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 .
Office Action Summary
Claim(s) 1, 4, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reid et al (US 2018/0168731 A1) in view of Boddington et al (US 2021/0177522 A1).
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reid et al (US 2018/0168731 A1) in view of Boddington et al (US 2021/0177522 A1), further in view of Shannon et al (US 2023/0360768 A1).
Claim(s) 5-9 and 11 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1, 4, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reid et al (US 2018/0168731 A1) in view of Boddington et al (US 2021/0177522 A1).
Regarding claim(s) 1, Reid teaches an analysis method for optimizing number and position of screws used in a long bone fracture fixation surgery, comprising:
performing a computed tomography (CT) scan on a surgical patient to obtain at least one medical image and establishing a long bone 3D model based on the medical image (Figure 1; Paragraph [0006]: “[…] an input module for receiving the individual patient data such as imaging data of fracture injuries. The imaging data may include CT scan, MRI, or X-ray imaging”; Paragraph [0007]: “[…] finite element modeling of a series of possible fixation constructs for the fracture injury. This can use modeling directly from segmenting CT images […]”; and Paragraph [0037]: “an image-based 3D model is determined based on patient imaging data such as CT scans. MRI, and x-rays etc. of specific bone fractures”);
performing an X-ray scan on the surgical patient to obtain at least one X-ray image (Figure1; Figure 2; and Paragraph [0006]: “[…] an input module for receiving the individual patient data such as imaging data of fracture injuries. The imaging data may include CT scan, MRI, or X-ray imaging. The individual patient data further includes bone density, bone shape, soft tissue anatomy, patient age, sex, weight, smoking status, and other data relevant for fracture fixation”);
selecting a bone plate 3D model from a model database (Figure 11; and Paragraph [0050]: “all possible fixation designs have been previously computed with results stored in a database that can be quickly retrieved by the system. In this example shown in FIG. 11, two types of plates may be selected, a 7-hole plate or a 9-hole plate […]”);
importing the bone plate 3D model into the long bone 3D model, and setting an initial fixation position of the bone plate 3D model (Figure 3; Figure 4; Figure 11; Paragraph [0014]: “[…] a database for storing the design data of fixation designs, finite element models and surrogate models of designs”; Paragraph [0015]: “The fixation implant may be a plate fixated on a fractured bone with screws […]; and Paragraph [0055]: “FIG. 3 shows an example listing of combinations of plate length, fracture gap, number of screws, and associated screw positions for creating fracture fixation designs used for subsequent FEA simulations and surrogate model fitting […] For automatic creation of all fixation designs, modularized finite element models for bone, plate and screw are created […] assembled using […]”);
instructing a first artificial intelligence to comprehensively analyze the long bone 3D model, the initial fixation position of the bone plate 3D model, the long bone fracture condition, and the bone quality condition, and automatically selecting a plurality of alternative solutions in the model database (Paragraph [0012]: “computer is able to identify the fracture fixation constructs that correspond to the specified data points or ranges […]”; Paragraph [0013]: “The system may be capable of identifying candidates for optimal fixation constructs based on searching the results of the plurality of simulated designs or use of the surrogate models”; Paragraph [0014]: “system may include a database for storing the design data of fixation designs, finite element models and surrogate models of designs”; Paragraph [0037]: “Computer experiments refer to parametric variation of the inputs of a computer model to generate large numbers of designs […] to enable generation of hundreds of fracture fixation designs in an automated fashion”; Paragraph [0055]: “[…] For automatic creation of all fixation designs, modularized finite element models for bone, plate and screw are created […]”; and Paragraph [0076]: “Various other methodologies such as kriging models, Bayesian approach, neural network and multivariate adaptive regression spines can be used to develop surrogate models”);
