DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 13, 2025 has been entered. Claims 1, 11, and 17 have been amended. Claims 1, 3-11, and 13-22 have been examined and are currently pending.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Information Disclosure Statement
The Information Disclosure Statements filed November 13, 2025 and February 2, 2026 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
Claim Objections
Claims 21-22 are objected to because of the following informalities: the term “the improbability” lacks antecedent basis lines 5 and 4, respectively. Appropriate correction is required.
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.
Claims 1, 3-11, and 13-22 are 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.
Claims 1, 11, and 17 are 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. Independent claims 1, 11, and 17 recite the limitations, “receive a second input identifying a treatment target area of the organ via an interactive model on a graphical user interface; receive patient data for the patient; generate, based on the patient data, a probability map indicating a probability for treatment of the treatment target area of the organ; provide the second digital model and the probability map for display;” which are not supported within the applicant’s specification. The examiner cannot find support for the above-described limitations within the applicant’s specification.
Claims 1, 11, and 17 are 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. Independent claims 1, 11, and 17 recite the limitation, “storing the second digital model and the probability map in a data repository” which is not disclosed within the applicant’s specification. The applicant’s specification discloses, “In some examples a system includes a computing device. The computing device is configured to receive a first input identifying an organ of a patient, and receive a scanned image of the organ. The computing device is also configured to generate a first digital model of a type of the organ. Further, the computing device is configured to determine an alignment of the scanned image to the first digital model. The computing device is also configured to generate a second digital model comprising at least a portion of the scanned image and the first digital model. The computing device is further configured to store the second digital model in a data repository. In some examples, receiving the first input is in response to a selection of a portion of a displayed target definition map. In some examples, the organ is a heart. In some examples, the computing device is configured to provide the second digital model for display.” in paragraph 0138. Paragraph 0138 discloses the second digital model is stored in a data repository, not including the probability map as claimed.
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, 4-5, 7-11, 14, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. US Publication 20210158937 A1 in view of Herrmann et al. US Publication 20180177422 A1 in view of Harlev et al. US Publication 20200196885 A1 further in view of Mahapatra US Publication 20180228536 A1.
Claims 1, 11, and 17:
As per claims 1, 11, and 17, Wu teaches a system, method, and nontransitory computer readable medium comprising:
a memory storing instructions (paragraph 0056 “For example, the memory 316 may include a machine-readable medium configured to store data and/or instructions that, when executed, cause the processing unit 310, the computation unit 312, or the data rendering unit 314 to perform one or more of the functions described herein. Examples of a machine-readable medium may include volatile or non-volatile memory including but not limited to semiconductor memory (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), flash memory, and/or the like.”):
and at least one processor communicatively coupled to the memory and configured to execute the instructions (paragraph 0056 “Each of the data processing unit 310, the computation unit 312, or the data rendering unit 314 may comprise one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microcontroller, a reduced instruction set computer (RISC) processor, an application specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a physics processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), or a combination thereof. The data processing unit 310, computation unit 312, and/or data rendering unit 314 may also comprise other type(s) of circuits or processors capable of executing the functions described herein. Further, the data processing unit 310, the computation unit 312, or the data rendering unit 314 may utilize the memory 316 to facilitate one or more of the operations described herein. For example, the memory 316 may include a machine-readable medium configured to store data and/or instructions that, when executed, cause the processing unit 310, the computation unit 312, or the data rendering unit 314 to perform one or more of the functions described herein. Examples of a machine-readable medium may include volatile or non-volatile memory including but not limited to semiconductor memory (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), flash memory, and/or the like.”):
receive a scanned image of the organ (paragraphs 0005, 0017, and 0029 “The processing device 112 may also receive (e.g., in real-time) a scan image of the patient produced by the medical scanner 102…”);
