DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims in application 19/099,949 filed on 1/30/2025.
The instant application claims benefit to provisional application #63/419,471 with a priority date of 10/26/2022.
The Pre-Grant publication US#20260020910 is published on 1/22/2026.
Claims 1-24 are pending.
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.
Claims 1-24 are 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. The claimed invention is a computer implemented method (1-12) and to a computer-assisted surgical system (claim 13-24). Thus fall within one of the four statutory categories (Step 1: YES).
Claims 1 and 13 are directed to a computer-implemented method and a computer-assisted surgical system for planning a knee arthroplasty procedure by receiving anatomical information of a knee joint, patella information of a femur of a patient and femoral information of a femur of the patient. Then generating or converting three-dimensional (3D) models of at least one portion of the knee joint based on the anatomical information. Characterizing a knee morphology of the knee joint is followed. A patella risk classification is obtained based on the knee morphology. This patella risk classification indicating a risk of a patella complication of the knee arthroplasty procedure.
These are surgery related information received as input, analyzed to characterized from a knee joint morphology after generating 3D model for an outcome obtained from classified patella risk. The actions of receiving, converting, judging for characterizing, extracting, outputting phoneme errors falls within the “Certain Method of Organizing Human Activity” groupings of abstract ideas subject to the 2019 Revised Patent Subject Matter Eligibility Guidance. The operations also include feature identified as “Mental Processes” while citing some specific known specific instances of managing interactions between tutor and students in surgical assisted computer applications. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, and/or a certain method of managing interactions between people but for the recitation of generic computer components, then it falls within the “Mental Processes” and “Certain Method of Organizing Human Activity” groupings of abstract ideas, respectively. The analysis of skills and operation by trainees using verified algorithm such as for classification is use of existing mathematical relationships, formulas. Hence are mathematical concepts. Accordingly, the claims recite one or more groupings of abstract idea(s). (Step 2A: Prong 1 YES)..
The independent claims do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “knee arthroplasty procedure”, “a processor’, “a memory’, " patella information of a femur of a patient and femoral information of a femur of the patient ", " three-dimensional (3D) models”, “a patella morphology of the patella and a femur morphology of the femur based on the 3D models”, “risk of a patella complication of the knee arthroplasty procedure “ do not appear to include additional elements that are sufficient to be significantly more than the judicial exception because the limitations are merely use of generic computer functions and computer parts to apply or use of judicial exception for a stipulated outcome. It works to perform these and some other related functions that are common and routine functions. When a following instructions from software programs uses these common hardware components to perform surgical tasks. Hence not indicative of integration of a practical application (Step 2A: Prong 2 No).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art.
Fig.4 Para 0002 of the instant specification is generally referring to computer assisted surgical system to include generic computer functions for a hardware/ software in a standard surgical workflow environment
As an example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a specific field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
Claims 2-12, 14-24 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, taking the claimed elements individually yields no difference from taking them in combination because each element simply performs its respective functions and model application and calculations as discussed above. In other words, these claims merely apply an abstract idea to a programmable processor or computer and do not improve the performance of the process or computer itself or provide a technical solution to a problem in a technical field. They do not effect a transformation of a particular article to a different state or thing, the underlying computing elements remain the same. Instead, the additional features merely amount to an instruction to apply the abstract idea using generic, functional, and conventional components well-known in the art. The determination of patella landmarks for knee morphology, parameters like height points, depth/thickness points, inferior articular point, articular perimeter points, posterior ridge points, centroid of the patella, or patella neutral point computer generated results from evaluation of inputs. A femoral implant and morphology comprising an implant trochlear groove morphology based on an associated sulcus model are known specified objects. A surgical plan determining of an optimal femoral component for the patella risk classification based on various risk factors is configured to address at least one complication associated with the patella risk classification and/or surgical preparation factors are pre- or post- solution activities not contributing towards improvement of the device or procedure. Mere instructions to apply an exception using the generic computer components cannot provide an inventive concept. Therefore, for these reasons, 1-24 are not patent-eligible under 35 USC 101.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-24 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by US 20190147128 A1 to O'Connor.