analyzing a stress distribution of each of the alternative solutions by a computer-aided analysis system (Paragraph [0007]: “[…] finite element modeling of a series of possible fixation constructs for the fracture injury. This can use modeling directly from segmenting CT images […]”; Paragraph [0010]: “surrogate models relating the design parameters to the biomechanics outputs”; Paragraph [0017]: “to provide the biomechanics output including maximum stresses of the plate and the screws, stiffness of fracture fixation and strain at the fracture gap. Other related outputs include motions, predicted hardware fatigue life, predicted healing, and predicted hardware and surgical costs”; Paragraph [0037]: “Based on the bone and fracture geometry of this imaged-based model […] Computer experiments refer to parametric variation of the inputs of a computer model to generate large numbers of designs”; Paragraph [0078]: “Additional output variables may be included as one of the finite element model outputs, for example, principal stress, and the location of maximum stress”), and obtaining a first preferred solution after simulating analysis, wherein the first preferred solution comprises a number of the screws and a locking position of the screws (Paragraph [0013]: “The system may be capable of identifying candidates for optimal fixation constructs based on searching the results of the plurality of simulated designs or use of the surrogate models”; Paragraph [0016]: “configured to include the plate length, fracture gap size, number of the screws, positions of the screws and plate material as design input data”; and Paragraph [0055]: “FIG. 3 shows an example listing of combinations of plate length, fracture gap, number of screws, and associated screw positions for creating fracture fixation designs used for subsequent FEA simulations and surrogate model fitting […] For automatic creation of all fixation designs, modularized finite element models for bone, plate and screw are created […] assembled using […] Mesh convergence testing […] results (gap displacement, construct stiffness, and maximum stress) converge […]”).
Reid fails to teach to Boddington teaches to (Figure 4B; Paragraph [0094]: “images can include multiple imaging modalities such as X-ray, fluoroscopy, ultrasound, computed tomography, terahertz imaging, or magnetic resonance imaging […]”; and Paragraph [0105]: “The computing platform 100, which includes an artificial intelligence engine, utilizes and analyzes the information from the datasets. These information sets have been analyzed and structured and based upon the specific surgical application can include: procedural medical image datasets […] biomechanical testing such as Von Mises Stresses failure modes datasets […] 3D statistical models of human anatomy datasets […] bone quality index […]”);
importing the bone plate 3D model into the long bone 3D model, and setting an initial fixation position of the bone plate 3D model (Figure 20; Figure 21; Paragraph [0136]: “The treatment option 47 will include a determination of the recommended implants for fixation of this type of fracture classification, for example an implant plate with specific screw configurations and selections 49”; Paragraph [0158]: “a screw combination calculation can provide the user with a prediction […] This can provide a predictive output […]”; Paragraph [0159]: “Specific decision points in the workflow can be, for example, the screw order and placement position in the plate implant”; and Paragraph [0162]: ” implant alignment calculation can provide […] a predictive output of an optimal implant position or normal healing/abnormal healing expectation and probability of success 623”);
instructing a first artificial intelligence to comprehensively analyze the long bone 3D model, the initial fixation position of the bone plate 3D model, the long bone fracture condition, and the bone quality condition, and automatically selecting a plurality of alternative solutions in the model database (Figure 2B; Figure 4B; Paragraph [0096]: “Module 10 is composed of computer algorithms and data structures for the reconstruction and fitting of three-dimensional (3D) statistical models of anatomical shape to intraoperative two-dimensional or three-dimensional image data”; Paragraph [0101]: “fracture identification and […] Classification Dataset interprets the image and makes a classification of the bone, bone section, type and group of the fracture.”; Paragraph [0105]: “The computing platform 100, which includes an artificial intelligence engine, utilizes and analyzes the information from the datasets. These information sets have been analyzed and structured and based upon the specific surgical application can include: procedural medical image datasets […] biomechanical testing such as Von Mises Stresses failure modes datasets […] 3D statistical models of human anatomy datasets […] bone quality index […]”; and Paragraph [0107]: “[…] information from the relevant datasets will be selected for inclusion in the Artificial Intelligence (AI) Engine in the form of multiple trained classifiers, each with a weighted contribution to the final surgical outcome prediction”).