generate a first digital model of a type of the organ (paragraphs 0023 and 0029 “The images captured by the sensors may include two-dimensional (2D) or three-dimensional (3D) images depicting a patient, an object or a scene present in a medical environment. Each of the 2D or 3D images may comprise a plurality of pixels, lines, and/or vertices. The functional unit may be configured to analyze these images (e.g., at a pixel level) and generate a 2D or 3D model (e.g., a parametric model such as one based on a skinned multi-person linear (SMPL) model) of the patient, object or scene depicted in the images, for example, using a neural network (e.g., a convolutional neural network)…The 2D or 3D model may be represented, for example, by one or more of a 2D mesh, a 3D mesh, a 2D contour, a 3D contour, etc. to indicate the pose, shape and/or other anatomical characteristics of a patient and thereby to facilitate a plurality of downstream medical applications and services for the patient including, for example, patient positioning, medical protocol design, unified or correlated diagnoses and treatments, medical environment monitoring, surgical navigation, etc. For ease of description, when a 2D or 3D human model of a patient or a 2D or 3D model of an object or scene is referred to herein, it should be interpreted to include not only the model itself but also a representation of the model in any, graphical or visual form.”);
determine an alignment of the scanned image to the first digital model (paragraphs 0009 and 0029-0031 “For instance, the processing device 112 may receive a scan image of the patient from the repository 114 or the medical scanner 102, align the scan image with the 2D or 3D human model of the patient, and render the aligned image and 2D or 3D model visually (e.g., in an overlaid picture) to allow the scan image to be presented and analyzed with reference to anatomical characteristics (e.g., body shape and/or pose) of the patient as indicated by the model. This way, more insight may be gained into the organ(s) or tissue(s) of the patient captured in the scan image based on the additional information provided by the 2D or 3D model.” and “Using the 2D or 3D model as a common reference, the processing device 112 may be able to align multiple different scan images (e.g., from respective imaging modalities) of the patient together, for example, by aligning each scan image with the 2D or 3D model and thereafter aligning one scan image with another using the 2D or 3D model as an intermediate reference. When referred to herein, the alignment of two or more scan images or the alignment of a scan image with the 2D or 3D model may include overlaying one scan image with another scan image or overlaying the 2D or 3D model with the scan image.”);
generate a second digital model based on the alignment, the second digital model comprising at least a portion of the scanned image and the first digital model (paragraphs 0029-0031 “For instance, the processing device 112 may receive a scan image of the patient from the repository 114 or the medical scanner 102, align the scan image with the 2D or 3D human model of the patient, and render the aligned image and 2D or 3D model visually (e.g., in an overlaid picture) to allow the scan image to be presented and analyzed with reference to anatomical characteristics (e.g., body shape and/or pose) of the patient as indicated by the model. This way, more insight may be gained into the organ(s) or tissue(s) of the patient captured in the scan image based on the additional information provided by the 2D or 3D model.” and “Using the 2D or 3D model as a common reference, the processing device 112 may be able to align multiple different scan images (e.g., from respective imaging modalities) of the patient together, for example, by aligning each scan image with the 2D or 3D model and thereafter aligning one scan image with another using the 2D or 3D model as an intermediate reference. When referred to herein, the alignment of two or more scan images or the alignment of a scan image with the 2D or 3D model may include overlaying one scan image with another scan image or overlaying the 2D or 3D model with the scan image.”);
Wu does not teach receive a first input identifying an organ of a patient. However, Herrmann teaches Learning Techniques for Cardiac Arrhythmia Detection and further teaches, “…An input circuit can receive physiological information of a patient (e.g., electrical or mechanical information of the heart of the patient, respiratory information of the patient, etc.)…” (paragraph 0038). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include receive a first input identifying an organ of a patient as taught by Herrmann in order to determine an organ associated with the patient.
Wu and Herrmann do not teach receiving a second input identifying a treatment target area of the organ via an interactive model on a graphical user interface. However, Harlev teaches a Catheter-Based Identification of Cardiac Regions and further teaches, “In certain implementations, the method 800 can further include receiving a user input associated with a selection of a treatment region at block 807. As used herein, the treatment region can include one or more types of cardiac regions and, thus, for example, can include one or more of an atrium, a supraventricular region (e.g., a region including the atrium and/or one or more other anatomic structures situated above the ventricles), and a ventricle. The user input associated with the selection of the treatment region can be made in a variety of ways, including through interaction with a user interface on a catheter interface unit, interaction with a user interface on the catheter (e.g., on a handle of the catheter), voice commands, hand gestures, or a combination thereof. As an example, a list of one or more types of cardiac regions can be displayed on a graphical user interface (e.g., the graphical user interface 110), and the physician can select one or more of the types of cardiac regions through the use of an input device in communication with the graphical user interface.” (paragraph 0079). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include receiving a second input identifying a treatment target area of the organ via an interactive model on a graphical user interface as taught by Harlev in order to allow a physician or technician to select or point to a particular section of the organ to be treated.
