DETAILED ACTION
Claims 1-20 have been presented for examination.
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 .
Response to Arguments
Applicant’s arguments filed 6/24/2026 have been fully considered but they are not persuasive.
Following Applicants amendments, the previously presented 112 rejection is WITHDRAWN.
Applicants argue that the cited references fail to teach a GAN conditioned on “(i) a patient radiologic image, (ii) orthotic parameters, and (iii) a temporal progression parameter to generate a plurality of artificial radiologic images corresponding to discrete time points over a predicted treatment period” However, the prior art clearly recites all three points as shown in the rejection below. Shin teaches the image generation, Patel & Gohil teach at least one orthotic parameter, and Casey teaches the temporal progression parameter with discrete time points over a predicted treatment period correlated to each specific patient containing all the necessary data that can be used as an input to the GAN of Shin. Therefore, the prior art rejection is MAINTAINED.
Applicants further argue that Shin merely teaches the GAN for general purpose tools for generating or transforming medical images and does not teach conditioning the GAN based on “orthotic parameters”, “temporal progression” and “patient-specific anatomical progression”. However, Patel & Gohil teach at least one orthotic parameter, and Casey teaches the temporal progression parameter with discrete time points over a predicted treatment period correlated to each specific patient. These parameters can be used as the “training data” described in (Shin, page 8, "Applications of Generative Systems") used to condition the GAN. Therefore, the prior art rejection is MAINTAINED
Applicant further argues that Shin does not disclose a GAN that is configured to generate a plurality of radiologic images corresponding to the points above, and that Shin’s GAN outputs are static, single-instance images generated generated without any time progression or sequencing. However, Shin [Applications of Generative Systems] recites “GANs trained on radiographic imagery have been applied in the context of radiotherapy treatment plan generation,68 prediction of brain tumor growth patterns, 69 and acceleration of image recompilation in an existing picture archiving and communication system.” Shin does not disclose anything regarding the image generation of the GAN having outputs of only static single-instance images. Applicants appear to be reading interpretations not explicitly recited in the prior art. Shin can be trained using the treatment planning data with temporal and anatomical progression from Casey and the orthotic parameters from Patel & Gohil. Therefore, the prior art rejection is MAINTAINED.
Applicant further argues that paragraphs 127-129 of Casey not being an input to image-generating or at least one orthotic to be used during the treatment period. However, as cited in Shin shown above, the disease progression data for treatment plan generation from Casey can be used to train the GAN, and Patel and Gohil disclose at least one orthotic parameter that has been used to treat abnormal bone curvature. Therefore, the prior art rejection is MAINTAINED.
Applicant further argues that there is no motivation to combine the references to arrive at the claimed invention. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Casey discloses a medical treatment plan for a patient “suffering from an orthopedic or spinal disease or disorder, such as … irregular spinal curvature (e.g., scoliosis, lordosis, kyphosis.” (See Casey [0031]) Shin discloses a GAN that it used to treat “spinal deformities, degeneration and trauma”, which as disclosed in the specification are all abnormal bone curvatures, and “ML in spine imaging represents a significant addition to the neurosurgeon’s armamentarium—it has the capacity to directly address and manifest clinical needs and improve diagnostic and procedural quality and safety” (See Shin [372]) Patel & Gohil disclose an orthotic used to “halt the curve progression of the spine of persons with scoliosis” (See Patel & Gohil [Table 1]) Therefore, the prior art rejection is MAINTAINED.
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.
Claims 1-5, 7-12, 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2022/0000556, hereafter Casey in view of NPL: Machine Learning Applications of Surgical Imaging for the Diagnosis and Treatment of Spine Disorders: Current State of the Art, hereafter Shin further in view of NPL: Custom orthotics development process based on additive manufacturing, hereafter Patel & Gohil.
Regarding Claim 1: Casey discloses a method of effectuating abnormal bone curvature treatment using a
training … using treatment data for each of a plurality of abnormal bone curvature patients, wherein the treatment data for each abnormal bone curvature patient includes a plurality of radiologic images taken during at least a partial treatment period.
Casey [0031] “the system 100 is configured to generate a medical treatment plan for a patient. In some embodiments, the system 100 is configured to generate a medical treatment plan for a patient suffering from an orthopedic or spinal disease or disorder, such as trauma (e.g., fractures), cancer, deformity, degeneration, pain (e.g., back pain, leg pain), irregular spinal curvature (e.g., scoliosis, lordosis, kyphosis), irregular spinal displacement (e.g., spondylolisthesis, lateral displacement axial displacement), osteoarthritis, lumbar degenerative disc disease, cervical degenerative disc disease, lumbar spinal stenosis, or cervical spinal stenosis, or a combination thereof … the medical treatment plan can include at least one treatment procedure (e.g., a surgical procedure or intervention) and/or at least one medical device (e.g., an implanted medical device”
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0117], “FIG. 7A-7D illustrate an example of a patient data set 700 (e.g., as received in step 502 of the method 500). The patient data set 700 can include any of the information previously described with respect to the patient data set. For example, the patient data set 700 includes patient information 701 (e.g., patient identification no., patient MRN, patient name, sex, age, body mass index (BMI), surgery date, surgeon, etc., shown in FIGS. 7A and 7B), diagnostic information 702 (e.g., Oswestry Disability Index (ODI), VAS-back score, VAS-leg score, Pre-operative pelvic incidence, pre-operative lumbar lordosis, pre-operative PI-LL angel, pre-operative lumbar coronal cobb, etc., shown in FIGS. 7B and 7C), and image data 703 (x-ray, CT, MRI, etc., shown in FIG. 7D).”
