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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 25 June 2026 has been entered.
Formal Matters
Applicant's response, filed 25 June 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-10, 12-13, and 16-18 are currently pending and have been examined.
Claims 11 and 14-15 have been canceled.
Claim 18 has been added.
Claims 1-10, 12-13, and 16-18 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for parent Application No. EP21216806.6, filed on 30 May 2024.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 22 December 2021 claiming benefit to EP21216806.6.
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.
Claims 1-10, 12-13, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Roh et al. (US Patent 11571266)[hereinafter Roh] in view of Alexander Winkler-Schwartz et al., Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation, 2(8) JMA Network Open (August 2, 2019) [hereinafter Winkler-Schwartz].
As per claim 1, Roh teaches on the following limitations of the claim:
providing (S1) a pre-acquired first data set (DS1) of medical image data of a patient (P) is taught in the Detailed Description in col 39 lines 27-42, col 40 lines 16-26, and Fig. 9 reference character 906 (teaching on acquiring medical image data for a patient)
providing (S2) a pre-acquired second data set (DS2) of patient medical parameters and/or natural language data related to a medical condition of the patient (P) is taught in the Detailed Description in col 39 lines 43-58 and Fig. 9 reference character 902 (teaching on acquiring patient condition data including patient diagnostic information)
generating (S3) a three-dimensional virtual model (M) of at least a portion of the body of the patient (P) based on the pre-acquired first data set (DS1) and on the pre-acquired second data set (DS2) is taught in the Detailed Description in col and Fig. 9 reference character 908 (teaching on generating a virtual reality surgery planning environment for the patient using the patient medical image data and patient condition data)
applying (S4) a machine learning algorithm (A1) to the pre-acquired first data set (DS1) and the second data set (DS2) to identify at least one pathological feature comprised by the pre-acquired first data set (DS1) and the pre-acquired second data set (DS2), wherein the at least one pathological feature is indicated in the three-dimensional virtual model (M) is taught in the Detailed Description in col 20 lines 4-40 and col 34 line 48 - col 35 line 7 (teaching on the virtual reality surgery environment reflecting a machine learning algorithm output modeling surgical stresses, strains, deformation, etc. characteristics (treated as synonymous to a pathological features) from the image and condition data)
executing (S5) a virtual vascular intervention using the generated three-dimensional virtual model (M) and at least one surgical tool; and is taught in the Detailed Description in col 42 lines 8-22, col 34 line 48 - col 35 line 7, and Fig. 9 reference character 916 (teaching on a surgeon utilizing the patient's virtual reality surgery)
generating (S6) haptic feedback (F) to a user using operational data of the virtual vascular intervention, wherein the operational data comprises data of a position of the surgical tool within the three-dimensional virtual model (M) and/or data related to the at least one surgical tool is taught in the Detailed Description in col 29 lines 24-57, col 34 line 60 - col 35 line 7, and col 44 lines 44-60 (teaching on tracking the surgeon's interactions in the virtual reality surgery and providing haptic feedback when a surgical tool is aligned - Examiner notes that the haptic feedback embodiment is taught in the real surgical setting but Roh also teaches on the same control parameters and functionality is available in the virtual simulation)
recording the executed virtual vascular intervention using the generated three-dimensional virtual model (M); and controlling a surgical robot using the recording of the executed virtual vascular intervention to execute a surgical vascular intervention is taught in the Detailed Description in col 41 line 4 - col 42 line 7 (teaching on implementing the virtual reality surgery with a robotic surgical device on the live patient)
Roh fails to teach the following limitation of claim 1. Winkler-Schwartz, however, does teach the following:
a computer-implemented method for virtual vascular interventions, comprising the steps of is taught in the § The Simulator and § The Virtual Reality Tumor Resection Task on p. 3 (teaching on a virtual reality vascular surgery planning system)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the vascular surgery of Winkler-Schwartz for the virtual reality surgery planning of Roh. Thus, the simple substitution of one known element for another producing a predictable result of planning a vascular surgery instead of a neurological or orthopedic surgery renders the claim obvious.
