DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. This communication is in response to the communication filed 7/15/2024. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 101
3. 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.
3.1. Claims 1-20 are rejected under 35 U.S.C. § 101 because while the claims (1) are to a statutory category (i.e., process, machine, manufacture or composition of matter, the claims (2A1) recite an abstract idea (i.e., a law of nature, a natural phenomenon); (2A2) do not recite additional elements that integrate the abstract idea into a practical application; and (2B) are not directed to significantly more than the abstract idea itself.
In regards to (1), the claims are to a statutory category (i.e., statutory categories including a process, machine, manufacture or composition of matter). In particular, independent claims 1, 10 and 16, and their respective dependent claims are directed, in part, to methods for optimizing protocols for medical imaging scanners to scan a subject.
In regards to (2A1), the claims, as a whole, recite and are directed to an abstract idea because the claims include one or more limitations that correspond to an abstract idea including mental processes and/or certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer. For example, independent claims 1, 10 and 16, as a whole, are directed to optimizing protocols for medical imaging scanners to scan a subject by, inter alia, optimizing a planned protocol, modifying settings of a medical imaging scanner accordingly, and scanning a subject (e.g., patient) which are human activities and/or interactions and therefore, certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer. The dependent claims include all of the limitations of their respective independent claims and thus are directed to the same abstract idea identified for the independent claims but further describe the elements and/or recite field of use limitations.
Furthermore, assuming arguendo, the claims are not directed to certain methods of organizing human activities, the claims, nevertheless, are directed to an abstract idea because the claims, except for certain limitations (* identified below in bold), under the broadest reasonable interpretation, can be reasonably and practically performed in the human mind and/or with pen and paper using observation, evaluation, judgment and/or opinion. That is, other than reciting the certain additional elements, nothing in the claims precludes the limitations from being practically performed in the mind and/or with pen and paper.
CLAIM 1:
A computer-implemented method for optimizing protocols for medical imaging scanners, comprising:
receiving, at a processing system comprising one or more processors, a planned protocol from an organization for a medical imaging scanner;
utilizing, via the processing system, an artificial intelligence-based algorithm to generate suggested protocol parameters to optimize the planned protocol based at least on the planned protocol;
outputting, via the processing system, from the artificial intelligence-based algorithm an optimized protocol for the planned protocol based on one or more of the suggested protocol parameters;
modifying, via the processing system, settings of the medical imaging scanner when the optimized protocol is utilized for a scan of a subject with the medical imaging scanner; and
executing, via the processing system, a scan of the subject with the medical imaging scanner utilizing the optimized protocol.
CLAIM 2:
The computer-implemented method of claim 1, further comprising:
receiving, at the processing system, user input of desired optimization criteria;
receiving, at the processing system, information specific to hardware and software of the medical imaging scanner; and
utilizing, via the processing system, the artificial intelligence-based algorithm to generate suggested protocol parameters to optimize the planned protocol based on the planned protocol, the desired optimization criteria, and the information specific to the hardware and the software of the medical imaging scanner.
CLAIM 3:
The computer-implemented method of claim 2, further comprising:
receiving, at the processing system, additional user input of acceptance of one or more of the suggested protocol parameters and/or rejection of one or more of the suggested protocol parameters; and
outputting, via the processing system, from the artificial intelligence-based algorithm the optimized protocol for the planned protocol based on the one or more of the suggested protocol parameters accepted via the additional user input.
CLAIM 4
The computer-implemented method of claim 3, further comprising training, via the processing system, the artificial intelligence-based algorithm based on the one or more of the suggested protocol parameters accepted via the additional user input.
CLAIM 5:
The computer-implemented method of claim 1, further comprising:
receiving, at the processing system, a plurality of planned protocols from the organization for the medical imaging scanner;
receiving, at the processing system, respective scan outcomes for each planned protocol of the plurality of planned protocols;
utilizing, via the processing system, the artificial intelligence-based algorithm to group the plurality of planned protocols into different protocol sets based on the respective scan outcomes;
outputting, via the processing system, from the artificial intelligence-based algorithm the different protocol sets;
receiving, via the processing system, user input of acceptance of one or more planned protocols within each protocol set of the different protocol sets and/or rejection of one or more of the planned protocols within each protocol set of the different protocol sets; and
outputting, via the processing system, from the artificial intelligence-based algorithm optimized protocol sets for the plurality of planned protocols based on the one or more planned protocols within each protocol set of the different protocol sets accepted via the user input.