Wu, Herrmann, and Harlev do not teach receiving patient data for the patient. However, Mahapatra teaches Determining Ablation Location Using Probabilistic Decision-Making and further teaches, “Each ablation record includes data elements associated with the patient, such as age, condition, sex, weight, and/or additional data. Each ablation record also includes data elements associated with the EP condition, such as one or more type(s) of arrhythmia experienced (e.g., AF, ventricular tachycardia (VT), ventricular fibrillation (VF) atrial tachycardia (AT)), symptoms, and/or the type of EP condition being treated with an ablation procedure. Each ablation record also includes data elements associated with the ablation procedure, including what type of procedure was performed, any ablation locations at which ablation was performed, and an indication of outcome (e.g., successful, unsuccessful, partially successful). In some embodiments, ablation records include data elements identifying the physician that performed the EP procedure and/or whether the physician is an expert physician.” (paragraph 0023), “Cardiac mapping system 14 further includes and/or is in communication with a database 42. Database 42 includes a plurality of records of historical (i.e., already-performed) ablation procedures, or “ablation records.” The ablation records are generated and stored in database 42 manually and/or automatically…In some embodiments, a subset of the plurality of ablation records are associated with ablation procedures performed and observed/recorded by one or more medical professionals, such as medical directors. In some embodiments, a subset of the plurality of ablation records are associated with ablation procedures performed by expert physicians and recorded in database 42.” (paragraph 0022) and “For each candidate ablation procedure to be performed using ablation system 10 (shown in FIG. 1), method 300 also includes receiving 308 a plurality of patient parameters associated with a patient receiving the candidate ablation procedure (e.g., using input device 44, shown in FIG. 1)…” (paragraph 0036). Therefore, it would have been obvious to one ordinary skilled in the art at the time of filing to modify Wu to include receiving patient data for the patient as taught by Mahapatra in order to identify information pertaining to the patient.
Wu, Herrmann, and Harlev do not teach generate, based on the patient data, a probability map indicating a probability for treatment of the treatment target area of the organ. However, Mahapatra teaches Determining Ablation Location Using Probabilistic Decision-Making and further teaches, “In the example embodiment, processing apparatus 16 causes display of the identified candidate ablation locations on display device 40. More specifically, processing apparatus 16 causes display of the identified candidate ablation locations with respect to the three-dimensional model of the patient's heart 20, providing visual indicators to the physician of where within the model (and therefore where within the heart 20) to ablate. In some embodiments, processing apparatus 16 displays the identified candidate ablation locations using a color map, a probability map, or similar visual indicator overlaid on an existing three-dimensional model. For example, FIG. 2 illustrates one example cardiac model 200 displayed on a visual interface 202 of display device 40. Visual indicators 204 are displayed as overlays on cardiac model 200 to identify a candidate ablation location 206 to a physician performing an EP procedure. Additionally or alternatively, visual indicators such as arrows, icons, labels, highlighting, bolding, shading, annotations, and/or any other visual indicator are further displayed on visual interface 202 of display device 40.” (paragraph 0032), “Processing apparatus 16 employs the above-described probabilistic analysis to identify and output candidate ablation locations on display device 40 during each candidate ablation procedure. Data specific to the candidate ablation procedure is input to cardiac mapping system 14. Specifically, in the example embodiment, an input device 44 is used to input patient- and case-specific data associated with the candidate ablation procedure. Input device 44 may include, for example, a keyboard, mouse, touch screen interface, and/or any other suitable input device. Such information as a patient age, a patient gender, a patient weight, one or more known patient conditions, and/or one or more known patient symptoms are input using input device 44.” (paragraph 0030) and “In some embodiments, one or more patient conditions and/or symptoms are determined using diagnostic techniques. One or more diagnostic techniques may be performed using ablation system 10, such as complex fractionated atrial electrogram (CFAE), local activation time (LAT), direction of activation, curl/divergence analysis, phase mapping, dominant frequency mapping, and/or any other known diagnostic technique. Based on one or more diagnosed patient conditions (“candidate conditions”), processing apparatus 16 applies the algorithm described above to identify one or more candidate ablation locations and the corresponding probability that ablating in each ablation location will be successful (i.e., lead to a successful outcome for the patient). The algorithm factors in patient- and case-specific data to identify and display one or more candidate ablation locations that are most likely to provide successful patient outcomes if ablation is performed at those candidate ablation locations. Processing apparatus 16 may therefore provide probabilistic indicators to a physician to aid in decision-making during an EP procedure.” (paragraph 0031) and “Processing apparatus 16 also develops an algorithm based upon the sets of probability parameters for the plurality of ablation records, stored in database 42. The algorithm is configured to output a probability that ablating in a particular ablation location will be successful, based on the historical ablation records in database 42 and associated probability parameters.” (paragraph 0025). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include generate, based on the patient data, a probability map indicating a probability for treatment of the treatment target area of the organ as taught by Mahapatra in order to identify sections of the organ that are relevant with respect to one or more treatments.
