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 .
DETAILED ACTION
The present office action represents the first action on the merits.
Claims 25-44 are pending.
Priority
This application claims priority to Patent Application No. 18/906,766 dated 06 October 2023.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 14 May 2025 is in compliance with the provisions of 37 CFR 1.97 and has been fully considered by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 25-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 25 and 40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite systems, methods, devices, and techniques for determining personalized risk assessments for dislocations resulting from arthroplasty procedures, and therefore meet step 1.
Step 2A1
The limitations of (Claim 25) obtaining values for one or more non-modifiable risk factors of a patient, each non-modifiable risk factor defining an immutable patient characteristic that is determined to impact a likelihood that a dislocation results from an arthroplasty procedure that is planned for the patient; obtaining first candidate values for one or more modifiable risk factors of the patient, each modifiable risk factor defining a mutable characteristic of the patient or the arthroplasty procedure that is determined to impact the likelihood that the dislocation results from the arthroplasty procedure that is planned for the patient, wherein the first candidate values are determined to minimize the likelihood that the dislocation results from the arthroplasty procedure; obtaining second candidate values for the one or more modifiable risk factors of the patient, wherein the second candidate values are determined to maximize the likelihood that the dislocation results from the arthroplasty procedure; determining a personalized risk interval that represents a range of modifiable risk for the patient with respect to the arthroplasty procedure, including determining (i) a lower bound for the personalized risk interval based on the values for the one or more non-modifiable risk factors of the patient and the first candidate values for the one or more modifiable risk factors and (ii) an upper bound for the personalized risk interval based on the values for the one or more non-modifiable risk factors of the patient and the second candidate values for the one or more modifiable risk factors; and providing an output indicative of the personalized risk interval, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
The limitations of (Claim 40) obtaining user-indicated values for one or more non-modifiable risk factors of a patient, each non-modifiable risk factor defining an immutable patient characteristic that is determined to impact a likelihood that a dislocation results from an arthroplasty procedure that is planned for the patient; obtaining user-indicated values for one or more modifiable risk factors of the patient, each modifiable risk factor defining a mutable characteristic of the patient or the arthroplasty procedure that is determined to impact the likelihood that the dislocation results from the arthroplasty procedure that is planned for the patient; determining a personalized, modifiable risk score for the patient with respect to the dislocation and the arthroplasty procedure based on the user-indicated values for the one or more non-modifiable risk factors of the patient and the user-indicated values for the one or more modifiable risk factors; and providing an output indicative of the personalized, modifiable risk score for the patient, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting methods implemented by a computer, the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people (i.e., rules or instructions for a person or persons to follow) but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of a computer that implements the identified abstract idea. The computing elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., generic computer components, see, e.g., Page 33) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, obtaining values is considered insignificant extra solution activity such as pre-solution activity e.g., data gathering (performed by receiving/transmitting/etc.) See MPEP 2106.05(g).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Claims 26-39 and 41-44 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 26 merely describes determining the lower and upper bounds for the personalized risk interval. Claims 39 and 44 merely describe extracting the image features. Claim 41 merely describes determining a personalized, modifiable risk score. Claims 26, 39, 41, and 44 further recite a machine-learning model. The Examiner notes that the machine learning model is described in Claim 27 as encompassing a regression model, an artificial neural network, a transformer model, or an XGBoost model, which is simple enough to be included in the abstract idea (i.e., a regression analysis is an activity that humans perform and is thus part of the rules or instructions).
Claims 28, 38, 42, and 43 merely describe the arthroplasty procedure and the one or more non-modifiable risk factors. Claim 29 merely describes the sex, age, THA indication, body mass index, indication of a neurologic disease, and the surgery indication of the patient. Claim 30 merely describes the arthroplasty procedure and the one or more modifiable risk factors. Claim 31 merely describes the femoral head diameter, the surgical approach, the type of acetabular liner, and the type of revised component. Claim 32 merely describes identifying and selecting a first value of the modifiable risk factor. Claim 33 merely describes identifying and selecting a second value of the modifiable risk factor. Claim 34 merely describes the patient. Claim 35 merely describes obtaining a set of user-specified values and determining a personalized, modifiable risk score. Claims 36 and 37 merely describe providing the output. Claim 36 further recites the additional element of a remote computing system. Utilization of a remote computing system equates to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Claim 37 further recites the additional element of an interactive user interface on a screen of an electronic device, which is considered to “generally link” under both the practical application and significantly more analysis.
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.
