DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
No claim limitation has been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
Claims 1-20 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 claims, specifically independent claim 1 recites an abstract idea, specifically a mental process, for evaluating a knee joint that, under the broadest reasonable interpretation, is capable of being performed mentally and/or by a human with the aid of pen and paper. This judicial exception is not integrated into a practical application because the limitations “generating image data related to the knee joint during the patellar tendon reflex” and “determining a patellar tendon score from the sensor data and the imaging data, wherein the patellar tendon score represents a knee joint condition” amount to an observation, evaluation, or judgement that an orthopedist would perform mentally in determining a knee joint condition. In particular, under the broadest reasonable interpretation, these steps amount to an orthopedist viewing the knee joint with their eyes during the patellar tendon reflex and mentally evaluating how the knee responds to determine a patellar tendon score.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding claim 1, the limitation “tracking a knee joint during a patellar tendon reflex using at least one sensor to generate sensor data” is merely insignificant extra-solution activity, such as mere data gathering, recited at a high level of generality and/or in a well-understood, routine, and conventional way, of the information needed to carry out the claimed algorithm. The claimed “sensor” is well-understood, routine, and conventional in the art, as evidenced by Amirouche et al. (US 2004/0019382 A1; see [0034], which describes conventional sensor technology including pressure sensors, tension sensors, and angle sensors). It is a component that would routinely be used in applying the abstract idea.
This judicial exception is not integrated into a practical application because the claim does not recite any limitations that amount to an improvement in the functioning of a computer, or an improvement to other technology or technical field, apply or use the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement the judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Regarding dependent claims 2-4, 6, 11, and 16 the limitations of these dependent claim(s) merely add details to the “sensor” or “imaging data”. However EMG sensors, inertial measurement units, computer vision, load cells, fluoroscopic images, and wearable devices are all well-understood, routine, and conventional in the art (see [0002] of the specification as originally filed).
Regarding dependent claims 5, 7-10, and 12-15 the limitations of these dependent claim(s) merely add details to the algorithm which forms the abstract idea, but does not contain any further “additional elements”. Thus, the dependent claim(s) are not significantly more than the extended abstract idea.
The claims, specifically independent claim 17 recites an abstract idea, specifically a mental process, for balancing a knee joint that, under the broadest reasonable interpretation, is capable of being performed mentally and/or by a human with the aid of pen and paper. This judicial exception is not integrated into a practical application because the limitations “obtaining imaging data of a knee joint; generating a model of the knee joint”, “conducting a motion analysis of the knee joint by tracking relative motion between a tibia and a femur to generate a motion arc data”, “determining a center of rotation (COR) axis of the tibia relative to the femur based on the motion arc data”, “positioning a femoral trial to align a central axis of the femoral trial with the COR axis”, “positioning a tibial trial in a first position; assessing ligament tension of the knee joint”, and “selecting a femoral implant based on a natural joint line of the knee joint” amount to an observation, evaluation, or judgement that an orthopedist would perform manually in balancing a knee joint. All of the claimed steps represent activities that an orthopedist would perform manually with their hands and/or using pen and paper (e.g. to estimate motion arc data and a center of rotation axis) during a knee arthroplasty procedure
The claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
This judicial exception is not integrated into a practical application because the claim does not recite any limitations that amount to an improvement in the functioning of a computer, or an improvement to other technology or technical field, apply or use the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement the judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Regarding dependent claims 18 and 20, the limitations of these dependent claim(s) merely add details to the algorithm which forms the abstract idea, but does not contain any further “additional elements”. Thus, the dependent claim(s) are not significantly more than the extended abstract idea.
Regarding dependent claims 19 the limitations of these dependent claim(s) merely add details to the “imaging data”. However CT scans are well-understood, routine, and conventional in the art (see [0002] of the specification as originally filed).
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.
Claim(s) 1-3, 7, 12, and 14-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Slepian et al. (US Publication No. 2021/0275152 A1).