As per claim 2, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 1. Roh also discloses the following:
the computer-implemented method of claim 1, comprising recommending at least one implantable medical device and/or the at least one surgical tool for use in the virtual vascular intervention based on a third data set (DS3) comprising the pre-acquired first data set (DS1), the pre-acquired second data set (DS2), and/or the generated three-dimensional virtual model (M) and a fourth data set (DS4) comprising the at least one identified pathological feature is taught in the Detailed Description in col 26 line 47-60 - col 27 line 6, col 27 lines 20-33, and col 29 lines 24-57 (teaching on recommending an implant device and a surgical tool appropriate for the patient's needs in the virtual reality surgery environment)
As per claim 3, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 2. Roh also discloses the following:
the computer-implemented method of claim 2, comprising calculating a usefulness probability based on medical data of other patients (P2) having a similar medical condition for the at least one surgical tool and/or the at least one implantable medical device is taught in the Detailed Description in col 21 lines 36-45, col 26 line 47-60 - col 27 line 6, and col 43 lines 4-22 (teaching on an embodiment of the machine learning algorithm for recommending an implant device and a surgical tool appropriate for the patient's needs for an implantation plan wherein the model determines a probability value that a feature vector has a particular value trained on historically similar patient sets)
As per claim 4, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 3. Roh also discloses the following:
the computer-implemented method of claim 3, wherein an additional machine learning algorithm (A2) and/or a rule-based algorithm (A3) is applied to the third data set (DS3), the pre-acquired second data set (DS2) and/or the generated three-dimensional virtual model (M), and the fourth data set (DS4) to classify at least one class (C) representing at least one additional patient (P2) having a closest matching health condition is taught in the Detailed Description in col 21 lines 36-45, col 26 line 47-60 - col 27 line 6, and col 43 lines 4-22 (teaching on an implantation plan algorithm wherein historically similar patients are utilized to train a model to generate optimal insertion parameters or surgical steps (treated as synonymous to a fourth data set))
As per claim 5, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 4. Roh also discloses the following:
the computer-implemented method of claim 4, wherein the at least one class (C) is outputted by the additional machine learning algorithm (A2) and/or the rule-based algorithm (A3) in an order of similarity to the third data set (DS3), the pre-acquired second data set (DS2), and/or the generated three-dimensional virtual model (M) and the fourth data set (DS4) is taught in the Detailed Description in col 21 lines 36-45, col 26 line 47-60 - col 27 line 6, and col 43 lines 4-65 (teaching on an implantation plan algorithm wherein historically similar patients are utilized to train a model to generate optimal insertion parameters or surgical steps (treated as synonymous to a fourth data set) wherein the "most similar" parameters are presented and additional offset options are provided (treated as a less similar and therefore an ordered list of parameter options))
As per claim 6, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 1. Roh also discloses the following:
the computer-implemented method of claim 1, wherein a position, size and/or severity of the at least one pathological feature is annotated by the machine learning algorithm (A1) in the three-dimensional virtual model (M) is taught in the Detailed Description in col 20 lines 4-40 and col 34 line 48 - col 35 line 7 (teaching on the virtual reality surgery environment reflecting a machine learning algorithm output modeling surgical stresses, strains, deformation, etc. characteristics (treated as synonymous to a pathological features at particular locations) from the image and condition data)
As per claim 7, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 1. Roh also discloses the following:
the computer-implemented method of claim 1, wherein the pre-acquired first data set (DS1) comprises CT-data, MRI-data and/or ultrasound-data is taught in the Detailed Description in col 33 lines 13-21, col 11 lines 37-41, and Fig. 9 reference character 906 (teaching on acquiring medical image data for a patient from a MRI, CT, or ultrasound imaging device)
As per claim 8, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 1. Roh also discloses the following:
the computer-implemented method of claim 1, comprising generating a medical practitioner information request if the medical practitioner accepts or rejects the three-dimensional virtual model (M) and the at least one pathological feature is taught in the Detailed Description in col 27 lines 20-33 and col 28 lines 8-22 (teaching on the surgeon accepting or rejecting the surgical plan after stepping through the virtual reality surgery)
As per claim 9, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 8. Roh also discloses the following:
the computer-implemented method of claim 8, wherein the medical practitioner information request (R1) comprises requesting reasons from the medical practitioner when the medical practitioner rejects the three-dimensional virtual model (M) and the at least one pathological feature is taught in the Detailed Description in col 27 lines 20-33 and col 28 lines 8-22 (teaching on the surgeon accepting the surgical plan after stepping through the virtual reality surgery - Examiner notes that the broadest reasonable interpretation of a method claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04(II) on contingent limitations))
As per claim 10, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 9. Roh also discloses the following:
the computer-implemented method of claim 9, comprising correcting three-dimensional virtual model (M) and/or the identified at least one pathological feature in response to the reasons from the medical practitioner is taught in the Detailed Description in col 27 lines 20-33, col 28 lines 8-22, col 42 lines 8-22 (teaching on the surgeon accepting the surgical plan after stepping through the virtual reality surgery, noting that the plan can be modified by another user, and the updated parameters accepted later - Examiner notes that the broadest reasonable interpretation of a method claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (see MPEP 2111.04(II) on contingent limitations))
As per claim 12, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 4. Roh also discloses the following:
the computer-implemented method of claim 4, wherein the virtual vascular intervention is analyzed to determine feedback data comprising a duration of the virtual vascular intervention, a patient health condition after the virtual vascular intervention, a usage of the at least on surgical tool and/or at least one implantable medical device, and/or ID data of a medical practitioner performing the virtual vascular intervention is taught in the Detailed Description in col 33 line 57- col 34 line 60 (teaching on tracking the virtual model to determine a surgical tool speed or the implant site path and updating the model with the adjustments and predict surgical outcomes)
As per claim 13, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 12. Roh also discloses the following:
the computer-implemented method of claim 12, comprising training the additional machine learning algorithm (A2) and/or updating rules of the rule-based algorithm (A3) based upon the feedback data is taught in the Detailed Description in col 33 line 57- col 34 line 40 (teaching on updating the model with the adjustments)
As per claim 16, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 1. Roh also discloses the following:
the method of claim 1, wherein the haptic feedback provides feedback to the user as to whether the at least one surgical tool or device is used or placed correctly is taught in the Detailed Description in col 29 lines 24-57, col 34 line 60 - col 35 line 7, and col 44 lines 44-60 (teaching on tracking the surgeon's interactions in the virtual reality surgery and providing haptic feedback when a surgical tool is aligned (treated as synonymous to placed correctly))
As per claim 17, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 1. Roh also discloses the following:
the method of claim 1, wherein the at least one surgical tool comprises a catheter is taught in the Detailed Description in col 42 lines 8-22, col 34 line 48 - col 35 line 7, col 17 lines 29-42, and Fig. 9 reference character 916 (teaching on a surgeon utilizing the patient's virtual reality surgery wherein the surgery tools includes a catheter)
As per claim 18, the combination of Roh and Winkler-Schwartz discloses all of the limitations of claim 1. Roh also discloses the following:
the method of claim 1, wherein the haptic feedback (F) indicated whether the surgical tool is used or placed correctly is taught in the Detailed Description in col 29 lines 24-57, col 34 line 60 - col 35 line 7, and col 44 lines 44-60 (teaching on tracking the surgeon's interactions in the virtual reality surgery and providing haptic feedback when a surgical tool is aligned (treated as synonymous to placed correctly) - Examiner notes that the haptic feedback embodiment is taught in the real surgical setting but Roh also teaches on the same control parameters and functionality is available in the virtual simulation)
Response to Arguments
Applicant's arguments filed with respect to 35 USC § 103 have been fully considered but they are not persuasive.