Claims 25 and 28-37 are rejected under 35 U.S.C. 103 as being unpatentable over Westrich et al. (U.S. 2024/0136046) in view of Guo et al., “Risk factors for dislocation after revision total hip arthroplasty: A systematic review and meta-analysis,” referred to hereinafter as Westrich and Guo, respectively.
REGARDING CLAIM 25
Westrich teaches the computer-implemented method, comprising:
obtaining values for one or more non-modifiable risk factors of a patient, each non-modifiable risk factor defining an immutable patient characteristic that is determined to impact a likelihood that a dislocation results from an arthroplasty procedure that is planned for the patient; [Para. 0036 teaches obtaining values such as age, gender, and body mass index (BMI), non-modifiable risk factors of a patient.]
obtaining first candidate values for one or more modifiable risk factors of the patient, each modifiable risk factor defining a mutable characteristic of the patient or the arthroplasty procedure that is determined to impact the likelihood that the dislocation results from the arthroplasty procedure that is planned for the patient, wherein the first candidate values are determined to minimize the likelihood that the dislocation results from the arthroplasty procedure; [Para. 0052 teaches obtaining femoral implant size of 63 mm to <67 mm of the patient (first candidate values for a modifiable risk factor). This range, determined by highest probability of recommendation, is based on projected success rates, and therefore would minimize the likelihood that a dislocation results from the arthroplasty procedure.]
obtaining second candidate values for the one or more modifiable risk factors of the patient, wherein the second candidate values are determined to maximize the likelihood that the dislocation results from the arthroplasty procedure; [Para. 0052 teaches obtaining femoral implant size of 55 mm to <59 mm of the patient (first candidate values for a modifiable risk factor). This range, determined by lowest probability of recommendation, is based on projected success rates, and therefore would maximize the likelihood that a dislocation results from the arthroplasty procedure.]
Westrich may not explicitly teach
determining a personalized risk interval that represents a range of modifiable risk for the patient with respect to the arthroplasty procedure, including determining (i) a lower bound for the personalized risk interval based on the values for the one or more non-modifiable risk factors of the patient and the first candidate values for the one or more modifiable risk factors and (ii) an upper bound for the personalized risk interval based on the values for the one or more non-modifiable risk factors of the patient and the second candidate values for the one or more modifiable risk factors;
However, Guo teaches the following:
determining a personalized risk interval that represents a range of modifiable risk for the patient with respect to the arthroplasty procedure, including determining (i) a lower bound for the personalized risk interval based on the values for the one or more non-modifiable risk factors of the patient and the first candidate values for the one or more modifiable risk factors and (ii) an upper bound for the personalized risk interval based on the values for the one or more non-modifiable risk factors of the patient and the second candidate values for the one or more modifiable risk factors; [Page 126, Line 10-14 teaches patients with 32-mm (femoral) head (diameters) had a dislocation rate of 8.7% (upper bound), while using 36- and 40-mm diameter heads lowered that rate to 1.1% (lower bound).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Westrich to determine a risk interval as taught by Guo, with the motivation of preventing postoperative dislocation after THA (see Guo at Page 124, Line 18-19).
Westrich in view of Guo may not explicitly teach
and providing an output indicative of the personalized risk interval.
However, Westrich teaches the following:
and providing an output indicative of the personalized... [Para. 0061 teaches displaying femur implant sizes and corresponding fit percentages.]
Westrich may not explicitly teach that the output is indicative of the risk interval. However, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to combine the personalized output of Westrich with the risk interval of Guo, since the combination is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the risk interval of Guo as the output Westrich provides. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
REGARDING CLAIM 28
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich further teaches
wherein the arthroplasty procedure is a total hip arthroplasty procedure and the one or more non-modifiable risk factors comprise at least one of a THA indication of the patient, a sex of the patient, a body mass index of the patient, an age of the patient, an indication of a neurologic disease of the patient, a diagnosis of spine disease of the patient, a history for spine surgery of the patient, a surgery indication of the patient, or a comorbidity of the patient. [Para. 0087 teaches total hip arthroplasty. TABLE I teaches sex of the patient.]
REGARDING CLAIM 29
Westrich in view of Guo teaches the computer-implemented method of claim 28.