Claim 1. O'Connor teaches a computer-implemented method for planning a knee arthroplasty procedure (Fig.21a,b surgical computer; Para 0244,0275 knee arthroplasty reference ), comprising, via a processor of a computing device:
receiving anatomical information of a knee joint, the anatomical information comprising patella information of a femur of a patient and femoral information of a femur of the patient (TABLE-US-00001; Para 0097 Patella information; femur component),
generating three-dimensional (3D) models of at least one portion of the knee joint based on the anatomical information, the 3D models comprising a patella model and a femoral model (Para 0274 2D- 3D models registration);
characterizing a knee morphology of the knee joint, the knee morphology comprising a patella morphology of the patella and a femur morphology of the femur based on the 3D models (Para 0224, 0097 knee arthroplasty and anatomical femur and patella structures or knee morphology for surgeons in Table for a 3D models structure) ; and
determining a patella risk classification based on the knee morphology, the patella risk classification indicating a risk of a patella complication of the knee arthroplasty procedure (Para 0105, 0166 Processor determines the positive risk adjustments and the negative risk adjustment for patient input data like patella complications utilizes simulation results to account and classify for patient specific risk from machine learning method).
Claim 2. O’Connor teaches the computer-implemented method of claim 1, further comprising determining patella landmarks based on the patella model, wherein the knee morphology is determined based on the patella landmarks (para 0307 Landmarks that are accessible through the skin or even during surgery, such as by measuring the distances from the medial or lateral condyle articulating Patella).
Claim 3. O’Connor teaches the computer-implemented method of claim 2, the patella landmarks comprising one or more of width points, height points, depth/thickness points, inferior articular point, articular perimeter points, posterior ridge points, centroid of the patella, or patella neutral point (Para 0118 reference points evaluated such as an anatomical or mechanical axis for a distance measurement from a bone or bone landmark ; Para 0097, 0237 for depth, thickness, displacement points relative to femoral axis Patella landmark ).
Claim 4. O’Connor teaches the computer-implemented method of claim 1, the femur of the knee joint comprising a femoral implant, wherein the femoral information comprises determined information for the femoral implant (Para 0096 0097 shape of the selected implants according to the surgery parameters from the database determining knee joint & femoral implants information).
Claim 5. O’Connor teaches the computer-implemented method of claim 4, the femur morphology comprising an implant trochlear groove morphology (Para 0118 trochlear groove depth morphology).
Claim 6. O’Connor teaches the computer-implemented method of claim 5, the implant trochlear groove morphology determined based on a sulcus model associated with the femoral component (The femoral sulcus angle is formed by two lines joining the highest points on the medial and the lateral condyles which meet at the lowest point on the intra-condylar groove of the femur ; Para 0118 estimating current patient outcome then comprises applying multiple machine learning model parameters to anatomical measurements of the current patient's knee; anatomical measurements may include one or more of the long leg varus/valgus alignment, the trans epicondylar to posterior condylar axes angle, distal and posterior offset of each ligament (native and implanted), the posterior condylar offset ratio, the femoral joint line angle etc.; object viewed as femoral condyles and model associated with the femoral component).
Claim 7. O’Connor teaches the computer-implemented method of claim 1, the patella risk classification comprising at least one of a risk of maltracking of the patella, a risk of post-operative knee pain, or patellofemoral instability (Para 0116 Processor determines the positive risk adjustments and the negative risk adjustments by selecting the patient input data that has been previously identified as having a positive or negative relationship with the perceived satisfaction value, in the context of other conditionally dependent inputs; If showing patellofemoral instability indicate how gaps are affected by the force to better understand the degree of ligament laxity or hypermobility e.g. one of a risk of maltracking of the patella could be classified).
Claim 8. O’Connor teaches the computer-implemented method of claim 1, the patella risk classification based on one or more risk factors comprising at least one of a Wiberg index, patella size, pre-operative patellar shift, or femoral sulcus groove depth (Table US 00001 Para 0105 Information about femoral condyles and/or sulcus groove dept can be overlaid with image that the surgeon finds for software accurately locating the two-dimensional image relative to a three-dimensional model of the surgical scene if Femoral ML Shift Medial/Lateral shift of the femoral component axis relative to the tibial Flexion with Patella risk classification from a planning tool utilizes simulation results).