Casey [0065] “Following the treatment of the patient in accordance with the treatment plan, treatment progress can be monitored over one or more time periods to update the data analysis module 116 and/or treatment planning module 118. Post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models for developing patient-specific treatment plans, patient-specific medical devices, or combinations thereof.”
… discrete time points over a predicted treatment period of the new patient, … a temporal progression parameter parameter defining the discrete time points, and … predicted anatomical progression of the abnormal bone curvature of the new patient over the predicted treatment period.
Casey [0127] “In addition to designing patient-specific medical care based off reference patient data sets, the systems and methods of the present technology may also design patient-specific medical care based off disease progression for a particular patient. In some embodiments, the present technology therefore includes software modules (e.g., machine learning models or other algorithms) that can be used to analyze, predict, and/or model disease progression for a particular patient. The machine learning models can be trained based off a plurality of reference patient data sets that includes, in addition to the patient data described with respect to FIG. 1, disease progression metrics for each of the reference patients. The progression metrics can include measurements for disease metrics over a period of time. Suitable metrics may include spinopelvic parameters (e.g., lumbar lordosis, pelvic tilt, sagittal vertical axis (SVA), cobb angel, coronal offset, etc.), disability scores, functional ability scores, flexibility scores, VAS pain scores, or the like. The progression of the metrics for each reference patient can be correlated to other patient information for the specific reference patient (e.g., age, sex, height, weight, activity level, diet, etc.).”
Casey does not explicitly disclose training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN; and generating, using the trained GAN, a plurality of artificial radiologic images wherein the trained GAN is conditioned on the radiologic image of the new patient.
However, Shin discloses training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN. Casey and Shin are analogous because they both pertain to the field of orthopedics. Specifically, both references utilize neural networks to treat spinal deformities making the teachings of Shin logically relevant to one working in the field of Casey.
Shin [Page 373: Detection and Characterization of Spinal Deformities] “One of the most popular contexts for the application of discriminative neural networks (NNs) is the diagnosis and characterization of scoliosis and related spinal deformities.”
Shin [Pages 379-380: Applications of Generative Systems] “By definition, a GAN pits generator and discriminator networks against each other, ultimately producing objects that sufficiently imitate the training data to fool the discriminator network (Figure 2). As such, these systems can be tuned to focus on their generative or discriminative functions.”
generating, using the trained GAN, a plurality of artificial radiologic images wherein the trained GAN is conditioned on the radiologic image of the new patient.
Shin [Pages 379-380: Applications of Generative Systems] “GANs trained on radiographic imagery have been applied in the context of radiotherapy treatment plan generation, prediction of brain tumor growth patterns, and acceleration of image recompilation in an existing picture archiving and communication system … some have applied GANs to generate artificial medical imagery as a training set for other ML systems…. The most fascinating application of GAN involves intermodality conversion of imaging—magnetic resonance imaging to CT, CT to magnetic resonance imaging, and CT to cbCT.”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Casey and Shin do not disclose at least one orthotic used during the at least partial treatment period and including parameters of at least one orthotic to be used during the predicted treatment period.
However, Patel & Gohil disclose at least one orthotic used during the at least partial treatment period and including parameters of at least one orthotic to be used during the predicted treatment period. Patel & Gohil, Casey and Shin are analogous because they all pertain to the field of orthopedics. Specifically, all references pertain to treating spinal deformities making the teachings of Patel & Gohil logically relevant to one working in the field of Casey and Shin.
Patel & Gohil [Table 1] “Orthoses device type: Thoracic lumbo-sacral orthoses” … “Use: To halt the curve progression of the spine of persons with scoliosis”
[Figure 2] displays a visual of “Thoracic lumbo-sacral orthoses”
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of Patel & Gohil with Casey and Shin as the references deal with a method for treating utilize spinal deformities such that the Thoracic lumbo-sacral orthoses of Patel & Gohil could be used as the medical device of Casey and Shin to facilitate treatment of abnormal bone curvature by halting the curve progression of the spine (See Patel & Gohil [Table 1]).
Regarding Claim 2: Casey in view of Shin further in view of Patel & Gohil disclose the method of claim 1, further comprising: determining at least one orthotic for the identified abnormal bone curvature using the trained GAN and an associated treatment period for the at least one determined orthotic.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0041] “the server 106 can be configured with one or more algorithms that generate patient-specific treatment plan data (e.g., treatment procedures, medical devices) based on the reference data. In some embodiments, the patient-specific data is generated based on correlations between the patient data set 108 and the reference data. Optionally, the server 106 can predict outcomes, including recovery times”
As discussed in Claim 1, Casey doesn’t teach a trained GAN; however, Shin teaches a trained GAN used for diagnosing and generating images of bone abnormalities.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Regarding Claim 3, Casey in view of Shin further in view of Patel & Gohil disclose the method of claim 2, further comprising the at least one determined orthotic using the identified abnormal bone curvature per claim 2.