Reid teaches an individualized fracture fixation planning system in which patient specific imaging data are used to construct image-based finite element models of fractured long bones, automatically generate a plurality of fracture fixation construct designs, develop surrogate models for evaluating the generated fixation constructs, and identify candidates for optimal fixation constructs based on the results of simulated fixation designs or surrogate models. Reid further teaches that surrogate models are not limited to polynomial regression models and that various alternative methodologies, including neural networks, can be used to develop the disclosed surrogate models (see Reid in Paragraph [0076]). Boddington teaches applying artificial intelligence, including machine learning, deep learning, and neural-network-based prediction models, to comprehensively analyze patient-specific orthopedic information, including fracture characteristics, bone quality, anatomical information, implant information, and medical-image data, in order to generate patient-specific implant recommendations and orthopedic treatment planning.
Therefore, it would have been obvious to one of ordinary skill in the art to combine Reid and Boddington before the effective filing date of the claimed invention. The motivation for this combination of references would have been to implement the known neural-network methodology expressly contemplated by Reid within Reid's computerized fracture fixation optimization framework so that candidate fracture fixation constructs could be identified using a known orthopedic artificial-intelligence implementation while preserving Reid's finite-element-based optimization process. This motivation for the combination of Reid and Boddington is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 4, Reid as modified by Boddington teaches the analysis method as claimed in claim 1, where Reid teaches wherein each of the alternative solutions in the model database comprises a plurality of classification marks (Paragraph [0014]: “[…] a database for storing the design data of fixation designs, finite element models and surrogate models of designs. The database may be populated by running and saving computer simulations, before or during the preoperative planning process”; and Paragraph [0037]: “Computer experiments refer to parametric variation of the inputs of a computer model to generate large numbers of designs […] The fixation designs include variations in plate length, hardware material, screw locations, and fracture gap size”);
each of the classification marks comprises at least two items, comprising gender, age, height, weight, and job (Paragraph [0006]: “The individual patient data further includes bone density, bone shape, soft tissue anatomy, patient age, sex, weight, smoking status, and other data relevant for fracture fixation”);
after the classification mark corresponding to the condition of the surgical patient is inputted, a certain region of the alternative solutions in the model database is selected (Paragraph [0006]: “The individualized preoperative planning system may include an input module for receiving the individual patient data such as imaging data of fracture injuries”; Paragraph [0037]: “First, an image-based 3D model is determined based on patient imaging data […] Based on the bone and fracture geometry of this imaged-based model, 3D finite element models of the various fracture fixation designs can be constructed”; Paragraph [0012]: “[…] to plot the design parameters and the biomechanics with multivariate plots, by which the user is able to specify data points or ranges by clicking within the multivariate plots and the computer is able to identify the fracture fixation constructs that correspond to the specified data points or ranges clicked by the user within the plots”; and Paragraph [0013]: “The system may be capable of identifying candidates for optimal fixation constructs based on searching the results of the plurality of simulated designs or use of the surrogate models”).
Regarding claim(s) 10, Reid as modified by Boddington teaches the analysis method as claimed in claim 1, where Reid teaches wherein the computer-aided analysis technique adopts the finite element method to simulate and analyze a load state of each of the alternative solutions (Paragraph [0055]: “Quadratic tetrahedral elements are utilized for the plate model which is meshed from manufacturer-supplied CAD files, and hexahedral elements are used to model the bone and screws […] Using approximately 100,000 elements, results (gap displacement, construct stiffness, and maximum stress) converge”; Paragraph [0050]: “The immediate update is made possible because the finite element model simulations of all possible fixation designs have been previously computed with results stored in a database that can be quickly retrieved by the system”; and Paragraph [0037]: “Computer experiments refer to parametric variation of the inputs of a computer model to generate large numbers of designs […] The fixation designs include variations in plate length, hardware material, screw locations, and fracture gap size”), and further analyze an external deformation (Paragraph [0050]: “The interface displays 3D deformed bodies (bones) and 3D field plots based on the selected fracture type and design type”; and Paragraph [0038]: “Clinically important biomechanical outputs of the designs include but are not limited to maximum stress within the plates and screws, interfragmentary displacement, and construct stiffness”) and an internal stress at every site of the bone plate 3D model and the screws (Paragraph [0057]: “Maximum von Mises stresses of the plate and screws are determined […] The stiffness of the fracture fixation construct is computed […] The deformation gradient […] is calculated […] The Green strain tensor is then obtained from the deformation gradient. Maximum shear strain at the fracture gap is calculated”; and Paragraph [0050]: “Upon each adjustment of the construct, the resulting 3D stresses and strains across the fracture site and implant will be immediately animatedly displayed”).