Wu, Herrmann, and Harlev do not teach provide the second digital model and the probability map for display. However, Mahapatra teaches Determining Ablation Location Using Probabilistic Decision-Making and further teaches, “In the example embodiment, processing apparatus 16 causes display of the identified candidate ablation locations on display device 40. More specifically, processing apparatus 16 causes display of the identified candidate ablation locations with respect to the three-dimensional model of the patient's heart 20, providing visual indicators to the physician of where within the model (and therefore where within the heart 20) to ablate. In some embodiments, processing apparatus 16 displays the identified candidate ablation locations using a color map, a probability map, or similar visual indicator overlaid on an existing three-dimensional model. For example, FIG. 2 illustrates one example cardiac model 200 displayed on a visual interface 202 of display device 40. Visual indicators 204 are displayed as overlays on cardiac model 200 to identify a candidate ablation location 206 to a physician performing an EP procedure. Additionally or alternatively, visual indicators such as arrows, icons, labels, highlighting, bolding, shading, annotations, and/or any other visual indicator are further displayed on visual interface 202 of display device 40.” (paragraph 0032). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include provide the second digital model and the probability map for display as taught by Mahapatra in order to present or highlight sections of the organ that are relevant with respect to one or more treatments.
Wu, Herrmann, and Harlev do not teach and store the second digital model and the probability map in a data repository. However, Mahapatra teaches Determining Ablation Location Using Probabilistic Decision-Making and further teaches, “Method 300 includes generating 302 a database (e.g., database 42, shown in FIG. 1) including a plurality of ablation records. Each ablation record of the plurality of ablation records is associated with a corresponding ablation procedure that has already been performed. In some embodiments, generating 302 includes generating a subset of the plurality of ablation records based upon published data of the corresponding ablation procedures. In some embodiments, generating 302 includes generating a subset of the plurality of ablation records based upon historical ablation procedures performed by one or more ablation practitioners who meet at least one expert criterion. In some embodiments, each ablation record identifies a condition and an ablation location associated with the corresponding ablation procedure. Method 300 also includes generating 304 a set of probability parameters describing each ablation record of the plurality of ablation records.” (paragraph 0035), “Method 300 includes applying 312 the algorithm to determine at least one candidate ablation location based upon the respective probabilities associated with the at least one candidate condition, and displaying 314 the at least one candidate ablation location on a visual interface (e.g., visual interface 202, shown in FIG. 2) of an ablation system. In some embodiments, displaying 314 includes adding at least one visual indicator of a corresponding ablation location onto an ablation map of the ablation system, such as cardiac model 200 (shown in FIG. 2). More particularly, adding a visual indicator may include adding at least one color to the ablation map to form a color map or probability map identifying the at least one candidate ablation location. Additionally or alternatively, adding a visual indicator may include adding at least one probability indicator to a respective location on the ablation map corresponding to the at least one candidate ablation location. In some embodiments, displaying 314 includes associating each at least one candidate ablation location with a corresponding point on an ablation map of the ablation system, and adding at least one visual indicator onto each point on the ablation map associated with each at least one candidate ablation location.” (paragraph 0037) and “…For example, in some embodiments, method 300 further includes performing 316 the candidate ablation procedure at least at one candidate ablation location. Method 300 may further include monitoring 318 an outcome of the candidate ablation procedure. Method 300 may also include adding 320 a data record associated with the candidate ablation procedure to the database. Adding 320 may include adding a data element associated with the outcome of the candidate ablation procedure to the data record associated with the candidate ablation procedure...” (paragraph 0038). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include store the second digital model and the probability map in a data repository as taught by Mahapatra in order to maintain a record of sections of the organ that are relevant with respect to one or more treatments.