Westrich further teaches
the sex of the patient is associated with values that include male and female; the age of the patient is associated with values that include a number of years or decades since the patient’s birth; the THA indication of the patient is associated with values that include primary THA or revision THA; the body mass index of the patient is associated with values that include less than or equal to 18, greater than 18 and less than or equal to 25, greater than 25 and less than or equal to 30, greater than 30 and less than or equal to 35, greater than 35 and less than or equal to 40, or greater than 40; the indication of a neurologic disease is associated with values that include Parkinson disease, dementia, alcoholism, or fibromyalgia; and the surgery indication of the patient is associated with values that include osteoarthritis, osteonecrosis, inflammation, posttraumatic, or nonunion. [TABLE I teaches male and female. THA indication of the patient, a body mass index of the patient, an age of the patient, an indication of a neurologic disease of the patient, and a surgery indication of the patient were optional and the options were not taken.]
REGARDING CLAIM 30
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Guo further teaches
wherein the arthroplasty procedure is a total hip arthroplasty procedure and the one or more modifiable risk factors comprise at least one of a femoral head diameter, a type of acetabular liner, a type of revised component, or a surgical approach. [Page 123, Introduction teaches total hip arthroplasty and using of a (acetabular) liner with an elevated rim. Page 126, Col. 2, Line 3-4 teaches type of revised component.]
REGARDING CLAIM 31
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Guo further teaches
the femoral head diameter is associated with values of cemented and non-cemented; the surgical approach is associated with values of posterior, lateral, direct anterior, or trochanteric osteotomy; the type of acetabular liner is associated with values of standard, elevated, constrained, or dual-mobility; and the type of revised component is associated with values of acetabular component, femoral component, or both components. [Table 2 teaches elevated or constrained, and single component or both components.]
REGARDING CLAIM 32
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich further teaches
for each of the one or more modifiable risk factors: identifying a first value of the modifiable risk factor associated with a lowest risk of the dislocation from the arthroplasty procedure among all possible values for the modifiable risk factor; and selecting the first value for inclusion in the first candidate values. [Para. 0052 teaches obtaining femoral implant size of 63 mm to <67 mm of the patient (first candidate values for a modifiable risk factor). This range, determined by highest probability of recommendation, is based on projected success rates, and therefore would be associated with a lowest risk of dislocation from the arthroplasty procedure.]
REGARDING CLAIM 33
Westrich in view of Guo teaches the computer-implemented method of claim 32.
Westrich further teaches
for each of the one or more modifiable risk factors: identifying a second value of the modifiable risk factor associated with a highest risk of the dislocation from the arthroplasty procedure among all possible values for the modifiable risk factor; and selecting the second value for inclusion in the second candidate values. [Para. 0052 teaches obtaining femoral implant size of 55 mm to <59 mm of the patient (first candidate values for a modifiable risk factor). This range, determined by lowest probability of recommendation, is based on projected success rates, and therefore would be associated with a highest risk of dislocation from the arthroplasty procedure.]
REGARDING CLAIM 34
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich further teaches
wherein the patient is a human. [The reference discloses predicting implant size for arthroplasty using demographic variables. Arthroplasty and implant sizing are commonly performed in human orthopedic surgery, and the disclosure reasonably encompasses use in human patients.]
REGARDING CLAIM 35
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich further teaches
obtaining a set of user-specified values for the one or more modifiable risk factors of the patient; and [Para. 0035 teaches the user selecting an implant type (modifiable risk factor).]
determining a personalized, modifiable risk score for the patient based on the values for the one or more non-modifiable risk factors of the patient and the set of user-specified values for the one or more modifiable risk factors. [Para. 0035 teaches processing the information received in the user interface (non-modifiable and modifiable risk factors) to predict appropriate implant sizes and percentages of likelihood of suitable fitting.]
REGARDING CLAIM 36
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich in view of Guo may not explicitly teach
wherein providing the output indicative of the personalized risk interval comprises displaying an indication of the personalized risk interval, storing the indication of the personalized risk interval, or transmitting the indication of the personalized risk interval to a remote computing system.
However, Westrich teaches the following:
wherein providing the output indicative of the personalized... comprises displaying an indication of the personalized…, storing the indication of the personalized risk interval, or transmitting the indication of the personalized risk interval to a remote computing system. [Para. 0061 teaches displaying femur implant sizes and corresponding fit percentages.]
Westrich may not explicitly teach that the output is indicative of the risk interval. However, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to combine the personalized output of Westrich with the risk interval of Guo, since the combination is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the risk interval of Guo as the output Westrich provides. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
REGARDING CLAIM 37
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich in view of Guo may not explicitly teach
wherein providing the output indicative of the personalized risk interval comprises generating computer code comprising instructions that, when executed, cause an indication of the personalized interval to be presented in an interactive user interface on a screen of an electronic device.
