous image studies including confirmed diagnostic gradings (para. 43, 44, 58, 59, 72, and 99 of Nye).
(R) Referring to claim 26, Nye discloses wherein the trained neural network is trained to predict the grading of the diagnostic finding for the image study (para. 44, 58, 59, and 72 of Nye).
Subject Matter Free of Prior Art
Claims 7 and 8 are free of prior art.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 10, and 21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's additional arguments filed 3/23/26 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 3/23/26.
(1) Applicant submits that independent claim 1 is subject matter eligible. Applicant further submits that independent claims 10 and 21, which include similar terms as claim 1, are also subject matter eligible.
(A) As per the first argument, see 101 rejection above. The Examiner submits that the foregoing underlined limitations in the 101 rejection above constitute “a mental process” because measuring a patient positioning parameter indicating a deviation of a patient position in the image study to an optimal patient position for an image exam type of the image study; analyzing the image study and the measured patient positioning parameter to determine a relationship between positioning errors and an observed grading of a diagnostic finding for the image study, wherein the observed grading of the diagnostic finding includes one of a severity of the diagnostic finding or a certainty classification of the diagnostic finding; and generating a reference grading of the diagnostic finding for the optimal patient position, generate the reference grading by compensating for impact of the deviation of the patient position on the observed grading of the diagnostic finding based on a relationship between positioning errors and diagnostic gradings determined from previous image studies; and outputting the reference grading of the diagnostic finding for diagnosing or treating the patient amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or with pen and paper. Accordingly, the claim recites at least one abstract idea. The limitations regarding a “trained neural network” do not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f). Furthermore, “inaccurate grading” is not a technical problem. This judicial exception is not integrated into a practical application. In particular, the processor, non-transitory computer readable storage medium, and executable program are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of obtaining data, measuring data, analyzing data, generating data, outputting data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Conclusion
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687
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