Claims 4, 14 and 19:
As per claims 4, 14 and 19, Wu, Herrmann, Harlev, and Mahapatra do not teach the system, method, and nontransitory computer readable medium of claims 1, 11, and 17 as described above and Wu further teaches wherein the at least one processor is further configured to execute the instructions:
determine a corresponding portion of the second digital model based on the treatment target area of the organ (paragraph 0032);
and regenerate the second digital model to identify the corresponding portion (paragraph 0032).
Claim 5:
As per claim 5, Wu, Herrmann, Harlev, and Mahapatra do not teach the system of claim 4 as described above and Wu further teaches wherein regenerating the second digital model comprises associating the corresponding portion with a distinctive feature for display (paragraph 0032).
Claims 7, 16, and 20:
As per claims 7, 16, and 20, Wu, Herrmann, Harlev, and Mahapatra do not teach the system, method, and nontransitory computer readable medium of claims 1, 11, and 17 as described above and Harlev further teaches wherein the at least one processor is further configured to execute the instructions to:
obtain study data records for the patient, wherein each study data record identifies one of a plurality of study types and a study target area of a plurality of study target areas for studies performed on the patient (paragraph 0057). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include obtain study data records for the patient, wherein each study data record identifies one of a plurality of study types and a study target area of a plurality of study target areas for studies performed on the patient as taught by Harlev in order to analyze medical data associated with a patient.
determine a first number of each of the plurality of study types performed on the patient based on the study data records (paragraph 0057). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include determine a first number of each of the plurality of study types performed on the patient based on the study data records as taught by Harlev in order to determine the types of procedures performed on patient.
determine, for each of the plurality of study types, a second number of studies performed on the patient in each of the plurality of study target areas (paragraph 0064). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include determine, for each of the plurality of study types, a second number of studies performed on the patient in each of the plurality of study target areas as taught by determine regions or locations of where the procedure was performed.
generate a first map for each of the plurality of study types based on the first number and the second number that correspond to each of the plurality of study types (paragraph 0073). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include generate a first map for each of the plurality of study types based on the first number and the second number that correspond to each of the plurality of study types as taught by Harlev in order to provide a visual of the procedures performed.
and store the first map in the data repository (paragraph 0057). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include store the first map in the data repository as taught by Harlev in order to maintain a record and a corresponding map or display of the procedures performed.
Claim 8:
As per claim 8, Wu, Herrmann, Harlev, and Mahapatra teach the system of claim 7 as described above and Wu further teaches wherein each first map indicates a frequency of the corresponding study type on each of the plurality of study target areas (paragraph 0032).
Claim 9:
As per claim 9, Wu, Herrmann, Harlev, and Mahapatra teach the system of claim 7 as described above and Wu further teaches wherein the at least one processor is further configured to execute the instructions to:
generate a second map based on the first numbers and the second numbers, wherein the second map indicates a probability of treatment for each of the plurality of study target areas (paragraph 0032);
and store the second map in the data repository (paragraph 0032).
Claim 10:
As per claim 10, Wu, Herrmann, Harlev, and Mahapatra teach the system of claim 9 as described above and Wu further teaches wherein receiving the first input is in response to a selection of a portion of a displayed target definition map (paragraph 0032).
Claim(s) 3, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Herrmann, Harlev, and Mahapatra as applied to claims 1, 11, and 17 above, and in view of Hendriks et al. US Publication 20240081766 A1 further in view of Barley et al. US Publication 20200163584 A1.