However, Westrich teaches the following:
wherein providing the output indicative of the personalized… comprises generating computer code comprising instructions that, when executed, cause an indication of the personalized… to be presented in an interactive user interface on a screen of an electronic device. [Claim 25 teaches providing, by a processor, in a user interface operating on a computing device, the output.]
Westrich may not explicitly teach that the output is indicative of the risk interval. However, it would have been prima facie obvious to one of ordinary skill in the art at the time of filing to combine the personalized output of Westrich with the risk interval of Guo, since the combination is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the risk interval of Guo as the output Westrich provides. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claims 26, 27, 38, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Westrich in view of Guo and Roche et al. (U.S. 2021/0322100), referred to hereinafter as Roche.
REGARDING CLAIM 26
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich in view of Guo may not explicitly teach
determining the lower bound for the personalized risk interval comprises processing, with a machine-learning model, the values for the one or more non-modifiable risk factors of the patient and the first candidate values for the one or more modifiable risk factors; and determining the upper bound for the personalized risk interval comprises processing, with the machine-learning model, the values for the one or more non-modifiable risk factors of the patient and the second candidate values for the one or more modifiable risk factors.
However, Roche teaches the following:
determining the lower bound for the personalized risk interval comprises processing, with a machine-learning model, the values for the one or more non-modifiable risk factors of the patient and the first candidate values for the one or more modifiable risk factors; and determining the upper bound for the personalized risk interval comprises processing, with the machine-learning model, the values for the one or more non-modifiable risk factors of the patient and the second candidate values for the one or more modifiable risk factors. [Para. 0080 teaches using machine learning predictive models to extrapolate statistical trends and relationships to that of patient-specific data for a particular patient who would receive joint arthroplasty. Para. 0082 teaches using the predictive models to compare range of outcomes achieved with different implant types and different implant sizes (modifiable risk factors) for various defined diagnoses and comorbidities (non-modifiable risk factors).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Westrich in view of Guo to use a machine learning model to predict arthroplasty outcomes as taught by Roche, with the motivation of more accurately predicting post-operative outcome measures a particular patient may achieve (see Roche at Para. 0080).
REGARDING CLAIM 27
Westrich in view of Guo and Roche teaches the computer-implemented method of claim 26.
Roche further teaches
wherein the machine-learning model comprises at least one of a regression model, an artificial neural network, a transformer model, or an XGBoost model. [Para. 0091 teaches the predictive models include linear regression and XGBoost techniques.]
REGARDING CLAIM 38
Westrich in view of Guo teaches the computer-implemented method of claim 25.
Westrich further teaches
wherein the arthroplasty procedure is a total hip arthroplasty procedure… femoral or pelvic… [Para. 0087 teaches total hip arthroplasty.]
Westrich in view of Guo may not explicitly teach
…and the one or more non-modifiable risk factors comprise image features extracted from one or more pre-operative images of a… region of the patient.
However, Roche teaches the following:
…and the one or more non-modifiable risk factors comprise image features extracted from one or more pre-operative images of a… region of the patient. [Para. 0004 teaches a reconstruction plan is generated based on a pre-operative image of the joint. Para. 0129 teaches extracting parameters from the reconstruction plan.]
Motivation to combine the teaching of Roche with the teachings of Westrich and Guo is the same as that presented with respect to claim 26 and is therefore reiterated here.
REGARDING CLAIM 39
Westrich in view of Guo and Roche teaches the computer-implemented method of claim 38.
Roche further teaches
extracting the image features using a machine-learning model trained to predict whether or a likelihood that a patient exhibits… following the total… arthroplasty procedure based on one or more pre-operative images of the… region. [Para. 0004 teaches a reconstruction plan is generated based on a pre-operative image of the joint. Para. 0129 teaches the data inputs to the image-based prediction machine learning model include the parameters extracted from the reconstruction plan.]
Westrich further teaches
dislocation… hip… femoral or pelvic… [Para. 0052 teaches calculating the probability of recommending different sizes of femoral implants. This probability is based on projected success rates, and therefore would be associated with a risk of dislocation from the arthroplasty procedure. Para. 0087 teaches total hip arthroplasty.]
Claims 40-44 are rejected under 35 U.S.C. 103 as being unpatentable over Westrich in view of McGuan et al. (WO 2022/066693), referred to hereinafter as McGuan.