CLAIM 6:
The computer-implemented method of claim 1, further comprising:
utilizing, via the processing system, the artificial intelligence-based algorithm to apply changes to all other planned protocols from the organization for the medical imaging scanner based on respective changes to the planned protocol to generate the optimized protocol;
outputting, via the processing system, from the artificial intelligence-based algorithm the other planned protocols with applied changes;
receiving, via the processing system, user input of acceptance of one or more of the other planned protocols with the applied changes and/or rejection of one or more of the other planned protocols with the applied changes; and
outputting, via the processing system, from the artificial intelligence-based algorithm respective improved protocols for the other planned protocols where the applied changes are accepted via the user input.
CLAIM 7:
The computer-implemented method of claim 1, further comprising:
receiving, at the processing system, a plurality of performed protocols for the planned protocol, wherein each performed protocol of the plurality of performed protocols was performed during a respective scan with respective protocol parameters;
determining, via the processing system, for each performed protocol of the plurality of performed protocols respective differences in the respective protocol parameters from protocol parameters of the planned protocol;
separating, via the processing system, the respective differences in the respective protocol parameters into different categories; and
utilizing, via the processing system, the artificial intelligence-based algorithm to generate suggested protocol parameters to optimize the planned protocol based at least on the plurality of performed protocols and the different categories.
CLAIM 8:
The computer-implemented method of claim 1, further comprising:
receiving, at the processing system, a plurality of performed protocols for a plurality of planned protocols from the organization for the medical imaging scanner, wherein each performed protocol of the plurality of performed protocols was performed during a respective scan with respective protocol parameters;
determining, via the processing system, for each performed protocol of the plurality of performed protocols for each respective planned protocol of the plurality of planned protocols respective differences in the respective protocol parameters from protocol parameters of the respective planned protocols;
separating, via the processing system, the respective differences in the respective parameters into different categories for the plurality of performed protocols for each respective planned protocol of the plurality of planned protocols;
utilizing, via the processing system, the artificial intelligence-based algorithm to generate suggested protocol parameters to optimize the plurality of planned protocols based at least on the plurality of performed protocols and the different categories for the plurality of planned protocols; and
outputting, via the processing system, from the artificial intelligence-based algorithm respective planned protocols that can be improved with each suggested protocol parameter of the suggested protocol parameters.
CLAIM 9:
The computer-implemented method of claim 8, further comprising: receiving, at the processing system, user input of user preferences for the respective scan; and
utilizing, via the processing system, the artificial intelligence-based algorithm to generate suggested protocol parameters to optimize the plurality of planned protocols based on the user preferences, the plurality of performed protocols and the different categories for the plurality of planned protocols.
CLAIM 10:
A computer-implemented method for optimizing protocols for medical imaging scanners, comprising:
receiving, at a processing system comprising one or more processors, clinical requirements for a scan using a medical imaging scanner of an organization;
receiving, at the processing system, information specific to hardware and software of the medical imaging scanner;
receiving, at the processing system, user input of one or more desired outcomes for the scan; and
utilizing, via the processing system, a generative artificial intelligence-based model to generate a protocol for performing the scan using the medical imaging scanner based on the clinical requirements and the one or more desired outcomes for the scan.
CLAIM 11:
The computer-implemented method of claim 10, further comprising:
receiving, at the processing system, additional user input additional one or more desired outcomes for the scan;
receiving, at the processing system, context from the generative artificial intelligence-based model; and
utilizing, via the processing system, the generative artificial intelligence-based model to update the protocol to generate an updated protocol based on the context and the additional user input.
CLAIM 12:
The computer-implemented method of claim 10, wherein the generative artificial intelligence-based model comprises a radiology large language model specific to the organization.
CLAIM 13:
The computer-implemented method of claim 12, wherein the radiology large language model is fine-tuned based on protocols from the organization for the medical imaging scanner.
CLAIM 14:
The computer-implemented method of claim 13, wherein organization specific fine-tuning of the radiology large language model is isolated from external exposure.
CLAIM 15:
The computer-implemented method of claim 14, wherein, prior to the organization specific fine-tuning, the generative artificial intelligence-based model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the medical imaging scanner and the original equipment manufacturer data for the medical imaging scanner.