Casey does not disclose generating a plurality of artificial radiologic images corresponding to different points in a treatment period of the new patient using the identified abnormal bone curvature and the corresponding radiologic image for the new patient as inputs to the trained GAN.
However, Shin discloses training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN;
Shin [Page 373: Detection and Characterization of Spinal Deformities] “One of the most popular contexts for the application of discriminative neural networks (NNs) is the diagnosis and characterization of scoliosis and related spinal deformities.”
Shin [Pages 379-380: Applications of Generative Systems] “By definition, a GAN pits generator and discriminator networks against each other, ultimately producing objects that sufficiently imitate the training data to fool the discriminator network (Figure 2). As such, these systems can be tuned to focus on their generative or discriminative functions.”
and generating a plurality of artificial radiologic images corresponding to different points in a treatment period of the new patient using the identified abnormal bone curvature and the corresponding radiologic image for the new patient as inputs to the trained GAN.
Shin [Pages 379-380: Applications of Generative Systems] “GANs trained on radiographic imagery have been applied in the context of radiotherapy treatment plan generation, prediction of brain tumor growth patterns, and acceleration of image recompilation in an existing picture archiving and communication system … some have applied GANs to generate artificial medical imagery as a training set for other ML systems…. The most fascinating application of GAN involves intermodality conversion of imaging—magnetic resonance imaging to CT, CT to magnetic resonance imaging, and CT to cbCT.”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Regarding Claim 4, Casey in view of Shin further in view of Patel & Gohil disclose the method of claim 1, wherein the treatment period of the new patient is associated with a prescribed orthotic.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Regarding Claim 5, Casey in view of Shin further in view of Patel & Gohil disclose the method of claim 3, wherein the determined orthotic is a visualization of an artificial orthotic generated by the trained GAN.
Casey [0060] “As another example, the display 122 can show a design for a medical device to be implanted in the patient, such as a two- or three-dimensional model of the device design. The display 122 can also show patient information, such as two- or three-dimensional images or models of the patient's anatomy where the surgical procedure is to be performed and/or where the device is to be implanted.”
Regarding Claim 7, Casey in view of Shin further in view of Patel & Gohil disclose the method of claim 2, wherein the determined orthotic is based on the fastest recovery outcome or the most consistent recovery outcome.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0049] “the treatment planning module 118 can generate the treatment plan based on correlations between data sets. For example, the treatment planning module 118 can correlate treatment procedure data and/or medical device design data from similar patients with favorable outcomes (e.g., as identified by the data analysis module 116). Correlation analysis can include transforming correlation coefficient values to values or scores. The values/scores can be aggregated, filtered, or otherwise analyzed to determine one or more statistical significances. These correlations can be used to determine treatment procedure(s) and/or medical device design(s) that are optimal or likely to produce a favorable outcome for the patient to be treated.”
Regarding Claim 8: Casey discloses a computer program product for effectuating abnormal bone curvature treatment using a computer program product comprising: one or more computer-readable storage media and program instructions stored on one or more non-transitory computer-readable storage media capable of performing a method, the method comprising:
training … using treatment data for each of a plurality of abnormal bone curvature patients, wherein the treatment data for each abnormal bone curvature patient includes a plurality of radiologic images taken during at least a partial treatment period.
Casey [0031] “the system 100 is configured to generate a medical treatment plan for a patient. In some embodiments, the system 100 is configured to generate a medical treatment plan for a patient suffering from an orthopedic or spinal disease or disorder, such as trauma (e.g., fractures), cancer, deformity, degeneration, pain (e.g., back pain, leg pain), irregular spinal curvature (e.g., scoliosis, lordosis, kyphosis), irregular spinal displacement (e.g., spondylolisthesis, lateral displacement axial displacement), osteoarthritis, lumbar degenerative disc disease, cervical degenerative disc disease, lumbar spinal stenosis, or cervical spinal stenosis, or a combination thereof … the medical treatment plan can include at least one treatment procedure (e.g., a surgical procedure or intervention) and/or at least one medical device (e.g., an implanted medical device”
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0117], “FIG. 7A-7D illustrate an example of a patient data set 700 (e.g., as received in step 502 of the method 500). The patient data set 700 can include any of the information previously described with respect to the patient data set. For example, the patient data set 700 includes patient information 701 (e.g., patient identification no., patient MRN, patient name, sex, age, body mass index (BMI), surgery date, surgeon, etc., shown in FIGS. 7A and 7B), diagnostic information 702 (e.g., Oswestry Disability Index (ODI), VAS-back score, VAS-leg score, Pre-operative pelvic incidence, pre-operative lumbar lordosis, pre-operative PI-LL angel, pre-operative lumbar coronal cobb, etc., shown in FIGS. 7B and 7C), and image data 703 (x-ray, CT, MRI, etc., shown in FIG. 7D).”
Casey [0065] “Following the treatment of the patient in accordance with the treatment plan, treatment progress can be monitored over one or more time periods to update the data analysis module 116 and/or treatment planning module 118. Post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models for developing patient-specific treatment plans, patient-specific medical devices, or combinations thereof.”