Regarding claim 1, Slepian et al. discloses a method to evaluate a knee joint, the method comprising the steps of:
tracking a knee joint during a patellar tendon reflex using at least one sensor (104(2), 104(3)) to generate sensor data (see Figure 15 and [0052] – “In step 1512, method 1500 captures EMG data of the muscle. In one example of step 1512, computer 110 receives data from sensor 104(2) and stores the data as EMG data 122 within memory 114. In step 1514, method 1500 captures movement data of the limb/appendage. In one example of step 1514, computer 110 receives data from sensor 104(3) and stores the data as motion data 124 within memory 114”);
generating image data related to the knee joint during the patellar tendon reflex (see [0049] – “In certain embodiments, system 100 may include one or more imaging apparatuses (e.g., a camera, etc.) for capturing visual information of the reflex response”), and
determining a patellar tendon score from the sensor data and the imaging data (see Figure 15 and [0053] – “In one example of step 1520, reflex analyzer 130 generates quantitative evaluation 136 by analyzing one or both of motion signature 132 and EMG signature 134 in view of signature database 140. In step 1522, method 1500 displays the quantitative evaluation. In one example of step 1522, reflex analyzer 130 displays quantitative evaluation 136 on display 150”),
wherein the patellar tendon score represents a knee joint condition (see [0038] – “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures. Such changes in reflex response may predate overt symptoms and signs of a given disorder. For example, previously captured signatures of patient 102 and/or standardized signatures may be stored within a signature database 140 to allow comparison by reflex analyzer 130. In another example, database 140 may store signatures of healthy reflex responses and signatures of reflex responses corresponding to different diseases and/or disorders”).
Regarding claim 2, Slepian et al. discloses at least one sensor includes an electromyography (EMG) sensor configured to capture real-time muscle activation data during the patellar tendon reflex (see [0032] – “Sensor 104(2) may be configured with electrodes and/or electrical sensors that sense EMG or other electrical signals associated with the muscle and that is simultaneously captured by the system 100, and may be synchronized with the electro-mechanical activation of the stimulating device 106”).
Regarding claim 3, Slepian et al. discloses the at least one sensor may include an inertial measurement unit (IMU) configured to measure any of knee joint angles, velocity, and acceleration during the patellar tendon reflex (see [0033] – “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102”).
Regarding claim 7, Slepian et al. discloses comparing the patellar tendon score with a pre-operative baseline data to evaluate recovery progress (see [0038] – “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures. Such changes in reflex response may predate overt symptoms and signs of a given disorder. For example, previously captured signatures of patient 102 and/or standardized signatures may be stored within a signature database 140 to allow comparison by reflex analyzer 130. In another example, database 140 may store signatures of healthy reflex responses and signatures of reflex responses corresponding to different diseases and/or disorders”).
Regarding claim 12, Slepian et al. discloses integrating the patellar tendon score with a database of patellar tendon scores for comparative analysis across patient populations (see [0038] – “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures. Such changes in reflex response may predate overt symptoms and signs of a given disorder. For example, previously captured signatures of patient 102 and/or standardized signatures may be stored within a signature database 140 to allow comparison by reflex analyzer 130. In another example, database 140 may store signatures of healthy reflex responses and signatures of reflex responses corresponding to different diseases and/or disorders”).
Regarding claim 14, Slepian et al. discloses outputting the patellar tendon score on a user interface (see [0053] – “In step 1522, method 1500 displays the quantitative evaluation. In one example of step 1522, reflex analyzer 130 displays quantitative evaluation 136 on display 150”).
Regarding claim 15, Slepian et al. discloses the user interface displays any of a reflex speed, peak extension, and range of motion of the knee joint (see Figure 4B and [0042] – “FIG. 4B shows an example motion signature 450 for the patellar reflect of FIG. 4A with three curves 454, 456, and 458 representing acceleration in X, Y, and Z directions, respectively, of motion signature 132 for patient 102. Dashed lines 402 and 452 indicate a common normalized time that provides a reference between signatures 400 and 450”).