CLAIM 16:
A computer-implemented method for optimizing protocols for medical imaging scanners, comprising:
receiving, at a processing system comprising one or more processors, existing planned protocols from an organization for performing a scan with a first medical imaging scanner;
receiving, at the processing system, information specific to hardware and software of the first medical imaging scanner;
receiving, at the processing system, additional information specific to hardware and software of a second medical imaging scanner different from the first medical imaging scanner, wherein the second medical imaging scanner is of a different manufacturer and/or a different model from the first medical imaging scanner;
receiving, at the processing system, user input of one or more desired outcomes for a respective scan with the second medical imaging scanner; and
utilizing, via the processing system, a generative artificial intelligence-based model to generate a protocol for performing the respective scan using the second medical imaging scanner based on the existing planned protocols and the one or more desired outcomes for the respective scan.
CLAIM 17:
The computer-implemented method of claim 16, wherein the generative artificial intelligence-based model comprises a radiology large language model specific to the organization.
CLAIM 18:
The computer-implemented method of claim 17, wherein the radiology large language model is fine-tuned based on protocols from the organization for the first medical imaging scanner.
CLAIM 19:
The computer-implemented method of claim 18, wherein organization specific fine-tuning of the radiology large language model is isolated from external exposure.
CLAIM 20:
The computer-implemented method of claim 19, wherein, prior to the organization specific fine-tuning, the generative artificial intelligence-based model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the first medical imaging scanner and the original equipment manufacturer data for the first medical imaging scanner.
* The limitations that are in bold are considered “additional elements” that are further analyzed below in subsequent steps of the 101 analysis. The limitations that are not in bold are abstract and/or can be reasonably and practically performed in the human mind and/or with pen paper.
In regards to (2A2), the claims do not recite additional elements that integrate the abstract idea into a practical application. The additional elements in the claims (i.e., * identified above in bold) do not integrate the abstract idea into a practical application because the additional elements merely add insignificant extra-solution activity to the abstract idea; merely link the use of the judicial exception to a particular technological environment or field of use; and/or simply append technologies and functions, specified at a high level of generality, to the abstract idea (i.e., the additional elements do not amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer).
Here, the additional elements (e.g., computer, medical imaging scanners, processing system, one or more processors, artificial intelligence-based algorithm, etc.) are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea using generic computer technologies. Moreover, the claims recite “computer-implemented,” “via the processing system”, “an artificial intelligence-based algorithm to”, etc. devoid of any meaningful technological improvement details and thus, further evidence the additional elements are merely being used to leverage generic technologies to automate what otherwise could be done manually. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Furthermore, the additional elements do not recite improvements to the functioning of a computer, or to any other technology or technical field—the additional elements merely recite general purpose computer technology; the additional elements do not recite applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition—there is no actual administration of a particular treatment; the additional elements do not recite applying the judicial exception with, or by use of, a particular machine—the additional elements merely recite general purpose computer technology; the additional elements do not recite limitations effecting a transformation or reduction of a particular article to a different state or thing—the additional elements do not recite transformation such as a rubber mold process; the additional elements do not recite applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment—the additional elements merely leverage general purpose computer technology to link the abstract idea to a technological environment.
In regards to (2B), the claims, individually, as a whole and in combination with one another, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of (A) a generic computer structure(s) that serves to perform computer functions that serve to merely link the abstract idea to a particular technological environment (i.e., computers); and/or (B) functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Here, 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 technologies. Mere instructions to apply an exception using generic computer technologies cannot provide an inventive concept.
Moreover, paragraphs [0028]-[0029] of applicant's specification (US 2026/0018276) recites that the system/method is implemented using a computing system such as, a single computer, virtual machine, virtual container, host, server, laptop, and/or mobile device which are well-known general purpose or generic-type computers and/or technologies. The use of generic computer components recited at a high level of generality to process information through an unspecified processor/computer does not impose any meaningful limit on the computer implementation of the 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. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Furthermore, the additional elements are merely well-known general purpose computers, components and/or technologies that receive, transmit, store, display, generate and otherwise process information which are akin to functions that courts consider well-understood, routine, and conventional activities previously known to the pertinent industry, such as, performing repetitive calculations; receiving or transmitting data over a network; electronic recordkeeping; retrieving and storing information in memory; and sorting information (See, for example, MPEP § 2106).
Therefore, the claims are not patent-eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
4. 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.
4.1. Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Mohamed Sheik Kathi (US 2024/0339214; hereinafter Kathi), in view of Gajdos et al. (US 2023/0115439).