… discrete time points over a predicted treatment period of the new patient, … a temporal progression parameter defining the discrete time points, and … predicted anatomical progression of the abnormal bone curvature of the new patient over the predicted treatment period.
Casey [0127] “In addition to designing patient-specific medical care based off reference patient data sets, the systems and methods of the present technology may also design patient-specific medical care based off disease progression for a particular patient. In some embodiments, the present technology therefore includes software modules (e.g., machine learning models or other algorithms) that can be used to analyze, predict, and/or model disease progression for a particular patient. The machine learning models can be trained based off a plurality of reference patient data sets that includes, in addition to the patient data described with respect to FIG. 1, disease progression metrics for each of the reference patients. The progression metrics can include measurements for disease metrics over a period of time. Suitable metrics may include spinopelvic parameters (e.g., lumbar lordosis, pelvic tilt, sagittal vertical axis (SVA), cobb angel, coronal offset, etc.), disability scores, functional ability scores, flexibility scores, VAS pain scores, or the like. The progression of the metrics for each reference patient can be correlated to other patient information for the specific reference patient (e.g., age, sex, height, weight, activity level, diet, etc.).”
Casey does not explicitly disclose training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN; and generating, using the trained GAN, a plurality of artificial radiologic images wherein the trained GAN is conditioned on the radiologic image of the new patient.
However, Shin discloses training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN. Casey and Shin are analogous because they both pertain to the field of orthopedics. Specifically, both references utilize neural networks to treat spinal deformities making the teachings of Shin logically relevant to one working in the field of Casey.
Shin [Page 373: Detection and Characterization of Spinal Deformities] “One of the most popular contexts for the application of discriminative neural networks (NNs) is the diagnosis and characterization of scoliosis and related spinal deformities.”
Shin [Pages 379-380: Applications of Generative Systems] “By definition, a GAN pits generator and discriminator networks against each other, ultimately producing objects that sufficiently imitate the training data to fool the discriminator network (Figure 2). As such, these systems can be tuned to focus on their generative or discriminative functions.”
generating, using the trained GAN, a plurality of artificial radiologic images wherein the trained GAN is conditioned on the radiologic image of the new patient.
Shin [Pages 379-380: Applications of Generative Systems] “GANs trained on radiographic imagery have been applied in the context of radiotherapy treatment plan generation, prediction of brain tumor growth patterns, and acceleration of image recompilation in an existing picture archiving and communication system … some have applied GANs to generate artificial medical imagery as a training set for other ML systems…. The most fascinating application of GAN involves intermodality conversion of imaging—magnetic resonance imaging to CT, CT to magnetic resonance imaging, and CT to cbCT.”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Casey and Shin do not disclose at least one orthotic used during the at least partial treatment period and including parameters of at least one orthotic to be used during the predicted treatment period.
However, Patel & Gohil disclose at least one orthotic used during the at least partial treatment period and including parameters of at least one orthotic to be used during the predicted treatment period. Patel & Gohil, Casey and Shin are analogous because they all pertain to the field of orthopedics. Specifically, all references pertain to treating spinal deformities making the teachings of Patel & Gohil logically relevant to one working in the field of Casey and Shin.
Patel & Gohil [Table 1] “Orthoses device type: Thoracic lumbo-sacral orthoses” … “Use: To halt the curve progression of the spine of persons with scoliosis”
[Figure 2] displays a visual of “Thoracic lumbo-sacral orthoses”
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of Patel & Gohil with Casey and Shin as the references deal with a method for treating utilize spinal deformities such that the Thoracic lumbo-sacral orthoses of Patel & Gohil could be used as the medical device of Casey and Shin to facilitate treatment of abnormal bone curvature by halting the curve progression of the spine (See Patel & Gohil [Table 1]).
Regarding Claim 9: Casey in view of Shin further in view of Patel & Gohil disclose the computer program product of 8, further comprising: determining at least one orthotic for the identified abnormal bone curvature using the trained GAN and an associated treatment period for the at least one determined orthotic.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0041] “the server 106 can be configured with one or more algorithms that generate patient-specific treatment plan data (e.g., treatment procedures, medical devices) based on the reference data. In some embodiments, the patient-specific data is generated based on correlations between the patient data set 108 and the reference data. Optionally, the server 106 can predict outcomes, including recovery times”
As discussed in Claim 8, Casey doesn’t teach a trained GAN; however, Shin teaches a trained GAN used for diagnosing and generating images of bone abnormalities.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Regarding Claim 10, Casey in view Shin further in view of Patel & Gohil disclose the method of claim 9, further comprising the at least one determined orthotic using the identified abnormal bone curvature per claim 9.
Casey does not disclose generating a plurality of artificial radiologic images corresponding to different points in a treatment period of the new patient using the identified abnormal bone curvature and the corresponding radiologic image for the new patient as inputs to the trained GAN.
However, Shin discloses training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN;
Shin [Page 373: Detection and Characterization of Spinal Deformities] “One of the most popular contexts for the application of discriminative neural networks (NNs) is the diagnosis and characterization of scoliosis and related spinal deformities.”