Regarding claim 16, Slepian et al. discloses the at least one sensor includes a wearable device configured to provide real-time feedback during the patellar tendon reflex (see [0031] – “In certain embodiments, sensors 104(2) and (3) may form at least part of an array of sensors, for example configured with a fabric type material that allows the sensors to be ‘worn’ by patient 102. The array of sensors may form a network that is a smart patch, stamp, applique, fabric, mesh, band, stocking or glove-like configuration, or applied polymer apparatus, or that may be sprayed onto the patient 102. For example, sensors 104 may be configured as one or more of sock, a glove, a wraparound, and so on”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slepian et al., further in view of Jimenez et al. (WO 2026/101866 A1)
Regarding claim 4, it is noted Slepian et al. does not specifically teach generating the image data includes capturing real-time kinematic data of the knee joint using computer vision. However, Jimenez et al. teaches generating the image data includes capturing real-time kinematic data of the knee joint using computer vision (see [0047] – “In various examples, markerless navigation may be implemented via a camera or computer-vision system capable of determining and tracking the movement of patient knee joint anatomy (i.e., femur, tibia, and/or patella). Processes according to some examples allow pre-operative, intraoperative, and/or post-operative patella tracking determinations and/or simulated predictions using markerless navigation to provide the surgeon with simulation outputs of patello-femoral kinematics to compare the native (i.e., pre-operative) before and after the knee arthroplasty procedure” and [0055] – “Processes according to some examples may provide a technological feature and advantage of determining recommendations on selecting implant components, determining implant component positioning, and intraoperative testing of implant components (or trials) using markerless, computer-vision techniques in combination with AI/ML models to determine and/or predict post-surgical patellar-tracking outcomes. Processes according to some examples may also provide possible clinical benefits including, without limitation, reduced anterior knee pain and implant failure”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slepian et al. to include generating the image data includes capturing real-time kinematic data of the knee joint using computer vision, as disclosed in Jimenez et al., so as to allow pre-operative, intraoperative, and/or post-operative patella tracking determinations and/or simulated predictions using markerless navigation to provide the surgeon with simulation outputs of patello-femoral kinematics to compare the native (i.e., pre-operative) before and after the knee arthroplasty procedure (see Jimenez et al.: [0047])
Regarding claim 13, it is noted Slepian et al. does not specifically teach the method is performed pre-operatively, intra-operatively, and post-operatively to provide continuous assessment of the knee joint. However, Jimenez et al. teaches the method is performed pre-operatively, intra-operatively, and post-operatively to provide continuous assessment of the knee joint (see [0047] – “In various examples, markerless navigation may be implemented via a camera or computer-vision system capable of determining and tracking the movement of patient knee joint anatomy (i.e., femur, tibia, and/or patella). Processes according to some examples allow pre-operative, intraoperative, and/or post-operative patella tracking determinations and/or simulated predictions using markerless navigation to provide the surgeon with simulation outputs of patello-femoral kinematics to compare the native (i.e., pre-operative) before and after the knee arthroplasty procedure” and [0055] – “Processes according to some examples may provide a technological feature and advantage of determining recommendations on selecting implant components, determining implant component positioning, and intraoperative testing of implant components (or trials) using markerless, computer-vision techniques in combination with AI/ML models to determine and/or predict post-surgical patellar-tracking outcomes. Processes according to some examples may also provide possible clinical benefits including, without limitation, reduced anterior knee pain and implant failure”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slepian et al. to include the method is performed pre-operatively, intra-operatively, and post-operatively to provide continuous assessment of the knee joint, as disclosed in Jimenez et al., so as to provide recommendations on selecting implant components, determining implant component positioning and intraoperative testing of implant components and to predict post-surgical patellar tracking outcomes (see Jimenez et al.: [0055]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slepian et al., further in view of Zhang et al. (US Publication No. 2010/0106059 A1).
Regarding claim 5, it is noted Slepian et al. does not specifically teach calculating a knee joint stiffness parameter from the patellar tendon score. However, Zhang et al. teaches calculating a knee joint stiffness parameter from the patellar tendon score (see [0033] – “The neuromuscular evaluator can also be used for evaluating non-reflex properties such as the passive/active joint ROM (range of motion), active muscle strength, and joint stiffness… In the similar way, the clinician can exert force to patient's limb through the evaluator while the neuromuscular evaluator measures the resistance force and joint movement simultaneously to determine joint stiffness”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slepian et al. to include calculating a knee joint stiffness parameter from the patellar tendon score, as disclosed in Zhang et al., so as to evaluate non-reflex properties such as joint range of motion, active muscle strength, and joint stiffness (see Zhang et al.: [0033]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slepian et al., further in view of Freeman et al. (US Publication No. 2022/0192641 A1).