CLAIM 1
Kathi teaches a computer-implemented method for optimizing protocols for medical imaging scanners (Kathi: abstract), comprising:
receiving, at a processing system comprising one or more processors, a planned protocol from an organization for a medical imaging scanner (Kathi: abstract; ¶¶ [0037] “To identify the a plurality of protocol parameters the system uses the retrieved user medical information, the selected one or more medical examination protocols”; FIGS. 1-6);
utilizing, via the processing system, an artificial intelligence-based algorithm to generate suggested protocol parameters to optimize the planned protocol based at least on the planned protocol (Kathi: abstract; ¶¶ [0012] “method further discloses controlling the change in the at least one protocol parameter with respect to the extracted one or more protocol parameters, based on the at least one of priority level and suggested change”, [0034]-[0035] “algorithm”, [0037] “an artificial intelligence (AI) based protocol selection method and system for optimizing medical imaging examination”; FIGS. 1-6);
outputting, via the processing system, from the artificial intelligence-based algorithm an optimized protocol for the planned protocol based on one or more of the suggested protocol parameters (Kathi: abstract; ¶¶ [0108]-[0109] “providing the plurality of protocol parameters based on the processed received input data to a medical imaging apparatus for performing the medical imaging examination of the user”; FIGS. 1-6);
modifying, via the processing system, settings of the medical imaging scanner when the optimized protocol is utilized for a scan of a subject with the medical imaging scanner (Kathi: abstract; ¶¶ [00109] “providing the plurality of protocol parameters based on the processed received input data to a medical imaging apparatus for performing the medical imaging examination”; FIGS. 1-6).
Kathi may not teach the following:
executing, via the processing system, a scan of the subject with the medical imaging scanner utilizing the optimized protocol.
Gajdos, however, teaches the following:
executing, via the processing system, a scan of the subject with the medical imaging scanner utilizing the optimized protocol (Gajdos: abstract; ¶¶ [0053]-[0065] “a medical ultrasound scanner images a patient using settings”; FIGS. 1-5).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of facilitating image optimization (Gajdos: ¶¶ [0001]-[0008]).
4.2. Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Mohamed Sheik Kathi (US 2024/0339214; hereinafter Kathi), in view of Gajdos et al. (US 2023/0115439), and further in view of Meyer et al. (US 2018/0306883).
CLAIM 2
Kathi teaches the computer-implemented method of claim 1, further comprising: receiving, at the processing system, user input of desired optimization criteria (Kathi: abstract; ¶¶ [0058]-[0059] “an input data that is received from the receiving unit 106 to change the at least one protocol parameter based on at least one of a priority level and a pre-defined range of the at least one protocol parameter”, [0090] “parameters that indicates high-resolution CT scan”; FIGS. 1-6); receiving, at the processing system, information specific to the medical imaging scanner (Kathi: abstract; ¶¶ [0009] “providing the plurality of protocol parameters based on the processed input data to a medical imaging apparatus for performing the medical imaging”, [0037] “To identify the a plurality of protocol parameters the system uses the retrieved user medical information, the selected one or more medical examination protocols”; FIGS. 1-6); and utilizing, via the processing system, the artificial intelligence-based algorithm to generate suggested protocol parameters to optimize the planned protocol based on the planned protocol, the desired optimization criteria, and the information specific to the medical imaging scanner (Kathi: abstract; ¶¶ [0108]-[0109] “providing the plurality of protocol parameters based on the processed received input data to a medical imaging apparatus for performing the medical imaging examination of the user”; FIGS. 1-6).
Kathi and Gajdos may not teach the following: information specific to hardware and software of the medical scanner.
Meyer, however, teaches the following: information specific to hardware and software of the medical scanner (Meyer: abstract; ¶¶ [0033]-[0036] “Data acquisition protocols predefined by the manufacturer are made available by the manufacturer for various possible equipment versions of the magnetic resonance apparatus as far as hardware and software components”; FIGS. 1-2).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving image data quality and optimization of data acquisition protocols (Meyer: ¶¶ [0001]-[0012]).
CLAIM 3
Kathi may not teach the computer-implemented method of claim 2, further comprising: receiving, at the processing system, additional user input of acceptance of one or more of the suggested protocol parameters and/or rejection of one or more of the suggested protocol parameters; and outputting, via the processing system, from the artificial intelligence-based algorithm the optimized protocol for the planned protocol based on the one or more of the suggested protocol parameters accepted via the additional user input.
Gajdos, however, teaches receiving, at the processing system, additional user input of acceptance of one or more of the suggested protocol parameters and/or rejection of one or more of the suggested protocol parameters; and outputting, via the processing system, from the artificial intelligence-based algorithm the optimized protocol for the planned protocol based on the one or more of the suggested protocol parameters accepted via the additional user input (Gajdos: abstract; ¶¶ [0068] “user may accept, alter, or reject the settings or sub-sets of the settings”; FIGS. 1-5).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of facilitating image optimization (Gajdos: ¶¶ [0001]-[0008]).