Shin [Pages 379-380: Applications of Generative Systems] “By definition, a GAN pits generator and discriminator networks against each other, ultimately producing objects that sufficiently imitate the training data to fool the discriminator network (Figure 2). As such, these systems can be tuned to focus on their generative or discriminative functions.”
and generating a plurality of artificial radiologic images corresponding to different points in a treatment period of the new patient using the identified abnormal bone curvature and the corresponding radiologic image for the new patient as inputs to the trained GAN.
Shin [Pages 379-380: Applications of Generative Systems] “GANs trained on radiographic imagery have been applied in the context of radiotherapy treatment plan generation, prediction of brain tumor growth patterns, and acceleration of image recompilation in an existing picture archiving and communication system … some have applied GANs to generate artificial medical imagery as a training set for other ML systems…. The most fascinating application of GAN involves intermodality conversion of imaging—magnetic resonance imaging to CT, CT to magnetic resonance imaging, and CT to cbCT.”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Regarding Claim 11, Casey in view of Shin further in view of Patel & Gohil disclose the computer program product of claim 8, wherein the treatment period of the new patient is associated with a prescribed orthotic.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Regarding Claim 12, Casey in view of Shin further in view of Patel & Gohil disclose 10, wherein the determined orthotic is a visualization of an artificial orthotic generated by the trained GAN.
Casey [0060] “As another example, the display 122 can show a design for a medical device to be implanted in the patient, such as a two- or three-dimensional model of the device design. The display 122 can also show patient information, such as two- or three-dimensional images or models of the patient's anatomy where the surgical procedure is to be performed and/or where the device is to be implanted.”
Regarding Claim 14, Casey in view of Shin further in view of Patel & Gohil disclose the computer program product of claim 9, wherein the determined orthotic is based on the fastest recovery outcome or the most consistent recovery outcome.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0049] “the treatment planning module 118 can generate the treatment plan based on correlations between data sets. For example, the treatment planning module 118 can correlate treatment procedure data and/or medical device design data from similar patients with favorable outcomes (e.g., as identified by the data analysis module 116). Correlation analysis can include transforming correlation coefficient values to values or scores. The values/scores can be aggregated, filtered, or otherwise analyzed to determine one or more statistical significances. These correlations can be used to determine treatment procedure(s) and/or medical device design(s) that are optimal or likely to produce a favorable outcome for the patient to be treated.”
Regarding Claim 15: Casey discloses a computer system for effectuating abnormal bone curvature treatment using a
training … using treatment data for each of a plurality of abnormal bone curvature patients, wherein the treatment data for each abnormal bone curvature patient includes a plurality of radiologic images taken during at least a partial treatment period.
Casey [0031] “the system 100 is configured to generate a medical treatment plan for a patient. In some embodiments, the system 100 is configured to generate a medical treatment plan for a patient suffering from an orthopedic or spinal disease or disorder, such as trauma (e.g., fractures), cancer, deformity, degeneration, pain (e.g., back pain, leg pain), irregular spinal curvature (e.g., scoliosis, lordosis, kyphosis), irregular spinal displacement (e.g., spondylolisthesis, lateral displacement axial displacement), osteoarthritis, lumbar degenerative disc disease, cervical degenerative disc disease, lumbar spinal stenosis, or cervical spinal stenosis, or a combination thereof … the medical treatment plan can include at least one treatment procedure (e.g., a surgical procedure or intervention) and/or at least one medical device (e.g., an implanted medical device”
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0117], “FIG. 7A-7D illustrate an example of a patient data set 700 (e.g., as received in step 502 of the method 500). The patient data set 700 can include any of the information previously described with respect to the patient data set. For example, the patient data set 700 includes patient information 701 (e.g., patient identification no., patient MRN, patient name, sex, age, body mass index (BMI), surgery date, surgeon, etc., shown in FIGS. 7A and 7B), diagnostic information 702 (e.g., Oswestry Disability Index (ODI), VAS-back score, VAS-leg score, Pre-operative pelvic incidence, pre-operative lumbar lordosis, pre-operative PI-LL angel, pre-operative lumbar coronal cobb, etc., shown in FIGS. 7B and 7C), and image data 703 (x-ray, CT, MRI, etc., shown in FIG. 7D).”
Casey [0065] “Following the treatment of the patient in accordance with the treatment plan, treatment progress can be monitored over one or more time periods to update the data analysis module 116 and/or treatment planning module 118. Post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models for developing patient-specific treatment plans, patient-specific medical devices, or combinations thereof.”
… discrete time points over a predicted treatment period of the new patient, … a temporal progression parameter defining the discrete time points, and … predicted anatomical progression of the abnormal bone curvature of the new patient over the predicted treatment period.
Casey [0127] “In addition to designing patient-specific medical care based off reference patient data sets, the systems and methods of the present technology may also design patient-specific medical care based off disease progression for a particular patient. In some embodiments, the present technology therefore includes software modules (e.g., machine learning models or other algorithms) that can be used to analyze, predict, and/or model disease progression for a particular patient. The machine learning models can be trained based off a plurality of reference patient data sets that includes, in addition to the patient data described with respect to FIG. 1, disease progression metrics for each of the reference patients. The progression metrics can include measurements for disease metrics over a period of time. Suitable metrics may include spinopelvic parameters (e.g., lumbar lordosis, pelvic tilt, sagittal vertical axis (SVA), cobb angel, coronal offset, etc.), disability scores, functional ability scores, flexibility scores, VAS pain scores, or the like. The progression of the metrics for each reference patient can be correlated to other patient information for the specific reference patient (e.g., age, sex, height, weight, activity level, diet, etc.).”