First, Applicant asserts that the stresses, strains, and deformations in Roh are not synonymous to the claimed pathological features. Applicant assert that one of ordinary skill in the art would recognize that a pathological feature for cardiology and vascular treatment would be limited to disease-related abnormalities and must be structural as [the pathological feature] “is indicated in the three-dimensional model”. Applicant further states that the physical model of Roh could “simulate the base anatomy of a patient, not any pathological condition”. To the extent the Examiner understands Applicant's nuanced argument, Examiner disagrees. Relying on Applicant’s logic – one of ordinary skill in the art would recognize that a surgical planning system for a patient, undergoing surgery for a cardiovascular disease as taught by Roh, would consider characteristics that would necessarily be pathological. Roh clearly teaches on surgical planning for a fracture characteristic in the recited section of Examiner’s rejection – see col 34 line 55. A fracture is clearly a pathological feature.
Applicant asserts that Roh fails to teach on a patient specific virtual model that integrates data from a plurality of data sources. Examiner is not persuaded. First, the claimed invention states (emphasis added) “generating (S3) a three-dimensional virtual model (M) of at least a portion of the body of the patient (P) based on the pre-acquired first data set (DS1) and on the pre-acquired second data set (DS2)”> There is no requirement that the model “integrate image data with separate medical-condition datasets”. Roh teaches on generating a virtual reality surgery planning environment for the patient using the patient medical image data wherein the imaging data considers patient condition data in the Detailed Description in col and Fig. 9 reference character 908. Reference characters 902 and 906 in fig. 9 very clearly demonstrate that the virtual model is created based, in some way, from the logical flow of patient data and image data.
Applicant then asserts that Roh fails to teach on recording and controlling steps of the instant claims, stating that Roh merely teaches on robotic control during the surgery and not sole reliance of the surgery using the recording. Examiner is not persuaded. The recorded practice surgery of Roh is utilized during the patient surgery as the adjustments are applied to the surgical plan. There is no support in the claim language nor in the instant specification that the robotic surgery occurs without human intervention and solely with the recording as guidance. The claimed important advantage of “indirect robot control” is not realized in the instant claims OR originally filed disclosure. The instant disclosure only states “According to a further aspect of the invention, the virtual vascular intervention using the generated three-dimensional virtual model is recorded, wherein a recording of the executed virtual vascular intervention is used to control a surgical robot to execute a vascular intervention. The pre-recorded vascular intervention can thus advantageously be used to conduct an actual vascular intervention at a later time.” (p. 8 lines 5-9). Examiner notes this explicitly contradicts the statement in the specification that the recording is utilized to aid in surgical preparation of the actual surgical event (see at least p. 1 lines 21-32).
Applicant asserts that the Roh and Winkler-Schwartz are not reasonably combined as they “have nothing to do with each other”. Examiner is not persuaded. Examiner merely relies on Winkler-Schwartz for teaching the vascular specific surgical planning. Preparing/evaluating a surgeon’s skills does not amount to non-analogous art. it has been held that a prior art reference must either be in the field of the inventor's endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Finally, Applicant asserts that Roh fails to teach on newly added claim 18 reciting “wherein the haptic feedback (F) indicated whether the surgical tool is used or placed correctly”. Examiner disagrees. In the same paragraph previously relied upon for the independent claim, Roh teaches on providing haptic feedback when a surgical tool is aligned which is synonymous to “placed correctly”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
George et al., Simulation in Robotic Surgery, Comprehensive Healthcare Simulation: Surgery and Surgical Subspecialties 191-220 (Jan. 05, 2019) teaching on the state of the art for surgical simulation with respect to robotic surgery in the § History of Robotic Virtual Simulation on p. 191-120
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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