CLAIM 4
Kathi may not teach the computer-implemented method of claim 3, further comprising training, via the processing system, the artificial intelligence-based algorithm based on the one or more of the suggested protocol parameters accepted via the additional user input.
Gajdos, however, teaches training, via the processing system, the artificial intelligence-based algorithm based on the one or more of the suggested protocol parameters accepted via the additional user input (Gajdos: abstract; ¶¶ [0016]-[0019] “During an examination workflow, various images are generated in sequence where the user discards some images and alters the settings to provide an image captured for diagnosis. These low and high-quality images are derived from the user workflow, so there is no need to manually label them. This interaction for treatment, diagnosis, and/or prognosis of the patient indicates a ground truth (e.g., settings for positive (captured) or negative (discarded)) for a sample that is the image, patient information, location information, and/or user information. This ground truth is used to train or retrain a machine-learned network to be applied by the medical scanner or other scanner for examination of other patients”, [0021], [0095]; FIGS. 1-5).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of facilitating image optimization (Gajdos: ¶¶ [0001]-[0008]).
4.3. Claims 10-11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mohamed Sheik Kathi (US 2024/0339214; hereinafter Kathi), in view of Gajdos et al. (US 2023/0115439), further in view of Meyer et al. (US 2018/0306883), and further in view of Kozloski et al. (US 2021/0374599).
CLAIM 10
Kathi teaches a computer-implemented method for optimizing protocols for medical imaging scanners (Kathi: abstract), comprising:
receiving, at a processing system comprising one or more processors, clinical requirements for a scan using a medical imaging scanner of an organization (Kathi: abstract; ¶¶ [0105]-[106] “method 600 includes selecting one or more medical examination protocols based on at least a part of the identified user physical information”; FIGS. 1-6);
receiving, at the processing system, information specific to the medical imaging scanner (Kathi: abstract; ¶¶ [0009] “providing the plurality of protocol parameters based on the processed input data to a medical imaging apparatus for performing the medical imaging”, [0037] “To identify the a plurality of protocol parameters the system uses the retrieved user medical information, the selected one or more medical examination protocols”; FIGS. 1-6); and
utilizing, via the processing system, a artificial intelligence-based model to generate a protocol for performing the scan using the medical imaging scanner based on the clinical requirements and the one or more desired outcomes for the scan (Kathi: abstract; ¶¶ [0108]-[0109] “providing the plurality of protocol parameters based on the processed received input data to a medical imaging apparatus for performing the medical imaging examination of the user”; FIGS. 1-6).
Kathi may not teach the following:
information specific to hardware and software of the medical imaging scanner;
receiving, at the processing system, user input of one or more desired outcomes for the scan; and
generative artificial intelligence-based model.
Gajdos, however, teaches the following:
receiving, at the processing system, user input of one or more desired outcomes for the scan (Gajdos: abstract; ¶¶ [0017]-[0022], [0033]-[0038] “scanner is configured by the user to scan the patient”; FIGS. 1-5).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of facilitating image optimization (Gajdos: ¶¶ [0001]-[0008]).
Kathi and Gajdos may not teach the following:
information specific to hardware and software of the medical imaging scanner; and
generative artificial intelligence-based model.
Meyer, however, teaches the following:
information specific to hardware and software of the medical imaging scanner (Meyer: abstract; ¶¶ [0033]-[0036] “Data acquisition protocols predefined by the manufacturer are made available by the manufacturer for various possible equipment versions of the magnetic resonance apparatus as far as hardware and software components”; FIGS. 1-2).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving image data quality and optimization of data acquisition protocols (Meyer: ¶¶ [0001]-[0012]).
Kathi, Gajdos and Meyer may not teach the following:
generative artificial intelligence-based model.
Kozloski, however, teaches the following:
generative artificial intelligence-based model (Kozloski: abstract; ¶¶ [0045]-[0050] “parameters-generating model, p.sub.MN, provides a second output vector from A.sub.M, which is used to parameterize a Generative Pathophysiological Model”, “changes to the parameters p.sub.M in a manner that will be apparent to one having ordinary skill in the art of AI and generative classifiers”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving medical imaging (Kozloski: ¶¶ [0001]-[0010]).
CLAIM 11
Kathi may not teach the computer-implemented method of claim 10, further comprising: receiving, at the processing system, additional user input additional one or more desired outcomes for the scan; receiving, at the processing system, context from the generative artificial intelligence-based model; and utilizing, via the processing system, the generative artificial intelligence-based model to update the protocol to generate an updated protocol based on the context and the additional user input.