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reid et al (US 2018/0168731 A1) in view of Boddington et al (US 2021/0177522 A1), further in view of Shannon et al (US 2023/0360768 A1).
Regarding claim(s) 2, Reid as modified by Boddington teaches the analysis method as claimed in claim 1, where Boddington teaches further comprising a second artificial intelligence (Figure 2B; Figure 4A; and Paragraph [0102]: “the computing platform 100, which includes one or more Artificial Intelligence (AI) Engines […] deep neural networks and other image classifiers are trained to analyze and interpret visual features […] A trained neural network in this context can thus be thought of as a predictive model that produces a surgical outcome classification […]”).
Where Reid teaches the second artificial intelligence directly provides the surgery recommendation based on the trained screw stress/strain distribution as the first preferred solution (Paragraph [0055]: “combinations of plate length, fracture gap, number of screws, and associated screw positions […] A total of 774 fracture fixation designs […] For automatic creation of all fixation designs, modularized finite element models for bone, plate and screw are created […]”).
Reid and Boddington fails to teach wherein the second artificial intelligence calculates numerous amounts of real surgical data via a computer-aided analysis method, and then the machine learning is performed on the calculation results to generate a plurality of surgery recommendations in a mechanical analysis database; when the alternative solution selected by the first artificial intelligence is the same as or determined to be similar to one of the surgery recommendations generated by the second artificial intelligence.
However, Shannon teaches wherein the second artificial intelligence calculates numerous amounts of real surgical data via a computer-aided analysis method (Paragraph [0092]: “Dynamic analysis 43 with finite elements and/or musculoskeletal model will be performed on each spine-implant configuration, and a full set of data output […] comparing the motions, forces, moments, stress and strain analysis […] for each potentially suitable implant, whether an artificial disc […] or other implants such as pedicle screws, intervertebral rods, or intervertebral cages […]”; and Paragraph [0093]: “[…] a first model is created from a reference population using retrospective data (pre- and post-operative DICOMs) […] dynamic analysis is used […] The outputs of this model are the listed parameters in each axis of motion: ROMs, forces, moments, pressures, strains, stresses, etc. In step 53A, patient data is divided into “training set” and “testing set” […]”), and then the machine learning is performed on the calculation results to generate a plurality of surgery recommendations in a mechanical analysis database (Paragraph [0099]: “Module 601 performs a clustering machine learning algorithm […] to identify patients with similar anthropometric and biophysical parameters […] Module 602 randomly assigns the patients […] into either training or testing subsets. Module 603 applies segmentation and dynamic analysis […] In module 604, the prediction model is generated to correlate biomechanical parameters with successful vs. unsuccessful surgical outcomes”; Paragraph [0108]: “those interventions can be selected or recommended […] the surgeon is able to select one or more possible procedures most likely to result in a successful outcome”; and Paragraph [0109]: “[…] provides a ranked list of implants predicted to result in a successful outcome, and makes recommendations for the surgical spinal correction”);
when the alternative solution selected by the first artificial intelligence is the same as or determined to be similar to one of the surgery recommendations generated by the second artificial intelligence (Paragraph [0099]: “identify patients with similar anthropometric and biophysical parameters. While the exemplary machine learning algorithm in module 601 illustrates a neural network, a number of different methods may be used for performing clustering […] the new subject is first classified according to the clustered reference population which comprises the outcome of module 601”; and Paragraph [0107]: “the subject is matched to those prior patient results having similar biomechanical parameters and successful outcomes. As more patients are analyzed, higher accuracy and better performance of the algorithm and model is expected”), the second artificial intelligence directly provides the surgery recommendation based on the trained screw stress/strain distribution as the first preferred solution (Paragraph [0092]: “ Dynamic analysis […] comparing the motions, forces, moments, stress and strain analysis and more for each potentially suitable implant, whether an artificial disc, as in the artificial disc replacement procedure described, or other implants such as pedicle screws, intervertebral rods[…]”; Paragraph [0093]: “[…] The outputs of this model are the listed parameters in each axis of motion: ROMs, forces, moments, pressures, strains, stresses, etc. In step 53A, patient data is divided into “training set” and “testing set” […] In step 54A, the machine learning algorithm using […]”; and