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 .
Formal Matters
Applicant's response, filed 26 February 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, 12-14, and 16-17 are currently pending and have been examined.
Claims 1, 8-10, and 14 have been amended.
Claims 11 and 15 have been canceled.
Claims 16-17 have been added.
Claims 1-10, 12-14, and 16-17 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claim 14 is drawn to a method, which is a statutory category of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 14 recites a computer-implemented method for providing [an algorithm] (A1) for identifying at least one pathological feature related to a medical condition of the patient (P) in part performing the steps of providing (S1′) a first training data set (TD1) comprising a pre-acquired first data set (DS1) of medical image data of a patient (P); providing (S2′) a second training data set (TD2) comprising an identified at least one pathological feature comprised by the pre-acquired first data set (DS1) of the medical image data of the patient (P) and 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); and training (S3′) the [algorithm] (A1) by an optimization algorithm which calculates an extreme value of a loss function for identifying the at least one pathological feature; and using the trained machine learning algorithm (A1) to identify the at least one pathological feature and indicate the at least one pathological feature in the three- dimensional virtual model (M) for executing the virtual vascular intervention.
These steps of optimizing an algorithm according to a loss function amount to a mathematical concept which includes mathematical relationships, mathematical formulas or equations, and mathematical calculations. The mathematical concept need not be expressed in mathematical symbols but not merely limitations that are based on or involve a mathematical concept (MPEP § 2106.04(a)(2)(I)(A) citing the abstract idea grouping for mathematical concepts for mathematical relationships).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 14 recites a machine learning algorithm. The specification only provides that the machine learning are “based on using statistical techniques to train a data” and “create mathematical models” (see the instant specification on p. 7). The machine learning is only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 14 recites a machine learning algorithm. This element is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claim 14 is therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 14 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Roh et al. (US Patent 11571266)[hereinafter Roh].
Claim 14 is rejected because Roh teaches on all elements of the claim:
a computer-implemented method for providing a trained machine learning algorithm (A1) for identifying at least one pathological feature related to a medical condition of the patient (P), comprising the steps of 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)
providing (S1′) a first training data set (TD1) comprising 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 second training data set (TD2) comprising an identified at least one pathological feature comprised by the pre-acquired first data set (DS1) of the medical image data of the patient (P) and 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); and 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)
training (S3′) the machine learning algorithm (A1) by an optimization algorithm which calculates an extreme value of a loss function for identifying the at least one pathological feature; and is taught in the Detailed Description in col 20 line 59 - col 21 line 35 (teaching on training a CNN (which necessitates the use of a loss function for model optimization) for modeling surgical stresses, strains, deformation, etc. characteristics (treated as synonymous to a pathological features) from historical patient data)
using the trained machine learning algorithm (A1) to identify the at least one pathological feature and indicate the at least one pathological feature in the three-dimensional virtual model (M) for executing the virtual vascular intervention 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)
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 16-17 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)
Response to Arguments
Applicant's arguments filed 26 February 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. However, in view of the amendments, claim 1 reciting “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” amounts to a practical application under Step 2A Prong 2 because the judicial exception is applied with, or used by, a particular machine (see MPEP § 2106.05(b) Particular Machine). Under the factors for consideration, the machine (I) is particular of the elements and can be specifically identified (here a surgical robot), (II) integrates the recited judicial exception into a practical application (here the virtual model is implemented utilizing a surgical robot), and (III) poses meaningful limits on the claim (here the surgery performed by the robot is a vascular surgery generated by the recorded virtual environment).
Applicant’s assertion that claim 14 fails to recite a method of organizing human activity is not persuasive. Claim 14 has been rejected under the mathematical concept judicial exception. Examiner sustains the subject matter eligibility rejection of independent claim 14.
Applicant's arguments filed 26 February 2026 with respect to 35 USC § 102 have been fully considered but they are not persuasive. Applicant asserts that Roh fails to teach on indicating a pathological feature in the three-dimensional virtual model citing a portion of Roh Examiner did not utilize to teach on the specific added limitation. Examiner has rejected the limitation under Roh in the Detailed Description in col 20 lines 4-40 and col 34 line 48 - col 35 line 7teaching 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.
Applicant's arguments filed 26 February 2026 with respect to 35 USC § 103 have been fully considered but they are not persuasive. First Applicant asserts the same position as presented in the 35 USC § 102 rejection but does not address the rejection further. Examiner sustains the rejection and notes that Roh teaches on the additional elements added to the independent claim as per the rejection above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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