Claims 3, 13, and 18:
As per claims 3, 13, and 18, Wu, Herrmann, Harlev, and Mahapatra teach the system, method and nontransitory computer readable medium of claims 1, 11 and 17 as described above but do not teach wherein the at least one processor is further configured to execute the instructions to:
receive a third input identifying an adjustment to the alignment of the scanned image to the first digital model. However, Hendriks teaches Perfusion Angiography combined with Photoplethysmography Imaging for Peripheral Vascular Disease Assessment and further teaches “An image for view on a display is generated in the step “generate/send to display” and transmitted to a display unit, e.g. to a display of the workstation. The processing unit, which may comprise a dedicated graphical processor, is generating the image based on the combined perfusion map, e.g. generates a color-coded heat map for representing, on a common scale, the magnitude of the temporally aligned first and second changes of the perfusion states of the organ deep and superficial tissue at various pixel elements of the image to be displayed. Preferably, the image for display is generated by using a 2D/3D blending algorithm such as alpha-blending to determine the color value at each pixel element of the image if the image generated is the result of a combination of 2D and 3D views of the perfused organ. For example, the processing unit may determine the saturation value or an alpha-channel value of each pixel as a function of a scene lighting, a viewing perspective, and depth-overlapping image objects (e.g. overlapping polygons of meshed objects). This allows for the generation of image for display in which the color-coded combined perfusion map is illustrated as an overlay image to a 3D reconstruction image of the organ, using the established correspondences between 3D views and 2D views of the organ as provided by the merging module. Presenting the combined perfusion map as an overlay image to the 3D reconstruction of the organ facilitates orientation and improves the visual inspection experience by the person viewing the image. The image may further be generated interactively so as to provide regular image updates to the viewing person in response to changing viewing conditions, e.g. when interactively rotating the 3D reconstruction image with the combined perfusion map overlay according to different viewing angles, when interactively selecting a perfused organ tissue portion (e.g. skin or deep tissue) to be enabled or disabled in the displayed image, or when interactively switching between different derived quantities (e.g. time-to-peak, time-to-arrival, area under curve, etc.) that reflect changes in the aligned first and second changes of the perfusion state over time.” (paragraph 0061). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include receive a third input identifying an adjustment to the alignment of the scanned image to the first digital model as taught by Hendriks in order to allow different views associated with the model.
adjust the second digital model based on the third input. However, Hendriks teaches Perfusion Angiography combined with Photoplethysmography Imaging for Peripheral Vascular Disease Assessment and further teaches “An image for view on a display is generated in the step “generate/send to display” and transmitted to a display unit, e.g. to a display of the workstation. The processing unit, which may comprise a dedicated graphical processor, is generating the image based on the combined perfusion map, e.g. generates a color-coded heat map for representing, on a common scale, the magnitude of the temporally aligned first and second changes of the perfusion states of the organ deep and superficial tissue at various pixel elements of the image to be displayed. Preferably, the image for display is generated by using a 2D/3D blending algorithm such as alpha-blending to determine the color value at each pixel element of the image if the image generated is the result of a combination of 2D and 3D views of the perfused organ. For example, the processing unit may determine the saturation value or an alpha-channel value of each pixel as a function of a scene lighting, a viewing perspective, and depth-overlapping image objects (e.g. overlapping polygons of meshed objects). This allows for the generation of image for display in which the color-coded combined perfusion map is illustrated as an overlay image to a 3D reconstruction image of the organ, using the established correspondences between 3D views and 2D views of the organ as provided by the merging module. Presenting the combined perfusion map as an overlay image to the 3D reconstruction of the organ facilitates orientation and improves the visual inspection experience by the person viewing the image. The image may further be generated interactively so as to provide regular image updates to the viewing person in response to changing viewing conditions, e.g. when interactively rotating the 3D reconstruction image with the combined perfusion map overlay according to different viewing angles, when interactively selecting a perfused organ tissue portion (e.g. skin or deep tissue) to be enabled or disabled in the displayed image, or when interactively switching between different derived quantities (e.g. time-to-peak, time-to-arrival, area under curve, etc.) that reflect changes in the aligned first and second changes of the perfusion state over time.” (paragraph 0061). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include adjust the second digital model based on the third input as taught by Hendriks in order to capture different views associated with the model.
Hendriks does not teach and store the adjusted second digital model in the data repository. However, Barley teaches Non-Rigid-Body Morphing of Vessel Image using Intravascular Device Shape and further teaches, “A database 123 is stored in memory or is accessible over a network and includes historic data, models, and/or finite element representations of organs and their deformation response to particular instruments for particular procedures. The database 123 may be employed by either or both modules 115 and 117 to update the 3D images for a dynamic overlay.” (paragraph 0027). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include store the adjusted second digital model in the data repository as taught by Barley in order to maintain a record of changes made to second digital model.