Paragraph [0109]: “[…] provides a ranked list of implants predicted to result in a successful outcome, and makes recommendations for the surgical spinal correction”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Reid, Boddington, and Shannon before the effective filing date of the claimed invention. The motivation for this combination of references would have been to improve the accuracy and reliability of patient-specific fracture fixation planning by utilizing known artificial-intelligence prediction techniques together with machine-learning analysis of biomechanical parameters derived from historical surgical cases. This motivation for the combination of Reid, Boddington, and Shannon is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 3, Reid as modified by Boddington and Shannon teaches the analysis method as claimed in claim 2, where Shannon teaches wherein a final surgical plan conducted on the surgical patient, an outcome, and a postoperative tracking record are imported into the mechanical analysis database (Paragraph [0094]: “Clinical data are collected and analyzed from patients who have previously undergone corrective spinal surgery […] These data comprise both preoperative 61 and postoperative 62 imaging studies for each patient having undergone a spinal correction procedure […] the patients are postoperative for spinal fusion procedures […] The output of the dynamic analysis […] is input separately to the prediction machine learning algorithm 63”; and Paragraph [0095]: “[…] The prediction algorithm […] determines the relative success or failure of each patient's surgical spine correction procedure […] determines a correlation between the biomechanical properties associated with a successful outcome and those associated with failure”), which are provided to the second artificial intelligence to perform machine learning (Paragraph [0094]: “The output of the dynamic analysis […] is input separately to the prediction machine learning algorithm 63”), thereby expanding the surgery recommendations in the mechanical analysis database and improving the comprehensive analysis ability of the second artificial intelligence (Paragraph [0097]: “By applying the predicted post-operative biomechanical parameters […] this enables grading a predicted success […] enables a health practitioner to take an appropriate decision […] so as to optimize a chance of a successful orthopedic surgery […]”; Paragraph [0098]: “[…] the use of a reference population allows a virtual comparison […] with the intent of selecting the operation most likely to produce favorable biomechanical properties”; and Paragraph [0099]: “the prediction model is generated […] The model is trained on the training population and validated on the testing population […] Once the model has been trained and validated, in module 605 it may be applied to a new patient […]”).
Allowable Subject Matter
Claim(s) 5-9 and 11 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Relevant Prior Art Directed to State of Art
Campbell et al (US 20210366118 A1) are relevant prior art not applied in the rejection(s) above. Campbell discloses a method, comprising: imaging, intraoperatively, a fractured bone of a patient to obtain a representation of the fractured bone in a computing system, the fractured bone defining at least a first bone fragment, and a second bone fragment that is separated from the first bone fragment by a fracture; imaging a contralateral bone of the patient to obtain a representation of the contralateral bone in the computing system; generating, intraoperatively in the computing system: a 3D virtual model of the fractured bone from data presented in the representation of the fractured bone; and a 3D virtual model of the contralateral bone from data presented in the representation of the contralateral bone comparing, intraoperatively in the computing system, a first spatial dimension measured in the 3D virtual model of the fractured bone in the computing system, with a second spatial dimension measured in the 3D virtual model of the contralateral bone.
Casey et al (US 2021/0210189 A1) are relevant prior art not applied in the rejection(s) above. Casey discloses a computer-implemented method for designing a patient-specific implant, the method comprising: receiving a patient data set of a patient, the patient data set including data indicative of the patient's spinal pathology; using at least one trained machine-learning model to: identify one or more model patient data sets by comparing the patient data set to a plurality of reference patient data sets, wherein each identified model patient data set includes a scored treatment outcome for a reference patient that received surgical treatment with a respective implant to address a spinal pathology generally similar to the patient's spinal pathology, and based at least in part on the identified one or more model patient data sets, design a patient-specific implant to be implanted in the patient; and generating fabrication data configured to be used by a manufacturing system to manufacture the patient-specific implant.
Conclusion
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/JONGBONG NAH/Examiner, Art Unit 2674
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