Regarding claim 6, it is noted Slepian et al. does not specifically teach the sensor data includes measurements from load cells to determine biomechanical forces acting on the knee joint during the patellar tendon reflex. However, Freeman et al. teaches the sensor data includes measurements from load cells to determine biomechanical forces acting on the knee joint during the patellar tendon reflex (see [0025] – “Each bumper 120, 125 has a corresponding respective embedded impact force sensors 130, 135. Force Sensors detect and respond to the presence or a change in the amount of pressure on an actuator, which can be a ball, button, diaphragm, flat membrane, plunger, or pushbutton. Three example sensor types are force sensing resistor, load cell, and resistive” and [0055] – “The data collected may be used at a minimum, to determine a difference in time between impact and delay of response, which can be correlated to one or more patient conditions, including normal responses. Data related to the smart hammer may be utilized to determine whether the strike was a valid strike or not. For instance, a low impact force may not be sufficient to obtain a response that is adequate for the determined different in time between impact and response to be valid. Other data, such as the response peak and impact force to response velocity difference, indicated at 670 may be correlated to other patient conditions. The response decay curve 652 and oscillations of the response curve 645 may be correlated to further patient conditions. These correlations may be made via conducting tests and correlating resulting data to known patient conditions”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slepian et al. to include the sensor data includes measurements from load cells to determine biomechanical forces acting on the knee joint during the patellar tendon reflex, as disclosed in Freeman et al., so as to determine whether a strike was valid or not and to correlate impact forces and response data to specific patient conditions (see Freeman et al.: [0055]).
Claim(s) 8-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slepian et al., further in view of Node-Langlois (US Publication No. 2008/0108912 A1).
Regarding claim 8, it is noted Slepian et al. does not specifically teach selecting an implant based on the patellar tendon score. However, Node-Langlois teaches selecting an implant based on the patellar tendon score (see Figure 4 and [0031] – “The surgeon can track the trajectory of the patella on the display screen by using the virtual representation of the femur, tibia and patella, and combining the kinematic data received during flexion and extension of the knee with current parameters (frontal and sagittal angulations) and the implant manufacturer's implant parameters to determine the best position for the tibial, femoral and patellar implant components 82. The method further includes identifying bony areas of the femur, tibia and patella that need to be cut to achieve optimal placement of the femoral, tibial and patellar components 84. To ensure correct patellar component placement and alignment, the size, shape and kinematics of the patella and the size and shape of the patellar implant are taken into account. Another step includes performing the incision, cutting of damaged areas of the femur, tibia and patella, and attaching the femoral, tibial and patellar components 86. The navigation system allows a surgeon to navigate the proximal tibia cut (medial resection and lateral resection), and the distal femur cut (medial resection and lateral resection). The method further includes displaying a first visual representation of the patella based on the positional information of the patella obtained during the first series of flexion and extension (this is determined based on the relative position of the femur and tibia microsensors), and displaying with the first visual representation a second virtual representation of the current position of the patellar implant relative to first visual representation. The surgeon then confirms alignment of the first and second virtual representations that are superimposed on the displayed image 88. Another step includes tracking the microsensors during a second series of passive flexion and extension of the knee 90 to determine the position of the patellar implant relative to the tibia and femur implants. The method further includes displaying the first trajectory of the patella from the first series of flexion and extension and the trajectory of the patellar implant from the second series of flexion and extension. The surgeon can then confirm the trajectory of the original patella with the trajectory of the patellar implant that are superimposed on the displayed image 92. The x, y and z coordinates of the patella should be the same, and movement of the patella and the patellar implant within the patellofemoral joint should be the same as well”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slepian et al. to include selecting an implant based on the patellar tendon score, as disclosed in Node-Langlois, so as to allow the surgeon to implant knee prostheses by taking into account the size, shape and movement of the femur, tibia, and patella as well as femorotibial and patellofemoral kinematics (see Node-Langlois: [0022]).
Regarding claim 9, Node-Langlois teaches the implant is any of a femoral (24) or tibial implant (26) for the knee joint (see Figure 5 and [0032] – “In this figure, the surgeon has replaced the ends of the tibia and femur, and the underside of the patella with femoral, tibial, and patellar components. A femoral component 24 is attached to the reshaped end of the femur bone 18. A tibial component 26 is secured to the reshaped end of the tibia bone 20”).