Claim(s) 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Herrmann, Harlev, and Mahapatra do not teach as applied to claims 4 and 14 above, and further in view of Karin et al. US Publication 20140086918 A1.
Claims 6 and 15:
As per claims 6 and 15, Wu, Herrmann, Harlev, and Mahapatra do not teach the system and method of claims 4 and 14 as described above but do not teach wherein the at least one processor is further configured to execute the instructions to transmit treatment data identifying the treatment target area of the organ to a radioablation treatment system. However, Karin teaches Methods for Inhibiting Prostate Cancer and further teaches, “"Radioablation" is a medical procedure where part of the electrical conduction system of a tissue (e.g., tumor, heart, or other dysfunctional tissue) is ablated using the heat generated from the low frequency AC, pulses of DC, or high frequency alternating current to treat a medical disorder. Radio frequency ablation (RFA) uses high frequency alternating current and has the advantage over previously used low frequency AC or pulses of DC in that it does not directly stimulate nerves or heart muscle and can therefore often be used without the need for general anesthetic.” (paragraph 0034). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include wherein the computing device is further configured to transmit treatment data identifying the treatment target area of the organ to a radioablation treatment system as taught by Karin in order to identify an organ of a patients that needs a radioablation treatment.
Claim(s) 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, Herrmann, Harlev, and Mahapatra do not teach as applied to claims 1 and 11 above, and further in view of Bunch et al. US Publication 20140005562 A1 further in view of Merritt US Publication 20160166232 A1.
Claims 21 and 22:
As per claims 21 and 22, Wu, Herrmann, Harlev, and Mahapatra do not teach a system and method of claims 1 and 11 as described above but do not teach compare a value of the probability map for the treatment of the treatment target area of the organ to a threshold. However, Bunch teaches Atrial Fibrillation Treatment Systems and Methods and further teaches, “In some instances, a further stage may occur after stage 1710 in which one or more rankings are mapped to a representation of the heart. For example, a three-dimensional image or model of the heart may be marked with a color or other indicator to identify a potential driver, or target site for ablation. If multiple drivers are identified, with one driver having a higher frequency and one or more further drivers having a lower frequency, such that the higher frequency driver may have a higher likelihood of being a primary driver, the marking or identification may proceed in a manner to distinguish the various drivers from one another. For example, the higher frequency driver may be marked with a color or otherwise identified in a manner that signifies that the position on the heart is likely a driver and is thus a good candidate for ablation, and the one or more lower frequency positions may be identified as less likely positions at which a primary driver is located. Even where only a single potential driver is identified, a similar identification may take place. For example, in some instances, it may be determined that any potential driver having a frequency that is above a threshold value (e.g., having a period of no greater than 100 milliseconds, in some instances) is a good candidate for ablation and thus may be identified accordingly (e.g., by marking a three-dimensional map with a specific color or other indicator). Other thresholds may also be determined. For example, potential positions that exhibit periods at or below a lower threshold may be identified as good driver candidates, positions that exhibit periods between the lower threshold and an upper threshold may be identified as fair driver candidates, and positions that exhibit periods at or above the upper threshold may be identified as poor driver candidates. Other ranking values and identification systems are also contemplated.” (paragraph 0170). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include compare a value of the probability map for the treatment of the treatment target area of the organ to a threshold as taught by Bunch in order to identify or rank treatments target area suitable for the organ.