Claim 9. O’Connor teaches the computer-implemented method of claim 1, further comprising determining a surgical plan based on the patella risk classification, the surgical plan configured to address at least one complication associated with the patella risk classification (Para 0203 Risk factor analysis; higher risk for dislocation patients, surgeons with help of processing circuitry recommend taking lateral radiographs in several positions e.g., standing, sitting, flexed-standing in order to understand how the spine and pelvis complication interactions are associated during a variety of activities because of patella risk classification).
Claim 10. O’Connor teaches the computer-implemented method of claim 9, the surgical plan comprising a determination of an optimal femoral component for the patella risk classification (Para 0003, 0196 potential for a bias in favor of mechanically ‘safer’ or determines the optimal allocation of femoral component as that matters for a femoral component of patella risk classification).
Claim 11. O’Connor teaches the computer-implemented method of claim 9, the surgical plan comprising a determination of an optimal femoral component placement for the patella risk classification (Para 0122 processor may determine different or further component placement input parameters based on the simulated kinematic parameters other than tibial slope or rotation in order to optimize the estimated current patient outcome).
Claim 12. O’Connor teaches the computer-implemented method of claim 9, the surgical plan comprising a determination of an optimal femoral preparation factor for the patella risk classification, the femoral preparation factor comprising a resection depth (Para 0182 the deviation from the cut angle or any other inter-operative adjustments and resections can be entered into the system and after the knee operation processor determining optimal femoral preparation for a revised predicted satisfaction value based on the intra-operative data and the post-operative data for example in a arthroplasty resection depth values ).
Claim 13. O’Connor teaches a computer-assisted surgical system, comprising: at least one computing device, comprising: a display device; processing circuitry; and a memory coupled to the processing circuitry (Fig.1, Fig.8 computing device), the memory comprising instructions that, when executed by the processing circuitry, cause the processing circuitry to:
receive anatomical information of a knee joint, the anatomical information comprising patella information of a femur of a patient and femoral information of a femur of the patient, generate three-dimensional (3D) models of at least one portion of the knee joint based on the anatomical information, the 3D models comprising a patella model and a femoral model, characterize a knee morphology of the knee joint, the knee morphology comprising a patella morphology of the patella and a femur morphology of the femur based on the 3D models, and determine a patella risk classification based on the knee morphology, the patella risk classification indicating a risk of a patella complication of the knee arthroplasty procedure (Fig.21a,b surgical computer; Para 0244,0275 knee arthroplasty reference ;Para 0224, 0097 knee arthroplasty and anatomical femur and patella structures or knee morphology for surgeons in Table for a 3D models structure; (Para 0105, 0166 Processor determines the positive risk adjustments and the negative risk adjustment for patient input data like patella complications utilizes simulation results to account and classify for patient specific risk from machine learning method).
Claim 14. O’Connor teaches the system of claim 13, the instructions, when executed by the processing circuitry, to cause the processing circuitry to determine patella landmarks based on the patella model, wherein the knee morphology is determined based on the patella landmarks (para 0307 Landmarks that are accessible through the skin or even during surgery, such as by measuring the distances from the medial or lateral condyle articulating Patella).
Claim 15. O’Connor teaches the system of claim 14, the patella landmarks comprising one or more of width points, height points, depth/thickness points, inferior articular point, articular perimeter points, posterior ridge points, centroid of the patella, or patella neutral point (Para 0118 reference points evaluated such as an anatomical or mechanical axis for a distance measurement from a bone or bone landmark ; Para 0097, 0237 for depth, thickness, displacement points relative to femoral axis Patella landmark ).
Claim 16. O’Connor teaches the system of claim 13, the femur of the knee joint comprising a femoral implant, wherein the femoral information comprises determined information for the femoral implant (Para 0290 determined particular or femoral implants insertion information).