Regarding claim 10, Node-Langlois teaches positioning the implant based on patellar tendon score (see Figure 4 and [0031] – “The surgeon can track the trajectory of the patella on the display screen by using the virtual representation of the femur, tibia and patella, and combining the kinematic data received during flexion and extension of the knee with current parameters (frontal and sagittal angulations) and the implant manufacturer's implant parameters to determine the best position for the tibial, femoral and patellar implant components 82. The method further includes identifying bony areas of the femur, tibia and patella that need to be cut to achieve optimal placement of the femoral, tibial and patellar components 84. To ensure correct patellar component placement and alignment, the size, shape and kinematics of the patella and the size and shape of the patellar implant are taken into account. Another step includes performing the incision, cutting of damaged areas of the femur, tibia and patella, and attaching the femoral, tibial and patellar components 86. The navigation system allows a surgeon to navigate the proximal tibia cut (medial resection and lateral resection), and the distal femur cut (medial resection and lateral resection). The method further includes displaying a first visual representation of the patella based on the positional information of the patella obtained during the first series of flexion and extension (this is determined based on the relative position of the femur and tibia microsensors), and displaying with the first visual representation a second virtual representation of the current position of the patellar implant relative to first visual representation. The surgeon then confirms alignment of the first and second virtual representations that are superimposed on the displayed image 88. Another step includes tracking the microsensors during a second series of passive flexion and extension of the knee 90 to determine the position of the patellar implant relative to the tibia and femur implants. The method further includes displaying the first trajectory of the patella from the first series of flexion and extension and the trajectory of the patellar implant from the second series of flexion and extension. The surgeon can then confirm the trajectory of the original patella with the trajectory of the patellar implant that are superimposed on the displayed image 92. The x, y and z coordinates of the patella should be the same, and movement of the patella and the patellar implant within the patellofemoral joint should be the same as well”).
Regarding claim 11, it is noted Slepian et al. does not specifically teach the image data includes fluoroscopic images of patellar alignment during the patellar tendon reflex. However, Node-Langlois teaches the image data includes fluoroscopic images of patellar alignment during the patellar tendon reflex (see [0027] – “The acquired imaging data from the imaging system 38 may include CT imaging data, MR imaging data, PET imaging data, ultrasound imaging data, X-ray imaging data, or any other suitable imaging data, as well as any combinations thereof. In addition to the acquired imaging data from various modalities, real-time imaging data from various real-time imaging modalities may also be available”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Slepian et al. to include the image data includes fluoroscopic images of patellar alignment during the patellar tendon reflex, as disclosed in Node-Langlois, because fluoroscopic imaging is one of a number of well-known imaging techniques commonly used during knee replacement surgery.
Claim(s) 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dressler et al. (US Publication No. 2024/0008925 A1), further in view of Liu et al. (US Publication No. 2022/0387187 A1).
Regarding claim 17, Dressler et al. discloses a method for balancing a knee joint, the method comprising:
obtaining imaging data of a knee joint (see [0100] – “Returning to FIG. 3, the imaging device 304 may be embodied as any type of device or collection of devices capable of pre-operatively and/or intra-operatively generating medical images of the boney anatomy of the patient. In the illustrative embodiments, the imaging device 304 is embodied as a CT imaging machine capable of generating CT medical images. However, in other embodies, the imaging device 304 may be embodied an imaging device capable of generating X-Ray medical images and/or three-dimensional models. It is contemplated that the imaging device 304 need not be used in all embodiments”);
generating a model of the knee joint (see [0112] – “The statistical shape-function model illustratively used in block 404 is embodied as a mathematical model that uses the anatomical parameters to match or correlate the shape of the patient's knee bones, as defined by the anatomical parameters, to a library of “healthy” knee bones. The library of knee bones may be established based on a pool of individuals having healthy knee joints. Illustratively, target kinematics determined by the statistical shape-function model include the varus-valgus angle, the internal-external rotation, the medial-lateral translation, the anterior-posterior translation, and the superior-inferior translation of the patient's bones through a range of flexion. In other embodiments, the target kinematics may include additional or other kinematic parameters of the patient's knee joint. By matching or correlating anatomic characteristics or parameters of the patient's knee bones to a library of “healthy” joints and associated kinematics, the “healthy” or non-damaged/non-deteriorated kinematics of the patient's knee joint is determined, providing patient-specific target kinematics to be achieved via the knee prosthesis implantation”);
