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 .
Election/Restrictions
Applicant’s election without traverse of Group IV, claims 28-38 in the reply filed on 05/19/26 is acknowledged.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 28-46 are rejected under 35 U.S.C. 103 as being unpatentable over Villongco (U.S. Patent Application Publication 20190332729) in view of Kuck et al. (U.S. Patent Application Publication 20160331262) and Blake et al. (U.S. Patent Application Publication 20160022375).
As per claims 28 and 33, Villongco teaches: collecting an arrhythmia electrocardiogram (ECG) and patient characteristics of a patient; and
receiving a demarcated generic three-dimensional (3D) mesh representing a generic cardiac geometry by inputting the arrhythmia ECG and patient characteristics into a mapping system that outputs the demarcated generic 3D mesh with a region of interest (ROI) demarcated (Figs. 18, 20; ¶¶ [0066-0070]; [0117-0121]; [0128-0133]).
Kuck et al. teach collecting a 3D image of the heart of the patient; and
generating based on the 3D image a patient-specific 3D mesh representing the patient's heart (Fig. 7; ¶¶ [0055-0056])
Blake et al. teach generating a demarcated patient-specific 3D mesh based on the patient-specific 3D mesh and the demarcated generic 3D mesh, the demarcated patient- specific 3D mesh with the ROI demarcated to reflect differences in the patient's cardiac geometry and the generic cardiac geometry (Figs. 4, 8; ¶¶ [0045]; [0066]; [0088]).
It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the arrhythmia mapping system of Villongco with the patient specific mesh generation techniques of Kuck et al. and the mesh translation and registration techniques of Blake et al. One would have been motivated to make such a modification(s) in order to generate patient-specific cardiac treatment geometries corresponding to patient anatomy, because doing so would permit transferring identified cardiac regions from a generic cardiac geometry to a patient-specific cardiac geometry using known mesh registration techniques.
[Examiner note: Claims 28 and 33 are rejected together because the one or more computing systems of claim 33 are configured to perform substantially the same operations recited in the method of claim 28, including generation of a demarcated generic 3D mesh, generation of a patient-specific 3D mesh, and generation of a demarcated patient-specific mesh].
As per claim 29-30, Villongco as modified above, discloses a method as recited in claim 28, but does not explicitly disclose submitting the demarcated patient-specific 3D mesh to a stereotactic ablative radiotherapy device and/or generating a 3D image corresponding to the demarcated patient-specific 3D mesh and submitting the 3D image to a stereotactic ablative radiotherapy device.
It would have been obvious to one having ordinary skill in the art at the time the invention was made to further modify the method of Villongco such that a demarcated patient-specific 3D mesh and/or a 3D image corresponding to the demarcated patient-specific 3D mesh is submitted to a stereotactic ablative radiotherapy device, since providing treatment-planning data to a treatment device represents a predictable use of generated patient-specific treatment geometry.
As per claims 31-32, Villongco as modified above, discloses a method as recited in claim 28, but does not explicitly disclose generating a labeling of segments within the 3D image and generating a delivery plan based on the demarcated patient-specific 3D mesh, the labeled segments, and a target dose and wherein the delivery plan is generated using a planning machine learning model.
It would have been obvious to one having ordinary skill in the art at the time the invention was made to further modify the method of Villongco such that it incorporated the aforementioned limitations. One would have been motivated to make such a modification because treatment planning routinely utilizes anatomical structures and dose information to determine treatment delivery parameters. Additionally, machine learning models were known for generating treatment plans using patient-specific medical information.
As per claim 34-39, Villongco as modified above, discloses one or more computing systems as recited in claim 33, but does not explicitly disclose machine learning, delivery plan generation, image-generation, display and visualization functions.
It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the one or more systems of Villongco such that it incorporated the aforementioned functionality. One would have been motivated to make such a modification(s) because such functions represent routine computer-based processing and treatment-planning operations applied to patient-specific medical data.
As per claims 40-41, Villongco as modified above, discloses one or more computing systems as recited in claim 33, but does not explicitly disclose wherein the computer-executable instructions include instructions to apply a demarcation machine learning (ML) model to the patient-specific 3D mesh and the demarcated generic 3D mesh to generate a demarcated patient-specific 3D mesh and wherein the demarcation ML model is a neural network.
It would have been obvious to one having ordinary skill in the art at the time the invention was made to further modify the one or more computing systems of Villongco such that it incorporated the aforementioned limitations. One would have been motivated to make such modification(s) because machine learning models were known for mapping and adapting anatomical information between medical model representations. Additionally, layers, activation functions and weights represent well-known components of neural-network machine learning models.
As per claim 42-43, Villongco as modified above, discloses one or more computing systems as recited in claim 33, but does not explicitly disclose wherein the mapping system applies a mapping ML model that inputs the arrhythmia cardiogram and outputs the demarcated generic 3D mesh with the region of interest demarcated and wherein the mapping system identifies, from a library of associations between library cardiogram and library demarcated generic 3D meshes a library cardiogram that matches the arrhythmia cardiogram based on a matching criterion and outputs the library demarcated generic 3D mesh associated with the matching library cardiogram as the demarcated generic 3D mesh.
It would have been obvious to one having ordinary skill in the art at the time the invention was made to further modify the one or more computing systems of Villongco such that it incorporated the aforementioned limitations. One would have been motivated to make such a modification because machine learning models were known for classifying patient data and generating corresponding anatomical model outputs. Additionally, comparing patient data to stored reference data and selecting an associated model based on a matching criterion represents a known classification technique.
As per claims 44-46, Villongco as modified above, discloses a method as recited in claim 28, but does not explicitly disclose, wherein the generating of the demarcated patient-specific 3D mesh includes applying a demarcation machine learning (ML) model to the patient-specific 3D mesh and the demarcated generic 3D mesh to generate a demarcated patient-specific 3D mesh with the one or more ROls demarcated accounting for differences between the generic cardiac geometry and the patient's cardiac geometry and further comprising displaying the demarcated patient-specific 3D mesh with the ROI demarcated to help inform treating the patient.
It would have been obvious to one having ordinary skill in the art at the time the invention was made to further modify the method of Villongco such that it incorporated the aforementioned limitations. One would have been motivated to make such a modification because machine learning models are routinely trained using corresponding training datasets to perform a desired mapping function. Additionally, layers, activation functions and weights represent well-known components of neural-network machine learning models and generating visual representation of patient-specific anatomical information represents a routine visualization function.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COURTNEY D THOMAS whose telephone number is (571)272-2496. The examiner can normally be reached M-F: 9 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Makiya can be reached at 571-272-2273. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/COURTNEY D THOMAS/Primary Examiner, Art Unit 2884
Prosecution Insights