Gajdos, however, teaches the following: receiving, at the processing system, additional user input additional one or more desired outcomes for the scan (Gajdos: abstract; ¶¶ [0017]-[0022], [0033]-[0038] “scanner is configured by the user to scan the patient”; FIGS. 1-5); receiving, at the processing system, context from the artificial intelligence-based model (Gajdos: abstract; ¶¶ [0063]-[0072] “settings output by the machine-learned network”; FIGS. 1-5); and utilizing, via the processing system, the artificial intelligence-based model to update the protocol to generate an updated protocol based on the context and the additional user input (Gajdos: abstract; ¶¶ [0015] “Patient-specific ultrasound image optimization is provided. Artificial intelligence (AI)-based algorithms enable an ultrasound scanner to “learn from experience” by analyzing data produced as part of patient imaging to determine ground truth”, [0063]-[0072] “output is converted into imaging action, which results in updating the imaging parameters for generating a next image”; FIGS. 1-5).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of facilitating image optimization (Gajdos: ¶¶ [0001]-[0008]).
Kathi, Gajdos and Meyer may not teach the following: generative artificial intelligence-based model.
Kozloski, however, teaches the following: generative artificial intelligence-based model (Kozloski: abstract; ¶¶ [0045]-[0050] “parameters-generating model, p.sub.MN, provides a second output vector from A.sub.M, which is used to parameterize a Generative Pathophysiological Model”, “changes to the parameters p.sub.M in a manner that will be apparent to one having ordinary skill in the art of AI and generative classifiers”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving medical imaging (Kozloski: ¶¶ [0001]-[0010]).
CLAIM 16
Kathi teaches a computer-implemented method for optimizing protocols for medical imaging scanners (Kathi: abstract), comprising:
receiving, at a processing system comprising one or more processors, existing planned protocols from an organization for performing a scan with a first medical imaging scanner (Kathi: abstract; ¶¶ [0037] “To identify the a plurality of protocol parameters the system uses the retrieved user medical information, the selected one or more medical examination protocols”; FIGS. 1-6); and
receiving, at the processing system, information specific to the first medical imaging scanner (Kathi: abstract; ¶¶ [0009] “providing the plurality of protocol parameters based on the processed input data to a medical imaging apparatus for performing the medical imaging”, [0037] “To identify the a plurality of protocol parameters the system uses the retrieved user medical information, the selected one or more medical examination protocols”; FIGS. 1-6).
Kathi does not appear to explicitly teach the following:
information specific to hardware and software; and
receiving, at the processing system, additional information specific to a second medical imaging scanner different from the first medical imaging scanner, wherein the second medical imaging scanner is of a different manufacturer and/or a different model from the first medical imaging scanner;
utilizing, via the processing system, a artificial intelligence-based model to generate a protocol for performing the respective scan using the second medical imaging scanner based on the existing planned protocols and the one or more desired outcomes for the respective scan.
Gajdos, however, teaches the following:
receiving, at the processing system, user input of one or more desired outcomes for a respective scan with the second medical imaging scanner (Gajdos: abstract; ¶¶ [0017]-[0022], [0033]-[0038] “scanner is configured by the user to scan the patient”; FIGS. 1-5); and
utilizing, via the processing system, a artificial intelligence-based model to generate a protocol for performing the respective scan using the second medical imaging scanner based on the existing planned protocols and the one or more desired outcomes for the respective scan (Gajdos: abstract; ¶¶ [0015], [0063]-[0072] “output is converted into imaging action, which results in updating the imaging parameters for generating a next image”; FIGS. 1-5).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of facilitating image optimization (Gajdos: ¶¶ [0001]-[0008]).
Kathi and Gajdos may not teach the following:
information specific to hardware and software; and
receiving, at the processing system, additional information specific to hardware and software of a second medical imaging scanner different from the first medical imaging scanner, wherein the second medical imaging scanner is of a different manufacturer and/or a different model from the first medical imaging scanner; and
a generative artificial intelligence-based model.