conducting a motion analysis of the knee joint by tracking relative motion between a tibia and a femur to generate a motion arc data (see [0111] – “In other embodiments, the patient-specific target kinematics may be measured directly from the patient (e.g., intra-operatively) by moving the patient's leg through a range of motion while the camera 618 observes the patient arrays 622 attached to the patient's femur 110 and tibia 112, allowing the surgical device 602 to monitor the movements of the patient's leg in three-dimensional space such that the analysis device 302 can determine kinematics of the patient's knee joint based on such movements. As a result of block 404, the analysis device 302 now possesses knowledge of the position of the femur 110 and the tibia 112 relative to one another at a variety of flexion angles throughout the range of motion (which positions can also be translated to the global reference frame of the camera 618, as desired)”);
positioning a femoral trial (see [0114] – “As shown in FIG. 6, the analysis device 302 then proceeds to determine the predicted performance of a knee prosthesis across a number of implant alignment options in block 406. The term “implant alignment” as used herein refers to the position and orientation of the femoral prosthesis on the distal end of the patient's femur and the position and orientation of the tibial tray on the proximal end of the patient's tibia. In some embodiments, implant alignment may also refer to the position and orientation of a patella prosthesis. As noted above, the position and orientation of each prosthetic component can be varied in up to six degrees of freedom (medial-lateral, anterior-posterior, superior-inferior, flexion-extension, adduction-abduction, and internal-external). The analysis device 302 predicts the performance of the knee prosthesis over a range of flexion when the knee prosthesis is implanted at each implant alignment in a set of implant alignment options”);
positioning a tibial trial in a first position (see [0114] – “As shown in FIG. 6, the analysis device 302 then proceeds to determine the predicted performance of a knee prosthesis across a number of implant alignment options in block 406. The term “implant alignment” as used herein refers to the position and orientation of the femoral prosthesis on the distal end of the patient's femur and the position and orientation of the tibial tray on the proximal end of the patient's tibia. In some embodiments, implant alignment may also refer to the position and orientation of a patella prosthesis. As noted above, the position and orientation of each prosthetic component can be varied in up to six degrees of freedom (medial-lateral, anterior-posterior, superior-inferior, flexion-extension, adduction-abduction, and internal-external). The analysis device 302 predicts the performance of the knee prosthesis over a range of flexion when the knee prosthesis is implanted at each implant alignment in a set of implant alignment options”);
assessing ligament tension of the knee joint (see [0113] – “In the illustrative embodiment, the target kinematics includes a patient-specific target ligament elongation across a range of flexion for one or more ligaments of the patient's knee joint. For example, in the illustrative embodiment, the analysis device 302 may determine a target ligament elongation for the medial collateral ligament (MCL), the lateral collateral ligament (LCL), the posterior cruciate ligament (PCL), the anterolateral ligament, and the posterior capsule of the patient's relevant knee joint. The femoral and tibial attachment sites of each of these ligaments may be identified in block 402 (e.g., using imaging or intra-operative measurements) or may be determined from other identified anatomical parameters in block 404 (e.g., using a statistical shape model). The analysis device 302 then applies the target kinematics, using transformation matrices, to determine the relative position of patient's femur and tibia in a number of positions throughout a range of flexion and calculates a distance between the femoral and tibial attachment sites for each ligament in each of the number of positions. These calculations provide an indication of the target length of each ligament across the range of flexion”; see also [0128]), and
selecting a femoral implant based on a natural joint line of the knee joint (see [0114] – “As shown in FIG. 6, the analysis device 302 then proceeds to determine the predicted performance of a knee prosthesis across a number of implant alignment options in block 406. The term “implant alignment” as used herein refers to the position and orientation of the femoral prosthesis on the distal end of the patient's femur and the position and orientation of the tibial tray on the proximal end of the patient's tibia. In some embodiments, implant alignment may also refer to the position and orientation of a patella prosthesis. As noted above, the position and orientation of each prosthetic component can be varied in up to six degrees of freedom (medial-lateral, anterior-posterior, superior-inferior, flexion-extension, adduction-abduction, and internal-external). The analysis device 302 predicts the performance of the knee prosthesis over a range of flexion when the knee prosthesis is implanted at each implant alignment in a set of implant alignment options”).