Casey does not explicitly disclose training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN; and generating, using the trained GAN, a plurality of artificial radiologic images wherein the trained GAN is conditioned on the radiologic image of the new patient.
However, Shin discloses training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN. Casey and Shin are analogous because they both pertain to the field of orthopedics. Specifically, both references utilize neural networks to treat spinal deformities making the teachings of Shin logically relevant to one working in the field of Casey.
Shin [Page 373: Detection and Characterization of Spinal Deformities] “One of the most popular contexts for the application of discriminative neural networks (NNs) is the diagnosis and characterization of scoliosis and related spinal deformities.”
Shin [Pages 379-380: Applications of Generative Systems] “By definition, a GAN pits generator and discriminator networks against each other, ultimately producing objects that sufficiently imitate the training data to fool the discriminator network (Figure 2). As such, these systems can be tuned to focus on their generative or discriminative functions.”
generating, using the trained GAN, a plurality of artificial radiologic images wherein the trained GAN is conditioned on the radiologic image of the new patient.
Shin [Pages 379-380: Applications of Generative Systems] “GANs trained on radiographic imagery have been applied in the context of radiotherapy treatment plan generation, prediction of brain tumor growth patterns, and acceleration of image recompilation in an existing picture archiving and communication system … some have applied GANs to generate artificial medical imagery as a training set for other ML systems…. The most fascinating application of GAN involves intermodality conversion of imaging—magnetic resonance imaging to CT, CT to magnetic resonance imaging, and CT to cbCT.”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Casey and Shin do not disclose at least one orthotic used during the at least partial treatment period and including parameters of at least one orthotic to be used during the predicted treatment period.
However, Patel & Gohil disclose at least one orthotic used during the at least partial treatment period and including parameters of at least one orthotic to be used during the predicted treatment period. Patel & Gohil, Casey and Shin are analogous because they all pertain to the field of orthopedics. Specifically, all references pertain to treating spinal deformities making the teachings of Patel & Gohil logically relevant to one working in the field of Casey and Shin.
Patel & Gohil [Table 1] “Orthoses device type: Thoracic lumbo-sacral orthoses” … “Use: To halt the curve progression of the spine of persons with scoliosis”
[Figure 2] displays a visual of “Thoracic lumbo-sacral orthoses”
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of Patel & Gohil with Casey and Shin as the references deal with a method for treating utilize spinal deformities such that the Thoracic lumbo-sacral orthoses of Patel & Gohil could be used as the medical device of Casey and Shin to facilitate treatment of abnormal bone curvature by halting the curve progression of the spine (See Patel & Gohil [Table 1]).
Regarding Claim 16: Casey in view of Shin further in view of Patel & Gohil disclose the computer system of claim 15, further comprising: determining at least one orthotic for the identified abnormal bone curvature using the trained GAN and an associated treatment period for the at least one determined orthotic.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Casey [0041] “the server 106 can be configured with one or more algorithms that generate patient-specific treatment plan data (e.g., treatment procedures, medical devices) based on the reference data. In some embodiments, the patient-specific data is generated based on correlations between the patient data set 108 and the reference data. Optionally, the server 106 can predict outcomes, including recovery times”
As discussed in Claim 15, Casey doesn’t teach a trained GAN; however, Shin teaches a trained GAN used for diagnosing and generating images of bone abnormalities.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Regarding Claim 17, Casey in view of Shin further in view of Patel & Gohil disclose the method of claim 16, further comprising the at least one determined orthotic using the identified abnormal bone curvature per claim 16.
Casey does not disclose generating a plurality of artificial radiologic images corresponding to different points in a treatment period of the new patient using the identified abnormal bone curvature and the corresponding radiologic image for the new patient as inputs to the trained GAN.
However, Shin discloses training of a GAN, identifying an abnormal bone curvature in a radiologic image for a new patient using a trained GAN;
Shin [Page 373: Detection and Characterization of Spinal Deformities] “One of the most popular contexts for the application of discriminative neural networks (NNs) is the diagnosis and characterization of scoliosis and related spinal deformities.”
Shin [Pages 379-380: Applications of Generative Systems] “By definition, a GAN pits generator and discriminator networks against each other, ultimately producing objects that sufficiently imitate the training data to fool the discriminator network (Figure 2). As such, these systems can be tuned to focus on their generative or discriminative functions.”
and generating a plurality of artificial radiologic images corresponding to different points in a treatment period of the new patient using the identified abnormal bone curvature and the corresponding radiologic image for the new patient as inputs to the trained GAN.
Shin [Pages 379-380: Applications of Generative Systems] “GANs trained on radiographic imagery have been applied in the context of radiotherapy treatment plan generation, prediction of brain tumor growth patterns, and acceleration of image recompilation in an existing picture archiving and communication system … some have applied GANs to generate artificial medical imagery as a training set for other ML systems…. The most fascinating application of GAN involves intermodality conversion of imaging—magnetic resonance imaging to CT, CT to magnetic resonance imaging, and CT to cbCT.”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to recognize that applying the diagnostic and image generative part of the GAN of Shin to the treatment planning system of Casey would improve the diagnosis of spinal pathologies for the inputted radiological images and refine the treatment of complex spinal conditions (See Shin [Page 372]).