Bunch does not teach and provide for display a warning indicating the improbability of the treatment target area of the organ based on the comparison. However, Merritt teaches Devices, Systems, and Methods for In-Stent Restenosis Prediction and further teaches, “In FIG. 2D, the stent 208 has not fully expanded to compress the lesion 206 against luminal walls 245 of the vessel 210. Instead, the lesion 206 remains partially intact and capable of at least partially occluding flow through the vessel 210. The imaging device 203 can convey this information via imaging data to the clinician and the processing system 101 may estimate the restenosis probability value and communicate it visually and/or by audio to the clinician. For example, when the processing system 101 estimates the restenosis probability value of the stent 208, which is not fully expanded as shown in FIG. 2D, is above a threshold value the processing system may cause a beep or chime to be produced on a speaker coupled to the processing system 101. Additionally or alternatively, the processing system 101 may cause a notification to be displayed to the clinician. The notification or alert, such as a visual notification or audio notification, associated with the estimated restenosis probability value may indicate to the clinician that the stent 208 is not positioned satisfactorily and that ameliorative measures should be considered. These ameliorative measures may include performing corrections to the deployment of the stent 208 or performing an alternative procedure, such as an ablation procedure to remove material from the site of the lesion 206. In some embodiments, the processing system 101 may generate a recommended intervention (e.g., a higher pressure deployment of the stent 208, an ablation process, or a combination thereof, etc.) and communicate the recommendation to the clinician in a user interface provided by the processing system 101.” (paragraph 0040). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Wu to include provide for display a warning indicating the improbability of the treatment target area of the organ based on the comparison as taught by Merritt in order to notify a medical professional regarding one or more unusual target areas for the organ.
Response to Arguments
Applicant’s arguments, see pages 11-12, filed November 13, 2025, with respect to the rejection(s) of claim(s) 1, 4-5, 7-11, 14, 16-17, and 19-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wu, Herrmann, Harlev, and Mahapatra under 35 U.S.C. 103.
According to applicant’s argument on pages 10-11 of the remarks disclose, “As an initial matter, Applicant's specification discloses storing various data and information in a database throughout the specification. FIG. 1 illustrates an embodiment of the cardiac radioablation diagnosis and treatment system of the Applicant's disclosure. The system 100 includes a database 116 in communication with the target definition computing device 104. The target definition computing device 104 is thus connected to and operable to send and receive data to the database 116. (See e.g., Specification, para. [0043]). Furthermore, the Specification expressly states "wherein the second map indicates a probability of treatment for each of the plurality of study target areas, and storing the second map in a data repository." (Specification, para. [0145]). Thus, the Specification as whole and expressly discloses storing a probability map in a database or data repository. Applicant respectfully requests reconsideration and withdrawal of the rejections.” The examiner respectfully disagrees.
The applicant's cited paragraphs 0043 and 0145 and figure 1 of the applicant's specification do not recite or disclose the limitation, "storing the second digital model and the probability map in a data repository." Paragraph 0043 of the applicant's specification discloses a target definition computer device 104 in communication with treatment planning computer device 106 and database 116. Paragraph 0145 of the applicant's specification discloses, "In some examples, the method includes generating a second map based on the first numbers and the second numbers, wherein the second map indicates a probability of treatment for each of the plurality of study target areas, and storing the second map in a data repository." Paragraph 0145 does not disclose a "second digital model" and a "probability map" in stored a "data repository" as claimed.
The applicant’s specification discloses, “In some examples a system includes a computing device. The computing device is configured to receive a first input identifying an organ of a patient, and receive a scanned image of the organ. The computing device is also configured to generate a first digital model of a type of the organ. Further, the computing device is configured to determine an alignment of the scanned image to the first digital model. The computing device is also configured to generate a second digital model comprising at least a portion of the scanned image and the first digital model. The computing device is further configured to store the second digital model in a data repository. In some examples, receiving the first input is in response to a selection of a portion of a displayed target definition map. In some examples, the organ is a heart. In some examples, the computing device is configured to provide the second digital model for display.” in paragraph 0138. Paragraph 0138 discloses the second digital model is stored in a data repository, not including the probability map as claimed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rainer et al. US Publication 20240303808 A1 Method for Automatic Identification of Cardiac Segmented Regions
Rainer discloses method for automatic identification of segmented regions of a heart, the method being executed by a control unit and including the steps of: acquiring a heart mesh that is a 3D graphical representation of the heart, including a left ventricle, a right ventricle, a heart apex and a heart base; determining a heart base plane corresponding to the heart base; determining, based on the heart base and the heart apex, a left ventricular axis extending across the left ventricle, from the heart apex to the heart base; using the heart base plane and the left ventricular axis to identify segmented regions indicative of the left ventricle and the right ventricle, each segmented region being a respective portion of the heart mesh satisfying a respective first criterion about a distance range from the heart base plane and a respective second criterion about a circumferential angular range about the left ventricular axis.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3682