It is noted Dressler et al. does not specifically teach determining a center of rotation (COR) axis of the tibia relative to the femur based on the motion arc data. However, Liu et al. teaches determining a center of rotation (COR) axis of the tibia relative to the femur based on the motion arc data and positioning a femoral trial to align a central axis of the femoral trial with the COR axis (see [0023] – “In one embodiment, a method of determining a femoral implant placement on a femur in a knee joint of a patient includes: collecting data at a plurality of positions of a tibia of the patient relative to the femur, the plurality of positions collectively representative of at least part of a range of motion of the knee and including at least part of a distance between extension of the knee joint and flexion of the knee joint, the data at each position including: a dynamic flexion axis of the femur, the dynamic flexion axis being a center of rotation of a length of the tibia about the femur; calculating a reference dynamic flexion axis based on the dynamic flexion axis of the femur in at least two positions of the plurality of positions; and planning the femoral implant placement on the femur based on the reference dynamic flexion axis”; see also [0092]-[0094]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Dressler et al. to include determining a center of rotation (COR) axis of the tibia relative to the femur based on the motion arc data, as disclosed in Liu et al., so as to optimize a planned implanted position of the femoral implant and a size of the femoral implant (see Liu et al.: [0094]).
Regarding claim 18, Dressler et al. teaches adjusting the tibial trial in six degrees of freedom such that the ligament tension is balanced (see [0086] – “When preparing for and performing a knee replacement surgery, an orthopaedic surgeon determines the type, size, and implant alignment of each prosthetic component to be implanted in the patient's knee joint. The surgeon may adjust or select the alignment of each prosthetic component along six degrees of freedom (medial-lateral, anterior-posterior, superior-inferior, flexion-extension, adduction-abduction, and internal-external) to achieve desired knee joint mechanics”).
Regarding claim 19, Dressler et al. teaches the imaging data includes CT scans segmented to create a three-dimensional model of the knee joint (see [0100] – “Returning to FIG. 3, the imaging device 304 may be embodied as any type of device or collection of devices capable of pre-operatively and/or intra-operatively generating medical images of the boney anatomy of the patient. In the illustrative embodiments, the imaging device 304 is embodied as a CT imaging machine capable of generating CT medical images. However, in other embodies, the imaging device 304 may be embodied an imaging device capable of generating X-Ray medical images and/or three-dimensional models. It is contemplated that the imaging device 304 need not be used in all embodiments”).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dressler et al. and Liu et al., further in view of Levine (US Publication No. 2019/0192231 A1).
Regarding claim 20, it is noted neither Dressler et al. nor Liu et al. specifically teach the step of conducting a motion analysis includes tracking points on the tibia relative to the femur during a pendulum knee drop. However, Levine teaches the step of conducting a motion analysis includes tracking points on the tibia relative to the femur during a pendulum knee drop (see [0042] – “In the drop test example, the articulation portion 170 may be configured to drop the knee to allow a tibia of the first leg of the patient to drop and the performance of the knee to be assessed. In the drop test, the upper first leg portion 126 does not necessarily need to pivot at the hip pivot 129 and may remain substantially aligned with the body support 110, or rotated only a small angle β, such as between 10 and 20 degrees. In the drop test, the lower first leg portion 128 may be allowed to pivot (e.g., rotate, drop) to an angle α of about 90 degrees or more. This allows the tibia of the patient to drop and then the surgeon is able to assess flexion and soft tissue performance. Like the lift test, the assessment may include measuring the amount of force on a portion of the first leg 1 such as the knee joint. Measuring force may include determining a change in force in a mid-operative or post-operative state compared to a pre-operative state. In some examples, the articulation angle α may range between 45 and 135 degrees. Other angles may be specified by the surgeon preoperatively or intraoperatively”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Dressler et al. and Liu et al. to include the step of conducting a motion analysis includes tracking points on the tibia relative to the femur during a pendulum knee drop, as disclosed in Levine, so as to assess flexion and soft tissue performance (see Levine: [0042]).
Conclusion
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/DEVIN B HENSON/Primary Examiner, Art Unit 3791
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