REGARDING CLAIM 40
Westrich teaches the computer-implemented method, comprising:
obtaining user-indicated values for one or more non-modifiable risk factors of a patient, each non-modifiable risk factor defining an immutable patient characteristic that is determined to impact a likelihood that a dislocation results from an arthroplasty procedure that is planned for the patient; [Para. 0035 teaches a user entering patient information such as age and gender (non-modifiable risk factors).]
obtaining user-indicated values for one or more modifiable risk factors of the patient, each modifiable risk factor defining a mutable characteristic of the patient or the arthroplasty procedure that is determined to impact the likelihood that the dislocation results from the arthroplasty procedure that is planned for the patient; [Para. 0012 teaches receiving a user’s selection of at least one implant size (modifiable risk factor).]
Westrich may not explicitly teach
determining a personalized, modifiable risk score for the patient with respect to the dislocation and the arthroplasty procedure based on the user-indicated values for the one or more non-modifiable risk factors of the patient and the user-indicated values for the one or more modifiable risk factors; and
providing an output indicative of the personalized, modifiable risk score for the patient.
However, McGuan teaches the following:
determining a personalized, modifiable risk score for the patient with respect to the dislocation and the arthroplasty procedure based on the user-indicated values for the one or more non-modifiable risk factors of the patient and the user-indicated values for the one or more modifiable risk factors; and [Para. 0100 teaches patient information includes patient demographics, diagnoses, and medical history information (non-modifiable risk factors). Para. 0157 teaches a machine learning model predicting a surgery from an initial state. The initial state includes the patient information and implant characteristics. Para. 0206 teaches a user selecting an implant size and an implant position (modifiable risk factors). Para. 0209 teaches outputting a risk of dislocation for each implant placement.]
providing an output indicative of the personalized, modifiable risk score for the patient. [Para. 0209 teaches outputting a risk of dislocation for each implant placement.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Westrich to determine and output a personalized risk score as taught by McGuan, with the motivation of improving the chance of successful clinical outcomes and lessening the economic burden on facilities (see McGuan at Para. 0137).
REGARDING CLAIM 41
Westrich in view of McGuan teaches the computer-implemented method of claim 40.
McGuan further teaches
determining the personalized, modifiable risk score comprises processing, with a machine-learning model, the user-indicated values for the one or more non-modifiable risk factors of the patient and the user-indicated values for the one or more modifiable risk factors. [Para. 0153 teaches processing information such as implant type and dimension, and patient demographics to train a machine learning model.]
REGARDING CLAIM 42
Westrich in view of McGuan teaches the computer-implemented method of claim 40.
Westrich further teaches
wherein the arthroplasty procedure is a total hip arthroplasty procedure and the one or more non-modifiable risk factors comprise at least one of a THA indication of the patient, a sex of the patient, a body mass index of the patient, an age of the patient, an indication of a neurologic disease of the patient, a diagnosis of spine disease of the patient, a history for spine surgery of the patient, a surgery indication of the patient, or a comorbidity of the patient. [Para. 0087 teaches total hip arthroplasty. TABLE I teaches sex of the patient.]
REGARDING CLAIM 43
Westrich in view of McGuan teaches the computer-implemented method of claim 40.
McGuan further teaches
wherein the arthroplasty procedure is a total hip arthroplasty procedure and the one or more non-modifiable risk factors comprise image features extracted from one or more pre-operative images of a femoral or pelvic region of the patient. [Para. 0169 teaches total hip arthroplasty. Para. 0100 teaches the pre-operative data includes images related to the anatomical area of interest. Para. 0011 teaches images of the pelvic joint of the patient.]
REGARDING CLAIM 44
Westrich in view of McGuan teaches the computer-implemented method of claim 43.
McGuan further teaches
extracting the image features using a machine-learning model trained to predict whether or a likelihood that a patient exhibits dislocation following the total hip arthroplasty procedure based on one or more pre-operative images of the femoral or pelvic region. [Para. 0169 teaches total hip arthroplasty. Para. 0153 teaches processing pre-operative data to train a machine learning model. Para. 0100 teaches the pre-operative data includes images related to the anatomical area of interest. Para. 0011 teaches images of the pelvic joint of the patient. Para. 0176 teaches using a computer model to predict risk of dislocation.]
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Grammatopoulos et al. (U.S. 2023/0285082) which discloses a hip arthroplasty planning method.
Parker et al. (U.S. 11986245) which discloses systems and methods for imaging and modeling following arthroplasty.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAMRYN B LEWIS whose telephone number is (703)756-1807. The examiner can normally be reached Monday - Friday, 11:00 am - 8:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert W Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683