Regarding Claim 18, Casey in view of Shin further in view of Patel & Gohil disclose the computer system of claim 15, wherein the treatment period of the new patient is associated with a prescribed orthotic.
Casey [0032] “the system 100 generates a medical treatment plan that is customized for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment plan. The patient-specific treatment plan can include at least one patient-specific surgical procedure and/or at least one patient-specific medical device that are designed and/or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, condition, medical history).”
Regarding Claim 19, Casey in view of Shin further in view of Patel & Gohil disclose the computer system of claim 17, wherein the determined orthotic is a visualization of an artificial orthotic generated by the trained GAN.
Casey [0060] “As another example, the display 122 can show a design for a medical device to be implanted in the patient, such as a two- or three-dimensional model of the device design. The display 122 can also show patient information, such as two- or three-dimensional images or models of the patient's anatomy where the surgical procedure is to be performed and/or where the device is to be implanted.”
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2022/0000556, Casey in view of Shin further in view of Patel & Gohil further in view of NPL: The Objective Measurement of Spinal Orthosis Use for The Treatment of Adolescent Idiopathic Scoliosis, hereafter Nicholson.
Regarding Claim 6, Casey in view of Shin further in view of Patel & Gohil disclose the method of claim 5, wherein the artificial orthotic visualization and the associated treatment period are based on user input variables, and wherein the user input variables include length, breadth, and recovery time.
Casey [0039] “the treatment data includes medical device design data for at least one medical device used to treat the reference patient, such as physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, modulus, hardness), and/or biological properties (e.g., osteo-integration, cellular adhesion, anti-bacterial properties, anti-viral properties) … a reference patient data set can include outcome data representing an outcome of the treatment of the reference patient, such as corrected anatomical metrics, presence of fusion, HRQL (health related quality of life), activity level, return to work, complications, recovery times, efficacy, mortality, and/or follow-up surgeries.”
Casey [0065] “Following the treatment of the patient in accordance with the treatment plan, treatment progress can be monitored over one or more time periods to update the data analysis module 116 and/or treatment planning module 118. Post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models for developing patient-specific treatment plans, patient-specific medical devices, or combinations thereof.”
Casey and Shin and Patel & Gohil do not explicitly disclose usage/day being a value in the treatment data that could be inputted into the treatment planning module.
However, Nicholson discloses a temperature sensor could be used to track spinal orthosis daily usage. Nicholson, Patel & Gohil, Casey and Shin are analogous because they all pertain to the field of orthopedics. Specifically, all references pertain to treating spinal deformities making the teachings of Nicholson logically relevant to one working in the field of Patel & Gohil, Casey and Shin.
Nicholson [Pg. 2246: Indication of Time in Brace] “This was verified using six volunteers comparing a diary of their activity to the temperature profile of a data logger strapped next to their skin (Table 3). This clearly identified when the logger was next to the skin and when it was not, and the correspondence between diary and data logger had >90% agreement. Correlation of the average use interval between diary and data logger gave an R2 value of 0.998 (linear regression analysis P < 0.001). In a previous study by our research group, Lavelle et al31 also demonstrated good correspondence between total brace use measured using their temperature sensor-based timers and a diary of brace use filled in by their patients.”
Nicholson [Table 3] displays “Validation of Data Logger Use by Volunteers, Comparing Their Diary of Activity and the Temperature Profile of the Data Logger”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Nicholson with Casey, Shin and Patel & Gohil as the references deal with a method for treating spinal deformities. Nicholson would apply the orthotic usage data to the outcome treatment data of a reference patient of Casey, Shin and Patel & Gohil (see Casey [0039]) such that the orthotic usage data would be used as inputs in to train the treatment planning module. The addition of the temperature sensor to track orthotic usage data allows for a more objective measurement of tracking orthotic usage and provides an idea of the bracing habits of each patient (See Nicholson [Pg. 2248: Discussion].
Regarding Claim 13, Casey in view of Shin further in view of Patel & Gohil disclose the computer program product of claim 12, wherein the artificial orthotic visualization and the associated treatment period are based on user input variables, and wherein the user input variables include length, breadth, recovery time, and usage/day.
Casey [0039] “the treatment data includes medical device design data for at least one medical device used to treat the reference patient, such as physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, modulus, hardness), and/or biological properties (e.g., osteo-integration, cellular adhesion, anti-bacterial properties, anti-viral properties) … a reference patient data set can include outcome data representing an outcome of the treatment of the reference patient, such as corrected anatomical metrics, presence of fusion, HRQL (health related quality of life), activity level, return to work, complications, recovery times, efficacy, mortality, and/or follow-up surgeries.”
Casey [0065] “Following the treatment of the patient in accordance with the treatment plan, treatment progress can be monitored over one or more time periods to update the data analysis module 116 and/or treatment planning module 118. Post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models for developing patient-specific treatment plans, patient-specific medical devices, or combinations thereof.”
Casey, Shin and Patel & Gohil do not explicitly disclose usage/day being a value in the treatment data that could be inputted into the treatment planning module.