Claim 17. O’Connor teaches the system of claim 16, the femur morphology comprising an implant trochlear groove morphology (Para 0118 trochlear groove depth morphology).
Claim 18. O’Connor teaches the system of claim 17, the implant trochlear groove morphology determined based on a sulcus model associated with the femoral component (The femoral sulcus angle is formed by two lines joining the highest points on the medial and the lateral condyles which meet at the lowest point on the intra-condylar groove of the femur ; Para 0118 estimating current patient outcome then comprises applying multiple machine learning model parameters to anatomical measurements of the current patient's knee; anatomical measurements may include one or more of the long leg varus/valgus alignment, the trans epicondylar to posterior condylar axes angle, distal and posterior offset of each ligament (native and implanted), the posterior condylar offset ratio, the femoral joint line angle etc.; object viewed as femoral condyles and model associated with the femoral component).
Claim 19. O’Connor teaches the system of claim 13, the patella risk classification comprising at least one of a risk of maltracking of the patella, a risk of post-operative knee pain, or patellofemoral instability (Para 0237, 0289 determination of an estimate of reported knee instability or to capture images if showing patellofemoral instability how gaps are affected by the force to better understand the degree of ligament laxity, pre-op laxity or hypermobility e.g. one of a risk of maltracking of the patella).
Claim 20. O’Connor teaches the system of claim 13, the patella risk classification based on one or more risk factors comprising at least one of a Wiberg index, patella size, pre-operative patellar shift, or femoral sulcus groove depth (Table US 00001 Para 0105 Information about femoral condyles and/or sulcus groove dept can be overlaid with image that the surgeon finds for software accurately locating the two-dimensional image relative to a three-dimensional model of the surgical scene if Femoral MLShift Medial/Lateral shift of the femoral component axis relative to the tibial Flexion with Patella risk classification from a planning tool utilizes simulation results).
Claim 21. O’Connor teaches the system of claim 13, the instructions, when executed by the processing circuitry, to cause the processing circuitry to determine a surgical plan based on the patella risk classification, the surgical plan configured to address at least one complication associated with the patella risk classification (Para 0203 Rsik factor analysis; higher risk for dislocation patients, surgeons with help of processing circuitry recommend taking lateral radiographs in several positions e.g., standing, sitting, flexed-standing in order to understand how the spine and pelvis complication interactions are associated during a variety of activities because of patella risk classification).
Claim 22. O’Connor teaches the system of claim 21, the surgical plan comprising a determination of an optimal femoral component for the patella risk classification (Para 0003, 0196 potential for a bias in favour of mechanically ‘safer’ or determines the optimal allocation of femoral component as that matters for a femoral component of patella risk classification).
Claim 23. O’Connor teaches the system of claim 21, the surgical plan comprising a determination of an optimal femoral component placement for the patella risk classification Para 0122 processor may determine different or further component placement input parameters based on the simulated kinematic parameters other than tibial slope or rotation in order to optimize the estimated current patient outcome).
Claim 24. O’Connor teaches the system of claim 21, the surgical plan comprising a determination of an optimal femoral preparation factor for the patella risk classification, the femoral preparation factor comprising a resection depth ((Para 0182 the deviation from the cut angle or any other inter-operative adjustments and resections can be entered into the system and after the knee operation processor determining optimal femoral preparation for a revised predicted satisfaction value based on the intra-operative data and the post-operative data for example in a arthroplasty resection depth values).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20230372015 A1 MARINESCU TANASOCA; Ruxandra Cristiana et al.
AUTOMATIC PATELLAR TRACKING IN TOTAL KNEE ARTHROPLASTY
US 5682886 A Delp; Scott L. et al.
Computer-assisted surgical system
US 12440280 B2 Janna; Sied W. et al.
Methods and systems for implanting a joint implant
US 20050113846 A1
Surgical navigation systems and processes for unicompartmental knee arthroplasty
US 11564744 B2 Hampp; Emily et al. Carson, Christopher Patrick
Systems and methods for surgical planning using soft tissue attachment points
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.Z/Examiner, Art Unit 3715 August 3, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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