Meyer, however, teaches the following:
information specific to hardware and software (Meyer: abstract; ¶¶ [0033]-[0036] “Data acquisition protocols predefined by the manufacturer are made available by the manufacturer for various possible equipment versions of the magnetic resonance apparatus as far as hardware and software components”; FIGS. 1-2); and
receiving, at the processing system, additional information specific to hardware and software of a second medical imaging scanner different from the first medical imaging scanner, wherein the second medical imaging scanner is of a different manufacturer and/or a different model from the first medical imaging scanner (Meyer: abstract; ¶¶ [0033]-[0036] “Data acquisition protocols predefined by the manufacturer are made available by the manufacturer for various possible equipment versions of the magnetic resonance apparatus as far as hardware and software components”; FIGS. 1-2); and
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving image data quality and optimization of data acquisition protocols (Meyer: ¶¶ [0001]-[0012]).
Kathi, Gajdos and Meyer may not teach the following:
a generative artificial intelligence-based model.
Kozloski, however, teaches the following:
a generative artificial intelligence-based model (Kozloski: abstract; ¶¶ [0045]-[0050] “parameters-generating model, p.sub.MN, provides a second output vector from A.sub.M, which is used to parameterize a Generative Pathophysiological Model”, “changes to the parameters p.sub.M in a manner that will be apparent to one having ordinary skill in the art of AI and generative classifiers”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving medical imaging (Kozloski: ¶¶ [0001]-[0010]).
4.4. Claims 12-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mohamed Sheik Kathi (US 2024/0339214; hereinafter Kathi), in view of Gajdos et al. (US 2023/0115439), further in view of Meyer et al. (US 2018/0306883), and further in view of Kozloski et al. (US 2021/0374599), and further in view of Paul, Jr. et al. (US 12243646).
CLAIM 12
Kathi , Gajdos, Meyer, and Kozloski may not teach the computer-implemented method of claim 10, wherein the generative artificial intelligence-based model comprises a radiology large language model specific to the organization.
Paul, however, teaches wherein the generative artificial intelligence-based model comprises a radiology large language model specific to the organization (Paul: abstract; col. 1, lns. 15-18; col. 10, lns. 1-3; FIGS. 1-16).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving healthcare outcomes (Paul: col. 1, lns. 15-29).
CLAIM 13
Kathi teaches the computer-implemented method of claim 12, wherein the model is fine-tuned based on protocols from the organization for the medical imaging scanner (Kathi: abstract; ¶¶ [0090] “ML based trained parameter identifier 402-c-1 that provides protocol parameters associated with the selected one or more medical examination protocols based on the medical information”, [0100]; FIGS. 1-6).
Kathi , Gajdos, Meyer, and Kozloski may not teach radiology large language model.
Paul, however, teaches wherein the radiology large language model is fine-tuned (Paul: abstract; col. 29, lns. 14-55 “the LLM can be fine-tuned…based on clinical input”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving healthcare outcomes (Paul: col. 1, lns. 15-29).
CLAIM 14
Kathi teaches the computer-implemented method of claim 13, wherein organization specific fine-tuning of the model is isolated from external exposure (Kathi: abstract; ¶¶ [0090], [0100]; FIGS. 1-6).
Kathi , Gajdos, Meyer, and Kozloski may not teach radiology large language model.
Paul, however, teaches wherein the radiology large language model is fine-tuned (Paul: abstract; col. 29, lns. 14-55 “the LLM can be fine-tuned…based on clinical input”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving healthcare outcomes (Paul: col. 1, lns. 15-29).
CLAIM 17
Kathi, Gajdos, Meyer and Kozloski may not teach the computer-implemented method of claim 16, wherein the generative artificial intelligence-based model comprises a radiology large language model specific to the organization (Paul: abstract; col. 1, lns. 15-18; col. 10, lns. 1-3; FIGS. 1-16).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving healthcare outcomes (Paul: col. 1, lns. 15-29).
CLAIM 18
Kathi teaches the computer-implemented method of claim 17, wherein the model is fine-tuned based on protocols from the organization for the first medical imaging scanner (Kathi: abstract; ¶¶ [0090] “ML based trained parameter identifier 402-c-1 that provides protocol parameters associated with the selected one or more medical examination protocols based on the medical information”, [0100]; FIGS. 1-6).
Kathi , Gajdos, Meyer, and Kozloski may not teach radiology large language model.
Paul, however, teaches wherein the radiology large language model is fine-tuned (Paul: abstract; col. 29, lns. 14-55 “the LLM can be fine-tuned…based on clinical input”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving healthcare outcomes (Paul: col. 1, lns. 15-29).
CLAIM 19
Kathi teaches the computer-implemented method of claim 18, wherein organization specific fine-tuning of the model is isolated from external exposure (Kathi: abstract; ¶¶ [0090], [0100]; FIGS. 1-6).