However, Nicholson discloses a temperature sensor could be used to track spinal orthosis daily usage. Nicholson, Patel & Gohil, Casey and Shin are analogous because they all pertain to the field of orthopedics. Specifically, all references pertain to treating spinal deformities making the teachings of Nicholson logically relevant to one working in the field of Patel & Gohil, Casey and Shin.
Nicholson [Pg. 2246: Indication of Time in Brace] “This was verified using six volunteers comparing a diary of their activity to the temperature profile of a data logger strapped next to their skin (Table 3). This clearly identified when the logger was next to the skin and when it was not, and the correspondence between diary and data logger had >90% agreement. Correlation of the average use interval between diary and data logger gave an R2 value of 0.998 (linear regression analysis P < 0.001). In a previous study by our research group, Lavelle et al31 also demonstrated good correspondence between total brace use measured using their temperature sensor-based timers and a diary of brace use filled in by their patients.”
Nicholson [Table 3] displays “Validation of Data Logger Use by Volunteers, Comparing Their Diary of Activity and the Temperature Profile of the Data Logger”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Nicholson with Casey, Shin and Patel & Gohil as the references deal with a method for treating spinal deformities. Nicholson would apply the orthotic usage data to the outcome treatment data of a reference patient of Casey, Shin and Patel & Gohil (see Casey [0039]) such that the orthotic usage data would be used as inputs in to train the treatment planning module. The addition of the temperature sensor to track orthotic usage data allows for a more objective measurement of tracking orthotic usage and provides an idea of the bracing habits of each patient (See Nicholson [Pg. 2248: Discussion].
Regarding Claim 20, Casey in view of Shin further in view of Patel & Gohil disclose the computer program product of claim 19, wherein the artificial orthotic visualization and the associated treatment period are based on user input variables, and wherein the user input variables include length, breadth, recovery time, and usage/day.
Casey [0039] “the treatment data includes medical device design data for at least one medical device used to treat the reference patient, such as physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, modulus, hardness), and/or biological properties (e.g., osteo-integration, cellular adhesion, anti-bacterial properties, anti-viral properties) … a reference patient data set can include outcome data representing an outcome of the treatment of the reference patient, such as corrected anatomical metrics, presence of fusion, HRQL (health related quality of life), activity level, return to work, complications, recovery times, efficacy, mortality, and/or follow-up surgeries.”
Casey [0065] “Following the treatment of the patient in accordance with the treatment plan, treatment progress can be monitored over one or more time periods to update the data analysis module 116 and/or treatment planning module 118. Post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models for developing patient-specific treatment plans, patient-specific medical devices, or combinations thereof.”
Casey, Shin and Patel do not explicitly disclose usage/day being a value in the treatment data that could be inputted into the treatment planning module.
However, Nicholson discloses a temperature sensor could be used to track spinal orthosis daily usage. Nicholson, Patel & Gohil, Casey and Shin are analogous because they all pertain to the field of orthopedics. Specifically, all references pertain to treating spinal deformities making the teachings of Nicholson logically relevant to one working in the field of Patel & Gohil, Casey and Shin.
Nicholson [Pg. 2246: Indication of Time in Brace] “This was verified using six volunteers comparing a diary of their activity to the temperature profile of a data logger strapped next to their skin (Table 3). This clearly identified when the logger was next to the skin and when it was not, and the correspondence between diary and data logger had >90% agreement. Correlation of the average use interval between diary and data logger gave an R2 value of 0.998 (linear regression analysis P < 0.001). In a previous study by our research group, Lavelle et al31 also demonstrated good correspondence between total brace use measured using their temperature sensor-based timers and a diary of brace use filled in by their patients.”
Nicholson [Table 3] displays “Validation of Data Logger Use by Volunteers, Comparing Their Diary of Activity and the Temperature Profile of the Data Logger”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Nicholson with Casey, Shin and Patel & Gohil as the references deal with a method for treating spinal deformities. Nicholson would apply the orthotic usage data to the outcome treatment data of a reference patient of Casey, Shin and Patel & Gohil (see Casey [0039]) such that the orthotic usage data would be used as inputs in to train the treatment planning module. The addition of the temperature sensor to track orthotic usage data allows for a more objective measurement of tracking orthotic usage and provides an idea of the bracing habits of each patient (See Nicholson [Pg. 2248: Discussion].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
All Claims are rejected
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure
U.S. Patent Application No. 2020/0124691
V. I. Voronov, P. A. Dovgolevsky and L. I. Voronova, "Creating a Simulation System of Orthopedic Insoles Based on Data from Three-Dimensional Scan of the Feet," 2021 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), Yaroslavl, Russian Federation, 2021, pp. 565-569, doi: 10.1109/ITQMIS53292.2021.9642841
Ahn G, Choi BS, Ko S, Jo C, Han HS, Lee MC, Ro DH. High-resolution knee plain radiography image synthesis using style generative adversarial network adaptive discriminator augmentation. J Orthop Res. 2023 Jan;41(1):84-93. doi: 10.1002/jor.25325. Epub 2022 Oct 5. PMID: 35293648.
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STT
/SCOTT THANH BINH TRAN/Examiner, Art Unit 2186
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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