Kathi , Gajdos, Meyer, and Kozloski may not teach radiology large language model.
Paul, however, teaches wherein the radiology large language model is fine-tuned (Paul: abstract; col. 29, lns. 14-55 “the LLM can be fine-tuned…based on clinical input”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving healthcare outcomes (Paul: col. 1, lns. 15-29).
4.5. Claims 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mohamed Sheik Kathi (US 2024/0339214; hereinafter Kathi), in view of Gajdos et al. (US 2023/0115439), further in view of Meyer et al. (US 2018/0306883), further in view of Kozloski et al. (US 2021/0374599), and further in view of Paul, Jr. et al. (US 12243646), and further in view of Kunz et al. (US 2023/0290111).
CLAIM 15
Kathi, Gajdos and Meyer may not teach the computer-implemented method of claim 14, wherein, prior to the organization specific fine-tuning, the generative artificial intelligence-based model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the medical imaging scanner and the original equipment manufacturer data for the medical imaging scanner.
Kozloski, however, teaches the following: generative artificial intelligence-based model (Kozloski: abstract; ¶¶ [0045]-[0050] “parameters-generating model, p.sub.MN, provides a second output vector from A.sub.M, which is used to parameterize a Generative Pathophysiological Model”, “changes to the parameters p.sub.M in a manner that will be apparent to one having ordinary skill in the art of AI and generative classifiers”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving medical imaging (Kozloski: ¶¶ [0001]-[0010]).
Kathi, Gajdos, Meyer, Kozloski and Paul may not teach the following: wherein, prior to the organization specific fine-tuning, the model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the medical imaging scanner and the original equipment manufacturer data for the medical imaging scanner.
Kunz, however, teaches wherein, prior to the organization specific fine-tuning, the model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the medical imaging scanner and the original equipment manufacturer data for the medical imaging scanner (Kozloski: abstract; ¶¶ [0027]-[0028] “AI model to fine tune output based on the brand and/or model of scanner used for training data (e.g., a Hamamatsu, Philips, or Leica scanner)”; FIGS. 1-7).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method to process electronic images for model selection and training based on equipment manufacturer, as taught by Kunz, with the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving imaging accuracy (Kunz: ¶¶ [0027]-[0028]).
CLAIM 20
Kathi, Gajdos and Meyer may not teach the computer-implemented method of claim 19, wherein, prior to the organization specific fine-tuning, the artificial intelligence-based model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the first medical imaging scanner and the original equipment manufacturer data for the first medical imaging scanner.
Kozloski, however, teaches the following: generative artificial intelligence-based model (Kozloski: abstract; ¶¶ [0045]-[0050] “parameters-generating model, p.sub.MN, provides a second output vector from A.sub.M, which is used to parameterize a Generative Pathophysiological Model”, “changes to the parameters p.sub.M in a manner that will be apparent to one having ordinary skill in the art of AI and generative classifiers”; FIGS. 1-9).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving medical imaging (Kozloski: ¶¶ [0001]-[0010]).
Kathi, Gajdos, Meyer, Kozloski and Paul may not teach the following: wherein, prior to the organization specific fine-tuning, the artificial intelligence-based model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the first medical imaging scanner and the original equipment manufacturer data for the first medical imaging scanner.
Kunz, however, teaches wherein, prior to the organization specific fine-tuning, the artificial intelligence-based model is pre-trained based on original equipment manufacturer data for different manufacturers and different models of medical imaging scanners similar to the first medical imaging scanner and the original equipment manufacturer data for the first medical imaging scanner (Kozloski: abstract; ¶¶ [0027]-[0028] “AI model to fine tune output based on the brand and/or model of scanner used for training data (e.g., a Hamamatsu, Philips, or Leica scanner)”; FIGS. 1-7).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the system and method to process electronic images for model selection and training based on equipment manufacturer, as taught by Kunz, with the system and method for clinical decision support using generative-AI large language models, as taught by Paul, with the system and method for image translation using a generative AI model, as taught by Kozloski, with the method and MRI system with imaging data protocol updates, as taught by Meyer, the system and method for tuned medical ultrasound imaging, as taught by Gajdos, with the method and system for optimizing medical imaging examination, as taught by Kathi, with the motivation of improving imaging accuracy (Kunz: ¶¶ [0027]-[0028]).
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Tomaszewski whose telephone number is (313)446-4863. The examiner can normally be reached M-F 5:30 am - 2:30 pm.
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/MICHAEL TOMASZEWSKI/Primary Examiner, Art Unit 3681
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